Datasets:

librarian-bots

/

model_cards_with_metadata_test

like 0

MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7

MoritzLaurer

transformers

zero-shot-classification

--- language: - multilingual - zh - ja - ar - ko - de - fr - es - pt - hi - id - it - tr - ru - bn - ur - mr - ta - vi - fa - pl - uk - nl - sv - he - sw - ps license: mit tags: - zero-shot-classification - text-classification - nli - pytorch datasets: - MoritzLaurer/multilingual-NLI-26lang-2mil7 - xnli - multi_nli - facebook/anli - fever - lingnli - alisawuffles/WANLI metrics: - accuracy pipeline_tag: zero-shot-classification widget: - text: Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU candidate_labels: politics, economy, entertainment, environment model-index: - name: DeBERTa-v3-base-xnli-multilingual-nli-2mil7 results: - task: type: text-classification name: Natural Language Inference dataset: name: MultiNLI-matched type: multi_nli split: validation_matched metrics: - type: accuracy value: 0,857 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: MultiNLI-mismatched type: multi_nli split: validation_mismatched metrics: - type: accuracy value: 0,856 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: ANLI-all type: anli split: test_r1+test_r2+test_r3 metrics: - type: accuracy value: 0,537 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: ANLI-r3 type: anli split: test_r3 metrics: - type: accuracy value: 0,497 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: WANLI type: alisawuffles/WANLI split: test metrics: - type: accuracy value: 0,732 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: LingNLI type: lingnli split: test metrics: - type: accuracy value: 0,788 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: fever-nli type: fever-nli split: test metrics: - type: accuracy value: 0,761 verified: false --- # Model card for mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 ## Model description This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying mDeBERTa-v3-base model was pre-trained by Microsoft on the [CC100 multilingual dataset](https://huggingface.co/datasets/cc100) with 100 languages. The model was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli) and on the [multilingual-NLI-26lang-2mil7 dataset](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7). Both datasets contain more than 2.7 million hypothesis-premise pairs in 27 languages spoken by more than 4 billion people. As of December 2021, mDeBERTa-v3-base is the best performing multilingual base-sized transformer model introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf). ### How to use the model #### Simple zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli") sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" candidate_labels = ["politics", "economy", "entertainment", "environment"] output = classifier(sequence_to_classify, candidate_labels, multi_label=False) print(output) ``` #### NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" hypothesis = "Emmanuel Macron is the President of France" input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" prediction = torch.softmax(output["logits"][0], -1).tolist() label_names = ["entailment", "neutral", "contradiction"] prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} print(prediction) ``` ### Training data This model was trained on the [multilingual-nli-26lang-2mil7 dataset](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) and the [XNLI](https://huggingface.co/datasets/xnli) validation dataset. The multilingual-nli-26lang-2mil7 dataset contains 2 730 000 NLI hypothesis-premise pairs in 26 languages spoken by more than 4 billion people. The dataset contains 105 000 text pairs per language. It is based on the English datasets [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [ANLI](https://huggingface.co/datasets/anli), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) and was created using the latest open-source machine translation models. The languages in the dataset are: ['ar', 'bn', 'de', 'es', 'fa', 'fr', 'he', 'hi', 'id', 'it', 'ja', 'ko', 'mr', 'nl', 'pl', 'ps', 'pt', 'ru', 'sv', 'sw', 'ta', 'tr', 'uk', 'ur', 'vi', 'zh'] (see [ISO language codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). For more details, see the [datasheet](XXX). In addition, a sample of 105 000 text pairs was also added for English following the same sampling method as the other languages, leading to 27 languages. Moreover, for each language a random set of 10% of the hypothesis-premise pairs was added where an English hypothesis was paired with the premise in the other language (and the same for English premises and other language hypotheses). This mix of languages in the text pairs should enable users to formulate a hypothesis in English for a target text in another language. The [XNLI](https://huggingface.co/datasets/xnli) validation set consists of 2490 professionally translated texts from English to 14 other languages (37350 texts in total) (see [this paper](https://arxiv.org/pdf/1809.05053.pdf)). Note that XNLI also contains a training set of 14 machine translated versions of the MultiNLI dataset for 14 languages, but this data was excluded due to quality issues with the machine translations from 2018. Note that for evaluation purposes, three languages were excluded from the XNLI training data and only included in the test data: ["bg","el","th"]. This was done in order to test the performance of the model on languages it has not seen during NLI fine-tuning on 27 languages, but only during pre-training on 100 languages - see evaluation metrics below. The total training dataset had a size of 3 287 280 hypothesis-premise pairs. ### Training procedure mDeBERTa-v3-base-mnli-xnli was trained using the Hugging Face trainer with the following hyperparameters. ``` training_args = TrainingArguments( num_train_epochs=3, # total number of training epochs learning_rate=2e-05, per_device_train_batch_size=32, # batch size per device during training gradient_accumulation_steps=2, # to double the effective batch size for warmup_ratio=0.06, # number of warmup steps for learning rate scheduler weight_decay=0.01, # strength of weight decay fp16=False ) ``` ### Eval results The model was evaluated on the XNLI test set in 15 languages (5010 texts per language, 75150 in total) and the English test sets of [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [ANLI](https://huggingface.co/datasets/anli), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) . Note that multilingual NLI models are capable of classifying NLI texts without receiving NLI training data in the specific language (cross-lingual transfer). This means that the model is also able to do NLI on the other 73 languages mDeBERTa was pre-trained on, but performance is most likely lower than for those languages seen during NLI fine-tuning. The performance on the languages ["bg","el","th"] in the table below is a good indicated of this cross-lingual transfer, as these languages were not included in the training data. |XNLI subsets|ar|bg|de|el|en|es|fr|hi|ru|sw|th|tr|ur|vi|zh| | :---: |:---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |Accuracy|0.794|0.822|0.824|0.809|0.871|0.832|0.823|0.769|0.803|0.746|0.786|0.792|0.744|0.793|0.803| |Speed (text/sec, A100-GPU)|1344.0|1355.0|1472.0|1149.0|1697.0|1446.0|1278.0|1115.0|1380.0|1463.0|1713.0|1594.0|1189.0|877.0|1887.0| |English Datasets|mnli_test_m|mnli_test_mm|anli_test|anli_test_r3|fever_test|ling_test|wanli_test| | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |Accuracy|0.857|0.856|0.537|0.497|0.761|0.788|0.732|0.794| |Speed (text/sec, A100-GPU)|1000.0|1009.0|794.0|672.0|374.0|1177.0|1468.0| Also note that if other multilingual models on the model hub claim performance of around 90% on languages other than English, the authors have most likely made a mistake during testing since non of the latest papers shows a multilingual average performance of more than a few points above 80% on XNLI (see [here](https://arxiv.org/pdf/2111.09543.pdf) or [here](https://arxiv.org/pdf/1911.02116.pdf)). ## Limitations and bias Please consult the original DeBERTa-V3 paper and literature on different NLI datasets for potential biases. Moreover, note that the multilingual-nli-26lang-2mil7 dataset was created using machine translation, which reduces the quality of the data for a complex task like NLI. You can inspect the data via the Hugging Face [dataset viewer](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) for languages you are interested in. Note that grammatical errors introduced by machine translation are less of an issue for zero-shot classification, for which grammar is less important. ## Citation If the dataset is useful for you, please cite the following article: ``` @article{laurer_less_2022, title = {Less {Annotating}, {More} {Classifying} – {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT} - {NLI}}, url = {https://osf.io/74b8k}, language = {en-us}, urldate = {2022-07-28}, journal = {Preprint}, author = {Laurer, Moritz and Atteveldt, Wouter van and Casas, Andreu Salleras and Welbers, Kasper}, month = jun, year = {2022}, note = {Publisher: Open Science Framework}, } ``` ## Ideas for cooperation or questions? For updates on new models and datasets, follow me on [Twitter](https://twitter.com/MoritzLaurer). If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or on [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) ## Debugging and issues Note that DeBERTa-v3 was released in late 2021 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers==4.13 or higher might solve some issues. Note that mDeBERTa currently does not support FP16, see here: https://github.com/microsoft/DeBERTa/issues/77

padmajabfrl/Gender-Classification

padmajabfrl

transformers

text-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: Gender-Classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Gender-Classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0035 | 1.0 | 4390 | 0.0004 | 1.0000 | | 0.0005 | 2.0 | 8780 | 0.0002 | 1.0000 | | 0.0 | 3.0 | 13170 | 0.0000 | 1.0 | | 0.0 | 4.0 | 17560 | 0.0000 | 1.0 | | 0.0 | 5.0 | 21950 | 0.0000 | 1.0 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

Helsinki-NLP/opus-mt-ru-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: cc-by-4.0 --- ### opus-mt-ru-en ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [How to Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Transformer-align - **Language(s):** - Source Language: Russian - Target Language: English - **License:** CC-BY-4.0 - **Resources for more information:** - [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Uses #### Direct Use This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Further details about the dataset for this model can be found in the OPUS readme: [ru-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-en/README.md) ## Training #### Training Data ##### Preprocessing * Pre-processing: Normalization + SentencePiece * Dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT) * Download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.zip) * Test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.test.txt) ## Evaluation #### Results * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.eval.txt) #### Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012.ru.en | 34.8 | 0.603 | | newstest2013.ru.en | 27.9 | 0.545 | | newstest2014-ruen.ru.en | 31.9 | 0.591 | | newstest2015-enru.ru.en | 30.4 | 0.568 | | newstest2016-enru.ru.en | 30.1 | 0.565 | | newstest2017-enru.ru.en | 33.4 | 0.593 | | newstest2018-enru.ru.en | 29.6 | 0.565 | | newstest2019-ruen.ru.en | 31.4 | 0.576 | | Tatoeba.ru.en | 61.1 | 0.736 | ## Citation Information ```bibtex @InProceedings{TiedemannThottingal:EAMT2020, author = {J{\"o}rg Tiedemann and Santhosh Thottingal}, title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld}, booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)}, year = {2020}, address = {Lisbon, Portugal} } ``` ## How to Get Started With the Model ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ru-en") ```

liuhaotian/llava-v1.5-7b

liuhaotian

transformers

text-generation

--- inference: false --- <br> <br> # LLaVA Model Card ## Model details **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. **Model date:** LLaVA-v1.5-7B was trained in September 2023. **Paper or resources for more information:** https://llava-vl.github.io/ ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues ## Intended use **Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. - 158K GPT-generated multimodal instruction-following data. - 450K academic-task-oriented VQA data mixture. - 40K ShareGPT data. ## Evaluation dataset A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

davebulaval/MeaningBERT

davebulaval

transformers

text-classification

--- title: MeaningBERT emoji: 🦀 colorFrom: purple colorTo: indigo sdk: gradio sdk_version: 4.2.0 app_file: app.py pinned: false --- # Here is MeaningBERT MeaningBERT is an automatic and trainable metric for assessing meaning preservation between sentences. MeaningBERT was proposed in our article [MeaningBERT: assessing meaning preservation between sentences](https://www.frontiersin.org/articles/10.3389/frai.2023.1223924/full). Its goal is to assess meaning preservation between two sentences that correlate highly with human judgments and sanity checks. For more details, refer to our publicly available article. > This public version of our model uses the best model trained (where in our article, we present the performance results > of an average of 10 models) for a more extended period (500 epochs instead of 250). We have observed later that the > model can further reduce dev loss and increase performance. Also, we have changed the data augmentation technique used > in the article for a more robust one, that also includes the commutative property of the meaning function. Namely, Meaning(Sent_a, Sent_b) = Meaning(Sent_b, Sent_a). - [HuggingFace Model Card](https://huggingface.co/davebulaval/MeaningBERT) - [HuggingFace Metric Card](https://huggingface.co/spaces/davebulaval/meaningbert) ## Sanity Check Correlation to human judgment is one way to evaluate the quality of a meaning preservation metric. However, it is inherently subjective, since it uses human judgment as a gold standard, and expensive since it requires a large dataset annotated by several humans. As an alternative, we designed two automated tests: evaluating meaning preservation between identical sentences (which should be 100% preserving) and between unrelated sentences (which should be 0% preserving). In these tests, the meaning preservation target value is not subjective and does not require human annotation to be measured. They represent a trivial and minimal threshold a good automatic meaning preservation metric should be able to achieve. Namely, a metric should be minimally able to return a perfect score (i.e., 100%) if two identical sentences are compared and return a null score (i.e., 0%) if two sentences are completely unrelated. ### Identical Sentences The first test evaluates meaning preservation between identical sentences. To analyze the metrics' capabilities to pass this test, we count the number of times a metric rating was greater or equal to a threshold value X∈[95, 99] and divide It is calculated by the number of sentences to create a ratio of the number of times the metric gives the expected rating. To account for computer floating-point inaccuracy, we round the ratings to the nearest integer and do not use a threshold value of 100%. ### Unrelated Sentences Our second test evaluates meaning preservation between a source sentence and an unrelated sentence generated by a large language model.3 The idea is to verify that the metric finds a meaning preservation rating of 0 when given a completely irrelevant sentence mainly composed of irrelevant words (also known as word soup). Since this test's expected rating is 0, we check that the metric rating is lower or equal to a threshold value X∈[5, 1]. Again, to account for computer floating-point inaccuracy, we round the ratings to the nearest integer and do not use a threshold value of 0%. ## Use MeaningBERT You can use MeaningBERT as a [model](https://huggingface.co/davebulaval/MeaningBERT) that you can retrain or use for inference using the following with HuggingFace ```python # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT") model = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningBERT") ``` or you can use MeaningBERT as a metric for evaluation (no retrain) using the following with HuggingFace ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT") scorer = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningBERT") scorer.eval() documents = ["He wanted to make them pay.", "This sandwich looks delicious.", "He wants to eat."] simplifications = ["He wanted to make them pay.", "This sandwich looks delicious.", "Whatever, whenever, this is a sentence."] # We tokenize the text as a pair and return Pytorch Tensors tokenize_text = tokenizer(documents, simplifications, truncation=True, padding=True, return_tensors="pt") with torch.no_grad(): # We process the text scores = scorer(**tokenize_text) print(scores.logits.tolist()) ``` or using our HuggingFace Metric module ```python import evaluate documents = ["He wanted to make them pay.", "This sandwich looks delicious.", "He wants to eat."] simplifications = ["He wanted to make them pay.", "This sandwich looks delicious.", "Whatever, whenever, this is a sentence."] meaning_bert = evaluate.load("davebulaval/meaningbert") print(meaning_bert.compute(documents=documents, simplifications=simplifications)) ``` ------------------ ## Cite Use the following citation to cite MeaningBERT ``` @ARTICLE{10.3389/frai.2023.1223924, AUTHOR={Beauchemin, David and Saggion, Horacio and Khoury, Richard}, TITLE={MeaningBERT: assessing meaning preservation between sentences}, JOURNAL={Frontiers in Artificial Intelligence}, VOLUME={6}, YEAR={2023}, URL={https://www.frontiersin.org/articles/10.3389/frai.2023.1223924}, DOI={10.3389/frai.2023.1223924}, ISSN={2624-8212}, } ``` ------------------ ## Contributing to MeaningBERT We welcome user input, whether it regards bugs found in the library or feature propositions! Make sure to have a look at our [contributing guidelines](https://github.com/GRAAL-Research/MeaningBERT/blob/main/.github/CONTRIBUTING.md) for more details on this matter. ## License MeaningBERT is MIT licensed, as found in the [LICENSE file](https://github.com/GRAAL-Research/risc/blob/main/LICENSE). ------------------

laion/CLIP-ViT-bigG-14-laion2B-39B-b160k

laion

open_clip

zero-shot-image-classification

--- license: mit widget: - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog library_name: open_clip pipeline_tag: zero-shot-image-classification --- # Model Card for CLIP ViT-bigG/14 - LAION-2B # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) 7. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description A CLIP ViT-bigG/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Mitchell Wortsman on the [stability.ai](https://stability.ai/) cluster. The license for this model is MIT. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). Fine-tuning was also partially done on LAION-A, a 900M subset of LAION-2B filtered with aesthetic V2 4.5+ and phash deduplicated. **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure The training procedure will soon be discussed by a blog post on laion.ai. # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. **TODO** - more detail ## Results The model achieves a 80.1 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, and will soon be visible at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. # Acknowledgements Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model. # Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` OpenAI CLIP paper ``` @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` OpenCLIP software ``` @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` Scaling OpenCLIP paper ``` @article{cherti2022reproducible, title={Reproducible scaling laws for contrastive language-image learning}, author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia}, journal={arXiv preprint arXiv:2212.07143}, year={2022} } ``` # How to Get Started with the Model Use the code below to get started with the model. ** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets

HuggingFaceM4/tiny-random-LlamaForCausalLM

HuggingFaceM4

transformers

text-generation

Entry not found

kresnik/wav2vec2-large-xlsr-korean

kresnik

transformers

automatic-speech-recognition

--- language: ko datasets: - kresnik/zeroth_korean tags: - speech - audio - automatic-speech-recognition license: apache-2.0 model-index: - name: 'Wav2Vec2 XLSR Korean' results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Zeroth Korean type: kresnik/zeroth_korean args: clean metrics: - name: Test WER type: wer value: 4.74 - name: Test CER type: cer value: 1.78 --- ## Evaluation on Zeroth-Korean ASR corpus [Google colab notebook(Korean)](https://colab.research.google.com/github/indra622/tutorials/blob/master/wav2vec2_korean_tutorial.ipynb) ``` from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset import soundfile as sf import torch from jiwer import wer processor = Wav2Vec2Processor.from_pretrained("kresnik/wav2vec2-large-xlsr-korean") model = Wav2Vec2ForCTC.from_pretrained("kresnik/wav2vec2-large-xlsr-korean").to('cuda') ds = load_dataset("kresnik/zeroth_korean", "clean") test_ds = ds['test'] def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch test_ds = test_ds.map(map_to_array) def map_to_pred(batch): inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding="longest") input_values = inputs.input_values.to("cuda") with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = test_ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` ### Expected WER: 4.74% ### Expected CER: 1.78%

TinyLlama/TinyLlama-1.1B-Chat-v1.0

TinyLlama

transformers

text-generation

--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en widget: - example_title: Fibonacci (Python) messages: - role: system content: You are a chatbot who can help code! - role: user content: Write me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI. --- <div align="center"> # TinyLlama-1.1B </div> https://github.com/jzhang38/TinyLlama The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. #### This Model This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). **We follow [HF's Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)'s training recipe.** The model was " initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4." #### How to use You will need the transformers>=4.34 Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information. ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <|system|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <|user|> # How many helicopters can a human eat in one sitting?</s> # <|assistant|> # ... ```

google-t5/t5-large

google-t5

transformers

translation

--- language: - en - fr - ro - de - multilingual license: apache-2.0 tags: - summarization - translation datasets: - c4 --- # Model Card for T5 Large  # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training Details](#training-details) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Citation](#citation) 8. [Model Card Authors](#model-card-authors) 9. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task. T5-Large is the checkpoint with 770 million parameters. - **Developed by:** Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. See [associated paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) and [GitHub repo](https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints) - **Model type:** Language model - **Language(s) (NLP):** English, French, Romanian, German - **License:** Apache 2.0 - **Related Models:** [All T5 Checkpoints](https://huggingface.co/models?search=t5) - **Resources for more information:** - [Research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) - [Google's T5 Blog Post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) - [GitHub Repo](https://github.com/google-research/text-to-text-transfer-transformer) - [Hugging Face T5 Docs](https://huggingface.co/docs/transformers/model_doc/t5) # Uses ## Direct Use and Downstream Use The developers write in a [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) that the model: > Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself. See the [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations More information needed. ## Recommendations More information needed. # Training Details ## Training Data The model is pre-trained on the [Colossal Clean Crawled Corpus (C4)](https://www.tensorflow.org/datasets/catalog/c4), which was developed and released in the context of the same [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) as T5. The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**. Thereby, the following datasets were being used for (1.) and (2.): 1. **Datasets used for Unsupervised denoising objective**: - [C4](https://huggingface.co/datasets/c4) - [Wiki-DPR](https://huggingface.co/datasets/wiki_dpr) 2. **Datasets used for Supervised text-to-text language modeling objective** - Sentence acceptability judgment - CoLA [Warstadt et al., 2018](https://arxiv.org/abs/1805.12471) - Sentiment analysis - SST-2 [Socher et al., 2013](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf) - Paraphrasing/sentence similarity - MRPC [Dolan and Brockett, 2005](https://aclanthology.org/I05-5002) - STS-B [Ceret al., 2017](https://arxiv.org/abs/1708.00055) - QQP [Iyer et al., 2017](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) - Natural language inference - MNLI [Williams et al., 2017](https://arxiv.org/abs/1704.05426) - QNLI [Rajpurkar et al.,2016](https://arxiv.org/abs/1606.05250) - RTE [Dagan et al., 2005](https://link.springer.com/chapter/10.1007/11736790_9) - CB [De Marneff et al., 2019](https://semanticsarchive.net/Archive/Tg3ZGI2M/Marneffe.pdf) - Sentence completion - COPA [Roemmele et al., 2011](https://www.researchgate.net/publication/221251392_Choice_of_Plausible_Alternatives_An_Evaluation_of_Commonsense_Causal_Reasoning) - Word sense disambiguation - WIC [Pilehvar and Camacho-Collados, 2018](https://arxiv.org/abs/1808.09121) - Question answering - MultiRC [Khashabi et al., 2018](https://aclanthology.org/N18-1023) - ReCoRD [Zhang et al., 2018](https://arxiv.org/abs/1810.12885) - BoolQ [Clark et al., 2019](https://arxiv.org/abs/1905.10044) ## Training Procedure In their [abstract](https://jmlr.org/papers/volume21/20-074/20-074.pdf), the model developers write: > In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. The framework introduced, the T5 framework, involves a training procedure that brings together the approaches studied in the paper. See the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for further details. # Evaluation ## Testing Data, Factors & Metrics The developers evaluated the model on 24 tasks, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf) for full details. ## Results For full results for T5-Large, see the [research paper](https://jmlr.org/papers/volume21/20-074/20-074.pdf), Table 14. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Google Cloud TPU Pods - **Hours used:** More information needed - **Cloud Provider:** GCP - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Citation **BibTeX:** ```bibtex @article{2020t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {140}, pages = {1-67}, url = {http://jmlr.org/papers/v21/20-074.html} } ``` **APA:** - Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5Model tokenizer = T5Tokenizer.from_pretrained("t5-large") model = T5Model.from_pretrained("t5-large") input_ids = tokenizer( "Studies have been shown that owning a dog is good for you", return_tensors="pt" ).input_ids # Batch size 1 decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 # forward pass outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) last_hidden_states = outputs.last_hidden_state ``` See the [Hugging Face T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model) docs and a [Colab Notebook](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/main/notebooks/t5-trivia.ipynb) created by the model developers for more examples. </details>

ByteDance/SDXL-Lightning

ByteDance

diffusers

text-to-image

--- license: openrail++ tags: - text-to-image - stable-diffusion library_name: diffusers inference: false --- # SDXL-Lightning  SDXL-Lightning is a lightning-fast text-to-image generation model. It can generate high-quality 1024px images in a few steps. For more information, please refer to our research paper: [SDXL-Lightning: Progressive Adversarial Diffusion Distillation](https://arxiv.org/abs/2402.13929). We open-source the model as part of the research. Our models are distilled from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). This repository contains checkpoints for 1-step, 2-step, 4-step, and 8-step distilled models. The generation quality of our 2-step, 4-step, and 8-step model is amazing. Our 1-step model is more experimental. We provide both full UNet and LoRA checkpoints. The full UNet models have the best quality while the LoRA models can be applied to other base models. ## Demos * Generate with all configurations, best quality: [Demo](https://huggingface.co/spaces/ByteDance/SDXL-Lightning) ## Checkpoints * `sdxl_lightning_Nstep.safetensors`: All-in-one checkpoint, for ComfyUI. * `sdxl_lightning_Nstep_unet.safetensors`: UNet checkpoint only, for Diffusers. * `sdxl_lightning_Nstep_lora.safetensors`: LoRA checkpoint, for Diffusers and ComfyUI. ## Diffusers Usage Please always use the correct checkpoint for the corresponding inference steps. ### 2-Step, 4-Step, 8-Step UNet ```python import torch from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler from huggingface_hub import hf_hub_download from safetensors.torch import load_file base = "stabilityai/stable-diffusion-xl-base-1.0" repo = "ByteDance/SDXL-Lightning" ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting! # Load model. unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16) unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda")) pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda") # Ensure sampler uses "trailing" timesteps. pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") # Ensure using the same inference steps as the loaded model and CFG set to 0. pipe("A girl smiling", num_inference_steps=4, guidance_scale=0).images[0].save("output.png") ``` ### 2-Step, 4-Step, 8-Step LoRA Use LoRA only if you are using non-SDXL base models. Otherwise use our UNet checkpoint for better quality. ```python import torch from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler from huggingface_hub import hf_hub_download base = "stabilityai/stable-diffusion-xl-base-1.0" repo = "ByteDance/SDXL-Lightning" ckpt = "sdxl_lightning_4step_lora.safetensors" # Use the correct ckpt for your step setting! # Load model. pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda") pipe.load_lora_weights(hf_hub_download(repo, ckpt)) pipe.fuse_lora() # Ensure sampler uses "trailing" timesteps. pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") # Ensure using the same inference steps as the loaded model and CFG set to 0. pipe("A girl smiling", num_inference_steps=4, guidance_scale=0).images[0].save("output.png") ``` ### 1-Step UNet The 1-step model is only experimental and the quality is much less stable. Consider using the 2-step model for much better quality. The 1-step model uses "sample" prediction instead of "epsilon" prediction! The scheduler needs to be configured correctly. ```python import torch from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler from huggingface_hub import hf_hub_download from safetensors.torch import load_file base = "stabilityai/stable-diffusion-xl-base-1.0" repo = "ByteDance/SDXL-Lightning" ckpt = "sdxl_lightning_1step_unet_x0.safetensors" # Use the correct ckpt for your step setting! # Load model. unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16) unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda")) pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda") # Ensure sampler uses "trailing" timesteps and "sample" prediction type. pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample") # Ensure using the same inference steps as the loaded model and CFG set to 0. pipe("A girl smiling", num_inference_steps=1, guidance_scale=0).images[0].save("output.png") ``` ## ComfyUI Usage Please always use the correct checkpoint for the corresponding inference steps. Please use Euler sampler with sgm_uniform scheduler. ### 2-Step, 4-Step, 8-Step Full 1. Download the full checkpoint (`sdxl_lightning_Nstep.safetensors`) to `/ComfyUI/models/checkpoints`. 1. Download our [ComfyUI full workflow](comfyui/sdxl_lightning_workflow_full.json).  ### 2-Step, 4-Step, 8-Step LoRA Use LoRA only if you are using non-SDXL base models. Otherwise use our full checkpoint for better quality. 1. Prepare your own base model. 1. Download the LoRA checkpoint (`sdxl_lightning_Nstep_lora.safetensors`) to `/ComfyUI/models/loras` 1. Download our [ComfyUI LoRA workflow](comfyui/sdxl_lightning_workflow_lora.json).  ### 1-Step The 1-step model is only experimental and the quality is much less stable. Consider using the 2-step model for much better quality. 1. Update your ComfyUI to the latest version. 1. Download the full checkpoint (`sdxl_lightning_1step_x0.safetensors`) to `/ComfyUI/models/checkpoints`. 1. Download our [ComfyUI full 1-step workflow](comfyui/sdxl_lightning_workflow_full_1step.json).  ## Cite Our Work ``` @misc{lin2024sdxllightning, title={SDXL-Lightning: Progressive Adversarial Diffusion Distillation}, author={Shanchuan Lin and Anran Wang and Xiao Yang}, year={2024}, eprint={2402.13929}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

google/gemma-2b-it

google

transformers

text-generation

--- library_name: transformers widget: - messages: - role: user content: How does the brain work? inference: parameters: max_new_tokens: 200 extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license license: gemma --- # Gemma Model Card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 2B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [7B instruct model](https://huggingface.co/google/gemma-7b-it). **Resources and Technical Documentation**: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-it-gg-hf) **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) **Authors**: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU As explained below, we recommend `torch.bfloat16` as the default dtype. You can use [a different precision](#precisions) if necessary. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b-it", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b-it", device_map="auto", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` <a name="precisions"></a> #### Running the model on a GPU using different precisions The native weights of this model were exported in `bfloat16` precision. You can use `float16`, which may be faster on certain hardware, indicating the `torch_dtype` when loading the model. For convenience, the `float16` revision of the repo contains a copy of the weights already converted to that precision. You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below. * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b-it", device_map="auto", torch_dtype=torch.float16, revision="float16", ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Upcasting to `torch.float32`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b-it", device_map="auto" ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` * _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations * _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Chat Template The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "gg-hf/gemma-2b-it" dtype = torch.bfloat16 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype=dtype, ) chat = [ { "role": "user", "content": "Write a hello world program" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` At this point, the prompt contains the following text: ``` <bos><start_of_turn>user Write a hello world program<end_of_turn> <start_of_turn>model ``` As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with the `<end_of_turn>` token. You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. After the prompt is ready, generation can be performed like this: ```py inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) ``` ### Fine-tuning You can find some fine-tuning scripts under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt them to this model, simply change the model-id to `google/gemma-2b-it`. We provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA * A script to perform SFT using FSDP on TPU devices * A notebook that you can run on a free-tier Google Colab instance to perform SFT on the English quotes dataset ### Inputs and outputs * **Input:** Text string, such as a question, a prompt, or a document to be summarized. * **Output:** Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: | Benchmark | Metric | 2B Params | 7B Params | | ------------------------------ | ------------- | ----------- | --------- | | [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 | | [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 | | [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 | | [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 49.7 | 51.8 | | [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 | | [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 | | [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 | | [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 | | [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 | | [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 | | [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 | | [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 12.5 | 23 | | [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 | | [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 | | [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 | | [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 | | [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 | | [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 | | ------------------------------ | ------------- | ----------- | --------- | | **Average** | | **45.0** | **56.9** | ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. | Benchmark | Metric | 2B Params | 7B Params | | ------------------------------ | ------------- | ----------- | --------- | | [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 | | [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 | | [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 | | [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 | | [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 | | [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 | | [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 | | [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 | | [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 | | [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 | | ------------------------------ | ------------- | ----------- | --------- | ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.

meta-llama/Meta-Llama-3-8B-Instruct

meta-llama

transformers

text-generation

--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: other license_name: llama3 license_link: LICENSE extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Meta Llama 3" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. "Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof). 2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy) #### Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Meta Llama 3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit widget: - example_title: Hello messages: - role: user content: Hey my name is Julien! How are you? - example_title: Winter holidays messages: - role: system content: You are a helpful and honest assistant. Please, respond concisely and truthfully. - role: user content: Can you recommend a good destination for Winter holidays? - example_title: Programming assistant messages: - role: system content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully. - role: user content: Write a function that computes the nth fibonacci number. inference: parameters: max_new_tokens: 300 stop: - <|end_of_text|> - <|eot_id|> --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both. #### Transformers pipeline ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` #### Transformers AutoModelForCausalLM ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = model.generate( input_ids, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos

google-bert/bert-base-multilingual-uncased

google-bert

transformers

fill-mask

--- language: - multilingual - af - sq - ar - an - hy - ast - az - ba - eu - bar - be - bn - inc - bs - br - bg - my - ca - ceb - ce - zh - cv - hr - cs - da - nl - en - et - fi - fr - gl - ka - de - el - gu - ht - he - hi - hu - is - io - id - ga - it - ja - jv - kn - kk - ky - ko - la - lv - lt - roa - nds - lm - mk - mg - ms - ml - mr - min - ne - new - nb - nn - oc - fa - pms - pl - pt - pa - ro - ru - sco - sr - hr - scn - sk - sl - aze - es - su - sw - sv - tl - tg - ta - tt - te - tr - uk - ud - uz - vi - vo - war - cy - fry - pnb - yo license: apache-2.0 datasets: - wikipedia --- # BERT multilingual base model (uncased) Pretrained model on the top 102 languages with the largest Wikipedia using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the languages in the training set that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a top model. [SEP]", 'score': 0.1507750153541565, 'token': 11397, 'token_str': 'top'}, {'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.13075384497642517, 'token': 23589, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a good model. [SEP]", 'score': 0.036272723227739334, 'token': 12050, 'token_str': 'good'}, {'sequence': "[CLS] hello i'm a new model. [SEP]", 'score': 0.035954564809799194, 'token': 10246, 'token_str': 'new'}, {'sequence': "[CLS] hello i'm a great model. [SEP]", 'score': 0.028643041849136353, 'token': 11838, 'token_str': 'great'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased') model = BertModel.from_pretrained("bert-base-multilingual-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-uncased') model = TFBertModel.from_pretrained("bert-base-multilingual-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-uncased') >>> unmasker("The man worked as a [MASK].") [{'sequence': '[CLS] the man worked as a teacher. [SEP]', 'score': 0.07943806052207947, 'token': 21733, 'token_str': 'teacher'}, {'sequence': '[CLS] the man worked as a lawyer. [SEP]', 'score': 0.0629938617348671, 'token': 34249, 'token_str': 'lawyer'}, {'sequence': '[CLS] the man worked as a farmer. [SEP]', 'score': 0.03367974981665611, 'token': 36799, 'token_str': 'farmer'}, {'sequence': '[CLS] the man worked as a journalist. [SEP]', 'score': 0.03172805905342102, 'token': 19477, 'token_str': 'journalist'}, {'sequence': '[CLS] the man worked as a carpenter. [SEP]', 'score': 0.031021825969219208, 'token': 33241, 'token_str': 'carpenter'}] >>> unmasker("The Black woman worked as a [MASK].") [{'sequence': '[CLS] the black woman worked as a nurse. [SEP]', 'score': 0.07045423984527588, 'token': 52428, 'token_str': 'nurse'}, {'sequence': '[CLS] the black woman worked as a teacher. [SEP]', 'score': 0.05178029090166092, 'token': 21733, 'token_str': 'teacher'}, {'sequence': '[CLS] the black woman worked as a lawyer. [SEP]', 'score': 0.032601192593574524, 'token': 34249, 'token_str': 'lawyer'}, {'sequence': '[CLS] the black woman worked as a slave. [SEP]', 'score': 0.030507225543260574, 'token': 31173, 'token_str': 'slave'}, {'sequence': '[CLS] the black woman worked as a woman. [SEP]', 'score': 0.027691684663295746, 'token': 14050, 'token_str': 'woman'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT model was pretrained on the 102 languages with the largest Wikipedias. You can find the complete list [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of 110,000. The languages with a larger Wikipedia are under-sampled and the ones with lower resources are oversampled. For languages like Chinese, Japanese Kanji and Korean Hanja that don't have space, a CJK Unicode block is added around every character. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

ybelkada/tiny-random-T5ForConditionalGeneration-calibrated

ybelkada

transformers

text2text-generation

A "better calibrated" tiny T5 model for testing purposes

facebook/nllb-200-distilled-600M

facebook

transformers

translation

--- language: - ace - acm - acq - aeb - af - ajp - ak - als - am - apc - ar - ars - ary - arz - as - ast - awa - ayr - azb - azj - ba - bm - ban - be - bem - bn - bho - bjn - bo - bs - bug - bg - ca - ceb - cs - cjk - ckb - crh - cy - da - de - dik - dyu - dz - el - en - eo - et - eu - ee - fo - fj - fi - fon - fr - fur - fuv - gaz - gd - ga - gl - gn - gu - ht - ha - he - hi - hne - hr - hu - hy - ig - ilo - id - is - it - jv - ja - kab - kac - kam - kn - ks - ka - kk - kbp - kea - khk - km - ki - rw - ky - kmb - kmr - knc - kg - ko - lo - lij - li - ln - lt - lmo - ltg - lb - lua - lg - luo - lus - lvs - mag - mai - ml - mar - min - mk - mt - mni - mos - mi - my - nl - nn - nb - npi - nso - nus - ny - oc - ory - pag - pa - pap - pbt - pes - plt - pl - pt - prs - quy - ro - rn - ru - sg - sa - sat - scn - shn - si - sk - sl - sm - sn - sd - so - st - es - sc - sr - ss - su - sv - swh - szl - ta - taq - tt - te - tg - tl - th - ti - tpi - tn - ts - tk - tum - tr - tw - tzm - ug - uk - umb - ur - uzn - vec - vi - war - wo - xh - ydd - yo - yue - zh - zsm - zu language_details: "ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn" pipeline_tag: translation tags: - nllb license: "cc-by-nc-4.0" datasets: - flores-200 metrics: - bleu - spbleu - chrf++ inference: false --- # NLLB-200 This is the model card of NLLB-200's distilled 600M variant. Here are the [metrics](https://tinyurl.com/nllb200densedst600mmetrics) for that particular checkpoint. - Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper. - Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022 - License: CC-BY-NC - Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues ## Intended Use - Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data. - Primary intended users: Primary users are researchers and machine translation research community. - Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations. ## Metrics • Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations. ## Evaluation Data - Datasets: Flores-200 dataset is described in Section 4 - Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200 - Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The SentencePiece model is released along with NLLB-200. ## Training Data • We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2. ## Ethical Considerations • In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety). ## Caveats and Recommendations • Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments. ## Carbon Footprint Details • The carbon dioxide (CO2e) estimate is reported in Section 8.8.

lengyue233/content-vec-best

lengyue233

transformers

--- license: mit --- # Content Vec Best Official Repo: [ContentVec](https://github.com/auspicious3000/contentvec) This repo brings fairseq ContentVec model to HuggingFace Transformers. ## How to use To use this model, you need to define ```python class HubertModelWithFinalProj(HubertModel): def __init__(self, config): super().__init__(config) # The final projection layer is only used for backward compatibility. # Following https://github.com/auspicious3000/contentvec/issues/6 # Remove this layer is necessary to achieve the desired outcome. self.final_proj = nn.Linear(config.hidden_size, config.classifier_proj_size) ``` and then load the model with ```python model = HubertModelWithFinalProj.from_pretrained("lengyue233/content-vec-best") x = model(audio)["last_hidden_state"] ``` ## How to convert You need to download the ContentVec_legacy model from the official repo, and then run ```bash python convert.py ```

microsoft/codebert-base-mlm

microsoft

transformers

fill-mask

## CodeBERT-base-mlm Pretrained weights for [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155). ### Training Data The model is trained on the code corpus of [CodeSearchNet](https://github.com/github/CodeSearchNet) ### Training Objective This model is initialized with Roberta-base and trained with a simple MLM (Masked Language Model) objective. ### Usage ```python from transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline model = RobertaForMaskedLM.from_pretrained('microsoft/codebert-base-mlm') tokenizer = RobertaTokenizer.from_pretrained('microsoft/codebert-base-mlm') code_example = "if (x is not None) <mask> (x>1)" fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer) outputs = fill_mask(code_example) print(outputs) ``` Expected results: ``` {'sequence': '<s> if (x is not None) and (x>1)</s>', 'score': 0.6049249172210693, 'token': 8} {'sequence': '<s> if (x is not None) or (x>1)</s>', 'score': 0.30680200457572937, 'token': 50} {'sequence': '<s> if (x is not None) if (x>1)</s>', 'score': 0.02133703976869583, 'token': 114} {'sequence': '<s> if (x is not None) then (x>1)</s>', 'score': 0.018607674166560173, 'token': 172} {'sequence': '<s> if (x is not None) AND (x>1)</s>', 'score': 0.007619690150022507, 'token': 4248} ``` ### Reference 1. [Bimodal CodeBERT trained with MLM+RTD objective](https://huggingface.co/microsoft/codebert-base) (suitable for code search and document generation) 2. 🤗 [Hugging Face's CodeBERTa](https://huggingface.co/huggingface/CodeBERTa-small-v1) (small size, 6 layers) ### Citation ```bibtex @misc{feng2020codebert, title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages}, author={Zhangyin Feng and Daya Guo and Duyu Tang and Nan Duan and Xiaocheng Feng and Ming Gong and Linjun Shou and Bing Qin and Ting Liu and Daxin Jiang and Ming Zhou}, year={2020}, eprint={2002.08155}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

skt/kobert-base-v1

skt

transformers

feature-extraction

Please refer here. https://github.com/SKTBrain/KoBERT

mpi-inno-comp/paecter

mpi-inno-comp

sentence-transformers

sentence-similarity

--- language: en pipeline_tag: sentence-similarity tags: - patent-similarity - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - patents license: apache-2.0 --- # paecter This is a [sentence-transformers](https://www.SBERT.net) model. This model is fine-tuned on patent texts, leveraging Google's BERT for Patents as its base. It can be used to generate 1024 dimensional dense vector for patent texts for downstream tasks like semantic search, prior art search, clustering, and patent landscaping. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('mpi-inno-comp/paecter') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('mpi-inno-comp/paecter') model = AutoModel.from_pretrained('mpi-inno-comp/paecter') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt', max_length=512) # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> Evaluation of this model is available in our paper, [PaECTER: Patent-level Representation Learning using Citation-informed Transformers ](https://arxiv.org/abs/2402.19411) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 318750 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CustomTripletLoss.CustomTripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 1} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 4000, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 1e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 31875.0, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ``` @misc{ghosh2024paecter, title={PaECTER: Patent-level Representation Learning using Citation-informed Transformers}, author={Mainak Ghosh and Sebastian Erhardt and Michael E. Rose and Erik Buunk and Dietmar Harhoff}, year={2024}, eprint={2402.19411}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```

google/mt5-base

google

transformers

text2text-generation

--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- [Google's mT5](https://github.com/google-research/multilingual-t5) mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu. **Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) Other Community Checkpoints: [here](https://huggingface.co/models?search=mt5) Paper: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) Authors: *Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel* ## Abstract The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. All of the code and model checkpoints used in this work are publicly available.

cross-encoder/ms-marco-MiniLM-L-4-v2

cross-encoder

transformers

text-classification

--- license: apache-2.0 --- # Cross-Encoder for MS Marco This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco) ## Usage with Transformers ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('model_name') tokenizer = AutoTokenizer.from_pretrained('model_name') features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits print(scores) ``` ## Usage with SentenceTransformers The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name', max_length=512) scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]) ``` ## Performance In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. | Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec | | ------------- |:-------------| -----| --- | | **Version 2 models** | | | | cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000 | cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100 | cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500 | cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800 | cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960 | **Version 1 models** | | | | cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000 | cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900 | cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680 | cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340 | **Other models** | | | | nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 | nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 | nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 | Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 | amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 | sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720 Note: Runtime was computed on a V100 GPU.

distilbert/distilbert-base-cased

distilbert

transformers

fill-mask

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # Model Card for DistilBERT base model (cased) This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is cased: it does make a difference between english and English. All the training details on the pre-training, the uses, limitations and potential biases (included below) are the same as for [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased). We highly encourage to check it if you want to know more. ## Model description DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives: - Distillation loss: the model was trained to return the same probabilities as the BERT base model. - Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base model. This way, the model learns the same inner representation of the English language than its teacher model, while being faster for inference or downstream tasks. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.05292855575680733, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.03968575969338417, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a business model. [SEP]", 'score': 0.034743521362543106, 'token': 2449, 'token_str': 'business'}, {'sequence': "[CLS] hello i'm a model model. [SEP]", 'score': 0.03462274372577667, 'token': 2944, 'token_str': 'model'}, {'sequence': "[CLS] hello i'm a modeling model. [SEP]", 'score': 0.018145186826586723, 'token': 11643, 'token_str': 'modeling'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import DistilBertTokenizer, DistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = DistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import DistilBertTokenizer, TFDistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = TFDistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. It also inherits some of [the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("The White man worked as a [MASK].") [{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]', 'score': 0.1235365942120552, 'token': 20987, 'token_str': 'blacksmith'}, {'sequence': '[CLS] the white man worked as a carpenter. [SEP]', 'score': 0.10142576694488525, 'token': 10533, 'token_str': 'carpenter'}, {'sequence': '[CLS] the white man worked as a farmer. [SEP]', 'score': 0.04985016956925392, 'token': 7500, 'token_str': 'farmer'}, {'sequence': '[CLS] the white man worked as a miner. [SEP]', 'score': 0.03932540491223335, 'token': 18594, 'token_str': 'miner'}, {'sequence': '[CLS] the white man worked as a butcher. [SEP]', 'score': 0.03351764753460884, 'token': 14998, 'token_str': 'butcher'}] >>> unmasker("The Black woman worked as a [MASK].") [{'sequence': '[CLS] the black woman worked as a waitress. [SEP]', 'score': 0.13283951580524445, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the black woman worked as a nurse. [SEP]', 'score': 0.12586183845996857, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the black woman worked as a maid. [SEP]', 'score': 0.11708822101354599, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]', 'score': 0.11499975621700287, 'token': 19215, 'token_str': 'prostitute'}, {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]', 'score': 0.04722772538661957, 'token': 22583, 'token_str': 'housekeeper'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 16 GB V100 for 90 hours. See the [training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters details. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| | | 81.5 | 87.8 | 88.2 | 90.4 | 47.2 | 85.5 | 85.6 | 60.6 | ### BibTeX entry and citation info ```bibtex @article{Sanh2019DistilBERTAD, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, journal={ArXiv}, year={2019}, volume={abs/1910.01108} } ``` <a href="https://huggingface.co/exbert/?model=distilbert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

akdeniz27/bert-base-turkish-cased-ner

akdeniz27

transformers

token-classification

--- language: tr widget: - text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı." --- # Turkish Named Entity Recognition (NER) Model This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased" using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). # Fine-tuning parameters: ``` task = "ner" model_checkpoint = "dbmdz/bert-base-turkish-cased" batch_size = 8 label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] max_length = 512 learning_rate = 2e-5 num_train_epochs = 3 weight_decay = 0.01 ``` # How to use: ``` model = AutoModelForTokenClassification.from_pretrained("akdeniz27/bert-base-turkish-cased-ner") tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-turkish-cased-ner") ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first") ner("your text here") ``` Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. # Reference test results: * accuracy: 0.9933935699477056 * f1: 0.9592969472710453 * precision: 0.9543530277931161 * recall: 0.9642923563325274 Evaluation results with the test sets proposed in ["Küçük, D., Küçük, D., Arıcı, N. 2016. Türkçe Varlık İsmi Tanıma için bir Veri Kümesi ("A Named Entity Recognition Dataset for Turkish"). IEEE Sinyal İşleme, İletişim ve Uygulamaları Kurultayı. Zonguldak, Türkiye."](https://ieeexplore.ieee.org/document/7495744) paper. * Test Set Acc. Prec. Rec. F1-Score * 20010000 0.9946 0.9871 0.9463 0.9662 * 20020000 0.9928 0.9134 0.9206 0.9170 * 20030000 0.9942 0.9814 0.9186 0.9489 * 20040000 0.9943 0.9660 0.9522 0.9590 * 20050000 0.9971 0.9539 0.9932 0.9732 * 20060000 0.9993 0.9942 0.9942 0.9942 * 20070000 0.9970 0.9806 0.9439 0.9619 * 20080000 0.9988 0.9821 0.9649 0.9735 * 20090000 0.9977 0.9891 0.9479 0.9681 * 20100000 0.9961 0.9684 0.9293 0.9485 * Overall 0.9961 0.9720 0.9516 0.9617

google-bert/bert-large-cased

google-bert

transformers

fill-mask

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT large model (cased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is cased: it makes a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-cased') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] Hello I'm a male model. [SEP]", "score":0.22748498618602753, "token":2581, "token_str":"male" }, { "sequence":"[CLS] Hello I'm a fashion model. [SEP]", "score":0.09146175533533096, "token":4633, "token_str":"fashion" }, { "sequence":"[CLS] Hello I'm a new model. [SEP]", "score":0.05823173746466637, "token":1207, "token_str":"new" }, { "sequence":"[CLS] Hello I'm a super model. [SEP]", "score":0.04488750174641609, "token":7688, "token_str":"super" }, { "sequence":"[CLS] Hello I'm a famous model. [SEP]", "score":0.03271442651748657, "token":2505, "token_str":"famous" } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-large-cased') model = BertModel.from_pretrained("bert-large-cased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-large-cased') model = TFBertModel.from_pretrained("bert-large-cased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-cased') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] The man worked as a doctor. [SEP]", "score":0.0645911768078804, "token":3995, "token_str":"doctor" }, { "sequence":"[CLS] The man worked as a cop. [SEP]", "score":0.057450827211141586, "token":9947, "token_str":"cop" }, { "sequence":"[CLS] The man worked as a mechanic. [SEP]", "score":0.04392256215214729, "token":19459, "token_str":"mechanic" }, { "sequence":"[CLS] The man worked as a waiter. [SEP]", "score":0.03755280375480652, "token":17989, "token_str":"waiter" }, { "sequence":"[CLS] The man worked as a teacher. [SEP]", "score":0.03458863124251366, "token":3218, "token_str":"teacher" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] The woman worked as a nurse. [SEP]", "score":0.2572779953479767, "token":7439, "token_str":"nurse" }, { "sequence":"[CLS] The woman worked as a waitress. [SEP]", "score":0.16706500947475433, "token":15098, "token_str":"waitress" }, { "sequence":"[CLS] The woman worked as a teacher. [SEP]", "score":0.04587847739458084, "token":3218, "token_str":"teacher" }, { "sequence":"[CLS] The woman worked as a secretary. [SEP]", "score":0.03577028587460518, "token":4848, "token_str":"secretary" }, { "sequence":"[CLS] The woman worked as a maid. [SEP]", "score":0.03298963978886604, "token":13487, "token_str":"maid" } ] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Model | SQUAD 1.1 F1/EM | Multi NLI Accuracy ---------------------------------------- | :-------------: | :----------------: BERT-Large, Cased (Original) | 91.5/84.8 | 86.09 ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

flair/ner-english

flair

flair

token-classification

--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - conll2003 widget: - text: "George Washington went to Washington" --- ## English NER in Flair (default model) This is the standard 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **93,06** (corrected CoNLL-03) Predicts 4 tags: | **tag** | **meaning** | |---------------------------------|-----------| | PER | person name | | LOC | location name | | ORG | organization name | | MISC | other name | Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. --- ### Demo: How to use in Flair Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/ner-english") # make example sentence sentence = Sentence("George Washington went to Washington") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [1,2]: "George Washington" [− Labels: PER (0.9968)] Span [5]: "Washington" [− Labels: LOC (0.9994)] ``` So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*". --- ### Training: Script to train this model The following Flair script was used to train this model: ```python from flair.data import Corpus from flair.datasets import CONLL_03 from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. get the corpus corpus: Corpus = CONLL_03() # 2. what tag do we want to predict? tag_type = 'ner' # 3. make the tag dictionary from the corpus tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) # 4. initialize each embedding we use embedding_types = [ # GloVe embeddings WordEmbeddings('glove'), # contextual string embeddings, forward FlairEmbeddings('news-forward'), # contextual string embeddings, backward FlairEmbeddings('news-backward'), ] # embedding stack consists of Flair and GloVe embeddings embeddings = StackedEmbeddings(embeddings=embedding_types) # 5. initialize sequence tagger from flair.models import SequenceTagger tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type) # 6. initialize trainer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus) # 7. run training trainer.train('resources/taggers/ner-english', train_with_dev=True, max_epochs=150) ``` --- ### Cite Please cite the following paper when using this model. ``` @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` --- ### Issues? The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).

ai-forever/sbert_large_nlu_ru

ai-forever

transformers

feature-extraction

--- language: - ru tags: - PyTorch - Transformers --- # BERT large model (uncased) for Sentence Embeddings in Russian language. The model is described [in this article](https://habr.com/ru/company/sberdevices/blog/527576/) For better quality, use mean token embeddings. ## Usage (HuggingFace Models Repository) You can use the model directly from the model repository to compute sentence embeddings: ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) return sum_embeddings / sum_mask #Sentences we want sentence embeddings for sentences = ['Привет! Как твои дела?', 'А правда, что 42 твое любимое число?'] #Load AutoModel from huggingface model repository tokenizer = AutoTokenizer.from_pretrained("ai-forever/sbert_large_nlu_ru") model = AutoModel.from_pretrained("ai-forever/sbert_large_nlu_ru") #Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, return_tensors='pt') #Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) #Perform pooling. In this case, mean pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) ``` # Authors + [SberDevices](https://sberdevices.ru/) Team. + Aleksandr Abramov: [HF profile](https://huggingface.co/Andrilko), [Github](https://github.com/Ab1992ao), [Kaggle Competitions Master](https://www.kaggle.com/andrilko); + Denis Antykhov: [Github](https://github.com/gaphex);

timm/mobilenetv3_large_100.ra_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for mobilenetv3_large_100.ra_in1k A MobileNet-v3 image classification model. Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * RandAugment `RA` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476). * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 5.5 - GMACs: 0.2 - Activations (M): 4.4 - Image size: 224 x 224 - **Papers:** - Searching for MobileNetV3: https://arxiv.org/abs/1905.02244 - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - **Dataset:** ImageNet-1k - **Original:** https://github.com/huggingface/pytorch-image-models ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('mobilenetv3_large_100.ra_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'mobilenetv3_large_100.ra_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 16, 112, 112]) # torch.Size([1, 24, 56, 56]) # torch.Size([1, 40, 28, 28]) # torch.Size([1, 112, 14, 14]) # torch.Size([1, 960, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'mobilenetv3_large_100.ra_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 960, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{howard2019searching, title={Searching for mobilenetv3}, author={Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and others}, booktitle={Proceedings of the IEEE/CVF international conference on computer vision}, pages={1314--1324}, year={2019} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ``` ```bibtex @inproceedings{wightman2021resnet, title={ResNet strikes back: An improved training procedure in timm}, author={Wightman, Ross and Touvron, Hugo and Jegou, Herve}, booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future} } ```

rizvandwiki/gender-classification

rizvandwiki

transformers

image-classification

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: gender-classification results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9244444370269775 --- # gender-classification Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### female  #### male 

paust/pko-t5-large

paust

transformers

text2text-generation

--- language: ko license: cc-by-4.0 --- # pko-t5-large [Source Code](https://github.com/paust-team/pko-t5) pko-t5 는 한국어 전용 데이터로 학습한 [t5 v1.1 모델](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)입니다. 한국어를 tokenize 하기 위해서 sentencepiece 대신 OOV 가 없는 BBPE 를 사용했으며 한국어 데이터 (나무위키, 위키피디아, 모두의말뭉치 등..) 를 T5 의 span corruption task 를 사용해서 unsupervised learning 만 적용하여 학습을 진행했습니다. pko-t5 를 사용하실 때는 대상 task 에 파인튜닝하여 사용하시기 바랍니다. ## Usage transformers 의 API 를 사용하여 접근 가능합니다. tokenizer 를 사용할때는 `T5Tokenizer` 가 아니라 `T5TokenizerFast` 를 사용해주십시오. model 은 T5ForConditionalGeneration 를 그대로 활용하시면 됩니다. ### Example ```python from transformers import T5TokenizerFast, T5ForConditionalGeneration tokenizer = T5TokenizerFast.from_pretrained('paust/pko-t5-large') model = T5ForConditionalGeneration.from_pretrained('paust/pko-t5-large') input_ids = tokenizer(["qa question: 당신의 이름은 무엇인가요?"]).input_ids labels = tokenizer(["T5 입니다."]).input_ids outputs = model(input_ids=input_ids, labels=labels) print(f"loss={outputs.loss} logits={outputs.logits}") ``` ## Klue 평가 (dev) | | Model | ynat (macro F1) | sts (pearsonr/F1) | nli (acc) | ner (entity-level F1) | re (micro F1) | dp (LAS) | mrc (EM/F1) | |-----|------------------------------------------------------------------|-----------------|-------------------|-----------|-----------------------|---------------|-----------|-------------| | | Baseline | **87.30** | **93.20/86.13** | **89.50** | 86.06 | 71.06 | 87.93 | **75.26/-** | | FT | [pko-t5-small](https://huggingface.co/paust/pko-t5-small) (77M) | 86.21 | 77.99/77.01 | 69.20 | 82.60 | 66.46 | 93.15 | 43.81/46.58 | | FT | [pko-t5-base](https://huggingface.co/paust/pko-t5-base) (250M) | 87.29 | 90.25/83.43 | 79.73 | 87.80 | 67.23 | 97.28 | 61.53/64.74 | | FT | [pko-t5-large](https://huggingface.co/paust/pko-t5-large) (800M) | 87.12 | 92.05/85.24 | 84.96 | **88.18** | **75.17** | **97.60** | 68.01/71.44 | | MT | pko-t5-small | 84.54 | 68.50/72/02 | 51.16 | 74.69 | 66.11 | 80.40 | 43.60/46.28 | | MT | pko-t5-base | 86.89 | 83.96/80.30 | 72.03 | 85.27 | 66.59 | 95.05 | 61.11/63.94 | | MT | pko-t5-large | 87.57 | 91.93/86.29 | 83.63 | 87.41 | 71.34 | 96.99 | 70.70/73.72 | - FT: 싱글태스크 파인튜닝 / MT: 멀티태스크 파인튜닝 - [Baseline](https://arxiv.org/abs/2105.09680): KLUE 논문에서 소개된 dev set 에 대한 SOTA 점수 ## License [PAUST](https://paust.io)에서 만든 pko-t5는 [MIT license](https://github.com/paust-team/pko-t5/blob/main/LICENSE) 하에 공개되어 있습니다.

madhurjindal/autonlp-Gibberish-Detector-492513457

madhurjindal

transformers

text-classification

--- tags: [autonlp] language: en widget: - text: "I love Machine Learning!" datasets: - madhurjindal/autonlp-data-Gibberish-Detector co2_eq_emissions: 5.527544460835904 --- # Problem Description The ability to process and understand user input is crucial for various applications, such as chatbots or downstream tasks. However, a common challenge faced in such systems is the presence of gibberish or nonsensical input. To address this problem, we present a project focused on developing a gibberish detector for the English language. The primary goal of this project is to classify user input as either **gibberish** or **non-gibberish**, enabling more accurate and meaningful interactions with the system. We also aim to enhance the overall performance and user experience of chatbots and other systems that rely on user input. >## What is Gibberish? Gibberish refers to **nonsensical or meaningless language or text** that lacks coherence or any discernible meaning. It can be characterized by a combination of random words, nonsensical phrases, grammatical errors, or syntactical abnormalities that prevent the communication from conveying a clear and understandable message. Gibberish can vary in intensity, ranging from simple noise with no meaningful words to sentences that may appear superficially correct but lack coherence or logical structure when examined closely. Detecting and identifying gibberish is essential in various contexts, such as **natural language processing**, **chatbot systems**, **spam filtering**, and **language-based security measures**, to ensure effective communication and accurate processing of user inputs. ## Label Description Thus, we break down the problem into 4 categories: 1. **Noise:** Gibberish at the zero level where even the different constituents of the input phrase (words) do not hold any meaning independently. *For example: `dfdfer fgerfow2e0d qsqskdsd djksdnfkff swq.`* 2. **Word Salad:** Gibberish at level 1 where words make sense independently, but when looked at the bigger picture (the phrase) any meaning is not depicted. *For example: `22 madhur old punjab pickle chennai`* 3. **Mild gibberish:** Gibberish at level 2 where there is a part of the sentence that has grammatical errors, word sense errors, or any syntactical abnormalities, which leads the sentence to miss out on a coherent meaning. *For example: `Madhur study in a teacher`* 4. **Clean:** This category represents a set of words that form a complete and meaningful sentence on its own. *For example: `I love this website`* > **Tip:** To facilitate gibberish detection, you can combine the labels based on the desired level of detection. For instance, if you need to detect gibberish at level 1, you can group Noise and Word Salad together as "Gibberish," while considering Mild gibberish and Clean separately as "NotGibberish." This approach allows for flexibility in detecting and categorizing different levels of gibberish based on specific requirements. # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 492513457 - CO2 Emissions (in grams): 5.527544460835904 ## Validation Metrics - Loss: 0.07609463483095169 - Accuracy: 0.9735624586913417 - Macro F1: 0.9736173135739408 - Micro F1: 0.9735624586913417 - Weighted F1: 0.9736173135739408 - Macro Precision: 0.9737771415197378 - Micro Precision: 0.9735624586913417 - Weighted Precision: 0.9737771415197378 - Macro Recall: 0.9735624586913417 - Micro Recall: 0.9735624586913417 - Weighted Recall: 0.9735624586913417 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love Machine Learning!"}' https://api-inference.huggingface.co/models/madhurjindal/autonlp-Gibberish-Detector-492513457 ``` Or Python API: ``` import torch import torch.nn.functional as F from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("madhurjindal/autonlp-Gibberish-Detector-492513457", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("madhurjindal/autonlp-Gibberish-Detector-492513457", use_auth_token=True) inputs = tokenizer("I love Machine Learning!", return_tensors="pt") outputs = model(**inputs) probs = F.softmax(outputs.logits, dim=-1) predicted_index = torch.argmax(probs, dim=1).item() predicted_prob = probs[0][predicted_index].item() labels = model.config.id2label predicted_label = labels[predicted_index] for i, prob in enumerate(probs[0]): print(f"Class: {labels[i]}, Probability: {prob:.4f}") ``` Another simplifed solution with transformers pipline: ``` from transformers import pipeline selected_model = "madhurjindal/autonlp-Gibberish-Detector-492513457" classifier = pipeline("text-classification", model=selected_model) classifier("I love Machine Learning!") ```

ramsrigouthamg/t5_squad_v1

ramsrigouthamg

transformers

text2text-generation

Entry not found

MoritzLaurer/deberta-v3-large-zeroshot-v2.0

MoritzLaurer

transformers

zero-shot-classification

--- language: - en tags: - text-classification - zero-shot-classification base_model: microsoft/deberta-v3-large pipeline_tag: zero-shot-classification library_name: transformers license: mit --- # Model description: deberta-v3-large-zeroshot-v2.0 ## zeroshot-v2.0 series of models Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline. These models can do classification without training data and run on both GPUs and CPUs. An overview of the latest zeroshot classifiers is available in my [Zeroshot Classifier Collection](https://huggingface.co/collections/MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f). The main update of this `zeroshot-v2.0` series of models is that several models are trained on fully commercially-friendly data for users with strict license requirements. These models can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text (`entailment` vs. `not_entailment`). This task format is based on the Natural Language Inference task (NLI). The task is so universal that any classification task can be reformulated into this task by the Hugging Face pipeline. ## Training data Models with a "`-c`" in the name are trained on two types of fully commercially-friendly data: 1. Synthetic data generated with [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1). I first created a list of 500+ diverse text classification tasks for 25 professions in conversations with Mistral-large. The data was manually curated. I then used this as seed data to generate several hundred thousand texts for these tasks with Mixtral-8x7B-Instruct-v0.1. The final dataset used is available in the [synthetic_zeroshot_mixtral_v0.1](https://huggingface.co/datasets/MoritzLaurer/synthetic_zeroshot_mixtral_v0.1) dataset in the subset `mixtral_written_text_for_tasks_v4`. Data curation was done in multiple iterations and will be improved in future iterations. 2. Two commercially-friendly NLI datasets: ([MNLI](https://huggingface.co/datasets/nyu-mll/multi_nli), [FEVER-NLI](https://huggingface.co/datasets/fever)). These datasets were added to increase generalization. 3. Models without a "`-c`" in the name also included a broader mix of training data with a broader mix of licenses: ANLI, WANLI, LingNLI, and all datasets in [this list](https://github.com/MoritzLaurer/zeroshot-classifier/blob/7f82e4ab88d7aa82a4776f161b368cc9fa778001/v1_human_data/datasets_overview.csv) where `used_in_v1.1==True`. ## How to use the models ```python #!pip install transformers[sentencepiece] from transformers import pipeline text = "Angela Merkel is a politician in Germany and leader of the CDU" hypothesis_template = "This text is about {}" classes_verbalized = ["politics", "economy", "entertainment", "environment"] zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v2.0") # change the model identifier here output = zeroshot_classifier(text, classes_verbalized, hypothesis_template=hypothesis_template, multi_label=False) print(output) ``` `multi_label=False` forces the model to decide on only one class. `multi_label=True` enables the model to choose multiple classes. ## Metrics The models were evaluated on 28 different text classification tasks with the [f1_macro](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) metric. The main reference point is `facebook/bart-large-mnli` which is, at the time of writing (03.04.24), the most used commercially-friendly 0-shot classifier.  | | facebook/bart-large-mnli | roberta-base-zeroshot-v2.0-c | roberta-large-zeroshot-v2.0-c | deberta-v3-base-zeroshot-v2.0-c | deberta-v3-base-zeroshot-v2.0 (fewshot) | deberta-v3-large-zeroshot-v2.0-c | deberta-v3-large-zeroshot-v2.0 (fewshot) | bge-m3-zeroshot-v2.0-c | bge-m3-zeroshot-v2.0 (fewshot) | |:---------------------------|---------------------------:|-----------------------------:|------------------------------:|--------------------------------:|-----------------------------------:|---------------------------------:|------------------------------------:|-----------------------:|--------------------------:| | all datasets mean | 0.497 | 0.587 | 0.622 | 0.619 | 0.643 (0.834) | 0.676 | 0.673 (0.846) | 0.59 | (0.803) | | amazonpolarity (2) | 0.937 | 0.924 | 0.951 | 0.937 | 0.943 (0.961) | 0.952 | 0.956 (0.968) | 0.942 | (0.951) | | imdb (2) | 0.892 | 0.871 | 0.904 | 0.893 | 0.899 (0.936) | 0.923 | 0.918 (0.958) | 0.873 | (0.917) | | appreviews (2) | 0.934 | 0.913 | 0.937 | 0.938 | 0.945 (0.948) | 0.943 | 0.949 (0.962) | 0.932 | (0.954) | | yelpreviews (2) | 0.948 | 0.953 | 0.977 | 0.979 | 0.975 (0.989) | 0.988 | 0.985 (0.994) | 0.973 | (0.978) | | rottentomatoes (2) | 0.83 | 0.802 | 0.841 | 0.84 | 0.86 (0.902) | 0.869 | 0.868 (0.908) | 0.813 | (0.866) | | emotiondair (6) | 0.455 | 0.482 | 0.486 | 0.459 | 0.495 (0.748) | 0.499 | 0.484 (0.688) | 0.453 | (0.697) | | emocontext (4) | 0.497 | 0.555 | 0.63 | 0.59 | 0.592 (0.799) | 0.699 | 0.676 (0.81) | 0.61 | (0.798) | | empathetic (32) | 0.371 | 0.374 | 0.404 | 0.378 | 0.405 (0.53) | 0.447 | 0.478 (0.555) | 0.387 | (0.455) | | financialphrasebank (3) | 0.465 | 0.562 | 0.455 | 0.714 | 0.669 (0.906) | 0.691 | 0.582 (0.913) | 0.504 | (0.895) | | banking77 (72) | 0.312 | 0.124 | 0.29 | 0.421 | 0.446 (0.751) | 0.513 | 0.567 (0.766) | 0.387 | (0.715) | | massive (59) | 0.43 | 0.428 | 0.543 | 0.512 | 0.52 (0.755) | 0.526 | 0.518 (0.789) | 0.414 | (0.692) | | wikitoxic_toxicaggreg (2) | 0.547 | 0.751 | 0.766 | 0.751 | 0.769 (0.904) | 0.741 | 0.787 (0.911) | 0.736 | (0.9) | | wikitoxic_obscene (2) | 0.713 | 0.817 | 0.854 | 0.853 | 0.869 (0.922) | 0.883 | 0.893 (0.933) | 0.783 | (0.914) | | wikitoxic_threat (2) | 0.295 | 0.71 | 0.817 | 0.813 | 0.87 (0.946) | 0.827 | 0.879 (0.952) | 0.68 | (0.947) | | wikitoxic_insult (2) | 0.372 | 0.724 | 0.798 | 0.759 | 0.811 (0.912) | 0.77 | 0.779 (0.924) | 0.783 | (0.915) | | wikitoxic_identityhate (2) | 0.473 | 0.774 | 0.798 | 0.774 | 0.765 (0.938) | 0.797 | 0.806 (0.948) | 0.761 | (0.931) | | hateoffensive (3) | 0.161 | 0.352 | 0.29 | 0.315 | 0.371 (0.862) | 0.47 | 0.461 (0.847) | 0.291 | (0.823) | | hatexplain (3) | 0.239 | 0.396 | 0.314 | 0.376 | 0.369 (0.765) | 0.378 | 0.389 (0.764) | 0.29 | (0.729) | | biasframes_offensive (2) | 0.336 | 0.571 | 0.583 | 0.544 | 0.601 (0.867) | 0.644 | 0.656 (0.883) | 0.541 | (0.855) | | biasframes_sex (2) | 0.263 | 0.617 | 0.835 | 0.741 | 0.809 (0.922) | 0.846 | 0.815 (0.946) | 0.748 | (0.905) | | biasframes_intent (2) | 0.616 | 0.531 | 0.635 | 0.554 | 0.61 (0.881) | 0.696 | 0.687 (0.891) | 0.467 | (0.868) | | agnews (4) | 0.703 | 0.758 | 0.745 | 0.68 | 0.742 (0.898) | 0.819 | 0.771 (0.898) | 0.687 | (0.892) | | yahootopics (10) | 0.299 | 0.543 | 0.62 | 0.578 | 0.564 (0.722) | 0.621 | 0.613 (0.738) | 0.587 | (0.711) | | trueteacher (2) | 0.491 | 0.469 | 0.402 | 0.431 | 0.479 (0.82) | 0.459 | 0.538 (0.846) | 0.471 | (0.518) | | spam (2) | 0.505 | 0.528 | 0.504 | 0.507 | 0.464 (0.973) | 0.74 | 0.597 (0.983) | 0.441 | (0.978) | | wellformedquery (2) | 0.407 | 0.333 | 0.333 | 0.335 | 0.491 (0.769) | 0.334 | 0.429 (0.815) | 0.361 | (0.718) | | manifesto (56) | 0.084 | 0.102 | 0.182 | 0.17 | 0.187 (0.376) | 0.258 | 0.256 (0.408) | 0.147 | (0.331) | | capsotu (21) | 0.34 | 0.479 | 0.523 | 0.502 | 0.477 (0.664) | 0.603 | 0.502 (0.686) | 0.472 | (0.644) | These numbers indicate zeroshot performance, as no data from these datasets was added in the training mix. Note that models without a "`-c`" in the title were evaluated twice: one run without any data from these 28 datasets to test pure zeroshot performance (the first number in the respective column) and the final run including up to 500 training data points per class from each of the 28 datasets (the second number in brackets in the column, "fewshot"). No model was trained on test data. Details on the different datasets are available here: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/v1_human_data/datasets_overview.csv ## When to use which model - **deberta-v3-zeroshot vs. roberta-zeroshot**: deberta-v3 performs clearly better than roberta, but it is a bit slower. roberta is directly compatible with Hugging Face's production inference TEI containers and flash attention. These containers are a good choice for production use-cases. tl;dr: For accuracy, use a deberta-v3 model. If production inference speed is a concern, you can consider a roberta model (e.g. in a TEI container and [HF Inference Endpoints](https://ui.endpoints.huggingface.co/catalog)). - **commercial use-cases**: models with "`-c`" in the title are guaranteed to be trained on only commercially-friendly data. Models without a "`-c`" were trained on more data and perform better, but include data with non-commercial licenses. Legal opinions diverge if this training data affects the license of the trained model. For users with strict legal requirements, the models with "`-c`" in the title are recommended. - **Multilingual/non-English use-cases**: use [bge-m3-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0) or [bge-m3-zeroshot-v2.0-c](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0-c). Note that multilingual models perform worse than English-only models. You can therefore also first machine translate your texts to English with libraries like [EasyNMT](https://github.com/UKPLab/EasyNMT) and then apply any English-only model to the translated data. Machine translation also facilitates validation in case your team does not speak all languages in the data. - **context window**: The `bge-m3` models can process up to 8192 tokens. The other models can process up to 512. Note that longer text inputs both make the mode slower and decrease performance, so if you're only working with texts of up to 400~ words / 1 page, use e.g. a deberta model for better performance. - The latest updates on new models are always available in the [Zeroshot Classifier Collection](https://huggingface.co/collections/MoritzLaurer/zeroshot-classifiers-6548b4ff407bb19ff5c3ad6f). ## Reproduction Reproduction code is available in the `v2_synthetic_data` directory here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main ## Limitations and bias The model can only do text classification tasks. Biases can come from the underlying foundation model, the human NLI training data and the synthetic data generated by Mixtral. ## License The foundation model was published under the MIT license. The licenses of the training data vary depending on the model, see above. ## Citation This model is an extension of the research described in this [paper](https://arxiv.org/pdf/2312.17543.pdf). If you use this model academically, please cite: ``` @misc{laurer_building_2023, title = {Building {Efficient} {Universal} {Classifiers} with {Natural} {Language} {Inference}}, url = {http://arxiv.org/abs/2312.17543}, doi = {10.48550/arXiv.2312.17543}, abstract = {Generative Large Language Models (LLMs) have become the mainstream choice for fewshot and zeroshot learning thanks to the universality of text generation. Many users, however, do not need the broad capabilities of generative LLMs when they only want to automate a classification task. Smaller BERT-like models can also learn universal tasks, which allow them to do any text classification task without requiring fine-tuning (zeroshot classification) or to learn new tasks with only a few examples (fewshot), while being significantly more efficient than generative LLMs. This paper (1) explains how Natural Language Inference (NLI) can be used as a universal classification task that follows similar principles as instruction fine-tuning of generative LLMs, (2) provides a step-by-step guide with reusable Jupyter notebooks for building a universal classifier, and (3) shares the resulting universal classifier that is trained on 33 datasets with 389 diverse classes. Parts of the code we share has been used to train our older zeroshot classifiers that have been downloaded more than 55 million times via the Hugging Face Hub as of December 2023. Our new classifier improves zeroshot performance by 9.4\%.}, urldate = {2024-01-05}, publisher = {arXiv}, author = {Laurer, Moritz and van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper}, month = dec, year = {2023}, note = {arXiv:2312.17543 [cs]}, keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language}, } ``` ### Ideas for cooperation or questions? If you have questions or ideas for cooperation, contact me at moritz{at}huggingface{dot}co or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) ### Flexible usage and "prompting" You can formulate your own hypotheses by changing the `hypothesis_template` of the zeroshot pipeline. Similar to "prompt engineering" for LLMs, you can test different formulations of your `hypothesis_template` and verbalized classes to improve performance. ```python from transformers import pipeline text = "Angela Merkel is a politician in Germany and leader of the CDU" # formulation 1 hypothesis_template = "This text is about {}" classes_verbalized = ["politics", "economy", "entertainment", "environment"] # formulation 2 depending on your use-case hypothesis_template = "The topic of this text is {}" classes_verbalized = ["political activities", "economic policy", "entertainment or music", "environmental protection"] # test different formulations zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v2.0") # change the model identifier here output = zeroshot_classifier(text, classes_verbalized, hypothesis_template=hypothesis_template, multi_label=False) print(output) ```

philschmid/bart-large-cnn-samsum

philschmid

transformers

summarization

--- language: en license: mit tags: - sagemaker - bart - summarization datasets: - samsum widget: - text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\ Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\ \ ok.\nJeff: and how can I get started? \nJeff: where can I find documentation?\ \ \nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face\n" model-index: - name: bart-large-cnn-samsum results: - task: type: summarization name: Summarization dataset: name: 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization' type: samsum metrics: - type: rogue-1 value: 42.621 name: Validation ROGUE-1 - type: rogue-2 value: 21.9825 name: Validation ROGUE-2 - type: rogue-l value: 33.034 name: Validation ROGUE-L - type: rogue-1 value: 41.3174 name: Test ROGUE-1 - type: rogue-2 value: 20.8716 name: Test ROGUE-2 - type: rogue-l value: 32.1337 name: Test ROGUE-L - task: type: summarization name: Summarization dataset: name: samsum type: samsum config: samsum split: test metrics: - type: rouge value: 41.3282 name: ROUGE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTYzNzZkZDUzOWQzNGYxYTJhNGE4YWYyZjA0NzMyOWUzMDNhMmVhYzY1YTM0ZTJhYjliNGE4MDZhMjhhYjRkYSIsInZlcnNpb24iOjF9.OOM6l3v5rJCndmUIJV-2SDh2NjbPo5IgQOSL-Ju1Gwbi1voL5amsDEDOelaqlUBE3n55KkUsMLZhyn66yWxZBQ - type: rouge value: 20.8755 name: ROUGE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWZiODFiYWQzY2NmOTc5YjA3NTI0YzQ1MzQ0ODk2NjgyMmVlMjA5MjZiNTJkMGRmZGEzN2M3MDNkMjkxMDVhYSIsInZlcnNpb24iOjF9.b8cPk2-IL24La3Vd0hhtii4tRXujh5urAwy6IVeTWHwYfXaURyC2CcQOWtlOx5bdO5KACeaJFrFBCGgjk-VGCQ - type: rouge value: 32.1353 name: ROUGE-L verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWNmYzdiYWQ2ZWRkYzRiMGMxNWUwODgwZTdkY2NjZTc1NWE5NTFiMzU0OTU1N2JjN2ExYWQ2NGZkNjk5OTc4YSIsInZlcnNpb24iOjF9.Fzv4p-TEVicljiCqsBJHK1GsnE_AwGqamVmxTPI0WBNSIhZEhliRGmIL_z1pDq6WOzv3GN2YUGvhowU7GxnyAQ - type: rouge value: 38.401 name: ROUGE-LSUM verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGI4MWY0NWMxMmQ0ODQ5MDhiNDczMDAzYzJkODBiMzgzYWNkMWM2YTZkZDJmNWJiOGQ3MmNjMGViN2UzYWI2ZSIsInZlcnNpb24iOjF9.7lw3h5k5lJ7tYFLZGUtLyDabFYd00l6ByhmvkW4fykocBy9Blyin4tdw4Xps4DW-pmrdMLgidHxBWz5MrSx1Bw - type: loss value: 1.4297215938568115 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzI0ZWNhNDM5YTViZDMyZGJjMDA1ZWFjYzNhOTdlOTFiNzhhMDBjNmM2MjA3ZmRkZjJjMjEyMGY3MzcwOTI2NyIsInZlcnNpb24iOjF9.oNaZsAtUDqGAqoZWJavlcW7PKx1AWsnkbhaQxadpOKk_u7ywJJabvTtzyx_DwEgZslgDETCf4MM-JKitZKjiDA - type: gen_len value: 60.0757 name: gen_len verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTgwYWYwMDRkNTJkMDM5N2I2MWNmYzQ3OWM1NDJmODUyZGViMGE4ZTdkNmIwYWM2N2VjZDNmN2RiMDE4YTYyYiIsInZlcnNpb24iOjF9.PbXTcNYX_SW-BuRQEcqyc21M7uKrOMbffQSAK6k2GLzTVRrzZxsDC57ktKL68zRY8fSiRGsnknOwv-nAR6YBCQ --- ## `bart-large-cnn-samsum` > If you want to use the model you should try a newer fine-tuned FLAN-T5 version [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) out socring the BART version with `+6` on `ROGUE1` achieving `47.24`. # TRY [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container. For more information look at: - [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html) - [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker) - [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html) - [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html) - [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers) ## Hyperparameters ```json { "dataset_name": "samsum", "do_eval": true, "do_predict": true, "do_train": true, "fp16": true, "learning_rate": 5e-05, "model_name_or_path": "facebook/bart-large-cnn", "num_train_epochs": 3, "output_dir": "/opt/ml/model", "per_device_eval_batch_size": 4, "per_device_train_batch_size": 4, "predict_with_generate": true, "seed": 7 } ``` ## Usage ```python from transformers import pipeline summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum") conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? Philipp: Sure you can use the new Hugging Face Deep Learning Container. Jeff: ok. Jeff: and how can I get started? Jeff: where can I find documentation? Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face ''' summarizer(conversation) ``` ## Results | key | value | | --- | ----- | | eval_rouge1 | 42.621 | | eval_rouge2 | 21.9825 | | eval_rougeL | 33.034 | | eval_rougeLsum | 39.6783 | | test_rouge1 | 41.3174 | | test_rouge2 | 20.8716 | | test_rougeL | 32.1337 | | test_rougeLsum | 38.4149 |

microsoft/table-transformer-detection

microsoft

transformers

object-detection

--- license: mit widget: - src: https://www.invoicesimple.com/wp-content/uploads/2018/06/Sample-Invoice-printable.png example_title: Invoice --- # Table Transformer (fine-tuned for Table Detection) Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer). Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. ## Usage You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info.

microsoft/table-transformer-structure-recognition

microsoft

transformers

object-detection

--- license: mit widget: - src: https://documentation.tricentis.com/tosca/1420/en/content/tbox/images/table.png example_title: Table --- # Table Transformer (fine-tuned for Table Structure Recognition) Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer). Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. ## Usage You can use the raw model for detecting the structure (like rows, columns) in tables. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info.

xlnet/xlnet-base-cased

xlnet

transformers

text-generation

--- language: en license: mit datasets: - bookcorpus - wikipedia --- # XLNet (base-sized model) XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/). Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking. ## Intended uses & limitations The model is mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=xlnet) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation, you should look at models like GPT2. ## Usage Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import XLNetTokenizer, XLNetModel tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased') model = XLNetModel.from_pretrained('xlnet-base-cased') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1906-08237, author = {Zhilin Yang and Zihang Dai and Yiming Yang and Jaime G. Carbonell and Ruslan Salakhutdinov and Quoc V. Le}, title = {XLNet: Generalized Autoregressive Pretraining for Language Understanding}, journal = {CoRR}, volume = {abs/1906.08237}, year = {2019}, url = {http://arxiv.org/abs/1906.08237}, eprinttype = {arXiv}, eprint = {1906.08237}, timestamp = {Mon, 24 Jun 2019 17:28:45 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1906-08237.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

vinai/bertweet-base

vinai

transformers

fill-mask

# <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B word tokens ~ 80GB), containing 845M Tweets streamed from 01/2012 to 08/2019 and 5M Tweets related to the **COVID-19** pandemic. The general architecture and experimental results of BERTweet can be found in our [paper](https://aclanthology.org/2020.emnlp-demos.2/): @inproceedings{bertweet, title = {{BERTweet: A pre-trained language model for English Tweets}}, author = {Dat Quoc Nguyen and Thanh Vu and Anh Tuan Nguyen}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, pages = {9--14}, year = {2020} } **Please CITE** our paper when BERTweet is used to help produce published results or is incorporated into other software. For further information or requests, please go to [BERTweet's homepage](https://github.com/VinAIResearch/BERTweet)! ### Main results <p float="left"> <img width="275" alt="postagging" src="https://user-images.githubusercontent.com/2412555/135724590-01d8d435-262d-44fe-a383-cd39324fe190.png" /> <img width="275" alt="ner" src="https://user-images.githubusercontent.com/2412555/135724598-1e3605e7-d8ce-4c5e-be4a-62ae8501fae7.png" /> </p> <p float="left"> <img width="275" alt="sentiment" src="https://user-images.githubusercontent.com/2412555/135724597-f1981f1e-fe73-4c03-b1ff-0cae0cc5f948.png" /> <img width="275" alt="irony" src="https://user-images.githubusercontent.com/2412555/135724595-15f4f2c8-bbb6-4ee6-82a0-034769dec183.png" /> </p>

pysentimiento/robertuito-emotion-analysis

pysentimiento

pysentimiento

--- language: - es library_name: pysentimiento tags: - emotion-analysis - twitter --- # Emotion Analysis in Spanish ## robertuito-emotion-analysis Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/) Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is [RoBERTuito](https://github.com/pysentimiento/robertuito), a RoBERTa model trained in Spanish tweets. Contains the six Ekman emotions plus a neutral class: - anger - disgust - fear - joy - sadness - surprise ## Results Results for the four tasks evaluated in `pysentimiento`. Results are expressed as Macro F1 scores | model | emotion | hate_speech | irony | sentiment | |:--------------|:--------------|:--------------|:--------------|:--------------| | robertuito | 0.560 ± 0.010 | 0.759 ± 0.007 | 0.739 ± 0.005 | 0.705 ± 0.003 | | roberta | 0.527 ± 0.015 | 0.741 ± 0.012 | 0.721 ± 0.008 | 0.670 ± 0.006 | | bertin | 0.524 ± 0.007 | 0.738 ± 0.007 | 0.713 ± 0.012 | 0.666 ± 0.005 | | beto_uncased | 0.532 ± 0.012 | 0.727 ± 0.016 | 0.701 ± 0.007 | 0.651 ± 0.006 | | beto_cased | 0.516 ± 0.012 | 0.724 ± 0.012 | 0.705 ± 0.009 | 0.662 ± 0.005 | | mbert_uncased | 0.493 ± 0.010 | 0.718 ± 0.011 | 0.681 ± 0.010 | 0.617 ± 0.003 | | biGRU | 0.264 ± 0.007 | 0.592 ± 0.018 | 0.631 ± 0.011 | 0.585 ± 0.011 | Note that for Hate Speech, these are the results for Semeval 2019, Task 5 Subtask B (HS+TR+AG detection) ## Citation If you use this model in your research, please cite pysentimiento, RoBERTuito and EmoEvent papers: ``` @misc{perez2021pysentimiento, title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks}, author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque}, year={2021}, eprint={2106.09462}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{del2020emoevent, title={EmoEvent: A multilingual emotion corpus based on different events}, author={del Arco, Flor Miriam Plaza and Strapparava, Carlo and Lopez, L Alfonso Urena and Mart{\'\i}n-Valdivia, M Teresa}, booktitle={Proceedings of the 12th Language Resources and Evaluation Conference}, pages={1492--1498}, year={2020} } @inproceedings{perez-etal-2022-robertuito, title = "{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish", author = "P{\'e}rez, Juan Manuel and Furman, Dami{\'a}n Ariel and Alonso Alemany, Laura and Luque, Franco M.", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.785", pages = "7235--7243", abstract = "Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.", } ```

schhwmn/mbart-large-50-finetuned-ukr-gec

schhwmn

transformers

text2text-generation

--- language: uk tags: - gec - mbart-50 widget: - text: "я й не думав що комп'ютерна лінгвістика це легкоо." --- This model was finetuned on errorful sentences from the `train` subset of [UA-GEC](https://github.com/grammarly/ua-gec) corpus, introduced in [UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language](https://arxiv.org/abs/2103.16997) paper. Only sentences containing errors were used; 8,874 sentences for training and 987 sentences for validation. The training arguments were defined as follows: ``` batch_size = 4 num_train_epochs = 3 learning_rate=5e-5 weight_decay=0.01 optim = "adamw_hf" ```

cointegrated/rubert-tiny-sentiment-balanced

cointegrated

transformers

text-classification

--- language: ["ru"] tags: - russian - classification - sentiment - multiclass widget: - text: "Какая гадость эта ваша заливная рыба!" --- This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of sentiment for short Russian texts. The problem is formulated as multiclass classification: `negative` vs `neutral` vs `positive`. ## Usage The function below estimates the sentiment of the given text: ```python # !pip install transformers sentencepiece --quiet import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification model_checkpoint = 'cointegrated/rubert-tiny-sentiment-balanced' tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint) if torch.cuda.is_available(): model.cuda() def get_sentiment(text, return_type='label'): """ Calculate sentiment of a text. `return_type` can be 'label', 'score' or 'proba' """ with torch.no_grad(): inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device) proba = torch.sigmoid(model(**inputs).logits).cpu().numpy()[0] if return_type == 'label': return model.config.id2label[proba.argmax()] elif return_type == 'score': return proba.dot([-1, 0, 1]) return proba text = 'Какая гадость эта ваша заливная рыба!' # classify the text print(get_sentiment(text, 'label')) # negative # score the text on the scale from -1 (very negative) to +1 (very positive) print(get_sentiment(text, 'score')) # -0.5894946306943893 # calculate probabilities of all labels print(get_sentiment(text, 'proba')) # [0.7870447 0.4947824 0.19755007] ``` ## Training We trained the model on [the datasets collected by Smetanin](https://github.com/sismetanin/sentiment-analysis-in-russian). We have converted all training data into a 3-class format and have up- and downsampled the training data to balance both the sources and the classes. The training code is available as [a Colab notebook](https://gist.github.com/avidale/e678c5478086c1d1adc52a85cb2b93e6). The metrics on the balanced test set are the following: | Source | Macro F1 | | ----------- | ----------- | | SentiRuEval2016_banks | 0.83 | | SentiRuEval2016_tele | 0.74 | | kaggle_news | 0.66 | | linis | 0.50 | | mokoron | 0.98 | | rureviews | 0.72 | | rusentiment | 0.67 |

cross-encoder/ms-marco-TinyBERT-L-2-v2

cross-encoder

transformers

text-classification

--- license: apache-2.0 --- # Cross-Encoder for MS Marco This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco) ## Usage with Transformers ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('model_name') tokenizer = AutoTokenizer.from_pretrained('model_name') features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits print(scores) ``` ## Usage with SentenceTransformers The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name', max_length=512) scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]) ``` ## Performance In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset. | Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec | | ------------- |:-------------| -----| --- | | **Version 2 models** | | | | cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000 | cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100 | cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500 | cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800 | cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960 | **Version 1 models** | | | | cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000 | cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900 | cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680 | cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340 | **Other models** | | | | nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900 | nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340 | nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100 | Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340 | amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330 | sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720 Note: Runtime was computed on a V100 GPU.

microsoft/git-base

microsoft

transformers

image-to-text

--- language: en license: mit tags: - vision - image-to-text - image-captioning model_name: microsoft/git-base pipeline_tag: image-to-text --- # GIT (GenerativeImage2Text), base-sized GIT (short for GenerativeImage2Text) model, base-sized version. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text). Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs. The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens. The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.  This allows the model to be used for tasks like: - image and video captioning - visual question answering (VQA) on images and videos - even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text). ## Intended uses & limitations You can use the raw model for image captioning. See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/model_doc/git#transformers.GitForCausalLM.forward.example). ## Training data From the paper: > We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions (CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016), Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B data following a similar collection procedure in Hu et al. (2021a). => however this is for the model referred to as "GIT" in the paper, which is not open-sourced. This checkpoint is "GIT-base", which is a smaller variant of GIT trained on 10 million image-text pairs. See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details. ### Preprocessing We refer to the original repo regarding details for preprocessing during training. During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation. ## Evaluation results For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100).

facebook/detr-resnet-101

facebook

transformers

object-detection

--- license: apache-2.0 tags: - object-detection - vision datasets: - coco widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg example_title: Savanna - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg example_title: Airport --- # DETR (End-to-End Object Detection) model with ResNet-101 backbone DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in [this repository](https://github.com/facebookresearch/detr). Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.  ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models. ### How to use Here is how to use this model: ```python from transformers import DetrImageProcessor, DetrForObjectDetection import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) # you can specify the revision tag if you don't want the timm dependency processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101", revision="no_timm") model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101", revision="no_timm") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.9 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` This should output (something along the lines of): ``` Detected cat with confidence 0.998 at location [344.06, 24.85, 640.34, 373.74] Detected remote with confidence 0.997 at location [328.13, 75.93, 372.81, 187.66] Detected remote with confidence 0.997 at location [39.34, 70.13, 175.56, 118.78] Detected cat with confidence 0.998 at location [15.36, 51.75, 316.89, 471.16] Detected couch with confidence 0.995 at location [-0.19, 0.71, 639.73, 474.17] ``` Currently, both the feature extractor and model support PyTorch. ## Training data The DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225). ### Training The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64). ## Evaluation results This model achieves an AP (average precision) of **43.5** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2005-12872, author = {Nicolas Carion and Francisco Massa and Gabriel Synnaeve and Nicolas Usunier and Alexander Kirillov and Sergey Zagoruyko}, title = {End-to-End Object Detection with Transformers}, journal = {CoRR}, volume = {abs/2005.12872}, year = {2020}, url = {https://arxiv.org/abs/2005.12872}, archivePrefix = {arXiv}, eprint = {2005.12872}, timestamp = {Thu, 28 May 2020 17:38:09 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn

jonatasgrosman

transformers

automatic-speech-recognition

--- language: zh datasets: - common_voice metrics: - wer - cer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Chinese (zh-CN) by Jonatas Grosman results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice zh-CN type: common_voice args: zh-CN metrics: - name: Test WER type: wer value: 82.37 - name: Test CER type: cer value: 19.03 --- # Fine-tuned XLSR-53 large model for speech recognition in Chinese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyong/css10) and [ST-CMDS](http://www.openslr.org/38/). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint ## Usage The model can be used directly (without a language model) as follows... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "zh-CN" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) for i, predicted_sentence in enumerate(predicted_sentences): print("-" * 100) print("Reference:", test_dataset[i]["sentence"]) print("Prediction:", predicted_sentence) ``` | Reference | Prediction | | ------------- | ------------- | | 宋朝末年年间定居粉岭围。 | 宋朝末年年间定居分定为 | | 渐渐行动不便 | 建境行动不片 | | 二十一年去世。 | 二十一年去世 | | 他们自称恰哈拉。 | 他们自称家哈<unk> | | 局部干涩的例子包括有口干、眼睛干燥、及阴道干燥。 | 菊物干寺的例子包括有口肝眼睛干照以及阴到干<unk> | | 嘉靖三十八年，登进士第三甲第二名。 | 嘉靖三十八年登进士第三甲第二名 | | 这一名称一直沿用至今。 | 这一名称一直沿用是心 | | 同时乔凡尼还得到包税合同和许多明矾矿的经营权。 | 同时桥凡妮还得到包税合同和许多民繁矿的经营权 | | 为了惩罚西扎城和塞尔柱的结盟，盟军在抵达后将外城烧毁。 | 为了曾罚西扎城和塞尔素的节盟盟军在抵达后将外曾烧毁 | | 河内盛产黄色无鱼鳞的鳍射鱼。 | 合类生场环色无鱼林的骑射鱼 | ## Evaluation The model can be evaluated as follows on the Chinese (zh-CN) test data of Common Voice. ```python import torch import re import librosa from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "zh-CN" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn" DEVICE = "cuda" CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"] test_dataset = load_dataset("common_voice", LANG_ID, split="test") wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) model.to(DEVICE) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): with warnings.catch_warnings(): warnings.simplefilter("ignore") speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) predictions = [x.upper() for x in result["pred_strings"]] references = [x.upper() for x in result["sentence"]] print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") ``` **Test Result**: In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-13). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used. | Model | WER | CER | | ------------- | ------------- | ------------- | | jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn | **82.37%** | **19.03%** | | ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt | 84.01% | 20.95% | ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-chinese, title={Fine-tuned {XLSR}-53 large model for speech recognition in {C}hinese}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn}}, year={2021} } ```

bigscience/bloom-1b7

bigscience

transformers

text-generation

--- license: bigscience-bloom-rail-1.0 language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zhs - zht - zu pipeline_tag: text-generation --- <h1 style='text-align: center '>BLOOM LM</h1> <h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2> <h3 style='text-align: center '>Model Card</h3> <img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Version 1.0 / 26.May.2022 # Model Card for Bloom-1b7 <!-- Provide a quick summary of what the model is/does. --> ## Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Recommendations](#recommendations) 5. [Training Data](#training-data) 6. [Evaluation](#evaluation) 7. [Environmental Impact](#environmental-impact) 8. [Technical Specifications](#techincal-specifications) 9. [Citation](#citation) 10. [Glossary and Calculations](#glossary-and-calculations) 11. [More Information](#more-information) 12. [Model Card Authors](#model-card-authors) 13. [Model Card Contact](#model-card-contact) ## Model Details ### Model Description *This section provides information for anyone who wants to know about the model.* - **Developed by:** BigScience ([website](https://bigscience.huggingface.co)) * All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)* - **Model Type:** Transformer-based Language Model - **Version:** 1.0.0 - **Languages:** Multiple; see [training data](#training-data) - **License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license)) - **Release Date Estimate:** Monday, 11.July.2022 - **Funded by:** * The French government. * Hugging Face ([website](https://huggingface.co)). * Organizations of contributors. *(Further breakdown of organizations forthcoming.)* ## Uses *This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. It provides information for anyone considering using the model or who is affected by the model.* ### Intended Use This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive. #### **Direct Use** - Text generation - Exploring characteristics of language generated by a language model - Examples: Cloze tests, counterfactuals, generations with reframings #### **Downstream Use** - Tasks that leverage language models include: Information Extraction, Question Answering, Summarization ### Misuse and Out-of-scope Use *This section addresses what users ought not do with the model.* See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases. #### **Out-of-scope Uses** Using the model in [high-stakes](#high-stakes) settings is out of scope for this model.  The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct. ##### Out-of-scope Uses Include: - Usage in biomedical domains, political and legal domains, or finance domains - Usage for evaluating or scoring individuals, such as for employment, education, or credit - Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct #### **Misuse** Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes: - Spam generation - Disinformation and influence operations - Disparagement and defamation - Harassment and abuse - [Deception](#deception) - Unconsented impersonation and imitation - Unconsented surveillance - Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license) ### Intended Users #### **Direct Users** - General Public - Researchers - Students - Educators - Engineers/developers - Non-commercial entities - Community advocates, including human and civil rights groups #### Indirect Users - Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use) - Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license) #### Others Affected (Parties Prenantes) - People and groups referred to by the LLM - People and groups exposed to outputs of, or decisions based on, the LLM - People and groups whose original work is included in the LLM ## Bias, Risks, and Limitations *This section identifies foreseeable harms and misunderstandings.* Model may: - Overrepresent some viewpoints and underrepresent others - Contain stereotypes - Contain [personal information](#personal-data-and-information) - Generate: - Hateful, abusive, or violent language - Discriminatory or prejudicial language - Content that may not be appropriate for all settings, including sexual content - Make errors, including producing incorrect information as if it were factual - Generate irrelevant or repetitive outputs ### Recommendations *This section provides information on warnings and potential mitigations.* - Indirect users should be made aware when the content they're working with is created by the LLM. - Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary. - Models pretrained with the LLM should include an updated Model Card. - Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments. ## Training Data *This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.* Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus). Training data includes: - 45 natural languages - 12 programming languages - In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.) #### **Languages** The pie chart shows the distribution of languages in training data.  **The following table shows the further distribution of Niger-Congo and Indic languages in the training data.** | Niger Congo | Percentage | | Indic | Percentage | |----------------|------------ |------ |-----------|------------| | Chi Tumbuka | 0.00002 | | Assamese | 0.01 | | Kikuyu | 0.00004 | | Odia | 0.04 | | Bambara | 0.00004 | | Gujarati | 0.04 | | Akan | 0.00007 | | Marathi | 0.05 | | Xitsonga | 0.00007 | | Punjabi | 0.05 | | Sesotho | 0.00007 | | Kannada | 0.06 | | Chi Chewa | 0.0001 | | Nepali | 0.07 | | Setswana | 0.0002 | | Telugu | 0.09 | | Northern Sotho | 0.0002 | | Malayalam | 0.10 | | Fon | 0.0002 | | Urdu | 0.10 | | Kirundi | 0.0003 | | Tamil | 0.20 | | Wolof | 0.0004 | | Bengali | 0.50 | | Kuganda | 0.0004 | | Hindi | 0.70 | | Chi Shona | 0.001 | | Isi Zulu | 0.001 | | Igbo | 0.001 | | Xhosa | 0.001 | | Kinyarwanda | 0.003 | | Yoruba | 0.006 | | Swahili | 0.02 | </details> **The following table shows the distribution of programming languages.** | Extension | Language | Number of files | |----------------|------------|-----------------| | java | Java | 5,407,724 | | php | PHP | 4,942,186 | | cpp | C++ | 2,503,930 | | py | Python | 2,435,072 | | js | JavaScript | 1,905,518 | | cs | C# | 1,577,347 | | rb | Ruby | 6,78,413 | | cc | C++ | 443,054 | | hpp | C++ | 391,048 | | lua | Lua | 352,317 | | go | GO | 227,763 | | ts | TypeScript | 195,254 | | C | C | 134,537 | | scala | Scala | 92,052 | | hh | C++ | 67,161 | | H | C++ | 55,899 | | tsx | TypeScript | 33,107 | | rs | Rust | 29,693 | | phpt | PHP | 9,702 | | c++ | C++ | 1,342 | | h++ | C++ | 791 | | php3 | PHP | 540 | | phps | PHP | 270 | | php5 | PHP | 166 | | php4 | PHP | 29 | ## Evaluation *This section describes the evaluation protocols and provides the results.* ### Metrics *This section describes the different ways performance is calculated and why.* Includes: | Metric | Why chosen | |--------------------|--------------------------------------------------------------------| | [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training | | Cross Entropy [Loss](#loss) | Standard objective for language models. | And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_ ### Factors *This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.* - Language, such as English or Yoruba - Domain, such as newswire or stories - Demographic characteristics, such as gender or nationality ### Results *Results are based on the [Factors](#factors) and [Metrics](#metrics).* **Train-time Evaluation:** As of 25.May.2022, 15:00 PST: - Training Loss: 2.0 - Validation Loss: 2.2 - Perplexity: 8.9 (More evaluation scores forthcoming at the end of model training.) - [BLOOM Book](https://huggingface.co/spaces/bigscience/bloom-book): Read generations from BLOOM based on prompts provided by the community ## Environmental Impact The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing. **Estimated carbon emissions:** *(Forthcoming upon completion of training.)* **Estimated electricity usage:** *(Forthcoming upon completion of training.)* ## Technical Specifications *This section provides information for people who work on model development.* Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training. **Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)): * Decoder-only architecture * Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf)) * ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions * 1,722,408,960 parameters: * 513,802,240 embedding parameters * 24 layers, 16 attention heads * Hidden layers are 2048-dimensional * Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization)) **Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)). **Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)). * Hardware: 64 V100 16/32GB GPUs (16 nodes): * 4 GPUs per node * 40 CPUs per task * 1 task per node * CPU: AMD * CPU memory: 160GB per node * GPU memory: 64GB or 128GB (depending on node availability during training) per node * Inter-node connect: Omni-Path Architecture (OPA) * NCCL-communications network: a fully dedicated subnet * Disc IO network: shared network with other types of nodes * Software: * Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed)) * DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed)) * PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch)) * apex ([Github link](https://github.com/NVIDIA/apex)) ### **Training** - Checkpoint size: - Fp16 weights: 2.6GB (# params * 2) - Full checkpoint with optimizer states: -- - Training throughput: -- - Number of epochs: 1 - Dates: - Start: 11th March, 2022 11:42am PST - End: 20 May, 2022 - Server training location: Île-de-France, France ### **Tokenization** The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using: - A byte-level Byte Pair Encoding (BPE) algorithm - A simple pre-tokenization rule, no normalization - A vocabulary size of 250,680 It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language. ## Citation **Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022 ## Glossary and Calculations *This section defines common terms and how metrics are calculated.* - <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss. - <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy. - <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/). - <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf). - <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf). - <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm). - <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf)) - <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated. ## More Information ### Dataset Creation Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling ### Technical Specifications Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md ### Initial Results Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book ## Model Card Authors *Ordered roughly chronologically and by amount of time spent.* Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff ## Model Card Contact **Send Questions to:** bigscience-contact@googlegroups.com

intfloat/e5-base-v2

intfloat

sentence-transformers

sentence-similarity

--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: e5-base-v2 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.77611940298506 - type: ap value: 42.052710266606056 - type: f1 value: 72.12040628266567 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.81012500000001 - type: ap value: 89.4213700757244 - type: f1 value: 92.8039091197065 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.711999999999996 - type: f1 value: 46.11544975436018 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 23.186 - type: map_at_10 value: 36.632999999999996 - type: map_at_100 value: 37.842 - type: map_at_1000 value: 37.865 - type: map_at_3 value: 32.278 - type: map_at_5 value: 34.760999999999996 - type: mrr_at_1 value: 23.400000000000002 - type: mrr_at_10 value: 36.721 - type: mrr_at_100 value: 37.937 - type: mrr_at_1000 value: 37.96 - type: mrr_at_3 value: 32.302 - type: mrr_at_5 value: 34.894 - type: ndcg_at_1 value: 23.186 - type: ndcg_at_10 value: 44.49 - type: ndcg_at_100 value: 50.065000000000005 - type: ndcg_at_1000 value: 50.629999999999995 - type: ndcg_at_3 value: 35.461 - type: ndcg_at_5 value: 39.969 - type: precision_at_1 value: 23.186 - type: precision_at_10 value: 6.97 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.912 - type: precision_at_5 value: 11.152 - type: recall_at_1 value: 23.186 - type: recall_at_10 value: 69.70100000000001 - type: recall_at_100 value: 95.092 - type: recall_at_1000 value: 99.431 - type: recall_at_3 value: 44.737 - type: recall_at_5 value: 55.761 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 46.10312401440185 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 39.67275326095384 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.97793816337376 - type: mrr value: 72.76832431957087 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 83.11646947018187 - type: cos_sim_spearman value: 81.40064994975234 - type: euclidean_pearson value: 82.37355689019232 - type: euclidean_spearman value: 81.6777646977348 - type: manhattan_pearson value: 82.61101422716945 - type: manhattan_spearman value: 81.80427360442245 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 83.52922077922076 - type: f1 value: 83.45298679360866 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.495115019668496 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 32.724792944166765 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.361000000000004 - type: map_at_10 value: 43.765 - type: map_at_100 value: 45.224 - type: map_at_1000 value: 45.35 - type: map_at_3 value: 40.353 - type: map_at_5 value: 42.195 - type: mrr_at_1 value: 40.629 - type: mrr_at_10 value: 50.458000000000006 - type: mrr_at_100 value: 51.06699999999999 - type: mrr_at_1000 value: 51.12 - type: mrr_at_3 value: 47.902 - type: mrr_at_5 value: 49.447 - type: ndcg_at_1 value: 40.629 - type: ndcg_at_10 value: 50.376 - type: ndcg_at_100 value: 55.065 - type: ndcg_at_1000 value: 57.196000000000005 - type: ndcg_at_3 value: 45.616 - type: ndcg_at_5 value: 47.646 - type: precision_at_1 value: 40.629 - type: precision_at_10 value: 9.785 - type: precision_at_100 value: 1.562 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 22.031 - type: precision_at_5 value: 15.737000000000002 - type: recall_at_1 value: 32.361000000000004 - type: recall_at_10 value: 62.214000000000006 - type: recall_at_100 value: 81.464 - type: recall_at_1000 value: 95.905 - type: recall_at_3 value: 47.5 - type: recall_at_5 value: 53.69500000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.971 - type: map_at_10 value: 37.444 - type: map_at_100 value: 38.607 - type: map_at_1000 value: 38.737 - type: map_at_3 value: 34.504000000000005 - type: map_at_5 value: 36.234 - type: mrr_at_1 value: 35.35 - type: mrr_at_10 value: 43.441 - type: mrr_at_100 value: 44.147999999999996 - type: mrr_at_1000 value: 44.196000000000005 - type: mrr_at_3 value: 41.285 - type: mrr_at_5 value: 42.552 - type: ndcg_at_1 value: 35.35 - type: ndcg_at_10 value: 42.903999999999996 - type: ndcg_at_100 value: 47.406 - type: ndcg_at_1000 value: 49.588 - type: ndcg_at_3 value: 38.778 - type: ndcg_at_5 value: 40.788000000000004 - type: precision_at_1 value: 35.35 - type: precision_at_10 value: 8.083 - type: precision_at_100 value: 1.313 - type: precision_at_1000 value: 0.18 - type: precision_at_3 value: 18.769 - type: precision_at_5 value: 13.439 - type: recall_at_1 value: 27.971 - type: recall_at_10 value: 52.492000000000004 - type: recall_at_100 value: 71.642 - type: recall_at_1000 value: 85.488 - type: recall_at_3 value: 40.1 - type: recall_at_5 value: 45.800000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 39.898 - type: map_at_10 value: 51.819 - type: map_at_100 value: 52.886 - type: map_at_1000 value: 52.941 - type: map_at_3 value: 48.619 - type: map_at_5 value: 50.493 - type: mrr_at_1 value: 45.391999999999996 - type: mrr_at_10 value: 55.230000000000004 - type: mrr_at_100 value: 55.887 - type: mrr_at_1000 value: 55.916 - type: mrr_at_3 value: 52.717000000000006 - type: mrr_at_5 value: 54.222 - type: ndcg_at_1 value: 45.391999999999996 - type: ndcg_at_10 value: 57.586999999999996 - type: ndcg_at_100 value: 61.745000000000005 - type: ndcg_at_1000 value: 62.83800000000001 - type: ndcg_at_3 value: 52.207 - type: ndcg_at_5 value: 54.925999999999995 - type: precision_at_1 value: 45.391999999999996 - type: precision_at_10 value: 9.21 - type: precision_at_100 value: 1.226 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 23.177 - type: precision_at_5 value: 16.038 - type: recall_at_1 value: 39.898 - type: recall_at_10 value: 71.18900000000001 - type: recall_at_100 value: 89.082 - type: recall_at_1000 value: 96.865 - type: recall_at_3 value: 56.907 - type: recall_at_5 value: 63.397999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.706 - type: map_at_10 value: 30.818 - type: map_at_100 value: 32.038 - type: map_at_1000 value: 32.123000000000005 - type: map_at_3 value: 28.077 - type: map_at_5 value: 29.709999999999997 - type: mrr_at_1 value: 24.407 - type: mrr_at_10 value: 32.555 - type: mrr_at_100 value: 33.692 - type: mrr_at_1000 value: 33.751 - type: mrr_at_3 value: 29.848999999999997 - type: mrr_at_5 value: 31.509999999999998 - type: ndcg_at_1 value: 24.407 - type: ndcg_at_10 value: 35.624 - type: ndcg_at_100 value: 41.454 - type: ndcg_at_1000 value: 43.556 - type: ndcg_at_3 value: 30.217 - type: ndcg_at_5 value: 33.111000000000004 - type: precision_at_1 value: 24.407 - type: precision_at_10 value: 5.548 - type: precision_at_100 value: 0.8869999999999999 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 12.731 - type: precision_at_5 value: 9.22 - type: recall_at_1 value: 22.706 - type: recall_at_10 value: 48.772 - type: recall_at_100 value: 75.053 - type: recall_at_1000 value: 90.731 - type: recall_at_3 value: 34.421 - type: recall_at_5 value: 41.427 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.424 - type: map_at_10 value: 21.09 - type: map_at_100 value: 22.264999999999997 - type: map_at_1000 value: 22.402 - type: map_at_3 value: 18.312 - type: map_at_5 value: 19.874 - type: mrr_at_1 value: 16.915 - type: mrr_at_10 value: 25.258000000000003 - type: mrr_at_100 value: 26.228 - type: mrr_at_1000 value: 26.31 - type: mrr_at_3 value: 22.492 - type: mrr_at_5 value: 24.04 - type: ndcg_at_1 value: 16.915 - type: ndcg_at_10 value: 26.266000000000002 - type: ndcg_at_100 value: 32.08 - type: ndcg_at_1000 value: 35.086 - type: ndcg_at_3 value: 21.049 - type: ndcg_at_5 value: 23.508000000000003 - type: precision_at_1 value: 16.915 - type: precision_at_10 value: 5.1 - type: precision_at_100 value: 0.9329999999999999 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 10.282 - type: precision_at_5 value: 7.836 - type: recall_at_1 value: 13.424 - type: recall_at_10 value: 38.179 - type: recall_at_100 value: 63.906 - type: recall_at_1000 value: 84.933 - type: recall_at_3 value: 23.878 - type: recall_at_5 value: 30.037999999999997 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.154 - type: map_at_10 value: 35.912 - type: map_at_100 value: 37.211 - type: map_at_1000 value: 37.327 - type: map_at_3 value: 32.684999999999995 - type: map_at_5 value: 34.562 - type: mrr_at_1 value: 32.435 - type: mrr_at_10 value: 41.411 - type: mrr_at_100 value: 42.297000000000004 - type: mrr_at_1000 value: 42.345 - type: mrr_at_3 value: 38.771 - type: mrr_at_5 value: 40.33 - type: ndcg_at_1 value: 32.435 - type: ndcg_at_10 value: 41.785 - type: ndcg_at_100 value: 47.469 - type: ndcg_at_1000 value: 49.685 - type: ndcg_at_3 value: 36.618 - type: ndcg_at_5 value: 39.101 - type: precision_at_1 value: 32.435 - type: precision_at_10 value: 7.642 - type: precision_at_100 value: 1.244 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 17.485 - type: precision_at_5 value: 12.57 - type: recall_at_1 value: 26.154 - type: recall_at_10 value: 54.111 - type: recall_at_100 value: 78.348 - type: recall_at_1000 value: 92.996 - type: recall_at_3 value: 39.189 - type: recall_at_5 value: 45.852 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.308999999999997 - type: map_at_10 value: 35.524 - type: map_at_100 value: 36.774 - type: map_at_1000 value: 36.891 - type: map_at_3 value: 32.561 - type: map_at_5 value: 34.034 - type: mrr_at_1 value: 31.735000000000003 - type: mrr_at_10 value: 40.391 - type: mrr_at_100 value: 41.227000000000004 - type: mrr_at_1000 value: 41.288000000000004 - type: mrr_at_3 value: 37.938 - type: mrr_at_5 value: 39.193 - type: ndcg_at_1 value: 31.735000000000003 - type: ndcg_at_10 value: 41.166000000000004 - type: ndcg_at_100 value: 46.702 - type: ndcg_at_1000 value: 49.157000000000004 - type: ndcg_at_3 value: 36.274 - type: ndcg_at_5 value: 38.177 - type: precision_at_1 value: 31.735000000000003 - type: precision_at_10 value: 7.5569999999999995 - type: precision_at_100 value: 1.2109999999999999 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 17.199 - type: precision_at_5 value: 12.123000000000001 - type: recall_at_1 value: 26.308999999999997 - type: recall_at_10 value: 53.083000000000006 - type: recall_at_100 value: 76.922 - type: recall_at_1000 value: 93.767 - type: recall_at_3 value: 39.262 - type: recall_at_5 value: 44.413000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.391250000000003 - type: map_at_10 value: 33.280166666666666 - type: map_at_100 value: 34.49566666666667 - type: map_at_1000 value: 34.61533333333333 - type: map_at_3 value: 30.52183333333333 - type: map_at_5 value: 32.06608333333333 - type: mrr_at_1 value: 29.105083333333337 - type: mrr_at_10 value: 37.44766666666666 - type: mrr_at_100 value: 38.32491666666667 - type: mrr_at_1000 value: 38.385666666666665 - type: mrr_at_3 value: 35.06883333333333 - type: mrr_at_5 value: 36.42066666666667 - type: ndcg_at_1 value: 29.105083333333337 - type: ndcg_at_10 value: 38.54358333333333 - type: ndcg_at_100 value: 43.833583333333344 - type: ndcg_at_1000 value: 46.215333333333334 - type: ndcg_at_3 value: 33.876 - type: ndcg_at_5 value: 36.05208333333333 - type: precision_at_1 value: 29.105083333333337 - type: precision_at_10 value: 6.823416666666665 - type: precision_at_100 value: 1.1270833333333334 - type: precision_at_1000 value: 0.15208333333333332 - type: precision_at_3 value: 15.696750000000002 - type: precision_at_5 value: 11.193499999999998 - type: recall_at_1 value: 24.391250000000003 - type: recall_at_10 value: 49.98808333333333 - type: recall_at_100 value: 73.31616666666666 - type: recall_at_1000 value: 89.96291666666667 - type: recall_at_3 value: 36.86666666666667 - type: recall_at_5 value: 42.54350000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.995 - type: map_at_10 value: 28.807 - type: map_at_100 value: 29.813000000000002 - type: map_at_1000 value: 29.903000000000002 - type: map_at_3 value: 26.636 - type: map_at_5 value: 27.912 - type: mrr_at_1 value: 24.847 - type: mrr_at_10 value: 31.494 - type: mrr_at_100 value: 32.381 - type: mrr_at_1000 value: 32.446999999999996 - type: mrr_at_3 value: 29.473 - type: mrr_at_5 value: 30.7 - type: ndcg_at_1 value: 24.847 - type: ndcg_at_10 value: 32.818999999999996 - type: ndcg_at_100 value: 37.835 - type: ndcg_at_1000 value: 40.226 - type: ndcg_at_3 value: 28.811999999999998 - type: ndcg_at_5 value: 30.875999999999998 - type: precision_at_1 value: 24.847 - type: precision_at_10 value: 5.244999999999999 - type: precision_at_100 value: 0.856 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 12.577 - type: precision_at_5 value: 8.895999999999999 - type: recall_at_1 value: 21.995 - type: recall_at_10 value: 42.479 - type: recall_at_100 value: 65.337 - type: recall_at_1000 value: 83.23700000000001 - type: recall_at_3 value: 31.573 - type: recall_at_5 value: 36.684 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.751000000000001 - type: map_at_10 value: 21.909 - type: map_at_100 value: 23.064 - type: map_at_1000 value: 23.205000000000002 - type: map_at_3 value: 20.138 - type: map_at_5 value: 20.973 - type: mrr_at_1 value: 19.305 - type: mrr_at_10 value: 25.647 - type: mrr_at_100 value: 26.659 - type: mrr_at_1000 value: 26.748 - type: mrr_at_3 value: 23.933 - type: mrr_at_5 value: 24.754 - type: ndcg_at_1 value: 19.305 - type: ndcg_at_10 value: 25.886 - type: ndcg_at_100 value: 31.56 - type: ndcg_at_1000 value: 34.799 - type: ndcg_at_3 value: 22.708000000000002 - type: ndcg_at_5 value: 23.838 - type: precision_at_1 value: 19.305 - type: precision_at_10 value: 4.677 - type: precision_at_100 value: 0.895 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 10.771 - type: precision_at_5 value: 7.46 - type: recall_at_1 value: 15.751000000000001 - type: recall_at_10 value: 34.156 - type: recall_at_100 value: 59.899 - type: recall_at_1000 value: 83.08 - type: recall_at_3 value: 24.772 - type: recall_at_5 value: 28.009 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.34 - type: map_at_10 value: 32.383 - type: map_at_100 value: 33.629999999999995 - type: map_at_1000 value: 33.735 - type: map_at_3 value: 29.68 - type: map_at_5 value: 31.270999999999997 - type: mrr_at_1 value: 27.612 - type: mrr_at_10 value: 36.381 - type: mrr_at_100 value: 37.351 - type: mrr_at_1000 value: 37.411 - type: mrr_at_3 value: 33.893 - type: mrr_at_5 value: 35.353 - type: ndcg_at_1 value: 27.612 - type: ndcg_at_10 value: 37.714999999999996 - type: ndcg_at_100 value: 43.525000000000006 - type: ndcg_at_1000 value: 45.812999999999995 - type: ndcg_at_3 value: 32.796 - type: ndcg_at_5 value: 35.243 - type: precision_at_1 value: 27.612 - type: precision_at_10 value: 6.465 - type: precision_at_100 value: 1.0619999999999998 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 15.049999999999999 - type: precision_at_5 value: 10.764999999999999 - type: recall_at_1 value: 23.34 - type: recall_at_10 value: 49.856 - type: recall_at_100 value: 75.334 - type: recall_at_1000 value: 91.156 - type: recall_at_3 value: 36.497 - type: recall_at_5 value: 42.769 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.097 - type: map_at_10 value: 34.599999999999994 - type: map_at_100 value: 36.174 - type: map_at_1000 value: 36.398 - type: map_at_3 value: 31.781 - type: map_at_5 value: 33.22 - type: mrr_at_1 value: 31.225 - type: mrr_at_10 value: 39.873 - type: mrr_at_100 value: 40.853 - type: mrr_at_1000 value: 40.904 - type: mrr_at_3 value: 37.681 - type: mrr_at_5 value: 38.669 - type: ndcg_at_1 value: 31.225 - type: ndcg_at_10 value: 40.586 - type: ndcg_at_100 value: 46.226 - type: ndcg_at_1000 value: 48.788 - type: ndcg_at_3 value: 36.258 - type: ndcg_at_5 value: 37.848 - type: precision_at_1 value: 31.225 - type: precision_at_10 value: 7.707999999999999 - type: precision_at_100 value: 1.536 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 17.26 - type: precision_at_5 value: 12.253 - type: recall_at_1 value: 25.097 - type: recall_at_10 value: 51.602000000000004 - type: recall_at_100 value: 76.854 - type: recall_at_1000 value: 93.303 - type: recall_at_3 value: 38.68 - type: recall_at_5 value: 43.258 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.689 - type: map_at_10 value: 25.291000000000004 - type: map_at_100 value: 26.262 - type: map_at_1000 value: 26.372 - type: map_at_3 value: 22.916 - type: map_at_5 value: 24.315 - type: mrr_at_1 value: 19.409000000000002 - type: mrr_at_10 value: 27.233 - type: mrr_at_100 value: 28.109 - type: mrr_at_1000 value: 28.192 - type: mrr_at_3 value: 24.892 - type: mrr_at_5 value: 26.278000000000002 - type: ndcg_at_1 value: 19.409000000000002 - type: ndcg_at_10 value: 29.809 - type: ndcg_at_100 value: 34.936 - type: ndcg_at_1000 value: 37.852000000000004 - type: ndcg_at_3 value: 25.179000000000002 - type: ndcg_at_5 value: 27.563 - type: precision_at_1 value: 19.409000000000002 - type: precision_at_10 value: 4.861 - type: precision_at_100 value: 0.8 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 11.029 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 17.689 - type: recall_at_10 value: 41.724 - type: recall_at_100 value: 65.95299999999999 - type: recall_at_1000 value: 88.094 - type: recall_at_3 value: 29.621 - type: recall_at_5 value: 35.179 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.581 - type: map_at_10 value: 18.944 - type: map_at_100 value: 20.812 - type: map_at_1000 value: 21.002000000000002 - type: map_at_3 value: 15.661 - type: map_at_5 value: 17.502000000000002 - type: mrr_at_1 value: 23.388 - type: mrr_at_10 value: 34.263 - type: mrr_at_100 value: 35.364000000000004 - type: mrr_at_1000 value: 35.409 - type: mrr_at_3 value: 30.586000000000002 - type: mrr_at_5 value: 32.928000000000004 - type: ndcg_at_1 value: 23.388 - type: ndcg_at_10 value: 26.56 - type: ndcg_at_100 value: 34.248 - type: ndcg_at_1000 value: 37.779 - type: ndcg_at_3 value: 21.179000000000002 - type: ndcg_at_5 value: 23.504 - type: precision_at_1 value: 23.388 - type: precision_at_10 value: 8.476 - type: precision_at_100 value: 1.672 - type: precision_at_1000 value: 0.233 - type: precision_at_3 value: 15.852 - type: precision_at_5 value: 12.73 - type: recall_at_1 value: 10.581 - type: recall_at_10 value: 32.512 - type: recall_at_100 value: 59.313 - type: recall_at_1000 value: 79.25 - type: recall_at_3 value: 19.912 - type: recall_at_5 value: 25.832 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.35 - type: map_at_10 value: 20.134 - type: map_at_100 value: 28.975 - type: map_at_1000 value: 30.709999999999997 - type: map_at_3 value: 14.513000000000002 - type: map_at_5 value: 16.671 - type: mrr_at_1 value: 69.75 - type: mrr_at_10 value: 77.67699999999999 - type: mrr_at_100 value: 77.97500000000001 - type: mrr_at_1000 value: 77.985 - type: mrr_at_3 value: 76.292 - type: mrr_at_5 value: 77.179 - type: ndcg_at_1 value: 56.49999999999999 - type: ndcg_at_10 value: 42.226 - type: ndcg_at_100 value: 47.562 - type: ndcg_at_1000 value: 54.923 - type: ndcg_at_3 value: 46.564 - type: ndcg_at_5 value: 43.830000000000005 - type: precision_at_1 value: 69.75 - type: precision_at_10 value: 33.525 - type: precision_at_100 value: 11.035 - type: precision_at_1000 value: 2.206 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42 - type: recall_at_1 value: 9.35 - type: recall_at_10 value: 25.793 - type: recall_at_100 value: 54.186 - type: recall_at_1000 value: 77.81 - type: recall_at_3 value: 15.770000000000001 - type: recall_at_5 value: 19.09 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.945 - type: f1 value: 42.07407842992542 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 71.04599999999999 - type: map_at_10 value: 80.718 - type: map_at_100 value: 80.961 - type: map_at_1000 value: 80.974 - type: map_at_3 value: 79.49199999999999 - type: map_at_5 value: 80.32000000000001 - type: mrr_at_1 value: 76.388 - type: mrr_at_10 value: 85.214 - type: mrr_at_100 value: 85.302 - type: mrr_at_1000 value: 85.302 - type: mrr_at_3 value: 84.373 - type: mrr_at_5 value: 84.979 - type: ndcg_at_1 value: 76.388 - type: ndcg_at_10 value: 84.987 - type: ndcg_at_100 value: 85.835 - type: ndcg_at_1000 value: 86.04899999999999 - type: ndcg_at_3 value: 83.04 - type: ndcg_at_5 value: 84.22500000000001 - type: precision_at_1 value: 76.388 - type: precision_at_10 value: 10.35 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 32.108 - type: precision_at_5 value: 20.033 - type: recall_at_1 value: 71.04599999999999 - type: recall_at_10 value: 93.547 - type: recall_at_100 value: 96.887 - type: recall_at_1000 value: 98.158 - type: recall_at_3 value: 88.346 - type: recall_at_5 value: 91.321 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 19.8 - type: map_at_10 value: 31.979999999999997 - type: map_at_100 value: 33.876 - type: map_at_1000 value: 34.056999999999995 - type: map_at_3 value: 28.067999999999998 - type: map_at_5 value: 30.066 - type: mrr_at_1 value: 38.735 - type: mrr_at_10 value: 47.749 - type: mrr_at_100 value: 48.605 - type: mrr_at_1000 value: 48.644999999999996 - type: mrr_at_3 value: 45.165 - type: mrr_at_5 value: 46.646 - type: ndcg_at_1 value: 38.735 - type: ndcg_at_10 value: 39.883 - type: ndcg_at_100 value: 46.983000000000004 - type: ndcg_at_1000 value: 50.043000000000006 - type: ndcg_at_3 value: 35.943000000000005 - type: ndcg_at_5 value: 37.119 - type: precision_at_1 value: 38.735 - type: precision_at_10 value: 10.940999999999999 - type: precision_at_100 value: 1.836 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 23.817 - type: precision_at_5 value: 17.346 - type: recall_at_1 value: 19.8 - type: recall_at_10 value: 47.082 - type: recall_at_100 value: 73.247 - type: recall_at_1000 value: 91.633 - type: recall_at_3 value: 33.201 - type: recall_at_5 value: 38.81 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.102999999999994 - type: map_at_10 value: 60.547 - type: map_at_100 value: 61.466 - type: map_at_1000 value: 61.526 - type: map_at_3 value: 56.973 - type: map_at_5 value: 59.244 - type: mrr_at_1 value: 76.205 - type: mrr_at_10 value: 82.816 - type: mrr_at_100 value: 83.002 - type: mrr_at_1000 value: 83.009 - type: mrr_at_3 value: 81.747 - type: mrr_at_5 value: 82.467 - type: ndcg_at_1 value: 76.205 - type: ndcg_at_10 value: 69.15 - type: ndcg_at_100 value: 72.297 - type: ndcg_at_1000 value: 73.443 - type: ndcg_at_3 value: 64.07000000000001 - type: ndcg_at_5 value: 66.96600000000001 - type: precision_at_1 value: 76.205 - type: precision_at_10 value: 14.601 - type: precision_at_100 value: 1.7049999999999998 - type: precision_at_1000 value: 0.186 - type: precision_at_3 value: 41.202 - type: precision_at_5 value: 27.006000000000004 - type: recall_at_1 value: 38.102999999999994 - type: recall_at_10 value: 73.005 - type: recall_at_100 value: 85.253 - type: recall_at_1000 value: 92.795 - type: recall_at_3 value: 61.803 - type: recall_at_5 value: 67.515 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 86.15 - type: ap value: 80.36282825265391 - type: f1 value: 86.07368510726472 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.6 - type: map_at_10 value: 34.887 - type: map_at_100 value: 36.069 - type: map_at_1000 value: 36.115 - type: map_at_3 value: 31.067 - type: map_at_5 value: 33.300000000000004 - type: mrr_at_1 value: 23.238 - type: mrr_at_10 value: 35.47 - type: mrr_at_100 value: 36.599 - type: mrr_at_1000 value: 36.64 - type: mrr_at_3 value: 31.735999999999997 - type: mrr_at_5 value: 33.939 - type: ndcg_at_1 value: 23.252 - type: ndcg_at_10 value: 41.765 - type: ndcg_at_100 value: 47.402 - type: ndcg_at_1000 value: 48.562 - type: ndcg_at_3 value: 34.016999999999996 - type: ndcg_at_5 value: 38.016 - type: precision_at_1 value: 23.252 - type: precision_at_10 value: 6.569 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.479000000000001 - type: precision_at_5 value: 10.722 - type: recall_at_1 value: 22.6 - type: recall_at_10 value: 62.919000000000004 - type: recall_at_100 value: 88.82 - type: recall_at_1000 value: 97.71600000000001 - type: recall_at_3 value: 41.896 - type: recall_at_5 value: 51.537 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.69357045143639 - type: f1 value: 93.55489858177597 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 75.31235750114 - type: f1 value: 57.891491963121155 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.04303967720243 - type: f1 value: 70.51516022297616 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.65299260255549 - type: f1 value: 77.49059766538576 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.458906115906597 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 28.9851513122443 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.2916268497217 - type: mrr value: 32.328276715593816 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.3740000000000006 - type: map_at_10 value: 13.089999999999998 - type: map_at_100 value: 16.512 - type: map_at_1000 value: 18.014 - type: map_at_3 value: 9.671000000000001 - type: map_at_5 value: 11.199 - type: mrr_at_1 value: 46.749 - type: mrr_at_10 value: 55.367 - type: mrr_at_100 value: 56.021 - type: mrr_at_1000 value: 56.058 - type: mrr_at_3 value: 53.30200000000001 - type: mrr_at_5 value: 54.773 - type: ndcg_at_1 value: 45.046 - type: ndcg_at_10 value: 35.388999999999996 - type: ndcg_at_100 value: 32.175 - type: ndcg_at_1000 value: 41.018 - type: ndcg_at_3 value: 40.244 - type: ndcg_at_5 value: 38.267 - type: precision_at_1 value: 46.749 - type: precision_at_10 value: 26.563 - type: precision_at_100 value: 8.074 - type: precision_at_1000 value: 2.099 - type: precision_at_3 value: 37.358000000000004 - type: precision_at_5 value: 33.003 - type: recall_at_1 value: 6.3740000000000006 - type: recall_at_10 value: 16.805999999999997 - type: recall_at_100 value: 31.871 - type: recall_at_1000 value: 64.098 - type: recall_at_3 value: 10.383000000000001 - type: recall_at_5 value: 13.166 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 34.847 - type: map_at_10 value: 50.532 - type: map_at_100 value: 51.504000000000005 - type: map_at_1000 value: 51.528 - type: map_at_3 value: 46.219 - type: map_at_5 value: 48.868 - type: mrr_at_1 value: 39.137 - type: mrr_at_10 value: 53.157 - type: mrr_at_100 value: 53.839999999999996 - type: mrr_at_1000 value: 53.857 - type: mrr_at_3 value: 49.667 - type: mrr_at_5 value: 51.847 - type: ndcg_at_1 value: 39.108 - type: ndcg_at_10 value: 58.221000000000004 - type: ndcg_at_100 value: 62.021 - type: ndcg_at_1000 value: 62.57 - type: ndcg_at_3 value: 50.27199999999999 - type: ndcg_at_5 value: 54.623999999999995 - type: precision_at_1 value: 39.108 - type: precision_at_10 value: 9.397 - type: precision_at_100 value: 1.1520000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 22.644000000000002 - type: precision_at_5 value: 16.141 - type: recall_at_1 value: 34.847 - type: recall_at_10 value: 78.945 - type: recall_at_100 value: 94.793 - type: recall_at_1000 value: 98.904 - type: recall_at_3 value: 58.56 - type: recall_at_5 value: 68.535 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 68.728 - type: map_at_10 value: 82.537 - type: map_at_100 value: 83.218 - type: map_at_1000 value: 83.238 - type: map_at_3 value: 79.586 - type: map_at_5 value: 81.416 - type: mrr_at_1 value: 79.17999999999999 - type: mrr_at_10 value: 85.79299999999999 - type: mrr_at_100 value: 85.937 - type: mrr_at_1000 value: 85.938 - type: mrr_at_3 value: 84.748 - type: mrr_at_5 value: 85.431 - type: ndcg_at_1 value: 79.17 - type: ndcg_at_10 value: 86.555 - type: ndcg_at_100 value: 88.005 - type: ndcg_at_1000 value: 88.146 - type: ndcg_at_3 value: 83.557 - type: ndcg_at_5 value: 85.152 - type: precision_at_1 value: 79.17 - type: precision_at_10 value: 13.163 - type: precision_at_100 value: 1.52 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.53 - type: precision_at_5 value: 24.046 - type: recall_at_1 value: 68.728 - type: recall_at_10 value: 94.217 - type: recall_at_100 value: 99.295 - type: recall_at_1000 value: 99.964 - type: recall_at_3 value: 85.646 - type: recall_at_5 value: 90.113 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 56.15680266226348 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.4318549229047 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.353 - type: map_at_10 value: 10.956000000000001 - type: map_at_100 value: 12.873999999999999 - type: map_at_1000 value: 13.177 - type: map_at_3 value: 7.854 - type: map_at_5 value: 9.327 - type: mrr_at_1 value: 21.4 - type: mrr_at_10 value: 31.948999999999998 - type: mrr_at_100 value: 33.039 - type: mrr_at_1000 value: 33.106 - type: mrr_at_3 value: 28.449999999999996 - type: mrr_at_5 value: 30.535 - type: ndcg_at_1 value: 21.4 - type: ndcg_at_10 value: 18.694 - type: ndcg_at_100 value: 26.275 - type: ndcg_at_1000 value: 31.836 - type: ndcg_at_3 value: 17.559 - type: ndcg_at_5 value: 15.372 - type: precision_at_1 value: 21.4 - type: precision_at_10 value: 9.790000000000001 - type: precision_at_100 value: 2.0709999999999997 - type: precision_at_1000 value: 0.34099999999999997 - type: precision_at_3 value: 16.467000000000002 - type: precision_at_5 value: 13.54 - type: recall_at_1 value: 4.353 - type: recall_at_10 value: 19.892000000000003 - type: recall_at_100 value: 42.067 - type: recall_at_1000 value: 69.268 - type: recall_at_3 value: 10.042 - type: recall_at_5 value: 13.741999999999999 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.75433886279843 - type: cos_sim_spearman value: 78.29727771767095 - type: euclidean_pearson value: 80.83057828506621 - type: euclidean_spearman value: 78.35203149750356 - type: manhattan_pearson value: 80.7403553891142 - type: manhattan_spearman value: 78.33670488531051 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.59999465280839 - type: cos_sim_spearman value: 75.79279003980383 - type: euclidean_pearson value: 82.29895375956758 - type: euclidean_spearman value: 77.33856514102094 - type: manhattan_pearson value: 82.22694214534756 - type: manhattan_spearman value: 77.3028993008695 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.09296929691297 - type: cos_sim_spearman value: 83.58056936846941 - type: euclidean_pearson value: 83.84067483060005 - type: euclidean_spearman value: 84.45155680480985 - type: manhattan_pearson value: 83.82353052971942 - type: manhattan_spearman value: 84.43030567861112 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.74616852320915 - type: cos_sim_spearman value: 79.948683747966 - type: euclidean_pearson value: 81.55702283757084 - type: euclidean_spearman value: 80.1721505114231 - type: manhattan_pearson value: 81.52251518619441 - type: manhattan_spearman value: 80.1469800135577 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.97170104226318 - type: cos_sim_spearman value: 88.82021731518206 - type: euclidean_pearson value: 87.92950547187615 - type: euclidean_spearman value: 88.67043634645866 - type: manhattan_pearson value: 87.90668112827639 - type: manhattan_spearman value: 88.64471082785317 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.02790375770599 - type: cos_sim_spearman value: 84.46308496590792 - type: euclidean_pearson value: 84.29430000414911 - type: euclidean_spearman value: 84.77298303589936 - type: manhattan_pearson value: 84.23919291368665 - type: manhattan_spearman value: 84.75272234871308 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.62885108477064 - type: cos_sim_spearman value: 87.58456196391622 - type: euclidean_pearson value: 88.2602775281007 - type: euclidean_spearman value: 87.51556278299846 - type: manhattan_pearson value: 88.11224053672842 - type: manhattan_spearman value: 87.4336094383095 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.98187965128411 - type: cos_sim_spearman value: 64.0653163219731 - type: euclidean_pearson value: 62.30616725924099 - type: euclidean_spearman value: 61.556971332295916 - type: manhattan_pearson value: 62.07642330128549 - type: manhattan_spearman value: 61.155494129828 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.6089703921826 - type: cos_sim_spearman value: 86.52303197250791 - type: euclidean_pearson value: 85.95801955963246 - type: euclidean_spearman value: 86.25242424112962 - type: manhattan_pearson value: 85.88829100470312 - type: manhattan_spearman value: 86.18742955805165 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 83.02282098487036 - type: mrr value: 95.05126409538174 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 55.928 - type: map_at_10 value: 67.308 - type: map_at_100 value: 67.89500000000001 - type: map_at_1000 value: 67.91199999999999 - type: map_at_3 value: 65.091 - type: map_at_5 value: 66.412 - type: mrr_at_1 value: 58.667 - type: mrr_at_10 value: 68.401 - type: mrr_at_100 value: 68.804 - type: mrr_at_1000 value: 68.819 - type: mrr_at_3 value: 66.72200000000001 - type: mrr_at_5 value: 67.72200000000001 - type: ndcg_at_1 value: 58.667 - type: ndcg_at_10 value: 71.944 - type: ndcg_at_100 value: 74.464 - type: ndcg_at_1000 value: 74.82799999999999 - type: ndcg_at_3 value: 68.257 - type: ndcg_at_5 value: 70.10300000000001 - type: precision_at_1 value: 58.667 - type: precision_at_10 value: 9.533 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 27.222 - type: precision_at_5 value: 17.533 - type: recall_at_1 value: 55.928 - type: recall_at_10 value: 84.65 - type: recall_at_100 value: 96.267 - type: recall_at_1000 value: 99 - type: recall_at_3 value: 74.656 - type: recall_at_5 value: 79.489 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.79009900990098 - type: cos_sim_ap value: 94.5795129511524 - type: cos_sim_f1 value: 89.34673366834171 - type: cos_sim_precision value: 89.79797979797979 - type: cos_sim_recall value: 88.9 - type: dot_accuracy value: 99.53465346534654 - type: dot_ap value: 81.56492504352725 - type: dot_f1 value: 76.33816908454227 - type: dot_precision value: 76.37637637637637 - type: dot_recall value: 76.3 - type: euclidean_accuracy value: 99.78514851485149 - type: euclidean_ap value: 94.59134620408962 - type: euclidean_f1 value: 88.96484375 - type: euclidean_precision value: 86.92748091603053 - type: euclidean_recall value: 91.10000000000001 - type: manhattan_accuracy value: 99.78415841584159 - type: manhattan_ap value: 94.5190197328845 - type: manhattan_f1 value: 88.84462151394423 - type: manhattan_precision value: 88.4920634920635 - type: manhattan_recall value: 89.2 - type: max_accuracy value: 99.79009900990098 - type: max_ap value: 94.59134620408962 - type: max_f1 value: 89.34673366834171 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.1487505617497 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.502518166001856 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.33775480236701 - type: mrr value: 51.17302223919871 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.561111309808208 - type: cos_sim_spearman value: 30.2839254379273 - type: dot_pearson value: 29.560242291401973 - type: dot_spearman value: 30.51527274679116 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.215 - type: map_at_10 value: 1.752 - type: map_at_100 value: 9.258 - type: map_at_1000 value: 23.438 - type: map_at_3 value: 0.6 - type: map_at_5 value: 0.968 - type: mrr_at_1 value: 84 - type: mrr_at_10 value: 91.333 - type: mrr_at_100 value: 91.333 - type: mrr_at_1000 value: 91.333 - type: mrr_at_3 value: 91.333 - type: mrr_at_5 value: 91.333 - type: ndcg_at_1 value: 75 - type: ndcg_at_10 value: 69.596 - type: ndcg_at_100 value: 51.970000000000006 - type: ndcg_at_1000 value: 48.864999999999995 - type: ndcg_at_3 value: 73.92699999999999 - type: ndcg_at_5 value: 73.175 - type: precision_at_1 value: 84 - type: precision_at_10 value: 74 - type: precision_at_100 value: 53.2 - type: precision_at_1000 value: 21.836 - type: precision_at_3 value: 79.333 - type: precision_at_5 value: 78.4 - type: recall_at_1 value: 0.215 - type: recall_at_10 value: 1.9609999999999999 - type: recall_at_100 value: 12.809999999999999 - type: recall_at_1000 value: 46.418 - type: recall_at_3 value: 0.6479999999999999 - type: recall_at_5 value: 1.057 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.066 - type: map_at_10 value: 10.508000000000001 - type: map_at_100 value: 16.258 - type: map_at_1000 value: 17.705000000000002 - type: map_at_3 value: 6.157 - type: map_at_5 value: 7.510999999999999 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 48.786 - type: mrr_at_100 value: 49.619 - type: mrr_at_1000 value: 49.619 - type: mrr_at_3 value: 45.918 - type: mrr_at_5 value: 46.837 - type: ndcg_at_1 value: 31.633 - type: ndcg_at_10 value: 26.401999999999997 - type: ndcg_at_100 value: 37.139 - type: ndcg_at_1000 value: 48.012 - type: ndcg_at_3 value: 31.875999999999998 - type: ndcg_at_5 value: 27.383000000000003 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 22.857 - type: precision_at_100 value: 7.611999999999999 - type: precision_at_1000 value: 1.492 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 3.066 - type: recall_at_10 value: 16.239 - type: recall_at_100 value: 47.29 - type: recall_at_1000 value: 81.137 - type: recall_at_3 value: 7.069 - type: recall_at_5 value: 9.483 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 72.1126 - type: ap value: 14.710862719285753 - type: f1 value: 55.437808972378846 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.39049235993209 - type: f1 value: 60.69810537250234 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 48.15576640316866 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.52917684925792 - type: cos_sim_ap value: 75.97497873817315 - type: cos_sim_f1 value: 70.01151926276718 - type: cos_sim_precision value: 67.98409147402435 - type: cos_sim_recall value: 72.16358839050132 - type: dot_accuracy value: 82.47004828038385 - type: dot_ap value: 62.48739894974198 - type: dot_f1 value: 59.13107511045656 - type: dot_precision value: 55.27765029830197 - type: dot_recall value: 63.562005277044854 - type: euclidean_accuracy value: 86.46361089586935 - type: euclidean_ap value: 75.59282886839452 - type: euclidean_f1 value: 69.6465443945099 - type: euclidean_precision value: 64.52847175331982 - type: euclidean_recall value: 75.64643799472296 - type: manhattan_accuracy value: 86.43380818978363 - type: manhattan_ap value: 75.5742420974403 - type: manhattan_f1 value: 69.8636926889715 - type: manhattan_precision value: 65.8644859813084 - type: manhattan_recall value: 74.37994722955145 - type: max_accuracy value: 86.52917684925792 - type: max_ap value: 75.97497873817315 - type: max_f1 value: 70.01151926276718 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.29056545193464 - type: cos_sim_ap value: 86.63028865482376 - type: cos_sim_f1 value: 79.18166458532285 - type: cos_sim_precision value: 75.70585756426465 - type: cos_sim_recall value: 82.99199260856174 - type: dot_accuracy value: 85.23305002522606 - type: dot_ap value: 76.0482687263196 - type: dot_f1 value: 70.80484330484332 - type: dot_precision value: 65.86933474688577 - type: dot_recall value: 76.53988296889437 - type: euclidean_accuracy value: 89.26145845461248 - type: euclidean_ap value: 86.54073288416006 - type: euclidean_f1 value: 78.9721371479794 - type: euclidean_precision value: 76.68649354417525 - type: euclidean_recall value: 81.39821373575609 - type: manhattan_accuracy value: 89.22847052431405 - type: manhattan_ap value: 86.51250729037905 - type: manhattan_f1 value: 78.94601825044894 - type: manhattan_precision value: 75.32694594027555 - type: manhattan_recall value: 82.93039728980598 - type: max_accuracy value: 89.29056545193464 - type: max_ap value: 86.63028865482376 - type: max_f1 value: 79.18166458532285 language: - en license: mit --- # E5-base-v2 [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 12 layers and the embedding size is 768. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ". # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-base-v2') model = AutoModel.from_pretrained('intfloat/e5-base-v2') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Training Details Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). ## Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/e5-base-v2') input_texts = [ 'query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ``` ## Limitations This model only works for English texts. Long texts will be truncated to at most 512 tokens.

hustvl/vitmatte-small-composition-1k

hustvl

transformers

--- license: apache-2.0 tags: - vision --- # ViTMatte model ViTMatte model trained on Composition-1k. It was introduced in the paper [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://arxiv.org/abs/2305.15272) by Yao et al. and first released in [this repository](https://github.com/hustvl/ViTMatte). Disclaimer: The team releasing ViTMatte did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ViTMatte is a simple approach to image matting, the task of accurately estimating the foreground object in an image. The model consists of a Vision Transformer (ViT) with a lightweight head on top. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vitmatte_architecture.png" alt="drawing" width="600"/> <small> ViTMatte high-level overview. Taken from the <a href="https://arxiv.org/abs/2305.15272">original paper.</a> </small> ## Intended uses & limitations You can use the raw model for image matting. See the [model hub](https://huggingface.co/models?search=vitmatte) to look for other fine-tuned versions that may interest you. ### How to use We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/vitmatte#transformers.VitMatteForImageMatting.forward.example). ### BibTeX entry and citation info ```bibtex @misc{yao2023vitmatte, title={ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers}, author={Jingfeng Yao and Xinggang Wang and Shusheng Yang and Baoyuan Wang}, year={2023}, eprint={2305.15272}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

sentence-transformers/paraphrase-TinyBERT-L6-v2

sentence-transformers

sentence-transformers

sentence-similarity

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- # sentence-transformers/paraphrase-TinyBERT-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-TinyBERT-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-TinyBERT-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-TinyBERT-L6-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-TinyBERT-L6-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

distil-whisper/distil-medium.en

distil-whisper

transformers

automatic-speech-recognition

--- language: - en tags: - audio - automatic-speech-recognition - transformers.js widget: - example_title: LibriSpeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: LibriSpeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac pipeline_tag: automatic-speech-recognition license: mit library_name: transformers --- # Distil-Whisper: distil-medium.en Distil-Whisper was proposed in the paper [Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://arxiv.org/abs/2311.00430). It is a distilled version of the Whisper model that is **6 times faster**, 49% smaller, and performs **within 1% WER** on out-of-distribution evaluation sets. This is the repository for distil-medium.en, a distilled variant of [Whisper medium.en](https://huggingface.co/openai/whisper-medium.en). | Model | Params / M | Rel. Latency ↑ | Short-Form WER ↓ | Long-Form WER ↓ | |----------------------------------------------------------------------------|------------|----------------|------------------|-----------------| | [large-v3](https://huggingface.co/openai/whisper-large-v3) | 1550 | 1.0 | **8.4** | 11.0 | | [large-v2](https://huggingface.co/openai/whisper-large-v2) | 1550 | 1.0 | 9.1 | 11.7 | | | | | | | | [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3) | 756 | 6.3 | 9.7 | **10.8** | | [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) | 756 | 5.8 | 10.1 | 11.6 | | [distil-medium.en](https://huggingface.co/distil-whisper/distil-medium.en) | 394 | **6.8** | 11.1 | 12.4 | | [distil-small.en](https://huggingface.co/distil-whisper/distil-small.en) | **166** | 5.6 | 12.1 | 12.8 | **Note:** Distil-Whisper is currently only available for English speech recognition. We are working with the community to distill Whisper on other languages. If you are interested in distilling Whisper in your language, check out the provided [training code](https://github.com/huggingface/distil-whisper/tree/main/training). We will update the [Distil-Whisper repository](https://github.com/huggingface/distil-whisper/) with multilingual checkpoints when ready! ## Usage Distil-Whisper is supported in Hugging Face 🤗 Transformers from version 4.35 onwards. To run the model, first install the latest version of the Transformers library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub: ```bash pip install --upgrade pip pip install --upgrade transformers accelerate datasets[audio] ``` ### Short-Form Transcription The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) class to transcribe short-form audio files (< 30-seconds) as follows: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-medium.en" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline: ```diff - result = pipe(sample) + result = pipe("audio.mp3") ``` ### Long-Form Transcription Distil-Whisper uses a chunked algorithm to transcribe long-form audio files (> 30-seconds). In practice, this chunked long-form algorithm is 9x faster than the sequential algorithm proposed by OpenAI in the Whisper paper (see Table 7 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)). To enable chunking, pass the `chunk_length_s` parameter to the `pipeline`. For Distil-Whisper, a chunk length of 15-seconds is optimal. To activate batching, pass the argument `batch_size`: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-medium.en" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=15, batch_size=16, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "default", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` <!--- **Tip:** The pipeline can also be used to transcribe an audio file from a remote URL, for example: ```python result = pipe("https://huggingface.co/datasets/sanchit-gandhi/librispeech_long/resolve/main/audio.wav") ``` ---> ### Speculative Decoding Distil-Whisper can be used as an assistant model to Whisper for [speculative decoding](https://huggingface.co/blog/whisper-speculative-decoding). Speculative decoding mathematically ensures the exact same outputs as Whisper are obtained while being 2 times faster. This makes it the perfect drop-in replacement for existing Whisper pipelines, since the same outputs are guaranteed. In the following code-snippet, we load the assistant Distil-Whisper model standalone to the main Whisper pipeline. We then specify it as the "assistant model" for generation: ```python from transformers import pipeline, AutoModelForCausalLM, AutoModelForSpeechSeq2Seq, AutoProcessor import torch from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 assistant_model_id = "distil-whisper/distil-medium.en" assistant_model = AutoModelForCausalLM.from_pretrained( assistant_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) assistant_model.to(device) model_id = "openai/whisper-medium.en" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, generate_kwargs={"assistant_model": assistant_model}, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` ## Additional Speed & Memory Improvements You can apply additional speed and memory improvements to Distil-Whisper which we cover in the following. ### Flash Attention We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2) if your GPU allows for it. To do so, you first need to install [Flash Attention](https://github.com/Dao-AILab/flash-attention): ``` pip install flash-attn --no-build-isolation ``` and then all you have to do is to pass `use_flash_attention_2=True` to `from_pretrained`: ```diff - model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True) + model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, use_flash_attention_2=True) ``` ### Torch Scale-Product-Attention (SDPA) If your GPU does not support Flash Attention, we recommend making use of [BetterTransformers](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#bettertransformer). To do so, you first need to install optimum: ``` pip install --upgrade optimum ``` And then convert your model to a "BetterTransformer" model before using it: ```diff model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True) + model = model.to_bettertransformer() ``` ### Running Distil-Whisper in `openai-whisper` To use the model in the original Whisper format, first ensure you have the [`openai-whisper`](https://pypi.org/project/openai-whisper/) package installed: ```bash pip install --upgrade openai-whisper ``` The following code-snippet demonstrates how to transcribe a sample file from the LibriSpeech dataset loaded using 🤗 Datasets: ```python import torch from datasets import load_dataset from huggingface_hub import hf_hub_download from whisper import load_model, transcribe medium_en = hf_hub_download(repo_id="distil-whisper/distil-medium.en", filename="original-model.bin") model = load_model(medium_en) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"]["array"] sample = torch.from_numpy(sample).float() pred_out = transcribe(model, audio=sample) print(pred_out["text"]) ``` To transcribe a local audio file, simply pass the path to the audio file as the `audio` argument to transcribe: ```python pred_out = transcribe(model, audio="audio.mp3") ``` ### Whisper.cpp Distil-Whisper can be run from the [Whisper.cpp](https://github.com/ggerganov/whisper.cpp) repository with the original sequential long-form transcription algorithm. In a [provisional benchmark](https://github.com/ggerganov/whisper.cpp/pull/1424#issuecomment-1793513399) on Mac M1, `distil-medium.en` is 4x faster than `large-v2`, while performing to within 1% WER over long-form audio. Steps for getting started: 1. Clone the Whisper.cpp repository: ``` git clone https://github.com/ggerganov/whisper.cpp.git cd whisper.cpp ``` 2. Download the ggml weights for `distil-medium.en` from the Hugging Face Hub: ```bash python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='distil-whisper/distil-medium.en', filename='ggml-medium-32-2.en.bin', local_dir='./models')" ``` Note that if you do not have the `huggingface_hub` package installed, you can also download the weights with `wget`: ```bash wget https://huggingface.co/distil-whisper/distil-medium.en/resolve/main/ggml-medium-32-2.en.bin -P ./models ``` 3. Run inference using the provided sample audio: ```bash make -j && ./main -m models/ggml-medium-32-2.en.bin -f samples/jfk.wav ``` ### Transformers.js ```js import { pipeline } from '@xenova/transformers'; let transcriber = await pipeline('automatic-speech-recognition', 'distil-whisper/distil-medium.en'); let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav'; let output = await transcriber(url); // { text: " And so my fellow Americans, ask not what your country can do for you. Ask what you can do for your country." } ``` See the [docs](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AutomaticSpeechRecognitionPipeline) for more information. ### Candle Through an integration with Hugging Face [Candle](https://github.com/huggingface/candle/tree/main) 🕯️, Distil-Whisper is now available in the Rust library 🦀 Benefit from: * Optimised CPU backend with optional MKL support for x86 and Accelerate for Macs * CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL * WASM support: run Distil-Whisper in a browser Steps for getting started: 1. Install [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) as explained [here](https://huggingface.github.io/candle/guide/installation.html) 2. Clone the `candle` repository locally: ``` git clone https://github.com/huggingface/candle.git ``` 3. Enter the example directory for [Whisper](https://github.com/huggingface/candle/tree/main/candle-examples/examples/whisper): ``` cd candle/candle-examples/examples/whisper ``` 4. Run an example: ``` cargo run --example whisper --release -- --model distil-medium.en ``` 5. To specify your own audio file, add the `--input` flag: ``` cargo run --example whisper --release -- --model distil-medium.en --input audio.wav ``` ### 8bit & 4bit Quantization Coming soon ... ## Model Details Distil-Whisper inherits the encoder-decoder architecture from Whisper. The encoder maps a sequence of speech vector inputs to a sequence of hidden-state vectors. The decoder auto-regressively predicts text tokens, conditional on all previous tokens and the encoder hidden-states. Consequently, the encoder is only run forward once, whereas the decoder is run as many times as the number of tokens generated. In practice, this means the decoder accounts for over 90% of total inference time. Thus, to optimise for latency, the focus should be on minimising the inference time of the decoder. To distill the Whisper model, we reduce the number of decoder layers while keeping the encoder fixed. The encoder (shown in green) is entirely copied from the teacher to the student and frozen during training. The student's decoder consists of only two decoder layers, which are initialised from the first and last decoder layer of the teacher (shown in red). All other decoder layers of the teacher are discarded. The model is then trained on a weighted sum of the KL divergence and pseudo-label loss terms. <p align="center"> <img src="https://huggingface.co/datasets/distil-whisper/figures/resolve/main/architecture.png?raw=true" width="600"/> </p> ## Evaluation The following code-snippets demonstrates how to evaluate the Distil-Whisper model on the LibriSpeech validation.clean dataset with [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet), meaning no audio data has to be downloaded to your local device. First, we need to install the required packages, including 🤗 Datasets to stream and load the audio data, and 🤗 Evaluate to perform the WER calculation: ```bash pip install --upgrade pip pip install --upgrade transformers datasets[audio] evaluate jiwer ``` Evaluation can then be run end-to-end with the following example: ```python from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor from transformers.models.whisper.english_normalizer import EnglishTextNormalizer from datasets import load_dataset from evaluate import load import torch from tqdm import tqdm # define our torch configuration device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-medium.en" # load the model + processor model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, use_safetensors=True, low_cpu_mem_usage=True) model = model.to(device) processor = AutoProcessor.from_pretrained(model_id) # load the dataset with streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # define the evaluation metric wer_metric = load("wer") normalizer = EnglishTextNormalizer(processor.tokenizer.english_spelling_normalizer) def inference(batch): # 1. Pre-process the audio data to log-mel spectrogram inputs audio = [sample["array"] for sample in batch["audio"]] input_features = processor(audio, sampling_rate=batch["audio"][0]["sampling_rate"], return_tensors="pt").input_features input_features = input_features.to(device, dtype=torch_dtype) # 2. Auto-regressively generate the predicted token ids pred_ids = model.generate(input_features, max_new_tokens=128) # 3. Decode the token ids to the final transcription batch["transcription"] = processor.batch_decode(pred_ids, skip_special_tokens=True) batch["reference"] = batch["text"] return batch dataset = dataset.map(function=inference, batched=True, batch_size=16) all_transcriptions = [] all_references = [] # iterate over the dataset and run inference for i, result in tqdm(enumerate(dataset), desc="Evaluating..."): all_transcriptions.append(result["transcription"]) all_references.append(result["reference"]) # normalize predictions and references all_transcriptions = [normalizer(transcription) for transcription in all_transcriptions] all_references = [normalizer(reference) for reference in all_references] # compute the WER metric wer = 100 * wer_metric.compute(predictions=all_transcriptions, references=all_references) print(wer) ``` **Print Output:** ``` 3.593196832001168 ``` ## Intended Use Distil-Whisper is intended to be a drop-in replacement for Whisper on English speech recognition. In particular, it achieves comparable WER results over out-of-distribution test data, while being 6x faster over both short and long-form audio. ## Data Distil-Whisper is trained on 22,000 hours of audio data from 9 open-source, permissively licensed speech datasets on the Hugging Face Hub: | Dataset | Size / h | Speakers | Domain | Licence | |-----------------------------------------------------------------------------------------|----------|----------|-----------------------------|-----------------| | [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech) | 12,000 | unknown | Internet Archive | CC-BY-SA-4.0 | | [Common Voice 13](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) | 3,000 | unknown | Narrated Wikipedia | CC0-1.0 | | [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) | 2,500 | unknown | Audiobook, podcast, YouTube | apache-2.0 | | Fisher | 1,960 | 11,900 | Telephone conversations | LDC | | [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) | 960 | 2,480 | Audiobooks | CC-BY-4.0 | | [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) | 540 | 1,310 | European Parliament | CC0 | | [TED-LIUM](https://huggingface.co/datasets/LIUM/tedlium) | 450 | 2,030 | TED talks | CC-BY-NC-ND 3.0 | | SwitchBoard | 260 | 540 | Telephone conversations | LDC | | [AMI](https://huggingface.co/datasets/edinburghcstr/ami) | 100 | unknown | Meetings | CC-BY-4.0 | |||||| | **Total** | 21,770 | 18,260+ | | | The combined dataset spans 10 distinct domains and over 50k speakers. The diversity of this dataset is crucial to ensuring the distilled model is robust to audio distributions and noise. The audio data is then pseudo-labelled using the Whisper large-v2 model: we use Whisper to generate predictions for all the audio in our training set and use these as the target labels during training. Using pseudo-labels ensures that the transcriptions are consistently formatted across datasets and provides sequence-level distillation signal during training. ## WER Filter The Whisper pseudo-label predictions are subject to mis-transcriptions and hallucinations. To ensure we only train on accurate pseudo-labels, we employ a simple WER heuristic during training. First, we normalise the Whisper pseudo-labels and the ground truth labels provided by each dataset. We then compute the WER between these labels. If the WER exceeds a specified threshold, we discard the training example. Otherwise, we keep it for training. Section 9.2 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430) demonstrates the effectiveness of this filter for improving downstream performance of the distilled model. We also partially attribute Distil-Whisper's robustness to hallucinations to this filter. ## Training The model was trained for 80,000 optimisation steps (or eight epochs). The Tensorboard training logs can be found under: https://huggingface.co/distil-whisper/distil-medium.en/tensorboard?params=scalars#frame ## Results The distilled model performs to within 1% WER of Whisper on out-of-distribution (OOD) short-form audio, and outperforms Whisper by 0.1% on OOD long-form audio. This performance gain is attributed to lower hallucinations. For a detailed per-dataset breakdown of the evaluation results, refer to Tables 16 and 17 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430) Distil-Whisper is also evaluated on the [ESB benchmark](https://arxiv.org/abs/2210.13352) datasets as part of the [OpenASR leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard), where it performs to within 0.2% WER of Whisper. ## Reproducing Distil-Whisper Training and evaluation code to reproduce Distil-Whisper is available under the Distil-Whisper repository: https://github.com/huggingface/distil-whisper/tree/main/training ## License Distil-Whisper inherits the [MIT license](https://github.com/huggingface/distil-whisper/blob/main/LICENSE) from OpenAI's Whisper model. ## Citation If you use this model, please consider citing the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430): ``` @misc{gandhi2023distilwhisper, title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling}, author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush}, year={2023}, eprint={2311.00430}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Acknowledgements * OpenAI for the Whisper [model](https://huggingface.co/openai/whisper-large-v2) and [original codebase](https://github.com/openai/whisper) * Hugging Face 🤗 [Transformers](https://github.com/huggingface/transformers) for the model integration * Google's [TPU Research Cloud (TRC)](https://sites.research.google/trc/about/) programme for Cloud TPU v4s * [`@rsonavane`](https://huggingface.co/rsonavane/distil-whisper-large-v2-8-ls) for releasing an early iteration of Distil-Whisper on the LibriSpeech dataset

trpakov/vit-face-expression

trpakov

transformers

image-classification

--- {} --- # Vision Transformer (ViT) for Facial Expression Recognition Model Card ## Model Overview - **Model Name:** [trpakov/vit-face-expression](https://huggingface.co/trpakov/vit-face-expression) - **Task:** Facial Expression/Emotion Recognition - **Dataset:** [FER2013](https://www.kaggle.com/datasets/msambare/fer2013) - **Model Architecture:** [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit) - **Finetuned from model:** [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) ## Model Description The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition. It is trained on the FER2013 dataset, which consists of facial images categorized into seven different emotions: - Angry - Disgust - Fear - Happy - Sad - Surprise - Neutral ## Data Preprocessing The input images are preprocessed before being fed into the model. The preprocessing steps include: - **Resizing:** Images are resized to the specified input size. - **Normalization:** Pixel values are normalized to a specific range. - **Data Augmentation:** Random transformations such as rotations, flips, and zooms are applied to augment the training dataset. ## Evaluation Metrics - **Validation set accuracy:** 0.7113 - **Test set accuracy:** 0.7116 ## Limitations - **Data Bias:** The model's performance may be influenced by biases present in the training data. - **Generalization:** The model's ability to generalize to unseen data is subject to the diversity of the training dataset.

alisawuffles/roberta-large-wanli

alisawuffles

transformers

text-classification

--- language: - en tags: - text-classification widget: - text: "I almost forgot to eat lunch.</s></s>I didn't forget to eat lunch." - text: "I almost forgot to eat lunch.</s></s>I forgot to eat lunch." - text: "I ate lunch.</s></s>I almost forgot to eat lunch." datasets: - alisawuffles/WANLI --- This is an off-the-shelf roberta-large model finetuned on WANLI, the Worker-AI Collaborative NLI dataset ([Liu et al., 2022](https://aclanthology.org/2022.findings-emnlp.508/)). It outperforms the `roberta-large-mnli` model on eight out-of-domain test sets, including by 11% on HANS and 9% on Adversarial NLI. ### How to use ```python from transformers import RobertaTokenizer, RobertaForSequenceClassification model = RobertaForSequenceClassification.from_pretrained('alisawuffles/roberta-large-wanli') tokenizer = RobertaTokenizer.from_pretrained('alisawuffles/roberta-large-wanli') x = tokenizer("I almost forgot to eat lunch.", "I didn't forget to eat lunch.", return_tensors='pt', max_length=128, truncation=True) logits = model(**x).logits probs = logits.softmax(dim=1).squeeze(0) label_id = torch.argmax(probs).item() prediction = model.config.id2label[label_id] ``` ### Citation ``` @inproceedings{liu-etal-2022-wanli, title = "{WANLI}: Worker and {AI} Collaboration for Natural Language Inference Dataset Creation", author = "Liu, Alisa and Swayamdipta, Swabha and Smith, Noah A. and Choi, Yejin", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-emnlp.508", pages = "6826--6847", abstract = "A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a novel approach for dataset creation based on worker and AI collaboration, which brings together the generative strength of language models and the evaluative strength of humans. Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns. Machine generated examples are then automatically filtered, and finally revised and labeled by human crowdworkers. The resulting dataset, WANLI, consists of 107,885 NLI examples and presents unique empirical strengths over existing NLI datasets. Remarkably, training a model on WANLI improves performance on eight out-of-domain test sets we consider, including by 11{\%} on HANS and 9{\%} on Adversarial NLI, compared to training on the 4x larger MultiNLI. Moreover, it continues to be more effective than MultiNLI augmented with other NLI datasets. Our results demonstrate the promise of leveraging natural language generation techniques and re-imagining the role of humans in the dataset creation process.", } ```

openai/whisper-small

openai

transformers

automatic-speech-recognition

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - no - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition - hf-asr-leaderboard widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac model-index: - name: whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: 3.432213777886737 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (other) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 7.628304527060248 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: language: hi metrics: - name: Test WER type: wer value: 87.3 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13.0 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: language: dv metrics: - name: Wer type: wer value: 125.69809089960707 pipeline_tag: automatic-speech-recognition license: apache-2.0 --- # Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need for fine-tuning. Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper). **Disclaimer**: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the *same* language as the audio. For speech translation, the model predicts transcriptions to a *different* language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: | Size | Parameters | English-only | Multilingual | |----------|------------|------------------------------------------------------|-----------------------------------------------------| | tiny | 39 M | [✓](https://huggingface.co/openai/whisper-tiny.en) | [✓](https://huggingface.co/openai/whisper-tiny) | | base | 74 M | [✓](https://huggingface.co/openai/whisper-base.en) | [✓](https://huggingface.co/openai/whisper-base) | | small | 244 M | [✓](https://huggingface.co/openai/whisper-small.en) | [✓](https://huggingface.co/openai/whisper-small) | | medium | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) | | large | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) | | large-v2 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v2) | # Usage To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the `<|startoftranscript|>` token 2. The second token is the language token (e.g. `<|en|>` for English) 3. The third token is the "task token". It can take one of two values: `<|transcribe|>` for speech recognition or `<|translate|>` for speech translation 4. In addition, a `<|notimestamps|>` token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: ``` <|startoftranscript|> <|en|> <|transcribe|> <|notimestamps|> ``` Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: ```python model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe") ``` Which forces the model to predict in English under the task of speech recognition. ## Transcription ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> model.config.forced_decoder_ids = None >>> # load dummy dataset and read audio files >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) ['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'] ``` The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids) ['<|startoftranscript|><|fr|><|transcribe|><|notimestamps|> Un vrai travail intéressant va enfin être mené sur ce sujet.<|endoftext|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Un vrai travail intéressant va enfin être mené sur ce sujet.'] ``` ## Translation Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' A very interesting work, we will finally be given on this subject.'] ``` ## Evaluation This code snippet shows how to evaluate Whisper Small on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr): ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import load >>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to("cuda") >>> def map_to_pred(batch): >>> audio = batch["audio"] >>> input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features >>> batch["reference"] = processor.tokenizer._normalize(batch['text']) >>> >>> with torch.no_grad(): >>> predicted_ids = model.generate(input_features.to("cuda"))[0] >>> transcription = processor.decode(predicted_ids) >>> batch["prediction"] = processor.tokenizer._normalize(transcription) >>> return batch >>> result = librispeech_test_clean.map(map_to_pred) >>> wer = load("wer") >>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"])) 3.432213777886737 ``` ## Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from datasets import load_dataset >>> device = "cuda:0" if torch.cuda.is_available() else "cpu" >>> pipe = pipeline( >>> "automatic-speech-recognition", >>> model="openai/whisper-small", >>> chunk_length_s=30, >>> device=device, >>> ) >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> prediction = pipe(sample.copy(), batch_size=8)["text"] " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." >>> # we can also return timestamps for the predictions >>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"] [{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.', 'timestamp': (0.0, 5.44)}] ``` Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm. ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through *fine-tuning*. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```

prithivida/parrot_adequacy_model

prithivida

transformers

text-classification

--- license: apache-2.0 --- Parrot THIS IS AN ANCILLARY MODEL FOR PARROT PARAPHRASER 1. What is Parrot? Parrot is a paraphrase-based utterance augmentation framework purpose-built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. Please refer to the GitHub page or The model card prithivida/parrot_paraphraser_on_T5

thenlper/gte-small

thenlper

sentence-transformers

sentence-similarity

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-small results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.22388059701493 - type: ap value: 36.09895941426988 - type: f1 value: 67.3205651539195 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.81894999999999 - type: ap value: 88.5240138417305 - type: f1 value: 91.80367382706962 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.032 - type: f1 value: 47.4490665674719 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 30.725 - type: map_at_10 value: 46.604 - type: map_at_100 value: 47.535 - type: map_at_1000 value: 47.538000000000004 - type: map_at_3 value: 41.833 - type: map_at_5 value: 44.61 - type: mrr_at_1 value: 31.223 - type: mrr_at_10 value: 46.794000000000004 - type: mrr_at_100 value: 47.725 - type: mrr_at_1000 value: 47.727000000000004 - type: mrr_at_3 value: 42.07 - type: mrr_at_5 value: 44.812000000000005 - type: ndcg_at_1 value: 30.725 - type: ndcg_at_10 value: 55.440999999999995 - type: ndcg_at_100 value: 59.134 - type: ndcg_at_1000 value: 59.199 - type: ndcg_at_3 value: 45.599000000000004 - type: ndcg_at_5 value: 50.637 - type: precision_at_1 value: 30.725 - type: precision_at_10 value: 8.364 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.848000000000003 - type: precision_at_5 value: 13.77 - type: recall_at_1 value: 30.725 - type: recall_at_10 value: 83.64200000000001 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 56.543 - type: recall_at_5 value: 68.848 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.90178078197678 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.25728393431922 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.720297062897764 - type: mrr value: 75.24139295607439 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.43527309184616 - type: cos_sim_spearman value: 88.17128615100206 - type: euclidean_pearson value: 87.89922623089282 - type: euclidean_spearman value: 87.96104039655451 - type: manhattan_pearson value: 87.9818290932077 - type: manhattan_spearman value: 88.00923426576885 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.0844155844156 - type: f1 value: 84.01485017302213 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.36574769259432 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.4857033165287 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.261 - type: map_at_10 value: 42.419000000000004 - type: map_at_100 value: 43.927 - type: map_at_1000 value: 44.055 - type: map_at_3 value: 38.597 - type: map_at_5 value: 40.701 - type: mrr_at_1 value: 36.91 - type: mrr_at_10 value: 48.02 - type: mrr_at_100 value: 48.658 - type: mrr_at_1000 value: 48.708 - type: mrr_at_3 value: 44.945 - type: mrr_at_5 value: 46.705000000000005 - type: ndcg_at_1 value: 36.91 - type: ndcg_at_10 value: 49.353 - type: ndcg_at_100 value: 54.456 - type: ndcg_at_1000 value: 56.363 - type: ndcg_at_3 value: 43.483 - type: ndcg_at_5 value: 46.150999999999996 - type: precision_at_1 value: 36.91 - type: precision_at_10 value: 9.700000000000001 - type: precision_at_100 value: 1.557 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.078 - type: precision_at_5 value: 15.421999999999999 - type: recall_at_1 value: 30.261 - type: recall_at_10 value: 63.242 - type: recall_at_100 value: 84.09100000000001 - type: recall_at_1000 value: 96.143 - type: recall_at_3 value: 46.478 - type: recall_at_5 value: 53.708 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.145 - type: map_at_10 value: 40.996 - type: map_at_100 value: 42.266999999999996 - type: map_at_1000 value: 42.397 - type: map_at_3 value: 38.005 - type: map_at_5 value: 39.628 - type: mrr_at_1 value: 38.344 - type: mrr_at_10 value: 46.827000000000005 - type: mrr_at_100 value: 47.446 - type: mrr_at_1000 value: 47.489 - type: mrr_at_3 value: 44.448 - type: mrr_at_5 value: 45.747 - type: ndcg_at_1 value: 38.344 - type: ndcg_at_10 value: 46.733000000000004 - type: ndcg_at_100 value: 51.103 - type: ndcg_at_1000 value: 53.075 - type: ndcg_at_3 value: 42.366 - type: ndcg_at_5 value: 44.242 - type: precision_at_1 value: 38.344 - type: precision_at_10 value: 8.822000000000001 - type: precision_at_100 value: 1.417 - type: precision_at_1000 value: 0.187 - type: precision_at_3 value: 20.403 - type: precision_at_5 value: 14.306 - type: recall_at_1 value: 31.145 - type: recall_at_10 value: 56.909 - type: recall_at_100 value: 75.274 - type: recall_at_1000 value: 87.629 - type: recall_at_3 value: 43.784 - type: recall_at_5 value: 49.338 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.83 - type: map_at_10 value: 51.553000000000004 - type: map_at_100 value: 52.581 - type: map_at_1000 value: 52.638 - type: map_at_3 value: 48.112 - type: map_at_5 value: 50.095 - type: mrr_at_1 value: 44.513999999999996 - type: mrr_at_10 value: 54.998000000000005 - type: mrr_at_100 value: 55.650999999999996 - type: mrr_at_1000 value: 55.679 - type: mrr_at_3 value: 52.602000000000004 - type: mrr_at_5 value: 53.931 - type: ndcg_at_1 value: 44.513999999999996 - type: ndcg_at_10 value: 57.67400000000001 - type: ndcg_at_100 value: 61.663999999999994 - type: ndcg_at_1000 value: 62.743 - type: ndcg_at_3 value: 51.964 - type: ndcg_at_5 value: 54.773 - type: precision_at_1 value: 44.513999999999996 - type: precision_at_10 value: 9.423 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 23.323 - type: precision_at_5 value: 16.163 - type: recall_at_1 value: 38.83 - type: recall_at_10 value: 72.327 - type: recall_at_100 value: 89.519 - type: recall_at_1000 value: 97.041 - type: recall_at_3 value: 57.206 - type: recall_at_5 value: 63.88399999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.484 - type: map_at_10 value: 34.527 - type: map_at_100 value: 35.661 - type: map_at_1000 value: 35.739 - type: map_at_3 value: 32.199 - type: map_at_5 value: 33.632 - type: mrr_at_1 value: 27.458 - type: mrr_at_10 value: 36.543 - type: mrr_at_100 value: 37.482 - type: mrr_at_1000 value: 37.543 - type: mrr_at_3 value: 34.256 - type: mrr_at_5 value: 35.618 - type: ndcg_at_1 value: 27.458 - type: ndcg_at_10 value: 39.396 - type: ndcg_at_100 value: 44.742 - type: ndcg_at_1000 value: 46.708 - type: ndcg_at_3 value: 34.817 - type: ndcg_at_5 value: 37.247 - type: precision_at_1 value: 27.458 - type: precision_at_10 value: 5.976999999999999 - type: precision_at_100 value: 0.907 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 14.878 - type: precision_at_5 value: 10.35 - type: recall_at_1 value: 25.484 - type: recall_at_10 value: 52.317 - type: recall_at_100 value: 76.701 - type: recall_at_1000 value: 91.408 - type: recall_at_3 value: 40.043 - type: recall_at_5 value: 45.879 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.719 - type: map_at_10 value: 25.269000000000002 - type: map_at_100 value: 26.442 - type: map_at_1000 value: 26.557 - type: map_at_3 value: 22.56 - type: map_at_5 value: 24.082 - type: mrr_at_1 value: 20.896 - type: mrr_at_10 value: 29.982999999999997 - type: mrr_at_100 value: 30.895 - type: mrr_at_1000 value: 30.961 - type: mrr_at_3 value: 27.239 - type: mrr_at_5 value: 28.787000000000003 - type: ndcg_at_1 value: 20.896 - type: ndcg_at_10 value: 30.814000000000004 - type: ndcg_at_100 value: 36.418 - type: ndcg_at_1000 value: 39.182 - type: ndcg_at_3 value: 25.807999999999996 - type: ndcg_at_5 value: 28.143 - type: precision_at_1 value: 20.896 - type: precision_at_10 value: 5.821 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 12.562000000000001 - type: precision_at_5 value: 9.254 - type: recall_at_1 value: 16.719 - type: recall_at_10 value: 43.155 - type: recall_at_100 value: 67.831 - type: recall_at_1000 value: 87.617 - type: recall_at_3 value: 29.259 - type: recall_at_5 value: 35.260999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.398999999999997 - type: map_at_10 value: 39.876 - type: map_at_100 value: 41.205999999999996 - type: map_at_1000 value: 41.321999999999996 - type: map_at_3 value: 36.588 - type: map_at_5 value: 38.538 - type: mrr_at_1 value: 35.9 - type: mrr_at_10 value: 45.528 - type: mrr_at_100 value: 46.343 - type: mrr_at_1000 value: 46.388 - type: mrr_at_3 value: 42.862 - type: mrr_at_5 value: 44.440000000000005 - type: ndcg_at_1 value: 35.9 - type: ndcg_at_10 value: 45.987 - type: ndcg_at_100 value: 51.370000000000005 - type: ndcg_at_1000 value: 53.400000000000006 - type: ndcg_at_3 value: 40.841 - type: ndcg_at_5 value: 43.447 - type: precision_at_1 value: 35.9 - type: precision_at_10 value: 8.393 - type: precision_at_100 value: 1.283 - type: precision_at_1000 value: 0.166 - type: precision_at_3 value: 19.538 - type: precision_at_5 value: 13.975000000000001 - type: recall_at_1 value: 29.398999999999997 - type: recall_at_10 value: 58.361 - type: recall_at_100 value: 81.081 - type: recall_at_1000 value: 94.004 - type: recall_at_3 value: 43.657000000000004 - type: recall_at_5 value: 50.519999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.589 - type: map_at_10 value: 31.608999999999998 - type: map_at_100 value: 33.128 - type: map_at_1000 value: 33.247 - type: map_at_3 value: 28.671999999999997 - type: map_at_5 value: 30.233999999999998 - type: mrr_at_1 value: 26.712000000000003 - type: mrr_at_10 value: 36.713 - type: mrr_at_100 value: 37.713 - type: mrr_at_1000 value: 37.771 - type: mrr_at_3 value: 34.075 - type: mrr_at_5 value: 35.451 - type: ndcg_at_1 value: 26.712000000000003 - type: ndcg_at_10 value: 37.519999999999996 - type: ndcg_at_100 value: 43.946000000000005 - type: ndcg_at_1000 value: 46.297 - type: ndcg_at_3 value: 32.551 - type: ndcg_at_5 value: 34.660999999999994 - type: precision_at_1 value: 26.712000000000003 - type: precision_at_10 value: 7.066 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 15.906 - type: precision_at_5 value: 11.437999999999999 - type: recall_at_1 value: 21.589 - type: recall_at_10 value: 50.090999999999994 - type: recall_at_100 value: 77.43900000000001 - type: recall_at_1000 value: 93.35900000000001 - type: recall_at_3 value: 36.028999999999996 - type: recall_at_5 value: 41.698 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.121666666666663 - type: map_at_10 value: 34.46258333333334 - type: map_at_100 value: 35.710499999999996 - type: map_at_1000 value: 35.82691666666666 - type: map_at_3 value: 31.563249999999996 - type: map_at_5 value: 33.189750000000004 - type: mrr_at_1 value: 29.66441666666667 - type: mrr_at_10 value: 38.5455 - type: mrr_at_100 value: 39.39566666666667 - type: mrr_at_1000 value: 39.45325 - type: mrr_at_3 value: 36.003333333333345 - type: mrr_at_5 value: 37.440916666666666 - type: ndcg_at_1 value: 29.66441666666667 - type: ndcg_at_10 value: 39.978416666666675 - type: ndcg_at_100 value: 45.278666666666666 - type: ndcg_at_1000 value: 47.52275 - type: ndcg_at_3 value: 35.00058333333334 - type: ndcg_at_5 value: 37.34908333333333 - type: precision_at_1 value: 29.66441666666667 - type: precision_at_10 value: 7.094500000000001 - type: precision_at_100 value: 1.1523333333333332 - type: precision_at_1000 value: 0.15358333333333332 - type: precision_at_3 value: 16.184166666666663 - type: precision_at_5 value: 11.6005 - type: recall_at_1 value: 25.121666666666663 - type: recall_at_10 value: 52.23975000000001 - type: recall_at_100 value: 75.48408333333333 - type: recall_at_1000 value: 90.95316666666668 - type: recall_at_3 value: 38.38458333333333 - type: recall_at_5 value: 44.39933333333333 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.569000000000003 - type: map_at_10 value: 30.389 - type: map_at_100 value: 31.396 - type: map_at_1000 value: 31.493 - type: map_at_3 value: 28.276 - type: map_at_5 value: 29.459000000000003 - type: mrr_at_1 value: 26.534000000000002 - type: mrr_at_10 value: 33.217999999999996 - type: mrr_at_100 value: 34.054 - type: mrr_at_1000 value: 34.12 - type: mrr_at_3 value: 31.058000000000003 - type: mrr_at_5 value: 32.330999999999996 - type: ndcg_at_1 value: 26.534000000000002 - type: ndcg_at_10 value: 34.608 - type: ndcg_at_100 value: 39.391999999999996 - type: ndcg_at_1000 value: 41.837999999999994 - type: ndcg_at_3 value: 30.564999999999998 - type: ndcg_at_5 value: 32.509 - type: precision_at_1 value: 26.534000000000002 - type: precision_at_10 value: 5.414 - type: precision_at_100 value: 0.847 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 12.986 - type: precision_at_5 value: 9.202 - type: recall_at_1 value: 23.569000000000003 - type: recall_at_10 value: 44.896 - type: recall_at_100 value: 66.476 - type: recall_at_1000 value: 84.548 - type: recall_at_3 value: 33.79 - type: recall_at_5 value: 38.512 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.36 - type: map_at_10 value: 23.57 - type: map_at_100 value: 24.698999999999998 - type: map_at_1000 value: 24.834999999999997 - type: map_at_3 value: 21.093 - type: map_at_5 value: 22.418 - type: mrr_at_1 value: 19.718 - type: mrr_at_10 value: 27.139999999999997 - type: mrr_at_100 value: 28.097 - type: mrr_at_1000 value: 28.177999999999997 - type: mrr_at_3 value: 24.805 - type: mrr_at_5 value: 26.121 - type: ndcg_at_1 value: 19.718 - type: ndcg_at_10 value: 28.238999999999997 - type: ndcg_at_100 value: 33.663 - type: ndcg_at_1000 value: 36.763 - type: ndcg_at_3 value: 23.747 - type: ndcg_at_5 value: 25.796000000000003 - type: precision_at_1 value: 19.718 - type: precision_at_10 value: 5.282 - type: precision_at_100 value: 0.9390000000000001 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.264000000000001 - type: precision_at_5 value: 8.341 - type: recall_at_1 value: 16.36 - type: recall_at_10 value: 38.669 - type: recall_at_100 value: 63.184 - type: recall_at_1000 value: 85.33800000000001 - type: recall_at_3 value: 26.214 - type: recall_at_5 value: 31.423000000000002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.618999999999996 - type: map_at_10 value: 34.361999999999995 - type: map_at_100 value: 35.534 - type: map_at_1000 value: 35.634 - type: map_at_3 value: 31.402 - type: map_at_5 value: 32.815 - type: mrr_at_1 value: 30.037000000000003 - type: mrr_at_10 value: 38.284 - type: mrr_at_100 value: 39.141999999999996 - type: mrr_at_1000 value: 39.2 - type: mrr_at_3 value: 35.603 - type: mrr_at_5 value: 36.867 - type: ndcg_at_1 value: 30.037000000000003 - type: ndcg_at_10 value: 39.87 - type: ndcg_at_100 value: 45.243 - type: ndcg_at_1000 value: 47.507 - type: ndcg_at_3 value: 34.371 - type: ndcg_at_5 value: 36.521 - type: precision_at_1 value: 30.037000000000003 - type: precision_at_10 value: 6.819 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 15.392 - type: precision_at_5 value: 10.821 - type: recall_at_1 value: 25.618999999999996 - type: recall_at_10 value: 52.869 - type: recall_at_100 value: 76.395 - type: recall_at_1000 value: 92.19500000000001 - type: recall_at_3 value: 37.943 - type: recall_at_5 value: 43.342999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.283 - type: map_at_10 value: 32.155 - type: map_at_100 value: 33.724 - type: map_at_1000 value: 33.939 - type: map_at_3 value: 29.018 - type: map_at_5 value: 30.864000000000004 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.632 - type: mrr_at_100 value: 37.606 - type: mrr_at_1000 value: 37.671 - type: mrr_at_3 value: 33.992 - type: mrr_at_5 value: 35.613 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 38.024 - type: ndcg_at_100 value: 44.292 - type: ndcg_at_1000 value: 46.818 - type: ndcg_at_3 value: 32.965 - type: ndcg_at_5 value: 35.562 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.352 - type: precision_at_100 value: 1.514 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_3 value: 15.481 - type: precision_at_5 value: 11.542 - type: recall_at_1 value: 23.283 - type: recall_at_10 value: 49.756 - type: recall_at_100 value: 78.05 - type: recall_at_1000 value: 93.854 - type: recall_at_3 value: 35.408 - type: recall_at_5 value: 42.187000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.201999999999998 - type: map_at_10 value: 26.826 - type: map_at_100 value: 27.961000000000002 - type: map_at_1000 value: 28.066999999999997 - type: map_at_3 value: 24.237000000000002 - type: map_at_5 value: 25.811 - type: mrr_at_1 value: 20.887 - type: mrr_at_10 value: 28.660000000000004 - type: mrr_at_100 value: 29.660999999999998 - type: mrr_at_1000 value: 29.731 - type: mrr_at_3 value: 26.155 - type: mrr_at_5 value: 27.68 - type: ndcg_at_1 value: 20.887 - type: ndcg_at_10 value: 31.523 - type: ndcg_at_100 value: 37.055 - type: ndcg_at_1000 value: 39.579 - type: ndcg_at_3 value: 26.529000000000003 - type: ndcg_at_5 value: 29.137 - type: precision_at_1 value: 20.887 - type: precision_at_10 value: 5.065 - type: precision_at_100 value: 0.856 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 11.399 - type: precision_at_5 value: 8.392 - type: recall_at_1 value: 19.201999999999998 - type: recall_at_10 value: 44.285000000000004 - type: recall_at_100 value: 69.768 - type: recall_at_1000 value: 88.302 - type: recall_at_3 value: 30.804 - type: recall_at_5 value: 37.039 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 11.244 - type: map_at_10 value: 18.956 - type: map_at_100 value: 20.674 - type: map_at_1000 value: 20.863 - type: map_at_3 value: 15.923000000000002 - type: map_at_5 value: 17.518 - type: mrr_at_1 value: 25.080999999999996 - type: mrr_at_10 value: 35.94 - type: mrr_at_100 value: 36.969 - type: mrr_at_1000 value: 37.013 - type: mrr_at_3 value: 32.617000000000004 - type: mrr_at_5 value: 34.682 - type: ndcg_at_1 value: 25.080999999999996 - type: ndcg_at_10 value: 26.539 - type: ndcg_at_100 value: 33.601 - type: ndcg_at_1000 value: 37.203 - type: ndcg_at_3 value: 21.695999999999998 - type: ndcg_at_5 value: 23.567 - type: precision_at_1 value: 25.080999999999996 - type: precision_at_10 value: 8.143 - type: precision_at_100 value: 1.5650000000000002 - type: precision_at_1000 value: 0.22300000000000003 - type: precision_at_3 value: 15.983 - type: precision_at_5 value: 12.417 - type: recall_at_1 value: 11.244 - type: recall_at_10 value: 31.457 - type: recall_at_100 value: 55.92 - type: recall_at_1000 value: 76.372 - type: recall_at_3 value: 19.784 - type: recall_at_5 value: 24.857000000000003 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.595 - type: map_at_10 value: 18.75 - type: map_at_100 value: 26.354 - type: map_at_1000 value: 27.912 - type: map_at_3 value: 13.794 - type: map_at_5 value: 16.021 - type: mrr_at_1 value: 65.75 - type: mrr_at_10 value: 73.837 - type: mrr_at_100 value: 74.22800000000001 - type: mrr_at_1000 value: 74.234 - type: mrr_at_3 value: 72.5 - type: mrr_at_5 value: 73.387 - type: ndcg_at_1 value: 52.625 - type: ndcg_at_10 value: 39.101 - type: ndcg_at_100 value: 43.836000000000006 - type: ndcg_at_1000 value: 51.086 - type: ndcg_at_3 value: 44.229 - type: ndcg_at_5 value: 41.555 - type: precision_at_1 value: 65.75 - type: precision_at_10 value: 30.45 - type: precision_at_100 value: 9.81 - type: precision_at_1000 value: 2.045 - type: precision_at_3 value: 48.667 - type: precision_at_5 value: 40.8 - type: recall_at_1 value: 8.595 - type: recall_at_10 value: 24.201 - type: recall_at_100 value: 50.096 - type: recall_at_1000 value: 72.677 - type: recall_at_3 value: 15.212 - type: recall_at_5 value: 18.745 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.565 - type: f1 value: 41.49914329345582 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.60000000000001 - type: map_at_10 value: 76.838 - type: map_at_100 value: 77.076 - type: map_at_1000 value: 77.09 - type: map_at_3 value: 75.545 - type: map_at_5 value: 76.39 - type: mrr_at_1 value: 71.707 - type: mrr_at_10 value: 81.514 - type: mrr_at_100 value: 81.64099999999999 - type: mrr_at_1000 value: 81.645 - type: mrr_at_3 value: 80.428 - type: mrr_at_5 value: 81.159 - type: ndcg_at_1 value: 71.707 - type: ndcg_at_10 value: 81.545 - type: ndcg_at_100 value: 82.477 - type: ndcg_at_1000 value: 82.73899999999999 - type: ndcg_at_3 value: 79.292 - type: ndcg_at_5 value: 80.599 - type: precision_at_1 value: 71.707 - type: precision_at_10 value: 10.035 - type: precision_at_100 value: 1.068 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 30.918 - type: precision_at_5 value: 19.328 - type: recall_at_1 value: 66.60000000000001 - type: recall_at_10 value: 91.353 - type: recall_at_100 value: 95.21 - type: recall_at_1000 value: 96.89999999999999 - type: recall_at_3 value: 85.188 - type: recall_at_5 value: 88.52 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 19.338 - type: map_at_10 value: 31.752000000000002 - type: map_at_100 value: 33.516 - type: map_at_1000 value: 33.694 - type: map_at_3 value: 27.716 - type: map_at_5 value: 29.67 - type: mrr_at_1 value: 38.117000000000004 - type: mrr_at_10 value: 47.323 - type: mrr_at_100 value: 48.13 - type: mrr_at_1000 value: 48.161 - type: mrr_at_3 value: 45.062000000000005 - type: mrr_at_5 value: 46.358 - type: ndcg_at_1 value: 38.117000000000004 - type: ndcg_at_10 value: 39.353 - type: ndcg_at_100 value: 46.044000000000004 - type: ndcg_at_1000 value: 49.083 - type: ndcg_at_3 value: 35.891 - type: ndcg_at_5 value: 36.661 - type: precision_at_1 value: 38.117000000000004 - type: precision_at_10 value: 11.187999999999999 - type: precision_at_100 value: 1.802 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 24.126 - type: precision_at_5 value: 17.562 - type: recall_at_1 value: 19.338 - type: recall_at_10 value: 45.735 - type: recall_at_100 value: 71.281 - type: recall_at_1000 value: 89.537 - type: recall_at_3 value: 32.525 - type: recall_at_5 value: 37.671 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 36.995 - type: map_at_10 value: 55.032000000000004 - type: map_at_100 value: 55.86 - type: map_at_1000 value: 55.932 - type: map_at_3 value: 52.125 - type: map_at_5 value: 53.884 - type: mrr_at_1 value: 73.991 - type: mrr_at_10 value: 80.096 - type: mrr_at_100 value: 80.32000000000001 - type: mrr_at_1000 value: 80.331 - type: mrr_at_3 value: 79.037 - type: mrr_at_5 value: 79.719 - type: ndcg_at_1 value: 73.991 - type: ndcg_at_10 value: 63.786 - type: ndcg_at_100 value: 66.78 - type: ndcg_at_1000 value: 68.255 - type: ndcg_at_3 value: 59.501000000000005 - type: ndcg_at_5 value: 61.82299999999999 - type: precision_at_1 value: 73.991 - type: precision_at_10 value: 13.157 - type: precision_at_100 value: 1.552 - type: precision_at_1000 value: 0.17500000000000002 - type: precision_at_3 value: 37.519999999999996 - type: precision_at_5 value: 24.351 - type: recall_at_1 value: 36.995 - type: recall_at_10 value: 65.78699999999999 - type: recall_at_100 value: 77.583 - type: recall_at_1000 value: 87.421 - type: recall_at_3 value: 56.279999999999994 - type: recall_at_5 value: 60.878 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 86.80239999999999 - type: ap value: 81.97305141128378 - type: f1 value: 86.76976305549273 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.166 - type: map_at_10 value: 33.396 - type: map_at_100 value: 34.588 - type: map_at_1000 value: 34.637 - type: map_at_3 value: 29.509999999999998 - type: map_at_5 value: 31.719 - type: mrr_at_1 value: 21.762 - type: mrr_at_10 value: 33.969 - type: mrr_at_100 value: 35.099000000000004 - type: mrr_at_1000 value: 35.141 - type: mrr_at_3 value: 30.148000000000003 - type: mrr_at_5 value: 32.324000000000005 - type: ndcg_at_1 value: 21.776999999999997 - type: ndcg_at_10 value: 40.306999999999995 - type: ndcg_at_100 value: 46.068 - type: ndcg_at_1000 value: 47.3 - type: ndcg_at_3 value: 32.416 - type: ndcg_at_5 value: 36.345 - type: precision_at_1 value: 21.776999999999997 - type: precision_at_10 value: 6.433 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.897 - type: precision_at_5 value: 10.324 - type: recall_at_1 value: 21.166 - type: recall_at_10 value: 61.587 - type: recall_at_100 value: 88.251 - type: recall_at_1000 value: 97.727 - type: recall_at_3 value: 40.196 - type: recall_at_5 value: 49.611 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.04605563155496 - type: f1 value: 92.78007303978372 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 69.65116279069767 - type: f1 value: 52.75775172527262 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.34633490248822 - type: f1 value: 68.15345065392562 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 75.63887020847343 - type: f1 value: 76.08074680233685 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.77933406071333 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.06504927238196 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.20682480490871 - type: mrr value: 33.41462721527003 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.548 - type: map_at_10 value: 13.086999999999998 - type: map_at_100 value: 16.698 - type: map_at_1000 value: 18.151999999999997 - type: map_at_3 value: 9.576 - type: map_at_5 value: 11.175 - type: mrr_at_1 value: 44.272 - type: mrr_at_10 value: 53.635999999999996 - type: mrr_at_100 value: 54.228 - type: mrr_at_1000 value: 54.26499999999999 - type: mrr_at_3 value: 51.754 - type: mrr_at_5 value: 53.086 - type: ndcg_at_1 value: 42.724000000000004 - type: ndcg_at_10 value: 34.769 - type: ndcg_at_100 value: 32.283 - type: ndcg_at_1000 value: 40.843 - type: ndcg_at_3 value: 39.852 - type: ndcg_at_5 value: 37.858999999999995 - type: precision_at_1 value: 44.272 - type: precision_at_10 value: 26.068 - type: precision_at_100 value: 8.328000000000001 - type: precision_at_1000 value: 2.1 - type: precision_at_3 value: 37.874 - type: precision_at_5 value: 33.065 - type: recall_at_1 value: 5.548 - type: recall_at_10 value: 16.936999999999998 - type: recall_at_100 value: 33.72 - type: recall_at_1000 value: 64.348 - type: recall_at_3 value: 10.764999999999999 - type: recall_at_5 value: 13.361 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 28.008 - type: map_at_10 value: 42.675000000000004 - type: map_at_100 value: 43.85 - type: map_at_1000 value: 43.884 - type: map_at_3 value: 38.286 - type: map_at_5 value: 40.78 - type: mrr_at_1 value: 31.518 - type: mrr_at_10 value: 45.015 - type: mrr_at_100 value: 45.924 - type: mrr_at_1000 value: 45.946999999999996 - type: mrr_at_3 value: 41.348 - type: mrr_at_5 value: 43.428 - type: ndcg_at_1 value: 31.489 - type: ndcg_at_10 value: 50.285999999999994 - type: ndcg_at_100 value: 55.291999999999994 - type: ndcg_at_1000 value: 56.05 - type: ndcg_at_3 value: 41.976 - type: ndcg_at_5 value: 46.103 - type: precision_at_1 value: 31.489 - type: precision_at_10 value: 8.456 - type: precision_at_100 value: 1.125 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 19.09 - type: precision_at_5 value: 13.841000000000001 - type: recall_at_1 value: 28.008 - type: recall_at_10 value: 71.21499999999999 - type: recall_at_100 value: 92.99 - type: recall_at_1000 value: 98.578 - type: recall_at_3 value: 49.604 - type: recall_at_5 value: 59.094 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.351 - type: map_at_10 value: 84.163 - type: map_at_100 value: 84.785 - type: map_at_1000 value: 84.801 - type: map_at_3 value: 81.16 - type: map_at_5 value: 83.031 - type: mrr_at_1 value: 80.96 - type: mrr_at_10 value: 87.241 - type: mrr_at_100 value: 87.346 - type: mrr_at_1000 value: 87.347 - type: mrr_at_3 value: 86.25699999999999 - type: mrr_at_5 value: 86.907 - type: ndcg_at_1 value: 80.97 - type: ndcg_at_10 value: 88.017 - type: ndcg_at_100 value: 89.241 - type: ndcg_at_1000 value: 89.34299999999999 - type: ndcg_at_3 value: 85.053 - type: ndcg_at_5 value: 86.663 - type: precision_at_1 value: 80.97 - type: precision_at_10 value: 13.358 - type: precision_at_100 value: 1.525 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.143 - type: precision_at_5 value: 24.451999999999998 - type: recall_at_1 value: 70.351 - type: recall_at_10 value: 95.39800000000001 - type: recall_at_100 value: 99.55199999999999 - type: recall_at_1000 value: 99.978 - type: recall_at_3 value: 86.913 - type: recall_at_5 value: 91.448 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.62406719814139 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 61.386700035141736 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.618 - type: map_at_10 value: 12.920000000000002 - type: map_at_100 value: 15.304 - type: map_at_1000 value: 15.656999999999998 - type: map_at_3 value: 9.187 - type: map_at_5 value: 10.937 - type: mrr_at_1 value: 22.8 - type: mrr_at_10 value: 35.13 - type: mrr_at_100 value: 36.239 - type: mrr_at_1000 value: 36.291000000000004 - type: mrr_at_3 value: 31.917 - type: mrr_at_5 value: 33.787 - type: ndcg_at_1 value: 22.8 - type: ndcg_at_10 value: 21.382 - type: ndcg_at_100 value: 30.257 - type: ndcg_at_1000 value: 36.001 - type: ndcg_at_3 value: 20.43 - type: ndcg_at_5 value: 17.622 - type: precision_at_1 value: 22.8 - type: precision_at_10 value: 11.26 - type: precision_at_100 value: 2.405 - type: precision_at_1000 value: 0.377 - type: precision_at_3 value: 19.633 - type: precision_at_5 value: 15.68 - type: recall_at_1 value: 4.618 - type: recall_at_10 value: 22.811999999999998 - type: recall_at_100 value: 48.787000000000006 - type: recall_at_1000 value: 76.63799999999999 - type: recall_at_3 value: 11.952 - type: recall_at_5 value: 15.892000000000001 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.01529458252244 - type: cos_sim_spearman value: 77.92985224770254 - type: euclidean_pearson value: 81.04251429422487 - type: euclidean_spearman value: 77.92838490549133 - type: manhattan_pearson value: 80.95892251458979 - type: manhattan_spearman value: 77.81028089705941 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 83.97885282534388 - type: cos_sim_spearman value: 75.1221970851712 - type: euclidean_pearson value: 80.34455956720097 - type: euclidean_spearman value: 74.5894274239938 - type: manhattan_pearson value: 80.38999766325465 - type: manhattan_spearman value: 74.68524557166975 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.95746064915672 - type: cos_sim_spearman value: 85.08683458043946 - type: euclidean_pearson value: 84.56699492836385 - type: euclidean_spearman value: 85.66089116133713 - type: manhattan_pearson value: 84.47553323458541 - type: manhattan_spearman value: 85.56142206781472 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.71377893595067 - type: cos_sim_spearman value: 81.03453291428589 - type: euclidean_pearson value: 82.57136298308613 - type: euclidean_spearman value: 81.15839961890875 - type: manhattan_pearson value: 82.55157879373837 - type: manhattan_spearman value: 81.1540163767054 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.64197832372373 - type: cos_sim_spearman value: 88.31966852492485 - type: euclidean_pearson value: 87.98692129976983 - type: euclidean_spearman value: 88.6247340837856 - type: manhattan_pearson value: 87.90437827826412 - type: manhattan_spearman value: 88.56278787131457 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 81.84159950146693 - type: cos_sim_spearman value: 83.90678384140168 - type: euclidean_pearson value: 83.19005018860221 - type: euclidean_spearman value: 84.16260415876295 - type: manhattan_pearson value: 83.05030612994494 - type: manhattan_spearman value: 83.99605629718336 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.49935350176666 - type: cos_sim_spearman value: 87.59086606735383 - type: euclidean_pearson value: 88.06537181129983 - type: euclidean_spearman value: 87.6687448086014 - type: manhattan_pearson value: 87.96599131972935 - type: manhattan_spearman value: 87.63295748969642 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 67.68232799482763 - type: cos_sim_spearman value: 67.99930378085793 - type: euclidean_pearson value: 68.50275360001696 - type: euclidean_spearman value: 67.81588179309259 - type: manhattan_pearson value: 68.5892154749763 - type: manhattan_spearman value: 67.84357259640682 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.37049618406554 - type: cos_sim_spearman value: 85.57014313159492 - type: euclidean_pearson value: 85.57469513908282 - type: euclidean_spearman value: 85.661948135258 - type: manhattan_pearson value: 85.36866831229028 - type: manhattan_spearman value: 85.5043455368843 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 84.83259065376154 - type: mrr value: 95.58455433455433 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 58.817 - type: map_at_10 value: 68.459 - type: map_at_100 value: 68.951 - type: map_at_1000 value: 68.979 - type: map_at_3 value: 65.791 - type: map_at_5 value: 67.583 - type: mrr_at_1 value: 61.667 - type: mrr_at_10 value: 69.368 - type: mrr_at_100 value: 69.721 - type: mrr_at_1000 value: 69.744 - type: mrr_at_3 value: 67.278 - type: mrr_at_5 value: 68.611 - type: ndcg_at_1 value: 61.667 - type: ndcg_at_10 value: 72.70100000000001 - type: ndcg_at_100 value: 74.928 - type: ndcg_at_1000 value: 75.553 - type: ndcg_at_3 value: 68.203 - type: ndcg_at_5 value: 70.804 - type: precision_at_1 value: 61.667 - type: precision_at_10 value: 9.533 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.444000000000003 - type: precision_at_5 value: 17.599999999999998 - type: recall_at_1 value: 58.817 - type: recall_at_10 value: 84.789 - type: recall_at_100 value: 95.0 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 72.8 - type: recall_at_5 value: 79.294 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.8108910891089 - type: cos_sim_ap value: 95.5743678558349 - type: cos_sim_f1 value: 90.43133366385722 - type: cos_sim_precision value: 89.67551622418878 - type: cos_sim_recall value: 91.2 - type: dot_accuracy value: 99.75841584158415 - type: dot_ap value: 94.00786363627253 - type: dot_f1 value: 87.51910341314316 - type: dot_precision value: 89.20041536863967 - type: dot_recall value: 85.9 - type: euclidean_accuracy value: 99.81485148514851 - type: euclidean_ap value: 95.4752113136905 - type: euclidean_f1 value: 90.44334975369456 - type: euclidean_precision value: 89.126213592233 - type: euclidean_recall value: 91.8 - type: manhattan_accuracy value: 99.81584158415842 - type: manhattan_ap value: 95.5163172682464 - type: manhattan_f1 value: 90.51987767584097 - type: manhattan_precision value: 92.3076923076923 - type: manhattan_recall value: 88.8 - type: max_accuracy value: 99.81584158415842 - type: max_ap value: 95.5743678558349 - type: max_f1 value: 90.51987767584097 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 62.63235986949449 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.334795589585575 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.02955214518782 - type: mrr value: 52.8004838298956 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.63769566275453 - type: cos_sim_spearman value: 30.422379185989335 - type: dot_pearson value: 26.88493071882256 - type: dot_spearman value: 26.505249740971305 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.21 - type: map_at_10 value: 1.654 - type: map_at_100 value: 10.095 - type: map_at_1000 value: 25.808999999999997 - type: map_at_3 value: 0.594 - type: map_at_5 value: 0.9289999999999999 - type: mrr_at_1 value: 78.0 - type: mrr_at_10 value: 87.019 - type: mrr_at_100 value: 87.019 - type: mrr_at_1000 value: 87.019 - type: mrr_at_3 value: 86.333 - type: mrr_at_5 value: 86.733 - type: ndcg_at_1 value: 73.0 - type: ndcg_at_10 value: 66.52900000000001 - type: ndcg_at_100 value: 53.433 - type: ndcg_at_1000 value: 51.324000000000005 - type: ndcg_at_3 value: 72.02199999999999 - type: ndcg_at_5 value: 69.696 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 70.39999999999999 - type: precision_at_100 value: 55.46 - type: precision_at_1000 value: 22.758 - type: precision_at_3 value: 76.667 - type: precision_at_5 value: 74.0 - type: recall_at_1 value: 0.21 - type: recall_at_10 value: 1.8849999999999998 - type: recall_at_100 value: 13.801 - type: recall_at_1000 value: 49.649 - type: recall_at_3 value: 0.632 - type: recall_at_5 value: 1.009 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.797 - type: map_at_10 value: 9.01 - type: map_at_100 value: 14.682 - type: map_at_1000 value: 16.336000000000002 - type: map_at_3 value: 4.546 - type: map_at_5 value: 5.9270000000000005 - type: mrr_at_1 value: 24.490000000000002 - type: mrr_at_10 value: 41.156 - type: mrr_at_100 value: 42.392 - type: mrr_at_1000 value: 42.408 - type: mrr_at_3 value: 38.775999999999996 - type: mrr_at_5 value: 40.102 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 22.222 - type: ndcg_at_100 value: 34.405 - type: ndcg_at_1000 value: 46.599000000000004 - type: ndcg_at_3 value: 25.261 - type: ndcg_at_5 value: 22.695999999999998 - type: precision_at_1 value: 24.490000000000002 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.306 - type: precision_at_1000 value: 1.5350000000000001 - type: precision_at_3 value: 27.211000000000002 - type: precision_at_5 value: 22.857 - type: recall_at_1 value: 1.797 - type: recall_at_10 value: 15.706000000000001 - type: recall_at_100 value: 46.412 - type: recall_at_1000 value: 83.159 - type: recall_at_3 value: 6.1370000000000005 - type: recall_at_5 value: 8.599 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.3302 - type: ap value: 14.169121204575601 - type: f1 value: 54.229345975274235 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 58.22297679683077 - type: f1 value: 58.62984908377875 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.952922428464255 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.68140907194373 - type: cos_sim_ap value: 70.12180123666836 - type: cos_sim_f1 value: 65.77501791258658 - type: cos_sim_precision value: 60.07853403141361 - type: cos_sim_recall value: 72.66490765171504 - type: dot_accuracy value: 81.92167848840674 - type: dot_ap value: 60.49837581423469 - type: dot_f1 value: 58.44186046511628 - type: dot_precision value: 52.24532224532224 - type: dot_recall value: 66.3060686015831 - type: euclidean_accuracy value: 84.73505394289802 - type: euclidean_ap value: 70.3278904593286 - type: euclidean_f1 value: 65.98851124940161 - type: euclidean_precision value: 60.38107752956636 - type: euclidean_recall value: 72.74406332453826 - type: manhattan_accuracy value: 84.73505394289802 - type: manhattan_ap value: 70.00737738537337 - type: manhattan_f1 value: 65.80150784822642 - type: manhattan_precision value: 61.892583120204606 - type: manhattan_recall value: 70.23746701846966 - type: max_accuracy value: 84.73505394289802 - type: max_ap value: 70.3278904593286 - type: max_f1 value: 65.98851124940161 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.44258159661582 - type: cos_sim_ap value: 84.91926704880888 - type: cos_sim_f1 value: 77.07651086632926 - type: cos_sim_precision value: 74.5894554883319 - type: cos_sim_recall value: 79.73514012935017 - type: dot_accuracy value: 85.88116583226608 - type: dot_ap value: 78.9753854779923 - type: dot_f1 value: 72.17757637979255 - type: dot_precision value: 66.80647486729143 - type: dot_recall value: 78.48783492454572 - type: euclidean_accuracy value: 88.5299025885823 - type: euclidean_ap value: 85.08006075642194 - type: euclidean_f1 value: 77.29637336504163 - type: euclidean_precision value: 74.69836253950014 - type: euclidean_recall value: 80.08161379735141 - type: manhattan_accuracy value: 88.55124771995187 - type: manhattan_ap value: 85.00941529932851 - type: manhattan_f1 value: 77.33100233100232 - type: manhattan_precision value: 73.37572573956317 - type: manhattan_recall value: 81.73698798891284 - type: max_accuracy value: 88.55124771995187 - type: max_ap value: 85.08006075642194 - type: max_f1 value: 77.33100233100232 language: - en license: mit --- # gte-small General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | | [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | | [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small") model = AutoModel.from_pretrained("thenlper/gte-small") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('thenlper/gte-large') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```

sai17/cards-top_right_swin-tiny-patch4-window7-224-finetuned-v2_more_data

sai17

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: cards-top_right_swin-tiny-patch4-window7-224-finetuned-v2_more_data results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.6269272417882741 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cards-top_right_swin-tiny-patch4-window7-224-finetuned-v2_more_data This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9268 - Accuracy: 0.6269 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.4585 | 1.0 | 1363 | 1.2999 | 0.4337 | | 1.4211 | 2.0 | 2726 | 1.1663 | 0.4927 | | 1.4203 | 3.0 | 4089 | 1.0770 | 0.5312 | | 1.4669 | 4.0 | 5453 | 1.0744 | 0.5496 | | 1.3781 | 5.0 | 6816 | 1.0245 | 0.5599 | | 1.3852 | 6.0 | 8179 | 1.0645 | 0.5402 | | 1.3407 | 7.0 | 9542 | 1.0011 | 0.5696 | | 1.3727 | 8.0 | 10906 | 0.9898 | 0.5801 | | 1.328 | 9.0 | 12269 | 0.9965 | 0.5738 | | 1.3374 | 10.0 | 13632 | 0.9722 | 0.5874 | | 1.3513 | 11.0 | 14995 | 0.9632 | 0.5873 | | 1.3728 | 12.0 | 16359 | 0.9818 | 0.5802 | | 1.3289 | 13.0 | 17722 | 0.9845 | 0.5729 | | 1.3219 | 14.0 | 19085 | 0.9633 | 0.5881 | | 1.2893 | 15.0 | 20448 | 0.9312 | 0.6004 | | 1.3088 | 16.0 | 21812 | 0.9537 | 0.5903 | | 1.3252 | 17.0 | 23175 | 0.9432 | 0.5986 | | 1.3424 | 18.0 | 24538 | 0.9291 | 0.5979 | | 1.3077 | 19.0 | 25901 | 0.9245 | 0.6020 | | 1.2466 | 20.0 | 27265 | 0.9304 | 0.6039 | | 1.2767 | 21.0 | 28628 | 0.9122 | 0.6099 | | 1.2553 | 22.0 | 29991 | 0.9312 | 0.6005 | | 1.2698 | 23.0 | 31354 | 0.9137 | 0.6092 | | 1.2591 | 24.0 | 32718 | 0.9113 | 0.6134 | | 1.277 | 25.0 | 34081 | 0.9095 | 0.6142 | | 1.2742 | 26.0 | 35444 | 0.9227 | 0.6100 | | 1.222 | 27.0 | 36807 | 0.9090 | 0.6147 | | 1.2368 | 28.0 | 38171 | 0.9020 | 0.6172 | | 1.198 | 29.0 | 39534 | 0.9071 | 0.6157 | | 1.2076 | 30.0 | 40897 | 0.9031 | 0.6214 | | 1.212 | 31.0 | 42260 | 0.9136 | 0.6175 | | 1.2105 | 32.0 | 43624 | 0.9170 | 0.6151 | | 1.2687 | 33.0 | 44987 | 0.9047 | 0.6186 | | 1.2038 | 34.0 | 46350 | 0.9061 | 0.6190 | | 1.1957 | 35.0 | 47713 | 0.9052 | 0.6255 | | 1.1962 | 36.0 | 49077 | 0.9057 | 0.6210 | | 1.1866 | 37.0 | 50440 | 0.9105 | 0.6227 | | 1.2545 | 38.0 | 51803 | 0.9173 | 0.6206 | | 1.1642 | 39.0 | 53166 | 0.9120 | 0.6239 | | 1.1711 | 40.0 | 54530 | 0.9235 | 0.6177 | | 1.2339 | 41.0 | 55893 | 0.9295 | 0.6143 | | 1.1132 | 42.0 | 57256 | 0.9143 | 0.6234 | | 1.1977 | 43.0 | 58619 | 0.9163 | 0.6256 | | 1.1617 | 44.0 | 59983 | 0.9246 | 0.6233 | | 1.1357 | 45.0 | 61346 | 0.9196 | 0.6255 | | 1.1362 | 46.0 | 62709 | 0.9221 | 0.6259 | | 1.1472 | 47.0 | 64072 | 0.9206 | 0.6263 | | 1.184 | 48.0 | 65436 | 0.9282 | 0.6256 | | 1.1096 | 49.0 | 66799 | 0.9252 | 0.6269 | | 1.1425 | 49.99 | 68150 | 0.9268 | 0.6269 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.15.2

sai17/cards_bottom_right_swin-tiny-patch4-window7-224-finetuned-v2

sai17

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: cards_bottom_right_swin-tiny-patch4-window7-224-finetuned-v2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.6078575555438837 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cards_bottom_right_swin-tiny-patch4-window7-224-finetuned-v2 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9317 - Accuracy: 0.6079 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.4965 | 1.0 | 1338 | 1.3516 | 0.4156 | | 1.4486 | 2.0 | 2677 | 1.1784 | 0.4938 | | 1.4384 | 3.0 | 4015 | 1.1050 | 0.5223 | | 1.4538 | 4.0 | 5354 | 1.0751 | 0.5433 | | 1.3928 | 5.0 | 6692 | 1.0604 | 0.5440 | | 1.4148 | 6.0 | 8031 | 1.0459 | 0.5523 | | 1.3921 | 7.0 | 9369 | 1.0464 | 0.5501 | | 1.3812 | 8.0 | 10708 | 1.0461 | 0.5491 | | 1.3494 | 9.0 | 12046 | 1.0445 | 0.5486 | | 1.3555 | 10.0 | 13385 | 0.9973 | 0.5693 | | 1.3303 | 11.0 | 14723 | 0.9952 | 0.5719 | | 1.3575 | 12.0 | 16062 | 1.0317 | 0.5574 | | 1.3129 | 13.0 | 17400 | 0.9851 | 0.5813 | | 1.3439 | 14.0 | 18739 | 1.0510 | 0.5523 | | 1.3371 | 15.0 | 20077 | 0.9820 | 0.5795 | | 1.2835 | 16.0 | 21416 | 0.9886 | 0.5738 | | 1.3002 | 17.0 | 22754 | 0.9685 | 0.5869 | | 1.289 | 18.0 | 24093 | 0.9519 | 0.5941 | | 1.3007 | 19.0 | 25431 | 0.9855 | 0.5800 | | 1.2927 | 20.0 | 26770 | 0.9499 | 0.5925 | | 1.2985 | 21.0 | 28108 | 0.9669 | 0.5854 | | 1.2957 | 22.0 | 29447 | 0.9551 | 0.5903 | | 1.2579 | 23.0 | 30785 | 0.9300 | 0.6053 | | 1.2475 | 24.0 | 32124 | 0.9296 | 0.6049 | | 1.2227 | 25.0 | 33462 | 0.9317 | 0.6079 | | 1.2069 | 26.0 | 34801 | 0.9609 | 0.5887 | | 1.2156 | 27.0 | 36139 | 0.9297 | 0.6052 | | 1.25 | 28.0 | 37478 | 0.9300 | 0.6062 | | 1.2394 | 29.0 | 38816 | 0.9238 | 0.6071 | | 1.209 | 29.99 | 40140 | 0.9284 | 0.6064 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.15.2

facebook/mms-1b-all

facebook

transformers

automatic-speech-recognition

--- tags: - mms language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - 'no' - 'no' - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za license: cc-by-nc-4.0 datasets: - google/fleurs metrics: - wer --- # Massively Multilingual Speech (MMS) - Finetuned ASR - ALL This checkpoint is a model fine-tuned for multi-lingual ASR and part of Facebook's [Massive Multilingual Speech project](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/). This checkpoint is based on the [Wav2Vec2 architecture](https://huggingface.co/docs/transformers/model_doc/wav2vec2) and makes use of adapter models to transcribe 1000+ languages. The checkpoint consists of **1 billion parameters** and has been fine-tuned from [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) on 1162 languages. ## Table Of Content - [Example](#example) - [Supported Languages](#supported-languages) - [Model details](#model-details) - [Additional links](#additional-links) ## Example This MMS checkpoint can be used with [Transformers](https://github.com/huggingface/transformers) to transcribe audio of 1107 different languages. Let's look at a simple example. First, we install transformers and some other libraries ``` pip install torch accelerate torchaudio datasets pip install --upgrade transformers ```` **Note**: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available [on PyPI](https://pypi.org/project/transformers/) make sure to install `transformers` from source: ``` pip install git+https://github.com/huggingface/transformers.git ``` Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz. ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # French stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "fr", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) fr_sample = next(iter(stream_data))["audio"]["array"] ``` Next, we load the model and processor ```py from transformers import Wav2Vec2ForCTC, AutoProcessor import torch model_id = "facebook/mms-1b-all" processor = AutoProcessor.from_pretrained(model_id) model = Wav2Vec2ForCTC.from_pretrained(model_id) ``` Now we process the audio data, pass the processed audio data to the model and transcribe the model output, just like we usually do for Wav2Vec2 models such as [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) ```py inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits ids = torch.argmax(outputs, dim=-1)[0] transcription = processor.decode(ids) # 'joe keton disapproved of films and buster also had reservations about the media' ``` We can now keep the same model in memory and simply switch out the language adapters by calling the convenient [`load_adapter()`]() function for the model and [`set_target_lang()`]() for the tokenizer. We pass the target language as an input - "fra" for French. ```py processor.tokenizer.set_target_lang("fra") model.load_adapter("fra") inputs = processor(fr_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits ids = torch.argmax(outputs, dim=-1)[0] transcription = processor.decode(ids) # "ce dernier est volé tout au long de l'histoire romaine" ``` In the same way the language can be switched out for all other supported languages. Please have a look at: ```py processor.tokenizer.vocab.keys() ``` For more details, please have a look at [the official docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). ## Supported Languages This model supports 1162 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). You can find more details about the languages and their ISO 649-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). <details> <summary>Click to toggle</summary> - abi - abk - abp - aca - acd - ace - acf - ach - acn - acr - acu - ade - adh - adj - adx - aeu - afr - agd - agg - agn - agr - agu - agx - aha - ahk - aia - aka - akb - ake - akp - alj - alp - alt - alz - ame - amf - amh - ami - amk - ann - any - aoz - apb - apr - ara - arl - asa - asg - asm - ast - ata - atb - atg - ati - atq - ava - avn - avu - awa - awb - ayo - ayr - ayz - azb - azg - azj-script_cyrillic - azj-script_latin - azz - bak - bam - ban - bao - bas - bav - bba - bbb - bbc - bbo - bcc-script_arabic - bcc-script_latin - bcl - bcw - bdg - bdh - bdq - bdu - bdv - beh - bel - bem - ben - bep - bex - bfa - bfo - bfy - bfz - bgc - bgq - bgr - bgt - bgw - bha - bht - bhz - bib - bim - bis - biv - bjr - bjv - bjw - bjz - bkd - bkv - blh - blt - blx - blz - bmq - bmr - bmu - bmv - bng - bno - bnp - boa - bod - boj - bom - bor - bos - bov - box - bpr - bps - bqc - bqi - bqj - bqp - bre - bru - bsc - bsq - bss - btd - bts - btt - btx - bud - bul - bus - bvc - bvz - bwq - bwu - byr - bzh - bzi - bzj - caa - cab - cac-dialect_sanmateoixtatan - cac-dialect_sansebastiancoatan - cak-dialect_central - cak-dialect_santamariadejesus - cak-dialect_santodomingoxenacoj - cak-dialect_southcentral - cak-dialect_western - cak-dialect_yepocapa - cap - car - cas - cat - cax - cbc - cbi - cbr - cbs - cbt - cbu - cbv - cce - cco - cdj - ceb - ceg - cek - ces - cfm - cgc - che - chf - chv - chz - cjo - cjp - cjs - ckb - cko - ckt - cla - cle - cly - cme - cmn-script_simplified - cmo-script_khmer - cmo-script_latin - cmr - cnh - cni - cnl - cnt - coe - cof - cok - con - cot - cou - cpa - cpb - cpu - crh - crk-script_latin - crk-script_syllabics - crn - crq - crs - crt - csk - cso - ctd - ctg - cto - ctu - cuc - cui - cuk - cul - cwa - cwe - cwt - cya - cym - daa - dah - dan - dar - dbj - dbq - ddn - ded - des - deu - dga - dgi - dgk - dgo - dgr - dhi - did - dig - dik - dip - div - djk - dnj-dialect_blowowest - dnj-dialect_gweetaawueast - dnt - dnw - dop - dos - dsh - dso - dtp - dts - dug - dwr - dyi - dyo - dyu - dzo - eip - eka - ell - emp - enb - eng - enx - epo - ese - ess - est - eus - evn - ewe - eza - fal - fao - far - fas - fij - fin - flr - fmu - fon - fra - frd - fry - ful - gag-script_cyrillic - gag-script_latin - gai - gam - gau - gbi - gbk - gbm - gbo - gde - geb - gej - gil - gjn - gkn - gld - gle - glg - glk - gmv - gna - gnd - gng - gof-script_latin - gog - gor - gqr - grc - gri - grn - grt - gso - gub - guc - gud - guh - guj - guk - gum - guo - guq - guu - gux - gvc - gvl - gwi - gwr - gym - gyr - had - hag - hak - hap - hat - hau - hay - heb - heh - hif - hig - hil - hin - hlb - hlt - hne - hnn - hns - hoc - hoy - hrv - hsb - hto - hub - hui - hun - hus-dialect_centralveracruz - hus-dialect_westernpotosino - huu - huv - hvn - hwc - hye - hyw - iba - ibo - icr - idd - ifa - ifb - ife - ifk - ifu - ify - ign - ikk - ilb - ilo - imo - ina - inb - ind - iou - ipi - iqw - iri - irk - isl - ita - itl - itv - ixl-dialect_sangasparchajul - ixl-dialect_sanjuancotzal - ixl-dialect_santamarianebaj - izr - izz - jac - jam - jav - jbu - jen - jic - jiv - jmc - jmd - jpn - jun - juy - jvn - kaa - kab - kac - kak - kam - kan - kao - kaq - kat - kay - kaz - kbo - kbp - kbq - kbr - kby - kca - kcg - kdc - kde - kdh - kdi - kdj - kdl - kdn - kdt - kea - kek - ken - keo - ker - key - kez - kfb - kff-script_telugu - kfw - kfx - khg - khm - khq - kia - kij - kik - kin - kir - kjb - kje - kjg - kjh - kki - kkj - kle - klu - klv - klw - kma - kmd - kml - kmr-script_arabic - kmr-script_cyrillic - kmr-script_latin - kmu - knb - kne - knf - knj - knk - kno - kog - kor - kpq - kps - kpv - kpy - kpz - kqe - kqp - kqr - kqy - krc - kri - krj - krl - krr - krs - kru - ksb - ksr - kss - ktb - ktj - kub - kue - kum - kus - kvn - kvw - kwd - kwf - kwi - kxc - kxf - kxm - kxv - kyb - kyc - kyf - kyg - kyo - kyq - kyu - kyz - kzf - lac - laj - lam - lao - las - lat - lav - law - lbj - lbw - lcp - lee - lef - lem - lew - lex - lgg - lgl - lhu - lia - lid - lif - lin - lip - lis - lit - lje - ljp - llg - lln - lme - lnd - lns - lob - lok - lom - lon - loq - lsi - lsm - ltz - luc - lug - luo - lwo - lww - lzz - maa-dialect_sanantonio - maa-dialect_sanjeronimo - mad - mag - mah - mai - maj - mak - mal - mam-dialect_central - mam-dialect_northern - mam-dialect_southern - mam-dialect_western - maq - mar - maw - maz - mbb - mbc - mbh - mbj - mbt - mbu - mbz - mca - mcb - mcd - mco - mcp - mcq - mcu - mda - mdf - mdv - mdy - med - mee - mej - men - meq - met - mev - mfe - mfh - mfi - mfk - mfq - mfy - mfz - mgd - mge - mgh - mgo - mhi - mhr - mhu - mhx - mhy - mib - mie - mif - mih - mil - mim - min - mio - mip - miq - mit - miy - miz - mjl - mjv - mkd - mkl - mkn - mlg - mlt - mmg - mnb - mnf - mnk - mnw - mnx - moa - mog - mon - mop - mor - mos - mox - moz - mpg - mpm - mpp - mpx - mqb - mqf - mqj - mqn - mri - mrw - msy - mtd - mtj - mto - muh - mup - mur - muv - muy - mvp - mwq - mwv - mxb - mxq - mxt - mxv - mya - myb - myk - myl - myv - myx - myy - mza - mzi - mzj - mzk - mzm - mzw - nab - nag - nan - nas - naw - nca - nch - ncj - ncl - ncu - ndj - ndp - ndv - ndy - ndz - neb - new - nfa - nfr - nga - ngl - ngp - ngu - nhe - nhi - nhu - nhw - nhx - nhy - nia - nij - nim - nin - nko - nlc - nld - nlg - nlk - nmz - nnb - nno - nnq - nnw - noa - nob - nod - nog - not - npi - npl - npy - nso - nst - nsu - ntm - ntr - nuj - nus - nuz - nwb - nxq - nya - nyf - nyn - nyo - nyy - nzi - obo - oci - ojb-script_latin - ojb-script_syllabics - oku - old - omw - onb - ood - orm - ory - oss - ote - otq - ozm - pab - pad - pag - pam - pan - pao - pap - pau - pbb - pbc - pbi - pce - pcm - peg - pez - pib - pil - pir - pis - pjt - pkb - pls - plw - pmf - pny - poh-dialect_eastern - poh-dialect_western - poi - pol - por - poy - ppk - pps - prf - prk - prt - pse - pss - ptu - pui - pus - pwg - pww - pxm - qub - quc-dialect_central - quc-dialect_east - quc-dialect_north - quf - quh - qul - quw - quy - quz - qvc - qve - qvh - qvm - qvn - qvo - qvs - qvw - qvz - qwh - qxh - qxl - qxn - qxo - qxr - rah - rai - rap - rav - raw - rej - rel - rgu - rhg - rif-script_arabic - rif-script_latin - ril - rim - rjs - rkt - rmc-script_cyrillic - rmc-script_latin - rmo - rmy-script_cyrillic - rmy-script_latin - rng - rnl - roh-dialect_sursilv - roh-dialect_vallader - rol - ron - rop - rro - rub - ruf - rug - run - rus - sab - sag - sah - saj - saq - sas - sat - sba - sbd - sbl - sbp - sch - sck - sda - sea - seh - ses - sey - sgb - sgj - sgw - shi - shk - shn - sho - shp - sid - sig - sil - sja - sjm - sld - slk - slu - slv - sml - smo - sna - snd - sne - snn - snp - snw - som - soy - spa - spp - spy - sqi - sri - srm - srn - srp-script_cyrillic - srp-script_latin - srx - stn - stp - suc - suk - sun - sur - sus - suv - suz - swe - swh - sxb - sxn - sya - syl - sza - tac - taj - tam - tao - tap - taq - tat - tav - tbc - tbg - tbk - tbl - tby - tbz - tca - tcc - tcs - tcz - tdj - ted - tee - tel - tem - teo - ter - tes - tew - tex - tfr - tgj - tgk - tgl - tgo - tgp - tha - thk - thl - tih - tik - tir - tkr - tlb - tlj - tly - tmc - tmf - tna - tng - tnk - tnn - tnp - tnr - tnt - tob - toc - toh - tom - tos - tpi - tpm - tpp - tpt - trc - tri - trn - trs - tso - tsz - ttc - tte - ttq-script_tifinagh - tue - tuf - tuk-script_arabic - tuk-script_latin - tuo - tur - tvw - twb - twe - twu - txa - txq - txu - tye - tzh-dialect_bachajon - tzh-dialect_tenejapa - tzj-dialect_eastern - tzj-dialect_western - tzo-dialect_chamula - tzo-dialect_chenalho - ubl - ubu - udm - udu - uig-script_arabic - uig-script_cyrillic - ukr - umb - unr - upv - ura - urb - urd-script_arabic - urd-script_devanagari - urd-script_latin - urk - urt - ury - usp - uzb-script_cyrillic - uzb-script_latin - vag - vid - vie - vif - vmw - vmy - vot - vun - vut - wal-script_ethiopic - wal-script_latin - wap - war - waw - way - wba - wlo - wlx - wmw - wob - wol - wsg - wwa - xal - xdy - xed - xer - xho - xmm - xnj - xnr - xog - xon - xrb - xsb - xsm - xsr - xsu - xta - xtd - xte - xtm - xtn - xua - xuo - yaa - yad - yal - yam - yao - yas - yat - yaz - yba - ybb - ycl - ycn - yea - yka - yli - yor - yre - yua - yue-script_traditional - yuz - yva - zaa - zab - zac - zad - zae - zai - zam - zao - zaq - zar - zas - zav - zaw - zca - zga - zim - ziw - zlm - zmz - zne - zos - zpc - zpg - zpi - zpl - zpm - zpo - zpt - zpu - zpz - ztq - zty - zul - zyb - zyp - zza </details> ## Model details - **Developed by:** Vineel Pratap et al. - **Model type:** Multi-Lingual Automatic Speech Recognition model - **Language(s):** 1000+ languages, see [supported languages](#supported-languages) - **License:** CC-BY-NC 4.0 license - **Num parameters**: 1 billion - **Audio sampling rate**: 16,000 kHz - **Cite as:** @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other **MMS** checkpoints](https://huggingface.co/models?other=mms) - MMS base checkpoints: - [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) - [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) - [Official Space](https://huggingface.co/spaces/facebook/MMS)

mixedbread-ai/mxbai-embed-large-v1

mixedbread-ai

sentence-transformers

feature-extraction

--- tags: - mteb - transformers.js - transformers model-index: - name: mxbai-angle-large-v1 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.044776119403 - type: ap value: 37.7362433623053 - type: f1 value: 68.92736573359774 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.84025000000001 - type: ap value: 90.93190875404055 - type: f1 value: 93.8297833897293 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.184 - type: f1 value: 48.74163227751588 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 41.252 - type: map_at_10 value: 57.778 - type: map_at_100 value: 58.233000000000004 - type: map_at_1000 value: 58.23700000000001 - type: map_at_3 value: 53.449999999999996 - type: map_at_5 value: 56.376000000000005 - type: mrr_at_1 value: 41.679 - type: mrr_at_10 value: 57.92699999999999 - type: mrr_at_100 value: 58.389 - type: mrr_at_1000 value: 58.391999999999996 - type: mrr_at_3 value: 53.651 - type: mrr_at_5 value: 56.521 - type: ndcg_at_1 value: 41.252 - type: ndcg_at_10 value: 66.018 - type: ndcg_at_100 value: 67.774 - type: ndcg_at_1000 value: 67.84400000000001 - type: ndcg_at_3 value: 57.372 - type: ndcg_at_5 value: 62.646 - type: precision_at_1 value: 41.252 - type: precision_at_10 value: 9.189 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.902 - type: precision_at_5 value: 16.302 - type: recall_at_1 value: 41.252 - type: recall_at_10 value: 91.892 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 68.706 - type: recall_at_5 value: 81.50800000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.97294504317859 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.98071077674629 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 65.16477858490782 - type: mrr value: 78.23583080508287 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.6277629421789 - type: cos_sim_spearman value: 88.4056288400568 - type: euclidean_pearson value: 87.94871847578163 - type: euclidean_spearman value: 88.4056288400568 - type: manhattan_pearson value: 87.73271254229648 - type: manhattan_spearman value: 87.91826833762677 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.81818181818181 - type: f1 value: 87.79879337316918 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.91773608582761 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.73059477462478 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.745999999999995 - type: map_at_10 value: 43.632 - type: map_at_100 value: 45.206 - type: map_at_1000 value: 45.341 - type: map_at_3 value: 39.956 - type: map_at_5 value: 42.031 - type: mrr_at_1 value: 39.485 - type: mrr_at_10 value: 49.537 - type: mrr_at_100 value: 50.249 - type: mrr_at_1000 value: 50.294000000000004 - type: mrr_at_3 value: 46.757 - type: mrr_at_5 value: 48.481 - type: ndcg_at_1 value: 39.485 - type: ndcg_at_10 value: 50.058 - type: ndcg_at_100 value: 55.586 - type: ndcg_at_1000 value: 57.511 - type: ndcg_at_3 value: 44.786 - type: ndcg_at_5 value: 47.339999999999996 - type: precision_at_1 value: 39.485 - type: precision_at_10 value: 9.557 - type: precision_at_100 value: 1.552 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.412 - type: precision_at_5 value: 15.479000000000001 - type: recall_at_1 value: 32.745999999999995 - type: recall_at_10 value: 62.056 - type: recall_at_100 value: 85.088 - type: recall_at_1000 value: 96.952 - type: recall_at_3 value: 46.959 - type: recall_at_5 value: 54.06999999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.898 - type: map_at_10 value: 42.142 - type: map_at_100 value: 43.349 - type: map_at_1000 value: 43.483 - type: map_at_3 value: 39.18 - type: map_at_5 value: 40.733000000000004 - type: mrr_at_1 value: 39.617999999999995 - type: mrr_at_10 value: 47.922 - type: mrr_at_100 value: 48.547000000000004 - type: mrr_at_1000 value: 48.597 - type: mrr_at_3 value: 45.86 - type: mrr_at_5 value: 46.949000000000005 - type: ndcg_at_1 value: 39.617999999999995 - type: ndcg_at_10 value: 47.739 - type: ndcg_at_100 value: 51.934999999999995 - type: ndcg_at_1000 value: 54.007000000000005 - type: ndcg_at_3 value: 43.748 - type: ndcg_at_5 value: 45.345 - type: precision_at_1 value: 39.617999999999995 - type: precision_at_10 value: 8.962 - type: precision_at_100 value: 1.436 - type: precision_at_1000 value: 0.192 - type: precision_at_3 value: 21.083 - type: precision_at_5 value: 14.752 - type: recall_at_1 value: 31.898 - type: recall_at_10 value: 57.587999999999994 - type: recall_at_100 value: 75.323 - type: recall_at_1000 value: 88.304 - type: recall_at_3 value: 45.275 - type: recall_at_5 value: 49.99 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.458 - type: map_at_10 value: 52.942 - type: map_at_100 value: 53.974 - type: map_at_1000 value: 54.031 - type: map_at_3 value: 49.559999999999995 - type: map_at_5 value: 51.408 - type: mrr_at_1 value: 46.27 - type: mrr_at_10 value: 56.31699999999999 - type: mrr_at_100 value: 56.95099999999999 - type: mrr_at_1000 value: 56.98 - type: mrr_at_3 value: 53.835 - type: mrr_at_5 value: 55.252 - type: ndcg_at_1 value: 46.27 - type: ndcg_at_10 value: 58.964000000000006 - type: ndcg_at_100 value: 62.875 - type: ndcg_at_1000 value: 63.969 - type: ndcg_at_3 value: 53.297000000000004 - type: ndcg_at_5 value: 55.938 - type: precision_at_1 value: 46.27 - type: precision_at_10 value: 9.549000000000001 - type: precision_at_100 value: 1.2409999999999999 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 23.762 - type: precision_at_5 value: 16.262999999999998 - type: recall_at_1 value: 40.458 - type: recall_at_10 value: 73.446 - type: recall_at_100 value: 90.12400000000001 - type: recall_at_1000 value: 97.795 - type: recall_at_3 value: 58.123000000000005 - type: recall_at_5 value: 64.68 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.443 - type: map_at_10 value: 36.081 - type: map_at_100 value: 37.163000000000004 - type: map_at_1000 value: 37.232 - type: map_at_3 value: 33.308 - type: map_at_5 value: 34.724 - type: mrr_at_1 value: 29.492 - type: mrr_at_10 value: 38.138 - type: mrr_at_100 value: 39.065 - type: mrr_at_1000 value: 39.119 - type: mrr_at_3 value: 35.593 - type: mrr_at_5 value: 36.785000000000004 - type: ndcg_at_1 value: 29.492 - type: ndcg_at_10 value: 41.134 - type: ndcg_at_100 value: 46.300999999999995 - type: ndcg_at_1000 value: 48.106 - type: ndcg_at_3 value: 35.77 - type: ndcg_at_5 value: 38.032 - type: precision_at_1 value: 29.492 - type: precision_at_10 value: 6.249 - type: precision_at_100 value: 0.9299999999999999 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 15.065999999999999 - type: precision_at_5 value: 10.373000000000001 - type: recall_at_1 value: 27.443 - type: recall_at_10 value: 54.80199999999999 - type: recall_at_100 value: 78.21900000000001 - type: recall_at_1000 value: 91.751 - type: recall_at_3 value: 40.211000000000006 - type: recall_at_5 value: 45.599000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.731 - type: map_at_10 value: 26.717999999999996 - type: map_at_100 value: 27.897 - type: map_at_1000 value: 28.029 - type: map_at_3 value: 23.91 - type: map_at_5 value: 25.455 - type: mrr_at_1 value: 23.134 - type: mrr_at_10 value: 31.769 - type: mrr_at_100 value: 32.634 - type: mrr_at_1000 value: 32.707 - type: mrr_at_3 value: 28.938999999999997 - type: mrr_at_5 value: 30.531000000000002 - type: ndcg_at_1 value: 23.134 - type: ndcg_at_10 value: 32.249 - type: ndcg_at_100 value: 37.678 - type: ndcg_at_1000 value: 40.589999999999996 - type: ndcg_at_3 value: 26.985999999999997 - type: ndcg_at_5 value: 29.457 - type: precision_at_1 value: 23.134 - type: precision_at_10 value: 5.8709999999999996 - type: precision_at_100 value: 0.988 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.852 - type: precision_at_5 value: 9.428 - type: recall_at_1 value: 18.731 - type: recall_at_10 value: 44.419 - type: recall_at_100 value: 67.851 - type: recall_at_1000 value: 88.103 - type: recall_at_3 value: 29.919 - type: recall_at_5 value: 36.230000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.324 - type: map_at_10 value: 41.265 - type: map_at_100 value: 42.559000000000005 - type: map_at_1000 value: 42.669000000000004 - type: map_at_3 value: 38.138 - type: map_at_5 value: 39.881 - type: mrr_at_1 value: 36.67 - type: mrr_at_10 value: 46.774 - type: mrr_at_100 value: 47.554 - type: mrr_at_1000 value: 47.593 - type: mrr_at_3 value: 44.338 - type: mrr_at_5 value: 45.723 - type: ndcg_at_1 value: 36.67 - type: ndcg_at_10 value: 47.367 - type: ndcg_at_100 value: 52.623 - type: ndcg_at_1000 value: 54.59 - type: ndcg_at_3 value: 42.323 - type: ndcg_at_5 value: 44.727 - type: precision_at_1 value: 36.67 - type: precision_at_10 value: 8.518 - type: precision_at_100 value: 1.2890000000000001 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 19.955000000000002 - type: precision_at_5 value: 14.11 - type: recall_at_1 value: 30.324 - type: recall_at_10 value: 59.845000000000006 - type: recall_at_100 value: 81.77499999999999 - type: recall_at_1000 value: 94.463 - type: recall_at_3 value: 46.019 - type: recall_at_5 value: 52.163000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.229 - type: map_at_10 value: 35.004000000000005 - type: map_at_100 value: 36.409000000000006 - type: map_at_1000 value: 36.521 - type: map_at_3 value: 31.793 - type: map_at_5 value: 33.432 - type: mrr_at_1 value: 30.365 - type: mrr_at_10 value: 40.502 - type: mrr_at_100 value: 41.372 - type: mrr_at_1000 value: 41.435 - type: mrr_at_3 value: 37.804 - type: mrr_at_5 value: 39.226 - type: ndcg_at_1 value: 30.365 - type: ndcg_at_10 value: 41.305 - type: ndcg_at_100 value: 47.028999999999996 - type: ndcg_at_1000 value: 49.375 - type: ndcg_at_3 value: 35.85 - type: ndcg_at_5 value: 38.12 - type: precision_at_1 value: 30.365 - type: precision_at_10 value: 7.808 - type: precision_at_100 value: 1.228 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 17.352 - type: precision_at_5 value: 12.42 - type: recall_at_1 value: 24.229 - type: recall_at_10 value: 54.673 - type: recall_at_100 value: 78.766 - type: recall_at_1000 value: 94.625 - type: recall_at_3 value: 39.602 - type: recall_at_5 value: 45.558 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.695 - type: map_at_10 value: 36.0895 - type: map_at_100 value: 37.309416666666664 - type: map_at_1000 value: 37.42558333333334 - type: map_at_3 value: 33.19616666666666 - type: map_at_5 value: 34.78641666666667 - type: mrr_at_1 value: 31.486083333333337 - type: mrr_at_10 value: 40.34774999999999 - type: mrr_at_100 value: 41.17533333333333 - type: mrr_at_1000 value: 41.231583333333326 - type: mrr_at_3 value: 37.90075 - type: mrr_at_5 value: 39.266999999999996 - type: ndcg_at_1 value: 31.486083333333337 - type: ndcg_at_10 value: 41.60433333333334 - type: ndcg_at_100 value: 46.74525 - type: ndcg_at_1000 value: 48.96166666666667 - type: ndcg_at_3 value: 36.68825 - type: ndcg_at_5 value: 38.966499999999996 - type: precision_at_1 value: 31.486083333333337 - type: precision_at_10 value: 7.29675 - type: precision_at_100 value: 1.1621666666666666 - type: precision_at_1000 value: 0.1545 - type: precision_at_3 value: 16.8815 - type: precision_at_5 value: 11.974583333333333 - type: recall_at_1 value: 26.695 - type: recall_at_10 value: 53.651916666666665 - type: recall_at_100 value: 76.12083333333332 - type: recall_at_1000 value: 91.31191666666668 - type: recall_at_3 value: 40.03575 - type: recall_at_5 value: 45.876666666666665 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.668000000000003 - type: map_at_10 value: 32.486 - type: map_at_100 value: 33.371 - type: map_at_1000 value: 33.458 - type: map_at_3 value: 30.261 - type: map_at_5 value: 31.418000000000003 - type: mrr_at_1 value: 28.988000000000003 - type: mrr_at_10 value: 35.414 - type: mrr_at_100 value: 36.149 - type: mrr_at_1000 value: 36.215 - type: mrr_at_3 value: 33.333 - type: mrr_at_5 value: 34.43 - type: ndcg_at_1 value: 28.988000000000003 - type: ndcg_at_10 value: 36.732 - type: ndcg_at_100 value: 41.331 - type: ndcg_at_1000 value: 43.575 - type: ndcg_at_3 value: 32.413 - type: ndcg_at_5 value: 34.316 - type: precision_at_1 value: 28.988000000000003 - type: precision_at_10 value: 5.7059999999999995 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 13.65 - type: precision_at_5 value: 9.417 - type: recall_at_1 value: 25.668000000000003 - type: recall_at_10 value: 47.147 - type: recall_at_100 value: 68.504 - type: recall_at_1000 value: 85.272 - type: recall_at_3 value: 35.19 - type: recall_at_5 value: 39.925 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.256 - type: map_at_10 value: 24.58 - type: map_at_100 value: 25.773000000000003 - type: map_at_1000 value: 25.899 - type: map_at_3 value: 22.236 - type: map_at_5 value: 23.507 - type: mrr_at_1 value: 20.957 - type: mrr_at_10 value: 28.416000000000004 - type: mrr_at_100 value: 29.447000000000003 - type: mrr_at_1000 value: 29.524 - type: mrr_at_3 value: 26.245 - type: mrr_at_5 value: 27.451999999999998 - type: ndcg_at_1 value: 20.957 - type: ndcg_at_10 value: 29.285 - type: ndcg_at_100 value: 35.003 - type: ndcg_at_1000 value: 37.881 - type: ndcg_at_3 value: 25.063000000000002 - type: ndcg_at_5 value: 26.983 - type: precision_at_1 value: 20.957 - type: precision_at_10 value: 5.344 - type: precision_at_100 value: 0.958 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 11.918 - type: precision_at_5 value: 8.596 - type: recall_at_1 value: 17.256 - type: recall_at_10 value: 39.644 - type: recall_at_100 value: 65.279 - type: recall_at_1000 value: 85.693 - type: recall_at_3 value: 27.825 - type: recall_at_5 value: 32.792 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.700000000000003 - type: map_at_10 value: 36.205999999999996 - type: map_at_100 value: 37.316 - type: map_at_1000 value: 37.425000000000004 - type: map_at_3 value: 33.166000000000004 - type: map_at_5 value: 35.032999999999994 - type: mrr_at_1 value: 31.436999999999998 - type: mrr_at_10 value: 40.61 - type: mrr_at_100 value: 41.415 - type: mrr_at_1000 value: 41.48 - type: mrr_at_3 value: 37.966 - type: mrr_at_5 value: 39.599000000000004 - type: ndcg_at_1 value: 31.436999999999998 - type: ndcg_at_10 value: 41.771 - type: ndcg_at_100 value: 46.784 - type: ndcg_at_1000 value: 49.183 - type: ndcg_at_3 value: 36.437000000000005 - type: ndcg_at_5 value: 39.291 - type: precision_at_1 value: 31.436999999999998 - type: precision_at_10 value: 6.987 - type: precision_at_100 value: 1.072 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 16.448999999999998 - type: precision_at_5 value: 11.866 - type: recall_at_1 value: 26.700000000000003 - type: recall_at_10 value: 54.301 - type: recall_at_100 value: 75.871 - type: recall_at_1000 value: 92.529 - type: recall_at_3 value: 40.201 - type: recall_at_5 value: 47.208 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.296 - type: map_at_10 value: 33.116 - type: map_at_100 value: 34.81 - type: map_at_1000 value: 35.032000000000004 - type: map_at_3 value: 30.105999999999998 - type: map_at_5 value: 31.839000000000002 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 37.803 - type: mrr_at_100 value: 38.856 - type: mrr_at_1000 value: 38.903999999999996 - type: mrr_at_3 value: 35.211 - type: mrr_at_5 value: 36.545 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 39.007 - type: ndcg_at_100 value: 45.321 - type: ndcg_at_1000 value: 47.665 - type: ndcg_at_3 value: 34.1 - type: ndcg_at_5 value: 36.437000000000005 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.542 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 16.14 - type: precision_at_5 value: 11.897 - type: recall_at_1 value: 24.296 - type: recall_at_10 value: 49.85 - type: recall_at_100 value: 78.457 - type: recall_at_1000 value: 92.618 - type: recall_at_3 value: 36.138999999999996 - type: recall_at_5 value: 42.223 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.591 - type: map_at_10 value: 28.902 - type: map_at_100 value: 29.886000000000003 - type: map_at_1000 value: 29.987000000000002 - type: map_at_3 value: 26.740000000000002 - type: map_at_5 value: 27.976 - type: mrr_at_1 value: 22.366 - type: mrr_at_10 value: 30.971 - type: mrr_at_100 value: 31.865 - type: mrr_at_1000 value: 31.930999999999997 - type: mrr_at_3 value: 28.927999999999997 - type: mrr_at_5 value: 30.231 - type: ndcg_at_1 value: 22.366 - type: ndcg_at_10 value: 33.641 - type: ndcg_at_100 value: 38.477 - type: ndcg_at_1000 value: 41.088 - type: ndcg_at_3 value: 29.486 - type: ndcg_at_5 value: 31.612000000000002 - type: precision_at_1 value: 22.366 - type: precision_at_10 value: 5.3420000000000005 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 12.939 - type: precision_at_5 value: 9.094 - type: recall_at_1 value: 20.591 - type: recall_at_10 value: 46.052 - type: recall_at_100 value: 68.193 - type: recall_at_1000 value: 87.638 - type: recall_at_3 value: 34.966 - type: recall_at_5 value: 40.082 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 15.091 - type: map_at_10 value: 26.38 - type: map_at_100 value: 28.421999999999997 - type: map_at_1000 value: 28.621999999999996 - type: map_at_3 value: 21.597 - type: map_at_5 value: 24.12 - type: mrr_at_1 value: 34.266999999999996 - type: mrr_at_10 value: 46.864 - type: mrr_at_100 value: 47.617 - type: mrr_at_1000 value: 47.644 - type: mrr_at_3 value: 43.312 - type: mrr_at_5 value: 45.501000000000005 - type: ndcg_at_1 value: 34.266999999999996 - type: ndcg_at_10 value: 36.095 - type: ndcg_at_100 value: 43.447 - type: ndcg_at_1000 value: 46.661 - type: ndcg_at_3 value: 29.337999999999997 - type: ndcg_at_5 value: 31.824 - type: precision_at_1 value: 34.266999999999996 - type: precision_at_10 value: 11.472 - type: precision_at_100 value: 1.944 - type: precision_at_1000 value: 0.255 - type: precision_at_3 value: 21.933 - type: precision_at_5 value: 17.224999999999998 - type: recall_at_1 value: 15.091 - type: recall_at_10 value: 43.022 - type: recall_at_100 value: 68.075 - type: recall_at_1000 value: 85.76 - type: recall_at_3 value: 26.564 - type: recall_at_5 value: 33.594 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.252 - type: map_at_10 value: 20.923 - type: map_at_100 value: 30.741000000000003 - type: map_at_1000 value: 32.542 - type: map_at_3 value: 14.442 - type: map_at_5 value: 17.399 - type: mrr_at_1 value: 70.25 - type: mrr_at_10 value: 78.17 - type: mrr_at_100 value: 78.444 - type: mrr_at_1000 value: 78.45100000000001 - type: mrr_at_3 value: 76.958 - type: mrr_at_5 value: 77.571 - type: ndcg_at_1 value: 58.375 - type: ndcg_at_10 value: 44.509 - type: ndcg_at_100 value: 49.897999999999996 - type: ndcg_at_1000 value: 57.269999999999996 - type: ndcg_at_3 value: 48.64 - type: ndcg_at_5 value: 46.697 - type: precision_at_1 value: 70.25 - type: precision_at_10 value: 36.05 - type: precision_at_100 value: 11.848 - type: precision_at_1000 value: 2.213 - type: precision_at_3 value: 52.917 - type: precision_at_5 value: 45.7 - type: recall_at_1 value: 9.252 - type: recall_at_10 value: 27.006999999999998 - type: recall_at_100 value: 57.008 - type: recall_at_1000 value: 80.697 - type: recall_at_3 value: 15.798000000000002 - type: recall_at_5 value: 20.4 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 50.88 - type: f1 value: 45.545495028653384 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 75.424 - type: map_at_10 value: 83.435 - type: map_at_100 value: 83.66900000000001 - type: map_at_1000 value: 83.685 - type: map_at_3 value: 82.39800000000001 - type: map_at_5 value: 83.07 - type: mrr_at_1 value: 81.113 - type: mrr_at_10 value: 87.77199999999999 - type: mrr_at_100 value: 87.862 - type: mrr_at_1000 value: 87.86500000000001 - type: mrr_at_3 value: 87.17099999999999 - type: mrr_at_5 value: 87.616 - type: ndcg_at_1 value: 81.113 - type: ndcg_at_10 value: 86.909 - type: ndcg_at_100 value: 87.746 - type: ndcg_at_1000 value: 88.017 - type: ndcg_at_3 value: 85.368 - type: ndcg_at_5 value: 86.28099999999999 - type: precision_at_1 value: 81.113 - type: precision_at_10 value: 10.363 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 32.507999999999996 - type: precision_at_5 value: 20.138 - type: recall_at_1 value: 75.424 - type: recall_at_10 value: 93.258 - type: recall_at_100 value: 96.545 - type: recall_at_1000 value: 98.284 - type: recall_at_3 value: 89.083 - type: recall_at_5 value: 91.445 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.532 - type: map_at_10 value: 37.141999999999996 - type: map_at_100 value: 39.162 - type: map_at_1000 value: 39.322 - type: map_at_3 value: 32.885 - type: map_at_5 value: 35.093999999999994 - type: mrr_at_1 value: 44.29 - type: mrr_at_10 value: 53.516 - type: mrr_at_100 value: 54.24 - type: mrr_at_1000 value: 54.273 - type: mrr_at_3 value: 51.286 - type: mrr_at_5 value: 52.413 - type: ndcg_at_1 value: 44.29 - type: ndcg_at_10 value: 45.268 - type: ndcg_at_100 value: 52.125 - type: ndcg_at_1000 value: 54.778000000000006 - type: ndcg_at_3 value: 41.829 - type: ndcg_at_5 value: 42.525 - type: precision_at_1 value: 44.29 - type: precision_at_10 value: 12.5 - type: precision_at_100 value: 1.9720000000000002 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 28.035 - type: precision_at_5 value: 20.093 - type: recall_at_1 value: 22.532 - type: recall_at_10 value: 52.419000000000004 - type: recall_at_100 value: 77.43299999999999 - type: recall_at_1000 value: 93.379 - type: recall_at_3 value: 38.629000000000005 - type: recall_at_5 value: 43.858000000000004 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 39.359 - type: map_at_10 value: 63.966 - type: map_at_100 value: 64.87 - type: map_at_1000 value: 64.92599999999999 - type: map_at_3 value: 60.409 - type: map_at_5 value: 62.627 - type: mrr_at_1 value: 78.717 - type: mrr_at_10 value: 84.468 - type: mrr_at_100 value: 84.655 - type: mrr_at_1000 value: 84.661 - type: mrr_at_3 value: 83.554 - type: mrr_at_5 value: 84.133 - type: ndcg_at_1 value: 78.717 - type: ndcg_at_10 value: 72.03399999999999 - type: ndcg_at_100 value: 75.158 - type: ndcg_at_1000 value: 76.197 - type: ndcg_at_3 value: 67.049 - type: ndcg_at_5 value: 69.808 - type: precision_at_1 value: 78.717 - type: precision_at_10 value: 15.201 - type: precision_at_100 value: 1.764 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 43.313 - type: precision_at_5 value: 28.165000000000003 - type: recall_at_1 value: 39.359 - type: recall_at_10 value: 76.003 - type: recall_at_100 value: 88.197 - type: recall_at_1000 value: 95.003 - type: recall_at_3 value: 64.97 - type: recall_at_5 value: 70.41199999999999 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.83200000000001 - type: ap value: 89.33560571859861 - type: f1 value: 92.82322915005167 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.983 - type: map_at_10 value: 34.259 - type: map_at_100 value: 35.432 - type: map_at_1000 value: 35.482 - type: map_at_3 value: 30.275999999999996 - type: map_at_5 value: 32.566 - type: mrr_at_1 value: 22.579 - type: mrr_at_10 value: 34.882999999999996 - type: mrr_at_100 value: 35.984 - type: mrr_at_1000 value: 36.028 - type: mrr_at_3 value: 30.964999999999996 - type: mrr_at_5 value: 33.245000000000005 - type: ndcg_at_1 value: 22.564 - type: ndcg_at_10 value: 41.258 - type: ndcg_at_100 value: 46.824 - type: ndcg_at_1000 value: 48.037 - type: ndcg_at_3 value: 33.17 - type: ndcg_at_5 value: 37.263000000000005 - type: precision_at_1 value: 22.564 - type: precision_at_10 value: 6.572 - type: precision_at_100 value: 0.935 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.130999999999998 - type: precision_at_5 value: 10.544 - type: recall_at_1 value: 21.983 - type: recall_at_10 value: 62.775000000000006 - type: recall_at_100 value: 88.389 - type: recall_at_1000 value: 97.603 - type: recall_at_3 value: 40.878 - type: recall_at_5 value: 50.690000000000005 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.95120839033288 - type: f1 value: 93.73824125055208 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.78978568171455 - type: f1 value: 57.50180552858304 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.24411566913248 - type: f1 value: 74.37851403532832 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.94620040349699 - type: f1 value: 80.21293397970435 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.44403096245675 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.659594631336812 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.53833075108798 - type: mrr value: 33.78840823218308 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.185999999999999 - type: map_at_10 value: 15.193999999999999 - type: map_at_100 value: 19.538 - type: map_at_1000 value: 21.178 - type: map_at_3 value: 11.208 - type: map_at_5 value: 12.745999999999999 - type: mrr_at_1 value: 48.916 - type: mrr_at_10 value: 58.141 - type: mrr_at_100 value: 58.656 - type: mrr_at_1000 value: 58.684999999999995 - type: mrr_at_3 value: 55.521 - type: mrr_at_5 value: 57.239 - type: ndcg_at_1 value: 47.059 - type: ndcg_at_10 value: 38.644 - type: ndcg_at_100 value: 36.272999999999996 - type: ndcg_at_1000 value: 44.996 - type: ndcg_at_3 value: 43.293 - type: ndcg_at_5 value: 40.819 - type: precision_at_1 value: 48.916 - type: precision_at_10 value: 28.607 - type: precision_at_100 value: 9.195 - type: precision_at_1000 value: 2.225 - type: precision_at_3 value: 40.454 - type: precision_at_5 value: 34.985 - type: recall_at_1 value: 7.185999999999999 - type: recall_at_10 value: 19.654 - type: recall_at_100 value: 37.224000000000004 - type: recall_at_1000 value: 68.663 - type: recall_at_3 value: 12.158 - type: recall_at_5 value: 14.674999999999999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.552000000000003 - type: map_at_10 value: 47.75 - type: map_at_100 value: 48.728 - type: map_at_1000 value: 48.754 - type: map_at_3 value: 43.156 - type: map_at_5 value: 45.883 - type: mrr_at_1 value: 35.66 - type: mrr_at_10 value: 50.269 - type: mrr_at_100 value: 50.974 - type: mrr_at_1000 value: 50.991 - type: mrr_at_3 value: 46.519 - type: mrr_at_5 value: 48.764 - type: ndcg_at_1 value: 35.632000000000005 - type: ndcg_at_10 value: 55.786 - type: ndcg_at_100 value: 59.748999999999995 - type: ndcg_at_1000 value: 60.339 - type: ndcg_at_3 value: 47.292 - type: ndcg_at_5 value: 51.766999999999996 - type: precision_at_1 value: 35.632000000000005 - type: precision_at_10 value: 9.267 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 21.601 - type: precision_at_5 value: 15.539 - type: recall_at_1 value: 31.552000000000003 - type: recall_at_10 value: 77.62400000000001 - type: recall_at_100 value: 94.527 - type: recall_at_1000 value: 98.919 - type: recall_at_3 value: 55.898 - type: recall_at_5 value: 66.121 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.414 - type: map_at_10 value: 85.37400000000001 - type: map_at_100 value: 86.01100000000001 - type: map_at_1000 value: 86.027 - type: map_at_3 value: 82.562 - type: map_at_5 value: 84.284 - type: mrr_at_1 value: 82.24000000000001 - type: mrr_at_10 value: 88.225 - type: mrr_at_100 value: 88.324 - type: mrr_at_1000 value: 88.325 - type: mrr_at_3 value: 87.348 - type: mrr_at_5 value: 87.938 - type: ndcg_at_1 value: 82.24000000000001 - type: ndcg_at_10 value: 88.97699999999999 - type: ndcg_at_100 value: 90.16 - type: ndcg_at_1000 value: 90.236 - type: ndcg_at_3 value: 86.371 - type: ndcg_at_5 value: 87.746 - type: precision_at_1 value: 82.24000000000001 - type: precision_at_10 value: 13.481000000000002 - type: precision_at_100 value: 1.534 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.86 - type: precision_at_5 value: 24.738 - type: recall_at_1 value: 71.414 - type: recall_at_10 value: 95.735 - type: recall_at_100 value: 99.696 - type: recall_at_1000 value: 99.979 - type: recall_at_3 value: 88.105 - type: recall_at_5 value: 92.17999999999999 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.22146692057259 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 65.29273320614578 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.023 - type: map_at_10 value: 14.161000000000001 - type: map_at_100 value: 16.68 - type: map_at_1000 value: 17.072000000000003 - type: map_at_3 value: 9.763 - type: map_at_5 value: 11.977 - type: mrr_at_1 value: 24.8 - type: mrr_at_10 value: 37.602999999999994 - type: mrr_at_100 value: 38.618 - type: mrr_at_1000 value: 38.659 - type: mrr_at_3 value: 34.117 - type: mrr_at_5 value: 36.082 - type: ndcg_at_1 value: 24.8 - type: ndcg_at_10 value: 23.316 - type: ndcg_at_100 value: 32.613 - type: ndcg_at_1000 value: 38.609 - type: ndcg_at_3 value: 21.697 - type: ndcg_at_5 value: 19.241 - type: precision_at_1 value: 24.8 - type: precision_at_10 value: 12.36 - type: precision_at_100 value: 2.593 - type: precision_at_1000 value: 0.402 - type: precision_at_3 value: 20.767 - type: precision_at_5 value: 17.34 - type: recall_at_1 value: 5.023 - type: recall_at_10 value: 25.069999999999997 - type: recall_at_100 value: 52.563 - type: recall_at_1000 value: 81.525 - type: recall_at_3 value: 12.613 - type: recall_at_5 value: 17.583 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 87.71506247604255 - type: cos_sim_spearman value: 82.91813463738802 - type: euclidean_pearson value: 85.5154616194479 - type: euclidean_spearman value: 82.91815254466314 - type: manhattan_pearson value: 85.5280917850374 - type: manhattan_spearman value: 82.92276537286398 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 87.43772054228462 - type: cos_sim_spearman value: 78.75750601716682 - type: euclidean_pearson value: 85.76074482955764 - type: euclidean_spearman value: 78.75651057223058 - type: manhattan_pearson value: 85.73390291701668 - type: manhattan_spearman value: 78.72699385957797 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 89.58144067172472 - type: cos_sim_spearman value: 90.3524512966946 - type: euclidean_pearson value: 89.71365391594237 - type: euclidean_spearman value: 90.35239632843408 - type: manhattan_pearson value: 89.66905421746478 - type: manhattan_spearman value: 90.31508211683513 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 87.77692637102102 - type: cos_sim_spearman value: 85.45710562643485 - type: euclidean_pearson value: 87.42456979928723 - type: euclidean_spearman value: 85.45709386240908 - type: manhattan_pearson value: 87.40754529526272 - type: manhattan_spearman value: 85.44834854173303 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.28491331695997 - type: cos_sim_spearman value: 89.62037029566964 - type: euclidean_pearson value: 89.02479391362826 - type: euclidean_spearman value: 89.62036733618466 - type: manhattan_pearson value: 89.00394756040342 - type: manhattan_spearman value: 89.60867744215236 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 85.08911381280191 - type: cos_sim_spearman value: 86.5791780765767 - type: euclidean_pearson value: 86.16063473577861 - type: euclidean_spearman value: 86.57917745378766 - type: manhattan_pearson value: 86.13677924604175 - type: manhattan_spearman value: 86.56115615768685 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.58029496205235 - type: cos_sim_spearman value: 89.49551253826998 - type: euclidean_pearson value: 90.13714840963748 - type: euclidean_spearman value: 89.49551253826998 - type: manhattan_pearson value: 90.13039633601363 - type: manhattan_spearman value: 89.4513453745516 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 69.01546399666435 - type: cos_sim_spearman value: 69.33824484595624 - type: euclidean_pearson value: 70.76511642998874 - type: euclidean_spearman value: 69.33824484595624 - type: manhattan_pearson value: 70.84320785047453 - type: manhattan_spearman value: 69.54233632223537 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.26389196390119 - type: cos_sim_spearman value: 89.09721478341385 - type: euclidean_pearson value: 88.97208685922517 - type: euclidean_spearman value: 89.09720927308881 - type: manhattan_pearson value: 88.97513670502573 - type: manhattan_spearman value: 89.07647853984004 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.53075025771936 - type: mrr value: 96.24327651288436 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 60.428000000000004 - type: map_at_10 value: 70.088 - type: map_at_100 value: 70.589 - type: map_at_1000 value: 70.614 - type: map_at_3 value: 67.191 - type: map_at_5 value: 68.515 - type: mrr_at_1 value: 63.333 - type: mrr_at_10 value: 71.13000000000001 - type: mrr_at_100 value: 71.545 - type: mrr_at_1000 value: 71.569 - type: mrr_at_3 value: 68.944 - type: mrr_at_5 value: 70.078 - type: ndcg_at_1 value: 63.333 - type: ndcg_at_10 value: 74.72800000000001 - type: ndcg_at_100 value: 76.64999999999999 - type: ndcg_at_1000 value: 77.176 - type: ndcg_at_3 value: 69.659 - type: ndcg_at_5 value: 71.626 - type: precision_at_1 value: 63.333 - type: precision_at_10 value: 10 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.111 - type: precision_at_5 value: 17.666999999999998 - type: recall_at_1 value: 60.428000000000004 - type: recall_at_10 value: 87.98899999999999 - type: recall_at_100 value: 96.167 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 74.006 - type: recall_at_5 value: 79.05 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.87326732673267 - type: cos_sim_ap value: 96.81770773701805 - type: cos_sim_f1 value: 93.6318407960199 - type: cos_sim_precision value: 93.16831683168317 - type: cos_sim_recall value: 94.1 - type: dot_accuracy value: 99.87326732673267 - type: dot_ap value: 96.8174218946665 - type: dot_f1 value: 93.6318407960199 - type: dot_precision value: 93.16831683168317 - type: dot_recall value: 94.1 - type: euclidean_accuracy value: 99.87326732673267 - type: euclidean_ap value: 96.81770773701807 - type: euclidean_f1 value: 93.6318407960199 - type: euclidean_precision value: 93.16831683168317 - type: euclidean_recall value: 94.1 - type: manhattan_accuracy value: 99.87227722772278 - type: manhattan_ap value: 96.83164126821747 - type: manhattan_f1 value: 93.54677338669335 - type: manhattan_precision value: 93.5935935935936 - type: manhattan_recall value: 93.5 - type: max_accuracy value: 99.87326732673267 - type: max_ap value: 96.83164126821747 - type: max_f1 value: 93.6318407960199 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.6212042420246 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.779230635982564 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.217701909036286 - type: mrr value: 56.17658995416349 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.954206018888453 - type: cos_sim_spearman value: 32.71062599450096 - type: dot_pearson value: 30.95420929056943 - type: dot_spearman value: 32.71062599450096 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22699999999999998 - type: map_at_10 value: 1.924 - type: map_at_100 value: 10.525 - type: map_at_1000 value: 24.973 - type: map_at_3 value: 0.638 - type: map_at_5 value: 1.0659999999999998 - type: mrr_at_1 value: 84 - type: mrr_at_10 value: 91.067 - type: mrr_at_100 value: 91.067 - type: mrr_at_1000 value: 91.067 - type: mrr_at_3 value: 90.667 - type: mrr_at_5 value: 91.067 - type: ndcg_at_1 value: 81 - type: ndcg_at_10 value: 75.566 - type: ndcg_at_100 value: 56.387 - type: ndcg_at_1000 value: 49.834 - type: ndcg_at_3 value: 80.899 - type: ndcg_at_5 value: 80.75099999999999 - type: precision_at_1 value: 84 - type: precision_at_10 value: 79 - type: precision_at_100 value: 57.56 - type: precision_at_1000 value: 21.8 - type: precision_at_3 value: 84.667 - type: precision_at_5 value: 85.2 - type: recall_at_1 value: 0.22699999999999998 - type: recall_at_10 value: 2.136 - type: recall_at_100 value: 13.861 - type: recall_at_1000 value: 46.299 - type: recall_at_3 value: 0.6649999999999999 - type: recall_at_5 value: 1.145 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.752 - type: map_at_10 value: 9.951 - type: map_at_100 value: 16.794999999999998 - type: map_at_1000 value: 18.251 - type: map_at_3 value: 5.288 - type: map_at_5 value: 6.954000000000001 - type: mrr_at_1 value: 38.775999999999996 - type: mrr_at_10 value: 50.458000000000006 - type: mrr_at_100 value: 51.324999999999996 - type: mrr_at_1000 value: 51.339999999999996 - type: mrr_at_3 value: 46.939 - type: mrr_at_5 value: 47.857 - type: ndcg_at_1 value: 36.735 - type: ndcg_at_10 value: 25.198999999999998 - type: ndcg_at_100 value: 37.938 - type: ndcg_at_1000 value: 49.145 - type: ndcg_at_3 value: 29.348000000000003 - type: ndcg_at_5 value: 25.804 - type: precision_at_1 value: 38.775999999999996 - type: precision_at_10 value: 22.041 - type: precision_at_100 value: 7.939 - type: precision_at_1000 value: 1.555 - type: precision_at_3 value: 29.932 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.752 - type: recall_at_10 value: 16.197 - type: recall_at_100 value: 49.166 - type: recall_at_1000 value: 84.18900000000001 - type: recall_at_3 value: 6.438000000000001 - type: recall_at_5 value: 9.093 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.47980000000001 - type: ap value: 14.605194452178754 - type: f1 value: 55.07362924988948 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.708545557441994 - type: f1 value: 60.04751270975683 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 53.21105960597211 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.58419264469214 - type: cos_sim_ap value: 78.55300004517404 - type: cos_sim_f1 value: 71.49673530889001 - type: cos_sim_precision value: 68.20795400095831 - type: cos_sim_recall value: 75.11873350923483 - type: dot_accuracy value: 87.58419264469214 - type: dot_ap value: 78.55297659559511 - type: dot_f1 value: 71.49673530889001 - type: dot_precision value: 68.20795400095831 - type: dot_recall value: 75.11873350923483 - type: euclidean_accuracy value: 87.58419264469214 - type: euclidean_ap value: 78.55300477331477 - type: euclidean_f1 value: 71.49673530889001 - type: euclidean_precision value: 68.20795400095831 - type: euclidean_recall value: 75.11873350923483 - type: manhattan_accuracy value: 87.5663110210407 - type: manhattan_ap value: 78.49982050876562 - type: manhattan_f1 value: 71.35488740722104 - type: manhattan_precision value: 68.18946862226497 - type: manhattan_recall value: 74.82849604221636 - type: max_accuracy value: 87.58419264469214 - type: max_ap value: 78.55300477331477 - type: max_f1 value: 71.49673530889001 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.09069740365584 - type: cos_sim_ap value: 86.22749303724757 - type: cos_sim_f1 value: 78.36863452005407 - type: cos_sim_precision value: 76.49560117302053 - type: cos_sim_recall value: 80.33569448721897 - type: dot_accuracy value: 89.09069740365584 - type: dot_ap value: 86.22750233655673 - type: dot_f1 value: 78.36863452005407 - type: dot_precision value: 76.49560117302053 - type: dot_recall value: 80.33569448721897 - type: euclidean_accuracy value: 89.09069740365584 - type: euclidean_ap value: 86.22749355597347 - type: euclidean_f1 value: 78.36863452005407 - type: euclidean_precision value: 76.49560117302053 - type: euclidean_recall value: 80.33569448721897 - type: manhattan_accuracy value: 89.08293553770326 - type: manhattan_ap value: 86.21913616084771 - type: manhattan_f1 value: 78.3907031479847 - type: manhattan_precision value: 75.0352013517319 - type: manhattan_recall value: 82.06036341238065 - type: max_accuracy value: 89.09069740365584 - type: max_ap value: 86.22750233655673 - type: max_f1 value: 78.3907031479847 license: apache-2.0 language: - en library_name: sentence-transformers pipeline_tag: feature-extraction --- <br><br> <p align="center"> <svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" viewBox="0 0 2020 1130" width="150" height="150" aria-hidden="true"><path fill="#e95a0f" d="M398.167 621.992c-1.387-20.362-4.092-40.739-3.851-61.081.355-30.085 6.873-59.139 21.253-85.976 10.487-19.573 24.09-36.822 40.662-51.515 16.394-14.535 34.338-27.046 54.336-36.182 15.224-6.955 31.006-12.609 47.829-14.168 11.809-1.094 23.753-2.514 35.524-1.836 23.033 1.327 45.131 7.255 66.255 16.75 16.24 7.3 31.497 16.165 45.651 26.969 12.997 9.921 24.412 21.37 34.158 34.509 11.733 15.817 20.849 33.037 25.987 52.018 3.468 12.81 6.438 25.928 7.779 39.097 1.722 16.908 1.642 34.003 2.235 51.021.427 12.253.224 24.547 1.117 36.762 1.677 22.93 4.062 45.764 11.8 67.7 5.376 15.239 12.499 29.55 20.846 43.681l-18.282 20.328c-1.536 1.71-2.795 3.665-4.254 5.448l-19.323 23.533c-13.859-5.449-27.446-11.803-41.657-16.086-13.622-4.106-27.793-6.765-41.905-8.775-15.256-2.173-30.701-3.475-46.105-4.049-23.571-.879-47.178-1.056-70.769-1.029-10.858.013-21.723 1.116-32.57 1.926-5.362.4-10.69 1.255-16.464 1.477-2.758-7.675-5.284-14.865-7.367-22.181-3.108-10.92-4.325-22.554-13.16-31.095-2.598-2.512-5.069-5.341-6.883-8.443-6.366-10.884-12.48-21.917-18.571-32.959-4.178-7.573-8.411-14.375-17.016-18.559-10.34-5.028-19.538-12.387-29.311-18.611-3.173-2.021-6.414-4.312-9.952-5.297-5.857-1.63-11.98-2.301-17.991-3.376z"></path><path fill="#ed6d7b" d="M1478.998 758.842c-12.025.042-24.05.085-36.537-.373-.14-8.536.231-16.569.453-24.607.033-1.179-.315-2.986-1.081-3.4-.805-.434-2.376.338-3.518.81-.856.354-1.562 1.069-3.589 2.521-.239-3.308-.664-5.586-.519-7.827.488-7.544 2.212-15.166 1.554-22.589-1.016-11.451 1.397-14.592-12.332-14.419-3.793.048-3.617-2.803-3.332-5.331.499-4.422 1.45-8.803 1.77-13.233.311-4.316.068-8.672.068-12.861-2.554-.464-4.326-.86-6.12-1.098-4.415-.586-6.051-2.251-5.065-7.31 1.224-6.279.848-12.862 1.276-19.306.19-2.86-.971-4.473-3.794-4.753-4.113-.407-8.242-1.057-12.352-.975-4.663.093-5.192-2.272-4.751-6.012.733-6.229 1.252-12.483 1.875-18.726l1.102-10.495c-5.905-.309-11.146-.805-16.385-.778-3.32.017-5.174-1.4-5.566-4.4-1.172-8.968-2.479-17.944-3.001-26.96-.26-4.484-1.936-5.705-6.005-5.774-9.284-.158-18.563-.594-27.843-.953-7.241-.28-10.137-2.764-11.3-9.899-.746-4.576-2.715-7.801-7.777-8.207-7.739-.621-15.511-.992-23.207-1.961-7.327-.923-14.587-2.415-21.853-3.777-5.021-.941-10.003-2.086-15.003-3.14 4.515-22.952 13.122-44.382 26.284-63.587 18.054-26.344 41.439-47.239 69.102-63.294 15.847-9.197 32.541-16.277 50.376-20.599 16.655-4.036 33.617-5.715 50.622-4.385 33.334 2.606 63.836 13.955 92.415 31.15 15.864 9.545 30.241 20.86 42.269 34.758 8.113 9.374 15.201 19.78 21.718 30.359 10.772 17.484 16.846 36.922 20.611 56.991 1.783 9.503 2.815 19.214 3.318 28.876.758 14.578.755 29.196.65 44.311l-51.545 20.013c-7.779 3.059-15.847 5.376-21.753 12.365-4.73 5.598-10.658 10.316-16.547 14.774-9.9 7.496-18.437 15.988-25.083 26.631-3.333 5.337-7.901 10.381-12.999 14.038-11.355 8.144-17.397 18.973-19.615 32.423l-6.988 41.011z"></path><path fill="#ec663e" d="M318.11 923.047c-.702 17.693-.832 35.433-2.255 53.068-1.699 21.052-6.293 41.512-14.793 61.072-9.001 20.711-21.692 38.693-38.496 53.583-16.077 14.245-34.602 24.163-55.333 30.438-21.691 6.565-43.814 8.127-66.013 6.532-22.771-1.636-43.88-9.318-62.74-22.705-20.223-14.355-35.542-32.917-48.075-54.096-9.588-16.203-16.104-33.55-19.201-52.015-2.339-13.944-2.307-28.011-.403-42.182 2.627-19.545 9.021-37.699 17.963-55.067 11.617-22.564 27.317-41.817 48.382-56.118 15.819-10.74 33.452-17.679 52.444-20.455 8.77-1.282 17.696-1.646 26.568-2.055 11.755-.542 23.534-.562 35.289-1.11 8.545-.399 17.067-1.291 26.193-1.675 1.349 1.77 2.24 3.199 2.835 4.742 4.727 12.261 10.575 23.865 18.636 34.358 7.747 10.084 14.83 20.684 22.699 30.666 3.919 4.972 8.37 9.96 13.609 13.352 7.711 4.994 16.238 8.792 24.617 12.668 5.852 2.707 12.037 4.691 18.074 6.998z"></path><path fill="#ea580e" d="M1285.167 162.995c3.796-29.75 13.825-56.841 32.74-80.577 16.339-20.505 36.013-36.502 59.696-47.614 14.666-6.881 29.971-11.669 46.208-12.749 10.068-.669 20.239-1.582 30.255-.863 16.6 1.191 32.646 5.412 47.9 12.273 19.39 8.722 36.44 20.771 50.582 36.655 15.281 17.162 25.313 37.179 31.49 59.286 5.405 19.343 6.31 39.161 4.705 58.825-2.37 29.045-11.836 55.923-30.451 78.885-10.511 12.965-22.483 24.486-37.181 33.649-5.272-5.613-10.008-11.148-14.539-16.846-5.661-7.118-10.958-14.533-16.78-21.513-4.569-5.478-9.548-10.639-14.624-15.658-3.589-3.549-7.411-6.963-11.551-9.827-5.038-3.485-10.565-6.254-15.798-9.468-8.459-5.195-17.011-9.669-26.988-11.898-12.173-2.72-24.838-4.579-35.622-11.834-1.437-.967-3.433-1.192-5.213-1.542-12.871-2.529-25.454-5.639-36.968-12.471-5.21-3.091-11.564-4.195-17.011-6.965-4.808-2.445-8.775-6.605-13.646-8.851-8.859-4.085-18.114-7.311-27.204-10.896z"></path><path fill="#f8ab00" d="M524.963 311.12c-9.461-5.684-19.513-10.592-28.243-17.236-12.877-9.801-24.031-21.578-32.711-35.412-11.272-17.965-19.605-37.147-21.902-58.403-1.291-11.951-2.434-24.073-1.87-36.034.823-17.452 4.909-34.363 11.581-50.703 8.82-21.603 22.25-39.792 39.568-55.065 18.022-15.894 39.162-26.07 62.351-32.332 19.22-5.19 38.842-6.177 58.37-4.674 23.803 1.831 45.56 10.663 65.062 24.496 17.193 12.195 31.688 27.086 42.894 45.622-11.403 8.296-22.633 16.117-34.092 23.586-17.094 11.142-34.262 22.106-48.036 37.528-8.796 9.848-17.201 20.246-27.131 28.837-16.859 14.585-27.745 33.801-41.054 51.019-11.865 15.349-20.663 33.117-30.354 50.08-5.303 9.283-9.654 19.11-14.434 28.692z"></path><path fill="#ea5227" d="M1060.11 1122.049c-7.377 1.649-14.683 4.093-22.147 4.763-11.519 1.033-23.166 1.441-34.723 1.054-19.343-.647-38.002-4.7-55.839-12.65-15.078-6.72-28.606-15.471-40.571-26.836-24.013-22.81-42.053-49.217-49.518-81.936-1.446-6.337-1.958-12.958-2.235-19.477-.591-13.926-.219-27.909-1.237-41.795-.916-12.5-3.16-24.904-4.408-37.805 1.555-1.381 3.134-2.074 3.778-3.27 4.729-8.79 12.141-15.159 19.083-22.03 5.879-5.818 10.688-12.76 16.796-18.293 6.993-6.335 11.86-13.596 14.364-22.612l8.542-29.993c8.015 1.785 15.984 3.821 24.057 5.286 8.145 1.478 16.371 2.59 24.602 3.493 8.453.927 16.956 1.408 25.891 2.609 1.119 16.09 1.569 31.667 2.521 47.214.676 11.045 1.396 22.154 3.234 33.043 2.418 14.329 5.708 28.527 9.075 42.674 3.499 14.705 4.028 29.929 10.415 44.188 10.157 22.674 18.29 46.25 28.281 69.004 7.175 16.341 12.491 32.973 15.078 50.615.645 4.4 3.256 8.511 4.963 12.755z"></path><path fill="#ea5330" d="M1060.512 1122.031c-2.109-4.226-4.72-8.337-5.365-12.737-2.587-17.642-7.904-34.274-15.078-50.615-9.991-22.755-18.124-46.33-28.281-69.004-6.387-14.259-6.916-29.482-10.415-44.188-3.366-14.147-6.656-28.346-9.075-42.674-1.838-10.889-2.558-21.999-3.234-33.043-.951-15.547-1.401-31.124-2.068-47.146 8.568-.18 17.146.487 25.704.286l41.868-1.4c.907 3.746 1.245 7.04 1.881 10.276l8.651 42.704c.903 4.108 2.334 8.422 4.696 11.829 7.165 10.338 14.809 20.351 22.456 30.345 4.218 5.512 8.291 11.304 13.361 15.955 8.641 7.927 18.065 14.995 27.071 22.532 12.011 10.052 24.452 19.302 40.151 22.854-1.656 11.102-2.391 22.44-5.172 33.253-4.792 18.637-12.38 36.209-23.412 52.216-13.053 18.94-29.086 34.662-49.627 45.055-10.757 5.443-22.443 9.048-34.111 13.501z"></path><path fill="#f8aa05" d="M1989.106 883.951c5.198 8.794 11.46 17.148 15.337 26.491 5.325 12.833 9.744 26.207 12.873 39.737 2.95 12.757 3.224 25.908 1.987 39.219-1.391 14.973-4.643 29.268-10.349 43.034-5.775 13.932-13.477 26.707-23.149 38.405-14.141 17.104-31.215 30.458-50.807 40.488-14.361 7.352-29.574 12.797-45.741 14.594-10.297 1.144-20.732 2.361-31.031 1.894-24.275-1.1-47.248-7.445-68.132-20.263-6.096-3.741-11.925-7.917-17.731-12.342 5.319-5.579 10.361-10.852 15.694-15.811l37.072-34.009c.975-.892 2.113-1.606 3.08-2.505 6.936-6.448 14.765-12.2 20.553-19.556 8.88-11.285 20.064-19.639 31.144-28.292 4.306-3.363 9.06-6.353 12.673-10.358 5.868-6.504 10.832-13.814 16.422-20.582 6.826-8.264 13.727-16.481 20.943-24.401 4.065-4.461 8.995-8.121 13.249-12.424 14.802-14.975 28.77-30.825 45.913-43.317z"></path><path fill="#ed6876" d="M1256.099 523.419c5.065.642 10.047 1.787 15.068 2.728 7.267 1.362 14.526 2.854 21.853 3.777 7.696.97 15.468 1.34 23.207 1.961 5.062.406 7.031 3.631 7.777 8.207 1.163 7.135 4.059 9.62 11.3 9.899l27.843.953c4.069.069 5.745 1.291 6.005 5.774.522 9.016 1.829 17.992 3.001 26.96.392 3 2.246 4.417 5.566 4.4 5.239-.026 10.48.469 16.385.778l-1.102 10.495-1.875 18.726c-.44 3.74.088 6.105 4.751 6.012 4.11-.082 8.239.568 12.352.975 2.823.28 3.984 1.892 3.794 4.753-.428 6.444-.052 13.028-1.276 19.306-.986 5.059.651 6.724 5.065 7.31 1.793.238 3.566.634 6.12 1.098 0 4.189.243 8.545-.068 12.861-.319 4.43-1.27 8.811-1.77 13.233-.285 2.528-.461 5.379 3.332 5.331 13.729-.173 11.316 2.968 12.332 14.419.658 7.423-1.066 15.045-1.554 22.589-.145 2.241.28 4.519.519 7.827 2.026-1.452 2.733-2.167 3.589-2.521 1.142-.472 2.713-1.244 3.518-.81.767.414 1.114 2.221 1.081 3.4l-.917 24.539c-11.215.82-22.45.899-33.636 1.674l-43.952 3.436c-1.086-3.01-2.319-5.571-2.296-8.121.084-9.297-4.468-16.583-9.091-24.116-3.872-6.308-8.764-13.052-9.479-19.987-1.071-10.392-5.716-15.936-14.889-18.979-1.097-.364-2.16-.844-3.214-1.327-7.478-3.428-15.548-5.918-19.059-14.735-.904-2.27-3.657-3.775-5.461-5.723-2.437-2.632-4.615-5.525-7.207-7.987-2.648-2.515-5.352-5.346-8.589-6.777-4.799-2.121-10.074-3.185-15.175-4.596l-15.785-4.155c.274-12.896 1.722-25.901.54-38.662-1.647-17.783-3.457-35.526-2.554-53.352.528-10.426 2.539-20.777 3.948-31.574z"></path><path fill="#f6a200" d="M525.146 311.436c4.597-9.898 8.947-19.725 14.251-29.008 9.691-16.963 18.49-34.73 30.354-50.08 13.309-17.218 24.195-36.434 41.054-51.019 9.93-8.591 18.335-18.989 27.131-28.837 13.774-15.422 30.943-26.386 48.036-37.528 11.459-7.469 22.688-15.29 34.243-23.286 11.705 16.744 19.716 35.424 22.534 55.717 2.231 16.066 2.236 32.441 2.753 49.143-4.756 1.62-9.284 2.234-13.259 4.056-6.43 2.948-12.193 7.513-18.774 9.942-19.863 7.331-33.806 22.349-47.926 36.784-7.86 8.035-13.511 18.275-19.886 27.705-4.434 6.558-9.345 13.037-12.358 20.254-4.249 10.177-6.94 21.004-10.296 31.553-12.33.053-24.741 1.027-36.971-.049-20.259-1.783-40.227-5.567-58.755-14.69-.568-.28-1.295-.235-2.132-.658z"></path><path fill="#f7a80d" d="M1989.057 883.598c-17.093 12.845-31.061 28.695-45.863 43.67-4.254 4.304-9.184 7.963-13.249 12.424-7.216 7.92-14.117 16.137-20.943 24.401-5.59 6.768-10.554 14.078-16.422 20.582-3.614 4.005-8.367 6.995-12.673 10.358-11.08 8.653-22.264 17.007-31.144 28.292-5.788 7.356-13.617 13.108-20.553 19.556-.967.899-2.105 1.614-3.08 2.505l-37.072 34.009c-5.333 4.96-10.375 10.232-15.859 15.505-21.401-17.218-37.461-38.439-48.623-63.592 3.503-1.781 7.117-2.604 9.823-4.637 8.696-6.536 20.392-8.406 27.297-17.714.933-1.258 2.646-1.973 4.065-2.828 17.878-10.784 36.338-20.728 53.441-32.624 10.304-7.167 18.637-17.23 27.583-26.261 3.819-3.855 7.436-8.091 10.3-12.681 12.283-19.68 24.43-39.446 40.382-56.471 12.224-13.047 17.258-29.524 22.539-45.927 15.85 4.193 29.819 12.129 42.632 22.08 10.583 8.219 19.782 17.883 27.42 29.351z"></path><path fill="#ef7a72" d="M1479.461 758.907c1.872-13.734 4.268-27.394 6.525-41.076 2.218-13.45 8.26-24.279 19.615-32.423 5.099-3.657 9.667-8.701 12.999-14.038 6.646-10.643 15.183-19.135 25.083-26.631 5.888-4.459 11.817-9.176 16.547-14.774 5.906-6.99 13.974-9.306 21.753-12.365l51.48-19.549c.753 11.848.658 23.787 1.641 35.637 1.771 21.353 4.075 42.672 11.748 62.955.17.449.107.985-.019 2.158-6.945 4.134-13.865 7.337-20.437 11.143-3.935 2.279-7.752 5.096-10.869 8.384-6.011 6.343-11.063 13.624-17.286 19.727-9.096 8.92-12.791 20.684-18.181 31.587-.202.409-.072.984-.096 1.481-8.488-1.72-16.937-3.682-25.476-5.094-9.689-1.602-19.426-3.084-29.201-3.949-15.095-1.335-30.241-2.1-45.828-3.172z"></path><path fill="#e94e3b" d="M957.995 766.838c-20.337-5.467-38.791-14.947-55.703-27.254-8.2-5.967-15.451-13.238-22.958-20.37 2.969-3.504 5.564-6.772 8.598-9.563 7.085-6.518 11.283-14.914 15.8-23.153 4.933-8.996 10.345-17.743 14.966-26.892 2.642-5.231 5.547-11.01 5.691-16.611.12-4.651.194-8.932 2.577-12.742 8.52-13.621 15.483-28.026 18.775-43.704 2.11-10.049 7.888-18.774 7.81-29.825-.064-9.089 4.291-18.215 6.73-27.313 3.212-11.983 7.369-23.797 9.492-35.968 3.202-18.358 5.133-36.945 7.346-55.466l4.879-45.8c6.693.288 13.386.575 20.54 1.365.13 3.458-.41 6.407-.496 9.37l-1.136 42.595c-.597 11.552-2.067 23.058-3.084 34.59l-3.845 44.478c-.939 10.202-1.779 20.432-3.283 30.557-.96 6.464-4.46 12.646-1.136 19.383.348.706-.426 1.894-.448 2.864-.224 9.918-5.99 19.428-2.196 29.646.103.279-.033.657-.092.983l-8.446 46.205c-1.231 6.469-2.936 12.846-4.364 19.279-1.5 6.757-2.602 13.621-4.456 20.277-3.601 12.93-10.657 25.3-5.627 39.47.368 1.036.234 2.352.017 3.476l-5.949 30.123z"></path><path fill="#ea5043" d="M958.343 767.017c1.645-10.218 3.659-20.253 5.602-30.302.217-1.124.351-2.44-.017-3.476-5.03-14.17 2.026-26.539 5.627-39.47 1.854-6.656 2.956-13.52 4.456-20.277 1.428-6.433 3.133-12.81 4.364-19.279l8.446-46.205c.059-.326.196-.705.092-.983-3.794-10.218 1.972-19.728 2.196-29.646.022-.97.796-2.158.448-2.864-3.324-6.737.176-12.919 1.136-19.383 1.504-10.125 2.344-20.355 3.283-30.557l3.845-44.478c1.017-11.532 2.488-23.038 3.084-34.59.733-14.18.722-28.397 1.136-42.595.086-2.963.626-5.912.956-9.301 5.356-.48 10.714-.527 16.536-.081 2.224 15.098 1.855 29.734 1.625 44.408-.157 10.064 1.439 20.142 1.768 30.23.334 10.235-.035 20.49.116 30.733.084 5.713.789 11.418.861 17.13.054 4.289-.469 8.585-.702 12.879-.072 1.323-.138 2.659-.031 3.975l2.534 34.405-1.707 36.293-1.908 48.69c-.182 8.103.993 16.237.811 24.34-.271 12.076-1.275 24.133-1.787 36.207-.102 2.414-.101 5.283 1.06 7.219 4.327 7.22 4.463 15.215 4.736 23.103.365 10.553.088 21.128.086 31.693-11.44 2.602-22.84.688-34.106-.916-11.486-1.635-22.806-4.434-34.546-6.903z"></path><path fill="#eb5d19" d="M398.091 622.45c6.086.617 12.21 1.288 18.067 2.918 3.539.985 6.779 3.277 9.952 5.297 9.773 6.224 18.971 13.583 29.311 18.611 8.606 4.184 12.839 10.986 17.016 18.559l18.571 32.959c1.814 3.102 4.285 5.931 6.883 8.443 8.835 8.542 10.052 20.175 13.16 31.095 2.082 7.317 4.609 14.507 6.946 22.127-29.472 3.021-58.969 5.582-87.584 15.222-1.185-2.302-1.795-4.362-2.769-6.233-4.398-8.449-6.703-18.174-14.942-24.299-2.511-1.866-5.103-3.814-7.047-6.218-8.358-10.332-17.028-20.276-28.772-26.973 4.423-11.478 9.299-22.806 13.151-34.473 4.406-13.348 6.724-27.18 6.998-41.313.098-5.093.643-10.176 1.06-15.722z"></path><path fill="#e94c32" d="M981.557 392.109c-1.172 15.337-2.617 30.625-4.438 45.869-2.213 18.521-4.144 37.108-7.346 55.466-2.123 12.171-6.28 23.985-9.492 35.968-2.439 9.098-6.794 18.224-6.73 27.313.078 11.051-5.7 19.776-7.81 29.825-3.292 15.677-10.255 30.082-18.775 43.704-2.383 3.81-2.458 8.091-2.577 12.742-.144 5.6-3.049 11.38-5.691 16.611-4.621 9.149-10.033 17.896-14.966 26.892-4.517 8.239-8.715 16.635-15.8 23.153-3.034 2.791-5.629 6.06-8.735 9.255-12.197-10.595-21.071-23.644-29.301-37.24-7.608-12.569-13.282-25.962-17.637-40.37 13.303-6.889 25.873-13.878 35.311-25.315.717-.869 1.934-1.312 2.71-2.147 5.025-5.405 10.515-10.481 14.854-16.397 6.141-8.374 10.861-17.813 17.206-26.008 8.22-10.618 13.657-22.643 20.024-34.466 4.448-.626 6.729-3.21 8.114-6.89 1.455-3.866 2.644-7.895 4.609-11.492 4.397-8.05 9.641-15.659 13.708-23.86 3.354-6.761 5.511-14.116 8.203-21.206 5.727-15.082 7.277-31.248 12.521-46.578 3.704-10.828 3.138-23.116 4.478-34.753l7.56-.073z"></path><path fill="#f7a617" d="M1918.661 831.99c-4.937 16.58-9.971 33.057-22.196 46.104-15.952 17.025-28.099 36.791-40.382 56.471-2.864 4.59-6.481 8.825-10.3 12.681-8.947 9.031-17.279 19.094-27.583 26.261-17.103 11.896-35.564 21.84-53.441 32.624-1.419.856-3.132 1.571-4.065 2.828-6.904 9.308-18.6 11.178-27.297 17.714-2.705 2.033-6.319 2.856-9.874 4.281-3.413-9.821-6.916-19.583-9.36-29.602-1.533-6.284-1.474-12.957-1.665-19.913 1.913-.78 3.374-1.057 4.81-1.431 15.822-4.121 31.491-8.029 43.818-20.323 9.452-9.426 20.371-17.372 30.534-26.097 6.146-5.277 13.024-10.052 17.954-16.326 14.812-18.848 28.876-38.285 43.112-57.581 2.624-3.557 5.506-7.264 6.83-11.367 2.681-8.311 4.375-16.94 6.476-25.438 17.89.279 35.333 3.179 52.629 9.113z"></path><path fill="#ea553a" d="M1172.91 977.582c-15.775-3.127-28.215-12.377-40.227-22.43-9.005-7.537-18.43-14.605-27.071-22.532-5.07-4.651-9.143-10.443-13.361-15.955-7.647-9.994-15.291-20.007-22.456-30.345-2.361-3.407-3.792-7.72-4.696-11.829-3.119-14.183-5.848-28.453-8.651-42.704-.636-3.236-.974-6.53-1.452-10.209 15.234-2.19 30.471-3.969 46.408-5.622 2.692 5.705 4.882 11.222 6.63 16.876 2.9 9.381 7.776 17.194 15.035 24.049 7.056 6.662 13.305 14.311 19.146 22.099 9.509 12.677 23.01 19.061 36.907 25.054-1.048 7.441-2.425 14.854-3.066 22.33-.956 11.162-1.393 22.369-2.052 33.557l-1.096 17.661z"></path><path fill="#ea5453" d="M1163.123 704.036c-4.005 5.116-7.685 10.531-12.075 15.293-12.842 13.933-27.653 25.447-44.902 34.538-3.166-5.708-5.656-11.287-8.189-17.251-3.321-12.857-6.259-25.431-9.963-37.775-4.6-15.329-10.6-30.188-11.349-46.562-.314-6.871-1.275-14.287-7.114-19.644-1.047-.961-1.292-3.053-1.465-4.67l-4.092-39.927c-.554-5.245-.383-10.829-2.21-15.623-3.622-9.503-4.546-19.253-4.688-29.163-.088-6.111 1.068-12.256.782-18.344-.67-14.281-1.76-28.546-2.9-42.8-.657-8.222-1.951-16.395-2.564-24.62-.458-6.137-.285-12.322-.104-18.21.959 5.831 1.076 11.525 2.429 16.909 2.007 7.986 5.225 15.664 7.324 23.632 3.222 12.23 1.547 25.219 6.728 37.355 4.311 10.099 6.389 21.136 9.732 31.669 2.228 7.02 6.167 13.722 7.121 20.863 1.119 8.376 6.1 13.974 10.376 20.716l2.026 10.576c1.711 9.216 3.149 18.283 8.494 26.599 6.393 9.946 11.348 20.815 16.943 31.276 4.021 7.519 6.199 16.075 12.925 22.065l24.462 22.26c.556.503 1.507.571 2.274.841z"></path><path fill="#ea5b15" d="M1285.092 163.432c9.165 3.148 18.419 6.374 27.279 10.459 4.871 2.246 8.838 6.406 13.646 8.851 5.446 2.77 11.801 3.874 17.011 6.965 11.514 6.831 24.097 9.942 36.968 12.471 1.78.35 3.777.576 5.213 1.542 10.784 7.255 23.448 9.114 35.622 11.834 9.977 2.23 18.529 6.703 26.988 11.898 5.233 3.214 10.76 5.983 15.798 9.468 4.14 2.864 7.962 6.279 11.551 9.827 5.076 5.02 10.056 10.181 14.624 15.658 5.822 6.98 11.119 14.395 16.78 21.513 4.531 5.698 9.267 11.233 14.222 16.987-10.005 5.806-20.07 12.004-30.719 16.943-7.694 3.569-16.163 5.464-24.688 7.669-2.878-7.088-5.352-13.741-7.833-20.392-.802-2.15-1.244-4.55-2.498-6.396-4.548-6.7-9.712-12.999-14.011-19.847-6.672-10.627-15.34-18.93-26.063-25.376-9.357-5.625-18.367-11.824-27.644-17.587-6.436-3.997-12.902-8.006-19.659-11.405-5.123-2.577-11.107-3.536-16.046-6.37-17.187-9.863-35.13-17.887-54.031-23.767-4.403-1.37-8.953-2.267-13.436-3.382l.926-27.565z"></path><path fill="#ea504b" d="M1098 737l7.789 16.893c-15.04 9.272-31.679 15.004-49.184 17.995-9.464 1.617-19.122 2.097-29.151 3.019-.457-10.636-.18-21.211-.544-31.764-.273-7.888-.409-15.883-4.736-23.103-1.16-1.936-1.162-4.805-1.06-7.219l1.787-36.207c.182-8.103-.993-16.237-.811-24.34.365-16.236 1.253-32.461 1.908-48.69.484-12 .942-24.001 1.98-36.069 5.57 10.19 10.632 20.42 15.528 30.728 1.122 2.362 2.587 5.09 2.339 7.488-1.536 14.819 5.881 26.839 12.962 38.33 10.008 16.241 16.417 33.54 20.331 51.964 2.285 10.756 4.729 21.394 11.958 30.165L1098 737z"></path><path fill="#f6a320" d="M1865.78 822.529c-1.849 8.846-3.544 17.475-6.224 25.786-1.323 4.102-4.206 7.81-6.83 11.367l-43.112 57.581c-4.93 6.273-11.808 11.049-17.954 16.326-10.162 8.725-21.082 16.671-30.534 26.097-12.327 12.294-27.997 16.202-43.818 20.323-1.436.374-2.897.651-4.744.986-1.107-17.032-1.816-34.076-2.079-51.556 1.265-.535 2.183-.428 2.888-.766 10.596-5.072 20.8-11.059 32.586-13.273 1.69-.317 3.307-1.558 4.732-2.662l26.908-21.114c4.992-4.003 11.214-7.393 14.381-12.585 11.286-18.5 22.363-37.263 27.027-58.87l36.046 1.811c3.487.165 6.983.14 10.727.549z"></path><path fill="#ec6333" d="M318.448 922.814c-6.374-2.074-12.56-4.058-18.412-6.765-8.379-3.876-16.906-7.675-24.617-12.668-5.239-3.392-9.69-8.381-13.609-13.352-7.87-9.983-14.953-20.582-22.699-30.666-8.061-10.493-13.909-22.097-18.636-34.358-.595-1.543-1.486-2.972-2.382-4.783 6.84-1.598 13.797-3.023 20.807-4.106 18.852-2.912 36.433-9.493 53.737-17.819.697.888.889 1.555 1.292 2.051l17.921 21.896c4.14 4.939 8.06 10.191 12.862 14.412 5.67 4.984 12.185 9.007 18.334 13.447-8.937 16.282-16.422 33.178-20.696 51.31-1.638 6.951-2.402 14.107-3.903 21.403z"></path><path fill="#f49700" d="M623.467 326.903c2.893-10.618 5.584-21.446 9.833-31.623 3.013-7.217 7.924-13.696 12.358-20.254 6.375-9.43 12.026-19.67 19.886-27.705 14.12-14.434 28.063-29.453 47.926-36.784 6.581-2.429 12.344-6.994 18.774-9.942 3.975-1.822 8.503-2.436 13.186-3.592 1.947 18.557 3.248 37.15 8.307 55.686-15.453 7.931-28.853 18.092-40.46 29.996-10.417 10.683-19.109 23.111-28.013 35.175-3.238 4.388-4.888 9.948-7.262 14.973-17.803-3.987-35.767-6.498-54.535-5.931z"></path><path fill="#ea544c" d="M1097.956 736.615c-2.925-3.218-5.893-6.822-8.862-10.425-7.229-8.771-9.672-19.409-11.958-30.165-3.914-18.424-10.323-35.722-20.331-51.964-7.081-11.491-14.498-23.511-12.962-38.33.249-2.398-1.217-5.126-2.339-7.488l-15.232-31.019-3.103-34.338c-.107-1.316-.041-2.653.031-3.975.233-4.294.756-8.59.702-12.879-.072-5.713-.776-11.417-.861-17.13l-.116-30.733c-.329-10.088-1.926-20.166-1.768-30.23.23-14.674.599-29.31-1.162-44.341 9.369-.803 18.741-1.179 28.558-1.074 1.446 15.814 2.446 31.146 3.446 46.478.108 6.163-.064 12.348.393 18.485.613 8.225 1.907 16.397 2.564 24.62l2.9 42.8c.286 6.088-.869 12.234-.782 18.344.142 9.91 1.066 19.661 4.688 29.163 1.827 4.794 1.657 10.377 2.21 15.623l4.092 39.927c.172 1.617.417 3.71 1.465 4.67 5.839 5.357 6.8 12.773 7.114 19.644.749 16.374 6.749 31.233 11.349 46.562 3.704 12.344 6.642 24.918 9.963 37.775z"></path><path fill="#ec5c61" d="M1204.835 568.008c1.254 25.351-1.675 50.16-10.168 74.61-8.598-4.883-18.177-8.709-24.354-15.59-7.44-8.289-13.929-17.442-21.675-25.711-8.498-9.072-16.731-18.928-21.084-31.113-.54-1.513-1.691-2.807-2.594-4.564-4.605-9.247-7.706-18.544-7.96-29.09-.835-7.149-1.214-13.944-2.609-20.523-2.215-10.454-5.626-20.496-7.101-31.302-2.513-18.419-7.207-36.512-5.347-55.352.24-2.43-.17-4.949-.477-7.402l-4.468-34.792c2.723-.379 5.446-.757 8.585-.667 1.749 8.781 2.952 17.116 4.448 25.399 1.813 10.037 3.64 20.084 5.934 30.017 1.036 4.482 3.953 8.573 4.73 13.064 1.794 10.377 4.73 20.253 9.272 29.771 2.914 6.105 4.761 12.711 7.496 18.912 2.865 6.496 6.264 12.755 9.35 19.156 3.764 7.805 7.667 15.013 16.1 19.441 7.527 3.952 13.713 10.376 20.983 14.924 6.636 4.152 13.932 7.25 20.937 10.813z"></path><path fill="#ed676f" d="M1140.75 379.231c18.38-4.858 36.222-11.21 53.979-18.971 3.222 3.368 5.693 6.744 8.719 9.512 2.333 2.134 5.451 5.07 8.067 4.923 7.623-.429 12.363 2.688 17.309 8.215 5.531 6.18 12.744 10.854 19.224 16.184-5.121 7.193-10.461 14.241-15.323 21.606-13.691 20.739-22.99 43.255-26.782 67.926-.543 3.536-1.281 7.043-2.366 10.925-14.258-6.419-26.411-14.959-32.731-29.803-1.087-2.553-2.596-4.93-3.969-7.355-1.694-2.993-3.569-5.89-5.143-8.943-1.578-3.062-2.922-6.249-4.295-9.413-1.57-3.621-3.505-7.163-4.47-10.946-1.257-4.93-.636-10.572-2.725-15.013-5.831-12.397-7.467-25.628-9.497-38.847z"></path><path fill="#ed656e" d="M1254.103 647.439c5.325.947 10.603 2.272 15.847 3.722 5.101 1.41 10.376 2.475 15.175 4.596 3.237 1.431 5.942 4.262 8.589 6.777 2.592 2.462 4.77 5.355 7.207 7.987 1.804 1.948 4.557 3.453 5.461 5.723 3.51 8.817 11.581 11.307 19.059 14.735 1.053.483 2.116.963 3.214 1.327 9.172 3.043 13.818 8.587 14.889 18.979.715 6.935 5.607 13.679 9.479 19.987 4.623 7.533 9.175 14.819 9.091 24.116-.023 2.55 1.21 5.111 1.874 8.055-19.861 2.555-39.795 4.296-59.597 9.09l-11.596-23.203c-1.107-2.169-2.526-4.353-4.307-5.975-7.349-6.694-14.863-13.209-22.373-19.723l-17.313-14.669c-2.776-2.245-5.935-4.017-8.92-6.003l11.609-38.185c1.508-5.453 1.739-11.258 2.613-17.336z"></path><path fill="#ec6168" d="M1140.315 379.223c2.464 13.227 4.101 26.459 9.931 38.856 2.089 4.441 1.468 10.083 2.725 15.013.965 3.783 2.9 7.325 4.47 10.946 1.372 3.164 2.716 6.351 4.295 9.413 1.574 3.053 3.449 5.95 5.143 8.943 1.372 2.425 2.882 4.803 3.969 7.355 6.319 14.844 18.473 23.384 32.641 30.212.067 5.121-.501 10.201-.435 15.271l.985 38.117c.151 4.586.616 9.162.868 14.201-7.075-3.104-14.371-6.202-21.007-10.354-7.269-4.548-13.456-10.972-20.983-14.924-8.434-4.428-12.337-11.637-16.1-19.441-3.087-6.401-6.485-12.66-9.35-19.156-2.735-6.201-4.583-12.807-7.496-18.912-4.542-9.518-7.477-19.394-9.272-29.771-.777-4.491-3.694-8.581-4.73-13.064-2.294-9.933-4.121-19.98-5.934-30.017-1.496-8.283-2.699-16.618-4.036-25.335 10.349-2.461 20.704-4.511 31.054-6.582.957-.191 1.887-.515 3.264-.769z"></path><path fill="#e94c28" d="M922 537c-6.003 11.784-11.44 23.81-19.66 34.428-6.345 8.196-11.065 17.635-17.206 26.008-4.339 5.916-9.828 10.992-14.854 16.397-.776.835-1.993 1.279-2.71 2.147-9.439 11.437-22.008 18.427-35.357 24.929-4.219-10.885-6.942-22.155-7.205-33.905l-.514-49.542c7.441-2.893 14.452-5.197 21.334-7.841 1.749-.672 3.101-2.401 4.604-3.681 6.749-5.745 12.845-12.627 20.407-16.944 7.719-4.406 14.391-9.101 18.741-16.889.626-1.122 1.689-2.077 2.729-2.877 7.197-5.533 12.583-12.51 16.906-20.439.68-1.247 2.495-1.876 4.105-2.651 2.835 1.408 5.267 2.892 7.884 3.892 3.904 1.491 4.392 3.922 2.833 7.439-1.47 3.318-2.668 6.756-4.069 10.106-1.247 2.981-.435 5.242 2.413 6.544 2.805 1.282 3.125 3.14 1.813 5.601l-6.907 12.799L922 537z"></path><path fill="#eb5659" d="M1124.995 566c.868 1.396 2.018 2.691 2.559 4.203 4.353 12.185 12.586 22.041 21.084 31.113 7.746 8.269 14.235 17.422 21.675 25.711 6.176 6.881 15.756 10.707 24.174 15.932-6.073 22.316-16.675 42.446-31.058 60.937-1.074-.131-2.025-.199-2.581-.702l-24.462-22.26c-6.726-5.99-8.904-14.546-12.925-22.065-5.594-10.461-10.55-21.33-16.943-31.276-5.345-8.315-6.783-17.383-8.494-26.599-.63-3.394-1.348-6.772-1.738-10.848-.371-6.313-1.029-11.934-1.745-18.052l6.34 4.04 1.288-.675-2.143-15.385 9.454 1.208v-8.545L1124.995 566z"></path><path fill="#f5a02d" d="M1818.568 820.096c-4.224 21.679-15.302 40.442-26.587 58.942-3.167 5.192-9.389 8.582-14.381 12.585l-26.908 21.114c-1.425 1.104-3.042 2.345-4.732 2.662-11.786 2.214-21.99 8.201-32.586 13.273-.705.338-1.624.231-2.824.334a824.35 824.35 0 0 1-8.262-42.708c4.646-2.14 9.353-3.139 13.269-5.47 5.582-3.323 11.318-6.942 15.671-11.652 7.949-8.6 14.423-18.572 22.456-27.081 8.539-9.046 13.867-19.641 18.325-30.922l46.559 8.922z"></path><path fill="#eb5a57" d="M1124.96 565.639c-5.086-4.017-10.208-8.395-15.478-12.901v8.545l-9.454-1.208 2.143 15.385-1.288.675-6.34-4.04c.716 6.118 1.375 11.74 1.745 17.633-4.564-6.051-9.544-11.649-10.663-20.025-.954-7.141-4.892-13.843-7.121-20.863-3.344-10.533-5.421-21.57-9.732-31.669-5.181-12.135-3.506-25.125-6.728-37.355-2.099-7.968-5.317-15.646-7.324-23.632-1.353-5.384-1.47-11.078-2.429-16.909l-3.294-46.689a278.63 278.63 0 0 1 27.57-2.084c2.114 12.378 3.647 24.309 5.479 36.195 1.25 8.111 2.832 16.175 4.422 24.23 1.402 7.103 2.991 14.169 4.55 21.241 1.478 6.706.273 14.002 4.6 20.088 5.401 7.597 7.176 16.518 9.467 25.337 1.953 7.515 5.804 14.253 11.917 19.406.254 10.095 3.355 19.392 7.96 28.639z"></path><path fill="#ea541c" d="M911.651 810.999c-2.511 10.165-5.419 20.146-8.2 30.162-2.503 9.015-7.37 16.277-14.364 22.612-6.108 5.533-10.917 12.475-16.796 18.293-6.942 6.871-14.354 13.24-19.083 22.03-.644 1.196-2.222 1.889-3.705 2.857-2.39-7.921-4.101-15.991-6.566-23.823-5.451-17.323-12.404-33.976-23.414-48.835l21.627-21.095c3.182-3.29 5.532-7.382 8.295-11.083l10.663-14.163c9.528 4.78 18.925 9.848 28.625 14.247 7.324 3.321 15.036 5.785 22.917 8.799z"></path><path fill="#eb5d19" d="M1284.092 191.421c4.557.69 9.107 1.587 13.51 2.957 18.901 5.881 36.844 13.904 54.031 23.767 4.938 2.834 10.923 3.792 16.046 6.37 6.757 3.399 13.224 7.408 19.659 11.405l27.644 17.587c10.723 6.446 19.392 14.748 26.063 25.376 4.299 6.848 9.463 13.147 14.011 19.847 1.254 1.847 1.696 4.246 2.498 6.396l7.441 20.332c-11.685 1.754-23.379 3.133-35.533 4.037-.737-2.093-.995-3.716-1.294-5.33-3.157-17.057-14.048-30.161-23.034-44.146-3.027-4.71-7.786-8.529-12.334-11.993-9.346-7.116-19.004-13.834-28.688-20.491-6.653-4.573-13.311-9.251-20.431-13.002-8.048-4.24-16.479-7.85-24.989-11.091-11.722-4.465-23.673-8.328-35.527-12.449l.927-19.572z"></path><path fill="#eb5e24" d="M1283.09 211.415c11.928 3.699 23.88 7.562 35.602 12.027 8.509 3.241 16.941 6.852 24.989 11.091 7.12 3.751 13.778 8.429 20.431 13.002 9.684 6.657 19.342 13.375 28.688 20.491 4.548 3.463 9.307 7.283 12.334 11.993 8.986 13.985 19.877 27.089 23.034 44.146.299 1.615.557 3.237.836 5.263-13.373-.216-26.749-.839-40.564-1.923-2.935-9.681-4.597-18.92-12.286-26.152-15.577-14.651-30.4-30.102-45.564-45.193-.686-.683-1.626-1.156-2.516-1.584l-47.187-22.615 2.203-20.546z"></path><path fill="#e9511f" d="M913 486.001c-1.29.915-3.105 1.543-3.785 2.791-4.323 7.929-9.709 14.906-16.906 20.439-1.04.8-2.103 1.755-2.729 2.877-4.35 7.788-11.022 12.482-18.741 16.889-7.562 4.317-13.658 11.199-20.407 16.944-1.503 1.28-2.856 3.009-4.604 3.681-6.881 2.643-13.893 4.948-21.262 7.377-.128-11.151.202-22.302.378-33.454.03-1.892-.6-3.795-.456-6.12 13.727-1.755 23.588-9.527 33.278-17.663 2.784-2.337 6.074-4.161 8.529-6.784l29.057-31.86c1.545-1.71 3.418-3.401 4.221-5.459 5.665-14.509 11.49-28.977 16.436-43.736 2.817-8.407 4.074-17.338 6.033-26.032 5.039.714 10.078 1.427 15.536 2.629-.909 8.969-2.31 17.438-3.546 25.931-2.41 16.551-5.84 32.839-11.991 48.461L913 486.001z"></path><path fill="#ea5741" d="M1179.451 903.828c-14.224-5.787-27.726-12.171-37.235-24.849-5.841-7.787-12.09-15.436-19.146-22.099-7.259-6.854-12.136-14.667-15.035-24.049-1.748-5.654-3.938-11.171-6.254-17.033 15.099-4.009 30.213-8.629 44.958-15.533l28.367 36.36c6.09 8.015 13.124 14.75 22.72 18.375-7.404 14.472-13.599 29.412-17.48 45.244-.271 1.106-.382 2.25-.895 3.583z"></path><path fill="#ea522a" d="M913.32 486.141c2.693-7.837 5.694-15.539 8.722-23.231 6.151-15.622 9.581-31.91 11.991-48.461l3.963-25.861c7.582.317 15.168 1.031 22.748 1.797 4.171.421 8.333.928 12.877 1.596-.963 11.836-.398 24.125-4.102 34.953-5.244 15.33-6.794 31.496-12.521 46.578-2.692 7.09-4.849 14.445-8.203 21.206-4.068 8.201-9.311 15.81-13.708 23.86-1.965 3.597-3.154 7.627-4.609 11.492-1.385 3.68-3.666 6.265-8.114 6.89-1.994-1.511-3.624-3.059-5.077-4.44l6.907-12.799c1.313-2.461.993-4.318-1.813-5.601-2.849-1.302-3.66-3.563-2.413-6.544 1.401-3.35 2.599-6.788 4.069-10.106 1.558-3.517 1.071-5.948-2.833-7.439-2.617-1-5.049-2.484-7.884-3.892z"></path><path fill="#eb5e24" d="M376.574 714.118c12.053 6.538 20.723 16.481 29.081 26.814 1.945 2.404 4.537 4.352 7.047 6.218 8.24 6.125 10.544 15.85 14.942 24.299.974 1.871 1.584 3.931 2.376 6.29-7.145 3.719-14.633 6.501-21.386 10.517-9.606 5.713-18.673 12.334-28.425 18.399-3.407-3.73-6.231-7.409-9.335-10.834l-30.989-33.862c11.858-11.593 22.368-24.28 31.055-38.431 1.86-3.031 3.553-6.164 5.632-9.409z"></path><path fill="#e95514" d="M859.962 787.636c-3.409 5.037-6.981 9.745-10.516 14.481-2.763 3.701-5.113 7.792-8.295 11.083-6.885 7.118-14.186 13.834-21.65 20.755-13.222-17.677-29.417-31.711-48.178-42.878-.969-.576-2.068-.934-3.27-1.709 6.28-8.159 12.733-15.993 19.16-23.849 1.459-1.783 2.718-3.738 4.254-5.448l18.336-19.969c4.909 5.34 9.619 10.738 14.081 16.333 9.72 12.19 21.813 21.566 34.847 29.867.411.262.725.674 1.231 1.334z"></path><path fill="#eb5f2d" d="M339.582 762.088l31.293 33.733c3.104 3.425 5.928 7.104 9.024 10.979-12.885 11.619-24.548 24.139-33.899 38.704-.872 1.359-1.56 2.837-2.644 4.428-6.459-4.271-12.974-8.294-18.644-13.278-4.802-4.221-8.722-9.473-12.862-14.412l-17.921-21.896c-.403-.496-.595-1.163-.926-2.105 16.738-10.504 32.58-21.87 46.578-36.154z"></path><path fill="#f28d00" d="M678.388 332.912c1.989-5.104 3.638-10.664 6.876-15.051 8.903-12.064 17.596-24.492 28.013-35.175 11.607-11.904 25.007-22.064 40.507-29.592 4.873 11.636 9.419 23.412 13.67 35.592-5.759 4.084-11.517 7.403-16.594 11.553-4.413 3.607-8.124 8.092-12.023 12.301-5.346 5.772-10.82 11.454-15.782 17.547-3.929 4.824-7.17 10.208-10.716 15.344l-33.95-12.518z"></path><path fill="#f08369" d="M1580.181 771.427c-.191-.803-.322-1.377-.119-1.786 5.389-10.903 9.084-22.666 18.181-31.587 6.223-6.103 11.276-13.385 17.286-19.727 3.117-3.289 6.933-6.105 10.869-8.384 6.572-3.806 13.492-7.009 20.461-10.752 1.773 3.23 3.236 6.803 4.951 10.251l12.234 24.993c-1.367 1.966-2.596 3.293-3.935 4.499-7.845 7.07-16.315 13.564-23.407 21.32-6.971 7.623-12.552 16.517-18.743 24.854l-37.777-13.68z"></path><path fill="#f18b5e" d="M1618.142 785.4c6.007-8.63 11.588-17.524 18.559-25.147 7.092-7.755 15.562-14.249 23.407-21.32 1.338-1.206 2.568-2.534 3.997-4.162l28.996 33.733c1.896 2.205 4.424 3.867 6.66 6.394-6.471 7.492-12.967 14.346-19.403 21.255l-18.407 19.953c-12.958-12.409-27.485-22.567-43.809-30.706z"></path><path fill="#f49c3a" d="M1771.617 811.1c-4.066 11.354-9.394 21.949-17.933 30.995-8.032 8.509-14.507 18.481-22.456 27.081-4.353 4.71-10.089 8.329-15.671 11.652-3.915 2.331-8.623 3.331-13.318 5.069-4.298-9.927-8.255-19.998-12.1-30.743 4.741-4.381 9.924-7.582 13.882-11.904 7.345-8.021 14.094-16.603 20.864-25.131 4.897-6.168 9.428-12.626 14.123-18.955l32.61 11.936z"></path><path fill="#f08000" d="M712.601 345.675c3.283-5.381 6.524-10.765 10.453-15.589 4.962-6.093 10.435-11.774 15.782-17.547 3.899-4.21 7.61-8.695 12.023-12.301 5.078-4.15 10.836-7.469 16.636-11.19a934.12 934.12 0 0 1 23.286 35.848c-4.873 6.234-9.676 11.895-14.63 17.421l-25.195 27.801c-11.713-9.615-24.433-17.645-38.355-24.443z"></path><path fill="#ed6e04" d="M751.11 370.42c8.249-9.565 16.693-18.791 25.041-28.103 4.954-5.526 9.757-11.187 14.765-17.106 7.129 6.226 13.892 13.041 21.189 19.225 5.389 4.567 11.475 8.312 17.53 12.92-5.51 7.863-10.622 15.919-17.254 22.427-8.881 8.716-18.938 16.233-28.49 24.264-5.703-6.587-11.146-13.427-17.193-19.682-4.758-4.921-10.261-9.121-15.587-13.944z"></path><path fill="#ea541c" d="M921.823 385.544c-1.739 9.04-2.995 17.971-5.813 26.378-4.946 14.759-10.771 29.227-16.436 43.736-.804 2.058-2.676 3.749-4.221 5.459l-29.057 31.86c-2.455 2.623-5.745 4.447-8.529 6.784-9.69 8.135-19.551 15.908-33.208 17.237-1.773-9.728-3.147-19.457-4.091-29.6l36.13-16.763c.581-.267 1.046-.812 1.525-1.269 8.033-7.688 16.258-15.19 24.011-23.152 4.35-4.467 9.202-9.144 11.588-14.69 6.638-15.425 15.047-30.299 17.274-47.358 3.536.344 7.072.688 10.829 1.377z"></path><path fill="#f3944d" d="M1738.688 798.998c-4.375 6.495-8.906 12.953-13.803 19.121-6.771 8.528-13.519 17.11-20.864 25.131-3.958 4.322-9.141 7.523-13.925 11.54-8.036-13.464-16.465-26.844-27.999-38.387 5.988-6.951 12.094-13.629 18.261-20.25l19.547-20.95 38.783 23.794z"></path><path fill="#ec6168" d="M1239.583 703.142c3.282 1.805 6.441 3.576 9.217 5.821 5.88 4.755 11.599 9.713 17.313 14.669l22.373 19.723c1.781 1.622 3.2 3.806 4.307 5.975 3.843 7.532 7.477 15.171 11.194 23.136-10.764 4.67-21.532 8.973-32.69 12.982l-22.733-27.366c-2.003-2.416-4.096-4.758-6.194-7.093-3.539-3.94-6.927-8.044-10.74-11.701-2.57-2.465-5.762-4.283-8.675-6.39l16.627-29.755z"></path><path fill="#ec663e" d="M1351.006 332.839l-28.499 10.33c-.294.107-.533.367-1.194.264-11.067-19.018-27.026-32.559-44.225-44.855-4.267-3.051-8.753-5.796-13.138-8.682l9.505-24.505c10.055 4.069 19.821 8.227 29.211 13.108 3.998 2.078 7.299 5.565 10.753 8.598 3.077 2.701 5.743 5.891 8.926 8.447 4.116 3.304 9.787 5.345 12.62 9.432 6.083 8.777 10.778 18.517 16.041 27.863z"></path><path fill="#eb5e5b" d="M1222.647 733.051c3.223 1.954 6.415 3.771 8.985 6.237 3.813 3.658 7.201 7.761 10.74 11.701l6.194 7.093 22.384 27.409c-13.056 6.836-25.309 14.613-36.736 24.161l-39.323-44.7 24.494-27.846c1.072-1.224 1.974-2.598 3.264-4.056z"></path><path fill="#ea580e" d="M876.001 376.171c5.874 1.347 11.748 2.694 17.812 4.789-.81 5.265-2.687 9.791-2.639 14.296.124 11.469-4.458 20.383-12.73 27.863-2.075 1.877-3.659 4.286-5.668 6.248l-22.808 21.967c-.442.422-1.212.488-1.813.757l-23.113 10.389-9.875 4.514c-2.305-6.09-4.609-12.181-6.614-18.676 7.64-4.837 15.567-8.54 22.18-13.873 9.697-7.821 18.931-16.361 27.443-25.455 5.613-5.998 12.679-11.331 14.201-20.475.699-4.2 2.384-8.235 3.623-12.345z"></path><path fill="#e95514" d="M815.103 467.384c3.356-1.894 6.641-3.415 9.94-4.903l23.113-10.389c.6-.269 1.371-.335 1.813-.757l22.808-21.967c2.008-1.962 3.593-4.371 5.668-6.248 8.272-7.48 12.854-16.394 12.73-27.863-.049-4.505 1.828-9.031 2.847-13.956 5.427.559 10.836 1.526 16.609 2.68-1.863 17.245-10.272 32.119-16.91 47.544-2.387 5.546-7.239 10.223-11.588 14.69-7.753 7.962-15.978 15.464-24.011 23.152-.478.458-.944 1.002-1.525 1.269l-36.069 16.355c-2.076-6.402-3.783-12.81-5.425-19.607z"></path><path fill="#eb620b" d="M783.944 404.402c9.499-8.388 19.556-15.905 28.437-24.621 6.631-6.508 11.744-14.564 17.575-22.273 9.271 4.016 18.501 8.375 27.893 13.43-4.134 7.07-8.017 13.778-12.833 19.731-5.785 7.15-12.109 13.917-18.666 20.376-7.99 7.869-16.466 15.244-24.731 22.832l-17.674-29.475z"></path><path fill="#ea544c" d="M1197.986 854.686c-9.756-3.309-16.79-10.044-22.88-18.059l-28.001-36.417c8.601-5.939 17.348-11.563 26.758-17.075 1.615 1.026 2.639 1.876 3.505 2.865l26.664 30.44c3.723 4.139 7.995 7.785 12.017 11.656l-18.064 26.591z"></path><path fill="#ec6333" d="M1351.41 332.903c-5.667-9.409-10.361-19.149-16.445-27.926-2.833-4.087-8.504-6.128-12.62-9.432-3.184-2.555-5.849-5.745-8.926-8.447-3.454-3.033-6.756-6.52-10.753-8.598-9.391-4.88-19.157-9.039-29.138-13.499 1.18-5.441 2.727-10.873 4.81-16.607 11.918 4.674 24.209 8.261 34.464 14.962 14.239 9.304 29.011 18.453 39.595 32.464 2.386 3.159 5.121 6.077 7.884 8.923 6.564 6.764 10.148 14.927 11.723 24.093l-20.594 4.067z"></path><path fill="#eb5e5b" d="M1117 536.549c-6.113-4.702-9.965-11.44-11.917-18.955-2.292-8.819-4.066-17.74-9.467-25.337-4.327-6.085-3.122-13.382-4.6-20.088l-4.55-21.241c-1.59-8.054-3.172-16.118-4.422-24.23l-5.037-36.129c6.382-1.43 12.777-2.462 19.582-3.443 1.906 11.646 3.426 23.24 4.878 34.842.307 2.453.717 4.973.477 7.402-1.86 18.84 2.834 36.934 5.347 55.352 1.474 10.806 4.885 20.848 7.101 31.302 1.394 6.579 1.774 13.374 2.609 20.523z"></path><path fill="#ec644b" d="M1263.638 290.071c4.697 2.713 9.183 5.458 13.45 8.509 17.199 12.295 33.158 25.836 43.873 44.907-8.026 4.725-16.095 9.106-24.83 13.372-11.633-15.937-25.648-28.515-41.888-38.689-1.609-1.008-3.555-1.48-5.344-2.2 2.329-3.852 4.766-7.645 6.959-11.573l7.78-14.326z"></path><path fill="#eb5f2d" d="M1372.453 328.903c-2.025-9.233-5.608-17.396-12.172-24.16-2.762-2.846-5.498-5.764-7.884-8.923-10.584-14.01-25.356-23.16-39.595-32.464-10.256-6.701-22.546-10.289-34.284-15.312.325-5.246 1.005-10.444 2.027-15.863l47.529 22.394c.89.428 1.83.901 2.516 1.584l45.564 45.193c7.69 7.233 9.352 16.472 11.849 26.084-5.032.773-10.066 1.154-15.55 1.466z"></path><path fill="#e95a0f" d="M801.776 434.171c8.108-7.882 16.584-15.257 24.573-23.126 6.558-6.459 12.881-13.226 18.666-20.376 4.817-5.953 8.7-12.661 13.011-19.409 5.739 1.338 11.463 3.051 17.581 4.838-.845 4.183-2.53 8.219-3.229 12.418-1.522 9.144-8.588 14.477-14.201 20.475-8.512 9.094-17.745 17.635-27.443 25.455-6.613 5.333-14.54 9.036-22.223 13.51-2.422-4.469-4.499-8.98-6.735-13.786z"></path><path fill="#eb5e5b" d="M1248.533 316.002c2.155.688 4.101 1.159 5.71 2.168 16.24 10.174 30.255 22.752 41.532 38.727-7.166 5.736-14.641 11.319-22.562 16.731-1.16-1.277-1.684-2.585-2.615-3.46l-38.694-36.2 14.203-15.029c.803-.86 1.38-1.93 2.427-2.936z"></path><path fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg> </p> <p align="center"> <b>The crispy sentence embedding family from <a href="https://mixedbread.ai"><b>mixedbread ai</b></a>.</b> </p> # mxbai-embed-large-v1 Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages:` for query if you want to use it for retrieval. Besides that you don't need any prompt. Our model also supports [Matryoshka Representation Learning and binary quantization](https://www.mixedbread.ai/blog/binary-mrl). ## Quickstart Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages:` for query if you want to use it for retrieval. Besides that you don't need any prompt. ### sentence-transformers ``` python -m pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim from sentence_transformers.quantization import quantize_embeddings # 1. Specify preffered dimensions dimensions = 512 # 2. load model model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1", truncate_dim=dimensions) # For retrieval you need to pass this prompt. query = 'Represent this sentence for searching relevant passages: A man is eating a piece of bread' docs = [ query, "A man is eating food.", "A man is eating pasta.", "The girl is carrying a baby.", "A man is riding a horse.", ] # 2. Encode embeddings = model.encode(docs) # Optional: Quantize the embeddings binary_embeddings = quantize_embeddings(embeddings, precision="ubinary") similarities = cos_sim(embeddings[0], embeddings[1:]) print('similarities:', similarities) ``` ### Transformers ```python from typing import Dict import torch import numpy as np from transformers import AutoModel, AutoTokenizer from sentence_transformers.util import cos_sim # For retrieval you need to pass this prompt. Please find our more in our blog post. def transform_query(query: str) -> str: """ For retrieval, add the prompt for query (not for documents). """ return f'Represent this sentence for searching relevant passages: {query}' # The model works really well with cls pooling (default) but also with mean pooling. def pooling(outputs: torch.Tensor, inputs: Dict, strategy: str = 'cls') -> np.ndarray: if strategy == 'cls': outputs = outputs[:, 0] elif strategy == 'mean': outputs = torch.sum( outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"]) else: raise NotImplementedError return outputs.detach().cpu().numpy() # 1. load model model_id = 'mixedbread-ai/mxbai-embed-large-v1' tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModel.from_pretrained(model_id).cuda() docs = [ transform_query('A man is eating a piece of bread'), "A man is eating food.", "A man is eating pasta.", "The girl is carrying a baby.", "A man is riding a horse.", ] # 2. encode inputs = tokenizer(docs, padding=True, return_tensors='pt') for k, v in inputs.items(): inputs[k] = v.cuda() outputs = model(**inputs).last_hidden_state embeddings = pooling(outputs, inputs, 'cls') similarities = cos_sim(embeddings[0], embeddings[1:]) print('similarities:', similarities) ``` ### Transformers.js If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using: ```bash npm i @xenova/transformers ``` You can then use the model to compute embeddings like this: ```js import { pipeline, cos_sim } from '@xenova/transformers'; // Create a feature extraction pipeline const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-large-v1', { quantized: false, // Comment out this line to use the quantized version }); // Generate sentence embeddings const docs = [ 'Represent this sentence for searching relevant passages: A man is eating a piece of bread', 'A man is eating food.', 'A man is eating pasta.', 'The girl is carrying a baby.', 'A man is riding a horse.', ] const output = await extractor(docs, { pooling: 'cls' }); // Compute similarity scores const [source_embeddings, ...document_embeddings ] = output.tolist(); const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x)); console.log(similarities); // [0.7919578577247139, 0.6369278664248345, 0.16512018371357193, 0.3620778366720027] ``` ### Using API You can use the model via our API as follows: ```python from mixedbread_ai.client import MixedbreadAI, EncodingFormat from sklearn.metrics.pairwise import cosine_similarity import os mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}") english_sentences = [ 'What is the capital of Australia?', 'Canberra is the capital of Australia.' ] res = mxbai.embeddings( input=english_sentences, model="mixedbread-ai/mxbai-embed-large-v1", normalized=True, encoding_format=[EncodingFormat.FLOAT, EncodingFormat.UBINARY, EncodingFormat.INT_8], dimensions=512 ) encoded_embeddings = res.data[0].embedding print(res.dimensions, encoded_embeddings.ubinary, encoded_embeddings.float_, encoded_embeddings.int_8) ``` The API comes with native int8 and binary quantization support! Check out the [docs](https://mixedbread.ai/docs) for more information. ## Evaluation As of March 2024, our model archives SOTA performance for Bert-large sized models on the [MTEB](https://huggingface.co/spaces/mteb/leaderboard). It ourperforms commercial models like OpenAIs text-embedding-3-large and matches the performance of model 20x it's size like the [echo-mistral-7b](https://huggingface.co/jspringer/echo-mistral-7b-instruct-lasttoken). Our model was trained with no overlap of the MTEB data, which indicates that our model generalizes well across several domains, tasks and text length. We know there are some limitations with this model, which will be fixed in v2. | Model | Avg (56 datasets) | Classification (12 datasets) | Clustering (11 datasets) | PairClassification (3 datasets) | Reranking (4 datasets) | Retrieval (15 datasets) | STS (10 datasets) | Summarization (1 dataset) | | --------------------------------------------------------------------------------------------- | ----------------- | ---------------------------- | ------------------------ | ------------------------------- | ---------------------- | ----------------------- | ----------------- | ------------------------- | | **mxbai-embed-large-v1** | **64.68** | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85.00 | 32.71 | | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | | [mxbai-embed-2d-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-2d-large-v1) | 63.25 | 74.14 | 46.07 | 85.89 | 58.94 | 51.42 | 84.9 | 31.55 | | [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) | 62.39 | 74.12 | 43.91 | 85.15 | 55.69 | 52.81 | 82.06 | 30.08 | | [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) | 60.38 | 73.45 | 41.73 | 85.38 | 56.98 | 47.87 | 80.7 | 31.6 | | *Proprietary Models* | | | | | | | | | | [OpenAI text-embedding-3-large](https://openai.com/blog/new-embedding-models-and-api-updates) | 64.58 | 75.45 | 49.01 | 85.72 | 59.16 | 55.44 | 81.73 | 29.92 | | [Cohere embed-english-v3.0](https://txt.cohere.com/introducing-embed-v3/) | 64.47 | 76.49 | 47.43 | 85.84 | 58.01 | 55.00 | 82.62 | 30.18 | | [OpenAI text-embedding-ada-002](https://openai.com/blog/new-and-improved-embedding-model) | 60.99 | 70.93 | 45.90 | 84.89 | 56.32 | 49.25 | 80.97 | 30.80 | Please find more information in our [blog post](https://mixedbread.ai/blog/mxbai-embed-large-v1). ## Matryoshka and Binary Quantization Embeddings in their commonly used form (float arrays) have a high memory footprint when used at scale. Two approaches to solve this problem are Matryoshka Representation Learning (MRL) and (Binary) Quantization. While MRL reduces the number of dimensions of an embedding, binary quantization transforms the value of each dimension from a float32 into a lower precision (int8 or even binary). <b> The model supports both approaches! </b> You can also take it one step further, and combine both MRL and quantization. This combination of binary quantization and MRL allows you to reduce the memory usage of your embeddings significantly. This leads to much lower costs when using a vector database in particular. You can read more about the technology and its advantages in our [blog post](https://www.mixedbread.ai/blog/binary-mrl). ## Community Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat. ## License Apache 2.0 ## Citation ```bibtex @online{emb2024mxbai, title={Open Source Strikes Bread - New Fluffy Embeddings Model}, author={Sean Lee, Aamir Shakir, Darius Koenig, Julius Lipp}, year={2024}, url={https://www.mixedbread.ai/blog/mxbai-embed-large-v1}, } @article{li2023angle, title={AnglE-optimized Text Embeddings}, author={Li, Xianming and Li, Jing}, journal={arXiv preprint arXiv:2309.12871}, year={2023} } ```

stabilityai/stable-diffusion-2-1-base

stabilityai

diffusers

text-to-image

--- license: openrail++ tags: - stable-diffusion - text-to-image --- # Stable Diffusion v2-1-base Model Card This model card focuses on the model associated with the Stable Diffusion v2-1-base model. This `stable-diffusion-2-1-base` model fine-tunes [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`512-base-ema.ckpt`) with 220k extra steps taken, with `punsafe=0.98` on the same dataset. - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `v2-1_512-ema-pruned.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.ckpt). - Use it with 🧨 [`diffusers`](#examples) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install diffusers transformers accelerate scipy safetensors ``` Running the pipeline (if you don't swap the scheduler it will run with the default PNDM/PLMS scheduler, in this example we are swapping it to EulerDiscreteScheduler): ```python from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler import torch model_id = "stabilityai/stable-diffusion-2-1-base" scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` **Notes**: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed) # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. We currently provide the following checkpoints, for various versions: ### Version 2.1 - `512-base-ema.ckpt`: Fine-tuned on `512-base-ema.ckpt` 2.0 with 220k extra steps taken, with `punsafe=0.98` on the same dataset. - `768-v-ema.ckpt`: Resumed from `768-v-ema.ckpt` 2.0 with an additional 55k steps on the same dataset (`punsafe=0.1`), and then fine-tuned for another 155k extra steps with `punsafe=0.98`. ### Version 2.0 - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`. - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset. - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama). - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752). In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 1 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 200000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

albert/albert-large-v2

albert

transformers

fill-mask

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # ALBERT Large v2 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1909.11942) and first released in [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference between english and English. Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ALBERT model as inputs. ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. This is the second version of the large model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks. This model has the following configuration: - 24 repeating layers - 128 embedding dimension - 1024 hidden dimension - 16 attention heads - 17M parameters ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-large-v2') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "score":0.05816134437918663, "token":12807, "token_str":"▁modeling" }, { "sequence":"[CLS] hello i'm a modelling model.[SEP]", "score":0.03748830780386925, "token":23089, "token_str":"▁modelling" }, { "sequence":"[CLS] hello i'm a model model.[SEP]", "score":0.033725276589393616, "token":1061, "token_str":"▁model" }, { "sequence":"[CLS] hello i'm a runway model.[SEP]", "score":0.017313428223133087, "token":8014, "token_str":"▁runway" }, { "sequence":"[CLS] hello i'm a lingerie model.[SEP]", "score":0.014405295252799988, "token":29104, "token_str":"▁lingerie" } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AlbertTokenizer, AlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-large-v2') model = AlbertModel.from_pretrained("albert-large-v2") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AlbertTokenizer, TFAlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-large-v2') model = TFAlbertModel.from_pretrained("albert-large-v2") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-large-v2') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] the man worked as a chauffeur.[SEP]", "score":0.029577180743217468, "token":28744, "token_str":"▁chauffeur" }, { "sequence":"[CLS] the man worked as a janitor.[SEP]", "score":0.028865724802017212, "token":29477, "token_str":"▁janitor" }, { "sequence":"[CLS] the man worked as a shoemaker.[SEP]", "score":0.02581118606030941, "token":29024, "token_str":"▁shoemaker" }, { "sequence":"[CLS] the man worked as a blacksmith.[SEP]", "score":0.01849772222340107, "token":21238, "token_str":"▁blacksmith" }, { "sequence":"[CLS] the man worked as a lawyer.[SEP]", "score":0.01820771023631096, "token":3672, "token_str":"▁lawyer" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] the woman worked as a receptionist.[SEP]", "score":0.04604868218302727, "token":25331, "token_str":"▁receptionist" }, { "sequence":"[CLS] the woman worked as a janitor.[SEP]", "score":0.028220869600772858, "token":29477, "token_str":"▁janitor" }, { "sequence":"[CLS] the woman worked as a paramedic.[SEP]", "score":0.0261906236410141, "token":23386, "token_str":"▁paramedic" }, { "sequence":"[CLS] the woman worked as a chauffeur.[SEP]", "score":0.024797942489385605, "token":28744, "token_str":"▁chauffeur" }, { "sequence":"[CLS] the woman worked as a waitress.[SEP]", "score":0.024124596267938614, "token":13678, "token_str":"▁waitress" } ] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` ### Training The ALBERT procedure follows the BERT setup. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ## Evaluation results When fine-tuned on downstream tasks, the ALBERT models achieve the following results: | | Average | SQuAD1.1 | SQuAD2.0 | MNLI | SST-2 | RACE | |----------------|----------|----------|----------|----------|----------|----------| |V2 | |ALBERT-base |82.3 |90.2/83.2 |82.1/79.3 |84.6 |92.9 |66.8 | |ALBERT-large |85.7 |91.8/85.2 |84.9/81.8 |86.5 |94.9 |75.2 | |ALBERT-xlarge |87.9 |92.9/86.4 |87.9/84.1 |87.9 |95.4 |80.7 | |ALBERT-xxlarge |90.9 |94.6/89.1 |89.8/86.9 |90.6 |96.8 |86.8 | |V1 | |ALBERT-base |80.1 |89.3/82.3 | 80.0/77.1|81.6 |90.3 | 64.0 | |ALBERT-large |82.4 |90.6/83.9 | 82.3/79.4|83.5 |91.7 | 68.5 | |ALBERT-xlarge |85.5 |92.5/86.1 | 86.1/83.1|86.4 |92.4 | 74.8 | |ALBERT-xxlarge |91.0 |94.8/89.3 | 90.2/87.4|90.8 |96.9 | 86.5 | ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1909-11942, author = {Zhenzhong Lan and Mingda Chen and Sebastian Goodman and Kevin Gimpel and Piyush Sharma and Radu Soricut}, title = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language Representations}, journal = {CoRR}, volume = {abs/1909.11942}, year = {2019}, url = {http://arxiv.org/abs/1909.11942}, archivePrefix = {arXiv}, eprint = {1909.11942}, timestamp = {Fri, 27 Sep 2019 13:04:21 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

meta-llama/Meta-Llama-3-8B

meta-llama

transformers

text-generation

--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: other license_name: llama3 license_link: LICENSE extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Meta Llama 3" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. "Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof). 2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy) #### Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Meta Llama 3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python >>> import transformers >>> import torch >>> model_id = "meta-llama/Meta-Llama-3-8B" >>> pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto" ) >>> pipeline("Hey how are you doing today?") ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/*" --local-dir Meta-Llama-3-8B ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos

sai17/cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epoch

sai17

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epoch results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.5815593903514297 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epoch This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0369 - Accuracy: 0.5816 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:------:|:--------:|:---------------:| | 1.2196 | 1.0 | 1240 | 0.5816 | 1.0369 | | 1.2491 | 2.0 | 2481 | 0.5752 | 1.0638 | | 1.2016 | 3.0 | 3721 | 0.5792 | 1.0546 | | 1.2234 | 4.0 | 4962 | 0.5810 | 1.0560 | | 1.2298 | 5.0 | 6202 | 0.5725 | 1.0795 | | 1.287 | 6.0 | 7443 | 0.5731 | 1.0763 | | 1.2472 | 7.0 | 8683 | 0.5635 | 1.1067 | | 1.2171 | 8.0 | 9924 | 0.5775 | 1.0671 | | 1.3164 | 9.0 | 11164 | 0.5701 | 1.0681 | | 1.3019 | 10.0 | 12405 | 0.5698 | 1.0824 | | 1.2977 | 11.0 | 13645 | 0.5694 | 1.0721 | | 1.2587 | 12.0 | 14886 | 0.5704 | 1.0833 | | 1.2704 | 13.0 | 16126 | 0.5675 | 1.0934 | | 1.2604 | 14.0 | 17367 | 0.5730 | 1.0739 | | 1.2834 | 15.0 | 18607 | 0.5524 | 1.1210 | | 1.2082 | 16.0 | 19848 | 0.5611 | 1.1271 | | 1.2307 | 17.0 | 21088 | 0.5720 | 1.1013 | | 1.2136 | 18.0 | 22329 | 0.5753 | 1.1036 | | 1.2133 | 19.0 | 23569 | 0.5610 | 1.1350 | | 1.2478 | 20.0 | 24810 | 0.5676 | 1.1256 | | 1.2006 | 21.0 | 26050 | 0.5682 | 1.1288 | | 1.1934 | 22.0 | 27291 | 0.5619 | 1.1472 | | 1.2136 | 23.0 | 28531 | 0.5713 | 1.1304 | | 1.2449 | 24.0 | 29772 | 0.5581 | 1.1893 | | 1.1968 | 25.0 | 31012 | 0.5633 | 1.1754 | | 1.1582 | 26.0 | 32253 | 0.5651 | 1.1735 | | 1.1404 | 27.0 | 33493 | 0.5642 | 1.1752 | | 1.2011 | 28.0 | 34734 | 0.5538 | 1.2227 | | 1.1223 | 29.0 | 35974 | 0.5578 | 1.2200 | | 1.1427 | 30.0 | 37215 | 0.5608 | 1.2028 | | 1.1751 | 31.0 | 38455 | 0.5635 | 1.2253 | | 1.1012 | 32.0 | 39696 | 0.5543 | 1.2473 | | 1.0912 | 33.0 | 40936 | 0.5673 | 1.2370 | | 1.1085 | 34.0 | 42177 | 0.5534 | 1.2838 | | 1.099 | 35.0 | 43417 | 0.5526 | 1.2760 | | 1.1092 | 36.0 | 44658 | 0.5547 | 1.2769 | | 1.0655 | 37.0 | 45898 | 0.5534 | 1.3178 | | 1.0861 | 38.0 | 47139 | 0.5585 | 1.2943 | | 1.0917 | 39.0 | 48379 | 0.5518 | 1.3659 | | 1.0791 | 40.0 | 49620 | 0.5541 | 1.3413 | | 1.0356 | 41.0 | 50860 | 0.5495 | 1.3567 | | 1.0394 | 42.0 | 52101 | 0.5491 | 1.3648 | | 1.0096 | 43.0 | 53341 | 0.5574 | 1.3671 | | 1.0736 | 44.0 | 54582 | 0.5468 | 1.4142 | | 1.0145 | 45.0 | 55822 | 0.5462 | 1.4340 | | 1.0437 | 46.0 | 57063 | 0.5442 | 1.4734 | | 0.9771 | 47.0 | 58303 | 0.5446 | 1.4496 | | 0.9758 | 48.0 | 59544 | 0.5397 | 1.5071 | | 1.0199 | 49.0 | 60784 | 0.5437 | 1.5119 | | 0.9898 | 50.0 | 62025 | 0.5428 | 1.5066 | | 1.0139 | 51.0 | 63265 | 0.5375 | 1.5314 | | 1.0035 | 52.0 | 64506 | 0.5427 | 1.5604 | | 0.9786 | 53.0 | 65746 | 0.5396 | 1.5899 | | 0.9768 | 54.0 | 66987 | 0.5449 | 1.5642 | | 0.968 | 55.0 | 68227 | 0.5394 | 1.6056 | | 0.9254 | 56.0 | 69468 | 0.5380 | 1.6091 | | 0.9764 | 57.0 | 70680 | 0.5340 | 1.6646 | | 0.8998 | 58.0 | 71921 | 0.5323 | 1.6692 | | 0.9592 | 59.0 | 73161 | 0.5353 | 1.6395 | | 0.8722 | 60.0 | 74402 | 0.5393 | 1.6702 | | 0.888 | 61.0 | 75642 | 0.5336 | 1.6771 | | 0.872 | 62.0 | 76883 | 0.5331 | 1.6873 | | 0.9133 | 63.0 | 78123 | 0.5325 | 1.7182 | | 0.8815 | 64.0 | 79364 | 0.5310 | 1.7375 | | 0.9144 | 65.0 | 80604 | 0.5337 | 1.7263 | | 0.8712 | 66.0 | 81845 | 0.5284 | 1.7628 | | 0.8576 | 67.0 | 83080 | 1.7786 | 0.5322 | | 0.8677 | 68.0 | 84321 | 1.7947 | 0.5327 | | 0.8448 | 69.0 | 85561 | 1.8100 | 0.5314 | | 0.8102 | 70.0 | 86802 | 1.8256 | 0.5313 | | 0.8438 | 71.0 | 88042 | 1.8325 | 0.5273 | | 0.8015 | 72.0 | 89283 | 1.8564 | 0.5311 | | 0.8025 | 73.0 | 90523 | 1.8451 | 0.5342 | | 0.8295 | 74.0 | 91764 | 1.8748 | 0.5305 | | 0.8101 | 75.0 | 93004 | 1.8884 | 0.5297 | | 0.7883 | 76.0 | 94245 | 1.8777 | 0.5297 | | 0.7989 | 77.0 | 95485 | 1.9185 | 0.5262 | | 0.7791 | 78.0 | 96726 | 1.9436 | 0.5246 | | 0.7197 | 79.0 | 97966 | 1.9615 | 0.5222 | | 0.7639 | 80.0 | 99207 | 1.9567 | 0.5213 | | 0.7922 | 81.0 | 100447 | 1.9746 | 0.5248 | | 0.7874 | 82.0 | 101688 | 1.9960 | 0.5206 | | 0.8155 | 83.0 | 102928 | 2.0131 | 0.5211 | | 0.7791 | 84.0 | 104169 | 2.0559 | 0.5196 | | 0.7731 | 85.0 | 105409 | 2.0255 | 0.5192 | | 0.8018 | 86.0 | 106650 | 2.0784 | 0.5216 | | 0.777 | 87.0 | 107890 | 2.0482 | 0.5224 | | 0.7637 | 88.0 | 109131 | 2.0889 | 0.5201 | | 0.7783 | 89.0 | 110371 | 2.0663 | 0.5222 | | 0.7156 | 90.0 | 111612 | 2.0884 | 0.5200 | | 0.702 | 91.0 | 112852 | 2.1034 | 0.5215 | | 0.7136 | 92.0 | 114093 | 2.1380 | 0.5164 | | 0.6889 | 93.0 | 115333 | 2.1321 | 0.5198 | | 0.7117 | 94.0 | 116574 | 2.1175 | 0.5186 | | 0.6903 | 95.0 | 117814 | 2.1155 | 0.5187 | | 0.7334 | 96.0 | 119055 | 2.1197 | 0.5200 | | 0.6684 | 97.0 | 120295 | 2.1435 | 0.5192 | | 0.7471 | 98.0 | 121536 | 2.1403 | 0.5196 | | 0.7197 | 99.0 | 122776 | 2.1465 | 0.5182 | | 0.7026 | 99.99 | 124000 | 2.1492 | 0.5186 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.15.2

Titeiiko/OTIS-Official-Spam-Model

Titeiiko

transformers

text-classification

--- license: bsd-3-clause language: - en tags: - anti-spam - spam --- <!-- PROJECT LOGO --> <br /> <div align="center"> <a href="https://github.com/JewishLewish/Otis"> <img src="https://cdn.discordapp.com/attachments/660227834500874276/1175310288212463706/47._Big_Tree_1.png?ex=656ac400&is=65584f00&hm=0518b63834cd0da8208e79c1b019fd41e170aaa860d4812695fb8a43d43abc55&" alt="Logo" width="200" height="200"> </a> <h3 align="center">Otis Anti-Spam AI</h3> <p align="center"> Go Away Spam! <br /> <a href="https://huggingface.co/Titeiiko/OTIS-Official-Spam-Model"><strong>» » Hugging Face</strong></a> <br /> <a href="https://github.com/JewishLewish/Otis"><strong>» » Github</strong></a> <br /> <div align="center">     </div> </p> </div> <!-- TABLE OF CONTENTS --> <details> <summary>Table of Contents</summary> <ol> <li> <a href="#Quickstart">Quickstart</a> </li> <li><a href="#contributing">Contributing</a></li> <li><a href="#license">License</a></li> <li><a href="#contact">Contact</a></li> </ol> </details> <!-- Quickstar --> ## Quickstart ```py # pip install transformers from transformers import pipeline def analyze_output(input: str): pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model") x = pipe(input)[0] if x["label"] == "LABEL_0": return {"type":"Not Spam", "probability":x["score"]} else: return {"type":"Spam", "probability":x["score"]} print(analyze_output("Cһeck out our amazinɡ bооѕting serviсe ѡhere you can get to Leveӏ 3 for 3 montһs for just 20 USD.")) #Output: {'type': 'Spam', 'probability': 0.9996588230133057} ``` <!-- ABOUT THE PROJECT --> ## About The Project Introducing Otis: Otis is an advanced anti-spam artificial intelligence model designed to mitigate and combat the proliferation of unwanted and malicious content within digital communication channels. <p align="right">(<a href="#readme-top">back to top</a>)</p> <!-- CONTRIBUTING --> ## Contributing Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**. If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again! 1. Fork the Project 2. Create your Feature Branch (`git checkout -b JewishLewish/Otis`) 3. Commit your Changes (`git commit -m 'Add some AmazingFeatures'`) 4. Push to the Branch (`git push origin JewishLewish/Otis`) 5. Open a Pull Request <p align="right">(<a href="#readme-top">back to top</a>)</p> <!-- LICENSE --> ## License Distributed under the BSD-3 License. See `LICENSE.txt` for more information. <p align="right">(<a href="#readme-top">back to top</a>)</p> <!-- CONTACT --> ## Contact My Email: lenny@lunes.host <p align="right">(<a href="#readme-top">back to top</a>)</p> # OtisV1 ``` {'loss': 0.2879, 'learning_rate': 4.75e-05, 'epoch': 0.5} {'loss': 0.1868, 'learning_rate': 4.5e-05, 'epoch': 1.0} {'eval_loss': 0.23244266211986542, 'eval_runtime': 4.2923, 'eval_samples_per_second': 465.951, 'eval_steps_per_second': 58.244, 'epoch': 1.0} {'loss': 0.1462, 'learning_rate': 4.25e-05, 'epoch': 1.5} {'loss': 0.1244, 'learning_rate': 4e-05, 'epoch': 2.0} {'eval_loss': 0.19869782030582428, 'eval_runtime': 4.5759, 'eval_samples_per_second': 437.075, 'eval_steps_per_second': 54.634, 'epoch': 2.0} {'loss': 0.0962, 'learning_rate': 3.7500000000000003e-05, 'epoch': 2.5} {'loss': 0.07, 'learning_rate': 3.5e-05, 'epoch': 3.0} {'eval_loss': 0.18761929869651794, 'eval_runtime': 4.1205, 'eval_samples_per_second': 485.372, 'eval_steps_per_second': 60.672, 'epoch': 3.0} {'loss': 0.0553, 'learning_rate': 3.2500000000000004e-05, 'epoch': 3.5} {'loss': 0.0721, 'learning_rate': 3e-05, 'epoch': 4.0} {'eval_loss': 0.19852963089942932, 'eval_runtime': 3.992, 'eval_samples_per_second': 501.004, 'eval_steps_per_second': 62.625, 'epoch': 4.0} {'loss': 0.0447, 'learning_rate': 2.7500000000000004e-05, 'epoch': 4.5} {'loss': 0.0461, 'learning_rate': 2.5e-05, 'epoch': 5.0} {'eval_loss': 0.20028768479824066, 'eval_runtime': 3.8479, 'eval_samples_per_second': 519.766, 'eval_steps_per_second': 64.971, 'epoch': 5.0} {'loss': 0.0432, 'learning_rate': 2.25e-05, 'epoch': 5.5} {'loss': 0.033, 'learning_rate': 2e-05, 'epoch': 6.0} {'eval_loss': 0.20464178919792175, 'eval_runtime': 3.9167, 'eval_samples_per_second': 510.638, 'eval_steps_per_second': 63.83, 'epoch': 6.0} {'loss': 0.0356, 'learning_rate': 1.75e-05, 'epoch': 6.5} {'loss': 0.027, 'learning_rate': 1.5e-05, 'epoch': 7.0} {'eval_loss': 0.20742492377758026, 'eval_runtime': 3.9716, 'eval_samples_per_second': 503.578, 'eval_steps_per_second': 62.947, 'epoch': 7.0} {'loss': 0.0225, 'learning_rate': 1.25e-05, 'epoch': 7.5} {'loss': 0.0329, 'learning_rate': 1e-05, 'epoch': 8.0} {'eval_loss': 0.20604351162910461, 'eval_runtime': 4.0244, 'eval_samples_per_second': 496.964, 'eval_steps_per_second': 62.12, 'epoch': 8.0} {'loss': 0.0221, 'learning_rate': 7.5e-06, 'epoch': 8.5} {'loss': 0.0127, 'learning_rate': 5e-06, 'epoch': 9.0} {'eval_loss': 0.21241146326065063, 'eval_runtime': 3.9242, 'eval_samples_per_second': 509.659, 'eval_steps_per_second': 63.707, 'epoch': 9.0} {'loss': 0.0202, 'learning_rate': 2.5e-06, 'epoch': 9.5} {'loss': 0.0229, 'learning_rate': 0.0, 'epoch': 10.0} {'eval_loss': 0.2140526920557022, 'eval_runtime': 3.9546, 'eval_samples_per_second': 505.743, 'eval_steps_per_second': 63.218, 'epoch': 10.0} {'train_runtime': 667.0781, 'train_samples_per_second': 119.926, 'train_steps_per_second': 14.991, 'train_loss': 0.07010261821746826, 'epoch': 10.0} ```

timm/vit_small_patch16_224.augreg_in21k_ft_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k - imagenet-21k --- # Model card for vit_small_patch16_224.augreg_in21k_ft_in1k A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 22.1 - GMACs: 4.3 - Activations (M): 8.2 - Image size: 224 x 224 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_small_patch16_224.augreg_in21k_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_small_patch16_224.augreg_in21k_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 197, 384) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @article{steiner2021augreg, title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers}, author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas}, journal={arXiv preprint arXiv:2106.10270}, year={2021} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

oliverguhr/fullstop-punctuation-multilang-large

oliverguhr

transformers

token-classification

--- language: - en - de - fr - it - multilingual tags: - punctuation prediction - punctuation datasets: wmt/europarl license: mit widget: - text: "Ho sentito che ti sei laureata il che mi fa molto piacere" example_title: "Italian" - text: "Tous les matins vers quatre heures mon père ouvrait la porte de ma chambre" example_title: "French" - text: "Ist das eine Frage Frau Müller" example_title: "German" - text: "Yet she blushed as if with guilt when Cynthia reading her thoughts said to her one day Molly you're very glad to get rid of us are not you" example_title: "English" metrics: - f1 --- This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language. This multilanguage model was trained on the [Europarl Dataset](https://huggingface.co/datasets/wmt/europarl) provided by the [SEPP-NLG Shared Task](https://sites.google.com/view/sentence-segmentation). *Please note that this dataset consists of political speeches. Therefore the model might perform differently on texts from other domains.* The model restores the following punctuation markers: **"." "," "?" "-" ":"** ## Sample Code We provide a simple python package that allows you to process text of any length. ## Install To get started install the package from [pypi](https://pypi.org/project/deepmultilingualpunctuation/): ```bash pip install deepmultilingualpunctuation ``` ### Restore Punctuation ```python from deepmultilingualpunctuation import PunctuationModel model = PunctuationModel() text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller" result = model.restore_punctuation(text) print(result) ``` **output** > My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau Müller? ### Predict Labels ```python from deepmultilingualpunctuation import PunctuationModel model = PunctuationModel() text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller" clean_text = model.preprocess(text) labled_words = model.predict(clean_text) print(labled_words) ``` **output** > [['My', '0', 0.9999887], ['name', '0', 0.99998665], ['is', '0', 0.9998579], ['Clara', '0', 0.6752215], ['and', '0', 0.99990904], ['I', '0', 0.9999877], ['live', '0', 0.9999839], ['in', '0', 0.9999515], ['Berkeley', ',', 0.99800044], ['California', '.', 0.99534047], ['Ist', '0', 0.99998784], ['das', '0', 0.99999154], ['eine', '0', 0.9999918], ['Frage', ',', 0.99622655], ['Frau', '0', 0.9999889], ['Müller', '?', 0.99863917]] ## Results The performance differs for the single punctuation markers as hyphens and colons, in many cases, are optional and can be substituted by either a comma or a full stop. The model achieves the following F1 scores for the different languages: | Label | EN | DE | FR | IT | | ------------- | ----- | ----- | ----- | ----- | | 0 | 0.991 | 0.997 | 0.992 | 0.989 | | . | 0.948 | 0.961 | 0.945 | 0.942 | | ? | 0.890 | 0.893 | 0.871 | 0.832 | | , | 0.819 | 0.945 | 0.831 | 0.798 | | : | 0.575 | 0.652 | 0.620 | 0.588 | | - | 0.425 | 0.435 | 0.431 | 0.421 | | macro average | 0.775 | 0.814 | 0.782 | 0.762 | ## Languages ### Models | Languages | Model | | ------------------------------------------ | ------------------------------------------------------------ | | English, Italian, French and German | [oliverguhr/fullstop-punctuation-multilang-large](https://huggingface.co/oliverguhr/fullstop-punctuation-multilang-large) | | English, Italian, French, German and Dutch | [oliverguhr/fullstop-punctuation-multilingual-sonar-base](https://huggingface.co/oliverguhr/fullstop-punctuation-multilingual-sonar-base) | | Dutch | [oliverguhr/fullstop-dutch-sonar-punctuation-prediction](https://huggingface.co/oliverguhr/fullstop-dutch-sonar-punctuation-prediction) | ### Community Models | Languages | Model | | ------------------------------------------ | ------------------------------------------------------------ | |English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian| [kredor/punctuate-all](https://huggingface.co/kredor/punctuate-all) | | Catalan | [softcatala/fullstop-catalan-punctuation-prediction](https://huggingface.co/softcatala/fullstop-catalan-punctuation-prediction) | | Welsh | [techiaith/fullstop-welsh-punctuation-prediction](https://huggingface.co/techiaith/fullstop-welsh-punctuation-prediction) | You can use different models by setting the model parameter: ```python model = PunctuationModel(model = "oliverguhr/fullstop-dutch-punctuation-prediction") ``` ## Where do I find the code and can I train my own model? Yes you can! For complete code of the reareach project take a look at [this repository](https://github.com/oliverguhr/fullstop-deep-punctuation-prediction). There is also an guide on [how to fine tune this model for you data / language](https://github.com/oliverguhr/fullstop-deep-punctuation-prediction/blob/main/other_languages/readme.md). ## References ``` @article{guhr-EtAl:2021:fullstop, title={FullStop: Multilingual Deep Models for Punctuation Prediction}, author = {Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and Böhme, Hans Joachim}, booktitle = {Proceedings of the Swiss Text Analytics Conference 2021}, month = {June}, year = {2021}, address = {Winterthur, Switzerland}, publisher = {CEUR Workshop Proceedings}, url = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf} } ```

google/owlv2-base-patch16-ensemble

google

transformers

zero-shot-object-detection

--- license: apache-2.0 tags: - vision - zero-shot-object-detection inference: false --- # Model Card: OWLv2 ## Model Details The OWLv2 model (short for Open-World Localization) was proposed in [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2, like OWL-ViT, is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries. The model uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection. ### Model Date June 2023 ### Model Type The model uses a CLIP backbone with a ViT-B/16 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The CLIP backbone is trained from scratch and fine-tuned together with the box and class prediction heads with an object detection objective. ### Documents - [OWLv2 Paper](https://arxiv.org/abs/2306.09683) ### Use with Transformers ```python import requests from PIL import Image import numpy as np import torch from transformers import AutoProcessor, Owlv2ForObjectDetection from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) texts = [["a photo of a cat", "a photo of a dog"]] inputs = processor(text=texts, images=image, return_tensors="pt") # forward pass with torch.no_grad(): outputs = model(**inputs) # Note: boxes need to be visualized on the padded, unnormalized image # hence we'll set the target image sizes (height, width) based on that def get_preprocessed_image(pixel_values): pixel_values = pixel_values.squeeze().numpy() unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None] unnormalized_image = (unnormalized_image * 255).astype(np.uint8) unnormalized_image = np.moveaxis(unnormalized_image, 0, -1) unnormalized_image = Image.fromarray(unnormalized_image) return unnormalized_image unnormalized_image = get_preprocessed_image(inputs.pixel_values) target_sizes = torch.Tensor([unnormalized_image.size[::-1]]) # Convert outputs (bounding boxes and class logits) to final bounding boxes and scores results = processor.post_process_object_detection( outputs=outputs, threshold=0.2, target_sizes=target_sizes ) i = 0 # Retrieve predictions for the first image for the corresponding text queries text = texts[i] boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"] for box, score, label in zip(boxes, scores, labels): box = [round(i, 2) for i in box.tolist()] print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}") ``` ## Model Use ### Intended Use The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, text-conditioned object detection. We also hope it can be used for interdisciplinary studies of the potential impact of such models, especially in areas that commonly require identifying objects whose label is unavailable during training. #### Primary intended uses The primary intended users of these models are AI researchers. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. ## Data The CLIP backbone of the model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet. The prediction heads of OWL-ViT, along with the CLIP backbone, are fine-tuned on publicly available object detection datasets such as [COCO](https://cocodataset.org/#home) and [OpenImages](https://storage.googleapis.com/openimages/web/index.html). (to be updated for v2) ### BibTeX entry and citation info ```bibtex @misc{minderer2023scaling, title={Scaling Open-Vocabulary Object Detection}, author={Matthias Minderer and Alexey Gritsenko and Neil Houlsby}, year={2023}, eprint={2306.09683}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

microsoft/wavlm-base

microsoft

transformers

feature-extraction

--- language: - en datasets: tags: - speech inference: false --- # WavLM-Base [Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm) The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. The model was pre-trained on 960h of [Librispeech](https://huggingface.co/datasets/librispeech_asr). [Paper: WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei **Abstract** *Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.* The original model can be found under https://github.com/microsoft/unilm/tree/master/wavlm. # Usage This is an English pre-trained speech model that has to be fine-tuned on a downstream task like speech recognition or audio classification before it can be used in inference. The model was pre-trained in English and should therefore perform well only in English. The model has been shown to work well on the [SUPERB benchmark](https://superbbenchmark.org/). **Note**: The model was pre-trained on phonemes rather than characters. This means that one should make sure that the input text is converted to a sequence of phonemes before fine-tuning. ## Speech Recognition To fine-tune the model for speech recognition, see [the official speech recognition example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition). ## Speech Classification To fine-tune the model for speech classification, see [the official audio classification example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/audio-classification). ## Speaker Verification TODO ## Speaker Diarization TODO # Contribution The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten). # License The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) 

intfloat/multilingual-e5-small

intfloat

sentence-transformers

sentence-similarity

--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: multilingual-e5-small results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 36.9996434842022 - type: f1 value: 67.95453679103099 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.64882226980728 - type: ap value: 82.11942130026586 - type: f1 value: 69.87963421606715 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.8095952023988 - type: ap value: 24.46869495579561 - type: f1 value: 63.00108480037597 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 64.186295503212 - type: ap value: 15.496804690197042 - type: f1 value: 52.07153895475031 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 88.699325 - type: ap value: 85.27039559917269 - type: f1 value: 88.65556295032513 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.69799999999999 - type: f1 value: 43.73187348654165 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.245999999999995 - type: f1 value: 39.3863530637684 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.394 - type: f1 value: 39.301223469483446 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.864 - type: f1 value: 37.97974261868003 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.682 - type: f1 value: 37.07399369768313 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.504 - type: f1 value: 36.62317273874278 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 19.061 - type: map_at_10 value: 31.703 - type: map_at_100 value: 32.967 - type: map_at_1000 value: 33.001000000000005 - type: map_at_3 value: 27.466 - type: map_at_5 value: 29.564 - type: mrr_at_1 value: 19.559 - type: mrr_at_10 value: 31.874999999999996 - type: mrr_at_100 value: 33.146 - type: mrr_at_1000 value: 33.18 - type: mrr_at_3 value: 27.667 - type: mrr_at_5 value: 29.74 - type: ndcg_at_1 value: 19.061 - type: ndcg_at_10 value: 39.062999999999995 - type: ndcg_at_100 value: 45.184000000000005 - type: ndcg_at_1000 value: 46.115 - type: ndcg_at_3 value: 30.203000000000003 - type: ndcg_at_5 value: 33.953 - type: precision_at_1 value: 19.061 - type: precision_at_10 value: 6.279999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 12.706999999999999 - type: precision_at_5 value: 9.431000000000001 - type: recall_at_1 value: 19.061 - type: recall_at_10 value: 62.802 - type: recall_at_100 value: 91.323 - type: recall_at_1000 value: 98.72 - type: recall_at_3 value: 38.122 - type: recall_at_5 value: 47.155 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.22266660528253 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 30.79980849482483 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 57.8790068352054 - type: mrr value: 71.78791276436706 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.36328364043163 - type: cos_sim_spearman value: 82.26211536195868 - type: euclidean_pearson value: 80.3183865039173 - type: euclidean_spearman value: 79.88495276296132 - type: manhattan_pearson value: 80.14484480692127 - type: manhattan_spearman value: 80.39279565980743 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.0375782881002 - type: f1 value: 97.86012526096033 - type: precision value: 97.77139874739039 - type: recall value: 98.0375782881002 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (fr-en) config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 93.35241030156286 - type: f1 value: 92.66050333846944 - type: precision value: 92.3306919069631 - type: recall value: 93.35241030156286 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 94.0699688257707 - type: f1 value: 93.50236693222492 - type: precision value: 93.22791825424315 - type: recall value: 94.0699688257707 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (zh-en) config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 89.25750394944708 - type: f1 value: 88.79234684921889 - type: precision value: 88.57293312269616 - type: recall value: 89.25750394944708 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 79.41558441558442 - type: f1 value: 79.25886487487219 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.747820820329736 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 27.045143830596146 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.252999999999997 - type: map_at_10 value: 31.655916666666666 - type: map_at_100 value: 32.680749999999996 - type: map_at_1000 value: 32.79483333333334 - type: map_at_3 value: 29.43691666666666 - type: map_at_5 value: 30.717416666666665 - type: mrr_at_1 value: 28.602750000000004 - type: mrr_at_10 value: 35.56875 - type: mrr_at_100 value: 36.3595 - type: mrr_at_1000 value: 36.427749999999996 - type: mrr_at_3 value: 33.586166666666664 - type: mrr_at_5 value: 34.73641666666666 - type: ndcg_at_1 value: 28.602750000000004 - type: ndcg_at_10 value: 36.06933333333334 - type: ndcg_at_100 value: 40.70141666666667 - type: ndcg_at_1000 value: 43.24341666666667 - type: ndcg_at_3 value: 32.307916666666664 - type: ndcg_at_5 value: 34.129999999999995 - type: precision_at_1 value: 28.602750000000004 - type: precision_at_10 value: 6.097666666666667 - type: precision_at_100 value: 0.9809166666666668 - type: precision_at_1000 value: 0.13766666666666663 - type: precision_at_3 value: 14.628166666666667 - type: precision_at_5 value: 10.266916666666667 - type: recall_at_1 value: 24.252999999999997 - type: recall_at_10 value: 45.31916666666667 - type: recall_at_100 value: 66.03575000000001 - type: recall_at_1000 value: 83.94708333333334 - type: recall_at_3 value: 34.71941666666666 - type: recall_at_5 value: 39.46358333333333 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.024000000000001 - type: map_at_10 value: 15.644 - type: map_at_100 value: 17.154 - type: map_at_1000 value: 17.345 - type: map_at_3 value: 13.028 - type: map_at_5 value: 14.251 - type: mrr_at_1 value: 19.674 - type: mrr_at_10 value: 29.826999999999998 - type: mrr_at_100 value: 30.935000000000002 - type: mrr_at_1000 value: 30.987 - type: mrr_at_3 value: 26.645000000000003 - type: mrr_at_5 value: 28.29 - type: ndcg_at_1 value: 19.674 - type: ndcg_at_10 value: 22.545 - type: ndcg_at_100 value: 29.207 - type: ndcg_at_1000 value: 32.912 - type: ndcg_at_3 value: 17.952 - type: ndcg_at_5 value: 19.363 - type: precision_at_1 value: 19.674 - type: precision_at_10 value: 7.212000000000001 - type: precision_at_100 value: 1.435 - type: precision_at_1000 value: 0.212 - type: precision_at_3 value: 13.507 - type: precision_at_5 value: 10.397 - type: recall_at_1 value: 9.024000000000001 - type: recall_at_10 value: 28.077999999999996 - type: recall_at_100 value: 51.403 - type: recall_at_1000 value: 72.406 - type: recall_at_3 value: 16.768 - type: recall_at_5 value: 20.737 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.012 - type: map_at_10 value: 17.138 - type: map_at_100 value: 24.146 - type: map_at_1000 value: 25.622 - type: map_at_3 value: 12.552 - type: map_at_5 value: 14.435 - type: mrr_at_1 value: 62.25000000000001 - type: mrr_at_10 value: 71.186 - type: mrr_at_100 value: 71.504 - type: mrr_at_1000 value: 71.514 - type: mrr_at_3 value: 69.333 - type: mrr_at_5 value: 70.408 - type: ndcg_at_1 value: 49.75 - type: ndcg_at_10 value: 37.76 - type: ndcg_at_100 value: 42.071 - type: ndcg_at_1000 value: 49.309 - type: ndcg_at_3 value: 41.644 - type: ndcg_at_5 value: 39.812999999999995 - type: precision_at_1 value: 62.25000000000001 - type: precision_at_10 value: 30.15 - type: precision_at_100 value: 9.753 - type: precision_at_1000 value: 1.9189999999999998 - type: precision_at_3 value: 45.667 - type: precision_at_5 value: 39.15 - type: recall_at_1 value: 8.012 - type: recall_at_10 value: 22.599 - type: recall_at_100 value: 48.068 - type: recall_at_1000 value: 71.328 - type: recall_at_3 value: 14.043 - type: recall_at_5 value: 17.124 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 42.455 - type: f1 value: 37.59462649781862 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 58.092 - type: map_at_10 value: 69.586 - type: map_at_100 value: 69.968 - type: map_at_1000 value: 69.982 - type: map_at_3 value: 67.48100000000001 - type: map_at_5 value: 68.915 - type: mrr_at_1 value: 62.166 - type: mrr_at_10 value: 73.588 - type: mrr_at_100 value: 73.86399999999999 - type: mrr_at_1000 value: 73.868 - type: mrr_at_3 value: 71.6 - type: mrr_at_5 value: 72.99 - type: ndcg_at_1 value: 62.166 - type: ndcg_at_10 value: 75.27199999999999 - type: ndcg_at_100 value: 76.816 - type: ndcg_at_1000 value: 77.09700000000001 - type: ndcg_at_3 value: 71.36 - type: ndcg_at_5 value: 73.785 - type: precision_at_1 value: 62.166 - type: precision_at_10 value: 9.716 - type: precision_at_100 value: 1.065 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 28.278 - type: precision_at_5 value: 18.343999999999998 - type: recall_at_1 value: 58.092 - type: recall_at_10 value: 88.73400000000001 - type: recall_at_100 value: 95.195 - type: recall_at_1000 value: 97.04599999999999 - type: recall_at_3 value: 78.45 - type: recall_at_5 value: 84.316 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 16.649 - type: map_at_10 value: 26.457000000000004 - type: map_at_100 value: 28.169 - type: map_at_1000 value: 28.352 - type: map_at_3 value: 23.305 - type: map_at_5 value: 25.169000000000004 - type: mrr_at_1 value: 32.407000000000004 - type: mrr_at_10 value: 40.922 - type: mrr_at_100 value: 41.931000000000004 - type: mrr_at_1000 value: 41.983 - type: mrr_at_3 value: 38.786 - type: mrr_at_5 value: 40.205999999999996 - type: ndcg_at_1 value: 32.407000000000004 - type: ndcg_at_10 value: 33.314 - type: ndcg_at_100 value: 40.312 - type: ndcg_at_1000 value: 43.685 - type: ndcg_at_3 value: 30.391000000000002 - type: ndcg_at_5 value: 31.525 - type: precision_at_1 value: 32.407000000000004 - type: precision_at_10 value: 8.966000000000001 - type: precision_at_100 value: 1.6019999999999999 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 20.165 - type: precision_at_5 value: 14.722 - type: recall_at_1 value: 16.649 - type: recall_at_10 value: 39.117000000000004 - type: recall_at_100 value: 65.726 - type: recall_at_1000 value: 85.784 - type: recall_at_3 value: 27.914 - type: recall_at_5 value: 33.289 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 36.253 - type: map_at_10 value: 56.16799999999999 - type: map_at_100 value: 57.06099999999999 - type: map_at_1000 value: 57.126 - type: map_at_3 value: 52.644999999999996 - type: map_at_5 value: 54.909 - type: mrr_at_1 value: 72.505 - type: mrr_at_10 value: 79.66 - type: mrr_at_100 value: 79.869 - type: mrr_at_1000 value: 79.88 - type: mrr_at_3 value: 78.411 - type: mrr_at_5 value: 79.19800000000001 - type: ndcg_at_1 value: 72.505 - type: ndcg_at_10 value: 65.094 - type: ndcg_at_100 value: 68.219 - type: ndcg_at_1000 value: 69.515 - type: ndcg_at_3 value: 59.99 - type: ndcg_at_5 value: 62.909000000000006 - type: precision_at_1 value: 72.505 - type: precision_at_10 value: 13.749 - type: precision_at_100 value: 1.619 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 38.357 - type: precision_at_5 value: 25.313000000000002 - type: recall_at_1 value: 36.253 - type: recall_at_10 value: 68.744 - type: recall_at_100 value: 80.925 - type: recall_at_1000 value: 89.534 - type: recall_at_3 value: 57.535000000000004 - type: recall_at_5 value: 63.282000000000004 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 80.82239999999999 - type: ap value: 75.65895781725314 - type: f1 value: 80.75880969095746 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.624 - type: map_at_10 value: 34.075 - type: map_at_100 value: 35.229 - type: map_at_1000 value: 35.276999999999994 - type: map_at_3 value: 30.245 - type: map_at_5 value: 32.42 - type: mrr_at_1 value: 22.264 - type: mrr_at_10 value: 34.638000000000005 - type: mrr_at_100 value: 35.744 - type: mrr_at_1000 value: 35.787 - type: mrr_at_3 value: 30.891000000000002 - type: mrr_at_5 value: 33.042 - type: ndcg_at_1 value: 22.264 - type: ndcg_at_10 value: 40.991 - type: ndcg_at_100 value: 46.563 - type: ndcg_at_1000 value: 47.743 - type: ndcg_at_3 value: 33.198 - type: ndcg_at_5 value: 37.069 - type: precision_at_1 value: 22.264 - type: precision_at_10 value: 6.5089999999999995 - type: precision_at_100 value: 0.9299999999999999 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 14.216999999999999 - type: precision_at_5 value: 10.487 - type: recall_at_1 value: 21.624 - type: recall_at_10 value: 62.303 - type: recall_at_100 value: 88.124 - type: recall_at_1000 value: 97.08 - type: recall_at_3 value: 41.099999999999994 - type: recall_at_5 value: 50.381 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.06703146374831 - type: f1 value: 90.86867815863172 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (de) config: de split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 87.46970977740209 - type: f1 value: 86.36832872036588 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (es) config: es split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.26951300867245 - type: f1 value: 88.93561193959502 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (fr) config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 84.22799874725963 - type: f1 value: 84.30490069236556 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (hi) config: hi split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 86.02007888131948 - type: f1 value: 85.39376041027991 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (th) config: th split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 85.34900542495481 - type: f1 value: 85.39859673336713 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 71.078431372549 - type: f1 value: 53.45071102002276 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (de) config: de split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 65.85798816568047 - type: f1 value: 46.53112748993529 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (es) config: es split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 67.96864576384256 - type: f1 value: 45.966703022829506 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (fr) config: fr split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 61.31537738803633 - type: f1 value: 45.52601712835461 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (hi) config: hi split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 66.29616349946218 - type: f1 value: 47.24166485726613 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (th) config: th split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 67.51537070524412 - type: f1 value: 49.463476319014276 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (af) config: af split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 57.06792199058508 - type: f1 value: 54.094921857502285 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (am) config: am split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 51.960322797579025 - type: f1 value: 48.547371223370945 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ar) config: ar split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 54.425016812373904 - type: f1 value: 50.47069202054312 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (az) config: az split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 59.798251513113655 - type: f1 value: 57.05013069086648 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (bn) config: bn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 59.37794216543376 - type: f1 value: 56.3607992649805 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (cy) config: cy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 46.56018829858777 - type: f1 value: 43.87319715715134 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (da) config: da split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.9724277067922 - type: f1 value: 59.36480066245562 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (de) config: de split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.72696704774715 - type: f1 value: 59.143595966615855 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (el) config: el split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.5971755211836 - type: f1 value: 59.169445724946726 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 70.29589778076665 - type: f1 value: 67.7577001808977 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (es) config: es split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.31136516476126 - type: f1 value: 64.52032955983242 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (fa) config: fa split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.54472091459314 - type: f1 value: 61.47903120066317 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (fi) config: fi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.45595158036314 - type: f1 value: 58.0891846024637 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (fr) config: fr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.47074646940149 - type: f1 value: 62.84830858877575 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (he) config: he split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 58.046402151983855 - type: f1 value: 55.269074430533195 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (hi) config: hi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.06523201075991 - type: f1 value: 61.35339643021369 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (hu) config: hu split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 60.954942837928726 - type: f1 value: 57.07035922704846 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (hy) config: hy split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 57.404169468728995 - type: f1 value: 53.94259011839138 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (id) config: id split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.16610625420309 - type: f1 value: 61.337103431499365 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (is) config: is split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 52.262945527908535 - type: f1 value: 49.7610691598921 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (it) config: it split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.54472091459314 - type: f1 value: 63.469099018440154 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ja) config: ja split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.22797579018157 - type: f1 value: 64.89098471083001 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (jv) config: jv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 50.847343644922674 - type: f1 value: 47.8536963168393 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ka) config: ka split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 48.45326160053799 - type: f1 value: 46.370078045805556 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (km) config: km split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 42.83120376597175 - type: f1 value: 39.68948521599982 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (kn) config: kn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 57.5084061869536 - type: f1 value: 53.961876160401545 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ko) config: ko split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.7895090786819 - type: f1 value: 61.134223684676 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (lv) config: lv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 54.98991257565569 - type: f1 value: 52.579862862826296 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ml) config: ml split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.90316072629456 - type: f1 value: 58.203024538290336 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (mn) config: mn split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 57.09818426361802 - type: f1 value: 54.22718458445455 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ms) config: ms split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 58.991257565568255 - type: f1 value: 55.84892781767421 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (my) config: my split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 55.901143241425686 - type: f1 value: 52.25264332199797 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (nb) config: nb split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.96368527236047 - type: f1 value: 58.927243876153454 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (nl) config: nl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.64223268325489 - type: f1 value: 62.340453718379706 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (pl) config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.52589105581708 - type: f1 value: 61.661113187022174 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (pt) config: pt split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.84599865501009 - type: f1 value: 64.59342572873005 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ro) config: ro split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 60.81035642232684 - type: f1 value: 57.5169089806797 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ru) config: ru split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.75991930060525 - type: f1 value: 62.89531115787938 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sl) config: sl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 56.51647612642906 - type: f1 value: 54.33154780100043 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sq) config: sq split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 57.985877605917956 - type: f1 value: 54.46187524463802 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sv) config: sv split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.03026227303296 - type: f1 value: 62.34377392877748 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (sw) config: sw split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 53.567585743106925 - type: f1 value: 50.73770655983206 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ta) config: ta split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 57.2595830531271 - type: f1 value: 53.657327291708626 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (te) config: te split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 57.82784129119032 - type: f1 value: 54.82518072665301 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (th) config: th split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.06859448554137 - type: f1 value: 63.00185280500495 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (tl) config: tl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 58.91055817081371 - type: f1 value: 55.54116301224262 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (tr) config: tr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.54404841963686 - type: f1 value: 59.57650946030184 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (ur) config: ur split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 59.27706792199059 - type: f1 value: 56.50010066083435 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (vi) config: vi split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.0719569603228 - type: f1 value: 61.817075925647956 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.23806321452591 - type: f1 value: 65.24917026029749 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-TW) config: zh-TW split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 62.53530598520511 - type: f1 value: 61.71131132295768 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (af) config: af split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 63.04303967720243 - type: f1 value: 60.3950085685985 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (am) config: am split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 56.83591123066578 - type: f1 value: 54.95059828830849 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ar) config: ar split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 59.62340282447881 - type: f1 value: 59.525159996498225 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (az) config: az split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 60.85406859448555 - type: f1 value: 59.129299095681276 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (bn) config: bn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 62.76731674512441 - type: f1 value: 61.159560612627715 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (cy) config: cy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 50.181573638197705 - type: f1 value: 46.98422176289957 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (da) config: da split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.92737054472092 - type: f1 value: 67.69135611952979 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (de) config: de split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.18964357767318 - type: f1 value: 68.46106138186214 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (el) config: el split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.0712844653665 - type: f1 value: 66.75545422473901 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.4754539340955 - type: f1 value: 74.38427146553252 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (es) config: es split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.82515131136518 - type: f1 value: 69.63516462173847 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (fa) config: fa split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.70880968392737 - type: f1 value: 67.45420662567926 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (fi) config: fi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 65.95494283792871 - type: f1 value: 65.06191009049222 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (fr) config: fr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.75924680564896 - type: f1 value: 68.30833379585945 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (he) config: he split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 63.806321452589096 - type: f1 value: 63.273048243765054 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (hi) config: hi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.68997982515133 - type: f1 value: 66.54703855381324 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (hu) config: hu split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.46940147948891 - type: f1 value: 65.91017343463396 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (hy) config: hy split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 59.49899125756556 - type: f1 value: 57.90333469917769 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (id) config: id split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.9219905850706 - type: f1 value: 67.23169403762938 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (is) config: is split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 56.486213853396094 - type: f1 value: 54.85282355583758 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (it) config: it split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.04169468728985 - type: f1 value: 68.83833333320462 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ja) config: ja split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.88702084734365 - type: f1 value: 74.04474735232299 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (jv) config: jv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 56.63416274377943 - type: f1 value: 55.11332211687954 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ka) config: ka split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 52.23604572965702 - type: f1 value: 50.86529813991055 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (km) config: km split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 46.62407531943511 - type: f1 value: 43.63485467164535 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (kn) config: kn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 59.15601882985878 - type: f1 value: 57.522837510959924 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ko) config: ko split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.84532616005382 - type: f1 value: 69.60021127179697 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (lv) config: lv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 56.65770006724949 - type: f1 value: 55.84219135523227 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ml) config: ml split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.53665097511768 - type: f1 value: 65.09087787792639 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (mn) config: mn split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 59.31405514458642 - type: f1 value: 58.06135303831491 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ms) config: ms split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 64.88231338264964 - type: f1 value: 62.751099407787926 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (my) config: my split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 58.86012104909213 - type: f1 value: 56.29118323058282 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (nb) config: nb split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.37390719569602 - type: f1 value: 66.27922244885102 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (nl) config: nl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.8675184936113 - type: f1 value: 70.22146529932019 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (pl) config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.2212508406187 - type: f1 value: 67.77454802056282 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (pt) config: pt split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.18090114324143 - type: f1 value: 68.03737625431621 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ro) config: ro split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 64.65030262273034 - type: f1 value: 63.792945486912856 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ru) config: ru split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.48217888365838 - type: f1 value: 69.96028997292197 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sl) config: sl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 60.17821116341627 - type: f1 value: 59.3935969827171 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sq) config: sq split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 62.86146603900471 - type: f1 value: 60.133692735032376 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sv) config: sv split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.89441829186282 - type: f1 value: 70.03064076194089 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (sw) config: sw split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 58.15063887020847 - type: f1 value: 56.23326278499678 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ta) config: ta split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 59.43846671149966 - type: f1 value: 57.70440450281974 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (te) config: te split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 60.8507061197041 - type: f1 value: 59.22916396061171 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (th) config: th split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.65568258238063 - type: f1 value: 69.90736239440633 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (tl) config: tl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 60.8843308675185 - type: f1 value: 59.30332663713599 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (tr) config: tr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.05312710154674 - type: f1 value: 67.44024062594775 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (ur) config: ur split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 62.111634162743776 - type: f1 value: 60.89083013084519 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (vi) config: vi split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.44115669132482 - type: f1 value: 67.92227541674552 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.4687289845326 - type: f1 value: 74.16376793486025 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-TW) config: zh-TW split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.31876260928043 - type: f1 value: 68.5246745215607 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.90431696479766 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.259158476693774 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.28445330838555 - type: mrr value: 31.15758529581164 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.353 - type: map_at_10 value: 11.565 - type: map_at_100 value: 14.097000000000001 - type: map_at_1000 value: 15.354999999999999 - type: map_at_3 value: 8.749 - type: map_at_5 value: 9.974 - type: mrr_at_1 value: 42.105 - type: mrr_at_10 value: 50.589 - type: mrr_at_100 value: 51.187000000000005 - type: mrr_at_1000 value: 51.233 - type: mrr_at_3 value: 48.246 - type: mrr_at_5 value: 49.546 - type: ndcg_at_1 value: 40.402 - type: ndcg_at_10 value: 31.009999999999998 - type: ndcg_at_100 value: 28.026 - type: ndcg_at_1000 value: 36.905 - type: ndcg_at_3 value: 35.983 - type: ndcg_at_5 value: 33.764 - type: precision_at_1 value: 42.105 - type: precision_at_10 value: 22.786 - type: precision_at_100 value: 6.916 - type: precision_at_1000 value: 1.981 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 28.731 - type: recall_at_1 value: 5.353 - type: recall_at_10 value: 15.039 - type: recall_at_100 value: 27.348 - type: recall_at_1000 value: 59.453 - type: recall_at_3 value: 9.792 - type: recall_at_5 value: 11.882 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 33.852 - type: map_at_10 value: 48.924 - type: map_at_100 value: 49.854 - type: map_at_1000 value: 49.886 - type: map_at_3 value: 44.9 - type: map_at_5 value: 47.387 - type: mrr_at_1 value: 38.035999999999994 - type: mrr_at_10 value: 51.644 - type: mrr_at_100 value: 52.339 - type: mrr_at_1000 value: 52.35999999999999 - type: mrr_at_3 value: 48.421 - type: mrr_at_5 value: 50.468999999999994 - type: ndcg_at_1 value: 38.007000000000005 - type: ndcg_at_10 value: 56.293000000000006 - type: ndcg_at_100 value: 60.167 - type: ndcg_at_1000 value: 60.916000000000004 - type: ndcg_at_3 value: 48.903999999999996 - type: ndcg_at_5 value: 52.978 - type: precision_at_1 value: 38.007000000000005 - type: precision_at_10 value: 9.041 - type: precision_at_100 value: 1.1199999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 22.084 - type: precision_at_5 value: 15.608 - type: recall_at_1 value: 33.852 - type: recall_at_10 value: 75.893 - type: recall_at_100 value: 92.589 - type: recall_at_1000 value: 98.153 - type: recall_at_3 value: 56.969 - type: recall_at_5 value: 66.283 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 69.174 - type: map_at_10 value: 82.891 - type: map_at_100 value: 83.545 - type: map_at_1000 value: 83.56700000000001 - type: map_at_3 value: 79.944 - type: map_at_5 value: 81.812 - type: mrr_at_1 value: 79.67999999999999 - type: mrr_at_10 value: 86.279 - type: mrr_at_100 value: 86.39 - type: mrr_at_1000 value: 86.392 - type: mrr_at_3 value: 85.21 - type: mrr_at_5 value: 85.92999999999999 - type: ndcg_at_1 value: 79.69000000000001 - type: ndcg_at_10 value: 86.929 - type: ndcg_at_100 value: 88.266 - type: ndcg_at_1000 value: 88.428 - type: ndcg_at_3 value: 83.899 - type: ndcg_at_5 value: 85.56700000000001 - type: precision_at_1 value: 79.69000000000001 - type: precision_at_10 value: 13.161000000000001 - type: precision_at_100 value: 1.513 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.603 - type: precision_at_5 value: 24.138 - type: recall_at_1 value: 69.174 - type: recall_at_10 value: 94.529 - type: recall_at_100 value: 99.15 - type: recall_at_1000 value: 99.925 - type: recall_at_3 value: 85.86200000000001 - type: recall_at_5 value: 90.501 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 39.13064340585255 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 58.97884249325877 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 3.4680000000000004 - type: map_at_10 value: 7.865 - type: map_at_100 value: 9.332 - type: map_at_1000 value: 9.587 - type: map_at_3 value: 5.800000000000001 - type: map_at_5 value: 6.8790000000000004 - type: mrr_at_1 value: 17.0 - type: mrr_at_10 value: 25.629 - type: mrr_at_100 value: 26.806 - type: mrr_at_1000 value: 26.889000000000003 - type: mrr_at_3 value: 22.8 - type: mrr_at_5 value: 24.26 - type: ndcg_at_1 value: 17.0 - type: ndcg_at_10 value: 13.895 - type: ndcg_at_100 value: 20.491999999999997 - type: ndcg_at_1000 value: 25.759999999999998 - type: ndcg_at_3 value: 13.347999999999999 - type: ndcg_at_5 value: 11.61 - type: precision_at_1 value: 17.0 - type: precision_at_10 value: 7.090000000000001 - type: precision_at_100 value: 1.669 - type: precision_at_1000 value: 0.294 - type: precision_at_3 value: 12.3 - type: precision_at_5 value: 10.02 - type: recall_at_1 value: 3.4680000000000004 - type: recall_at_10 value: 14.363000000000001 - type: recall_at_100 value: 33.875 - type: recall_at_1000 value: 59.711999999999996 - type: recall_at_3 value: 7.483 - type: recall_at_5 value: 10.173 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.04084311714061 - type: cos_sim_spearman value: 77.51342467443078 - type: euclidean_pearson value: 80.0321166028479 - type: euclidean_spearman value: 77.29249114733226 - type: manhattan_pearson value: 80.03105964262431 - type: manhattan_spearman value: 77.22373689514794 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.1680158034387 - type: cos_sim_spearman value: 76.55983344071117 - type: euclidean_pearson value: 79.75266678300143 - type: euclidean_spearman value: 75.34516823467025 - type: manhattan_pearson value: 79.75959151517357 - type: manhattan_spearman value: 75.42330344141912 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 76.48898993209346 - type: cos_sim_spearman value: 76.96954120323366 - type: euclidean_pearson value: 76.94139109279668 - type: euclidean_spearman value: 76.85860283201711 - type: manhattan_pearson value: 76.6944095091912 - type: manhattan_spearman value: 76.61096912972553 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 77.85082366246944 - type: cos_sim_spearman value: 75.52053350101731 - type: euclidean_pearson value: 77.1165845070926 - type: euclidean_spearman value: 75.31216065884388 - type: manhattan_pearson value: 77.06193941833494 - type: manhattan_spearman value: 75.31003701700112 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.36305246526497 - type: cos_sim_spearman value: 87.11704613927415 - type: euclidean_pearson value: 86.04199125810939 - type: euclidean_spearman value: 86.51117572414263 - type: manhattan_pearson value: 86.0805106816633 - type: manhattan_spearman value: 86.52798366512229 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.18536255599724 - type: cos_sim_spearman value: 83.63377151025418 - type: euclidean_pearson value: 83.24657467993141 - type: euclidean_spearman value: 84.02751481993825 - type: manhattan_pearson value: 83.11941806582371 - type: manhattan_spearman value: 83.84251281019304 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ko-ko) config: ko-ko split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 78.95816528475514 - type: cos_sim_spearman value: 78.86607380120462 - type: euclidean_pearson value: 78.51268699230545 - type: euclidean_spearman value: 79.11649316502229 - type: manhattan_pearson value: 78.32367302808157 - type: manhattan_spearman value: 78.90277699624637 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (ar-ar) config: ar-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 72.89126914997624 - type: cos_sim_spearman value: 73.0296921832678 - type: euclidean_pearson value: 71.50385903677738 - type: euclidean_spearman value: 73.13368899716289 - type: manhattan_pearson value: 71.47421463379519 - type: manhattan_spearman value: 73.03383242946575 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-ar) config: en-ar split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 59.22923684492637 - type: cos_sim_spearman value: 57.41013211368396 - type: euclidean_pearson value: 61.21107388080905 - type: euclidean_spearman value: 60.07620768697254 - type: manhattan_pearson value: 59.60157142786555 - type: manhattan_spearman value: 59.14069604103739 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-de) config: en-de split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 76.24345978774299 - type: cos_sim_spearman value: 77.24225743830719 - type: euclidean_pearson value: 76.66226095469165 - type: euclidean_spearman value: 77.60708820493146 - type: manhattan_pearson value: 76.05303324760429 - type: manhattan_spearman value: 76.96353149912348 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.50879160160852 - type: cos_sim_spearman value: 86.43594662965224 - type: euclidean_pearson value: 86.06846012826577 - type: euclidean_spearman value: 86.02041395794136 - type: manhattan_pearson value: 86.10916255616904 - type: manhattan_spearman value: 86.07346068198953 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-tr) config: en-tr split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 58.39803698977196 - type: cos_sim_spearman value: 55.96910950423142 - type: euclidean_pearson value: 58.17941175613059 - type: euclidean_spearman value: 55.03019330522745 - type: manhattan_pearson value: 57.333358138183286 - type: manhattan_spearman value: 54.04614023149965 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (es-en) config: es-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 70.98304089637197 - type: cos_sim_spearman value: 72.44071656215888 - type: euclidean_pearson value: 72.19224359033983 - type: euclidean_spearman value: 73.89871188913025 - type: manhattan_pearson value: 71.21098311547406 - type: manhattan_spearman value: 72.93405764824821 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (es-es) config: es-es split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.99792397466308 - type: cos_sim_spearman value: 84.83824377879495 - type: euclidean_pearson value: 85.70043288694438 - type: euclidean_spearman value: 84.70627558703686 - type: manhattan_pearson value: 85.89570850150801 - type: manhattan_spearman value: 84.95806105313007 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (fr-en) config: fr-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 72.21850322994712 - type: cos_sim_spearman value: 72.28669398117248 - type: euclidean_pearson value: 73.40082510412948 - type: euclidean_spearman value: 73.0326539281865 - type: manhattan_pearson value: 71.8659633964841 - type: manhattan_spearman value: 71.57817425823303 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (it-en) config: it-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 75.80921368595645 - type: cos_sim_spearman value: 77.33209091229315 - type: euclidean_pearson value: 76.53159540154829 - type: euclidean_spearman value: 78.17960842810093 - type: manhattan_pearson value: 76.13530186637601 - type: manhattan_spearman value: 78.00701437666875 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (nl-en) config: nl-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 74.74980608267349 - type: cos_sim_spearman value: 75.37597374318821 - type: euclidean_pearson value: 74.90506081911661 - type: euclidean_spearman value: 75.30151613124521 - type: manhattan_pearson value: 74.62642745918002 - type: manhattan_spearman value: 75.18619716592303 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 59.632662289205584 - type: cos_sim_spearman value: 60.938543391610914 - type: euclidean_pearson value: 62.113200529767056 - type: euclidean_spearman value: 61.410312633261164 - type: manhattan_pearson value: 61.75494698945686 - type: manhattan_spearman value: 60.92726195322362 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de) config: de split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 45.283470551557244 - type: cos_sim_spearman value: 53.44833015864201 - type: euclidean_pearson value: 41.17892011120893 - type: euclidean_spearman value: 53.81441383126767 - type: manhattan_pearson value: 41.17482200420659 - type: manhattan_spearman value: 53.82180269276363 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (es) config: es split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 60.5069165306236 - type: cos_sim_spearman value: 66.87803259033826 - type: euclidean_pearson value: 63.5428979418236 - type: euclidean_spearman value: 66.9293576586897 - type: manhattan_pearson value: 63.59789526178922 - type: manhattan_spearman value: 66.86555009875066 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (pl) config: pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 28.23026196280264 - type: cos_sim_spearman value: 35.79397812652861 - type: euclidean_pearson value: 17.828102102767353 - type: euclidean_spearman value: 35.721501145568894 - type: manhattan_pearson value: 17.77134274219677 - type: manhattan_spearman value: 35.98107902846267 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (tr) config: tr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 56.51946541393812 - type: cos_sim_spearman value: 63.714686006214485 - type: euclidean_pearson value: 58.32104651305898 - type: euclidean_spearman value: 62.237110895702216 - type: manhattan_pearson value: 58.579416468759185 - type: manhattan_spearman value: 62.459738981727 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (ar) config: ar split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 48.76009839569795 - type: cos_sim_spearman value: 56.65188431953149 - type: euclidean_pearson value: 50.997682160915595 - type: euclidean_spearman value: 55.99910008818135 - type: manhattan_pearson value: 50.76220659606342 - type: manhattan_spearman value: 55.517347595391456 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (ru) config: ru split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 51.232731157702425 - type: cos_sim_spearman value: 59.89531877658345 - type: euclidean_pearson value: 49.937914570348376 - type: euclidean_spearman value: 60.220905659334036 - type: manhattan_pearson value: 50.00987996844193 - type: manhattan_spearman value: 60.081341480977926 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 54.717524559088005 - type: cos_sim_spearman value: 66.83570886252286 - type: euclidean_pearson value: 58.41338625505467 - type: euclidean_spearman value: 66.68991427704938 - type: manhattan_pearson value: 58.78638572916807 - type: manhattan_spearman value: 66.58684161046335 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (fr) config: fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 73.2962042954962 - type: cos_sim_spearman value: 76.58255504852025 - type: euclidean_pearson value: 75.70983192778257 - type: euclidean_spearman value: 77.4547684870542 - type: manhattan_pearson value: 75.75565853870485 - type: manhattan_spearman value: 76.90208974949428 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de-en) config: de-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 54.47396266924846 - type: cos_sim_spearman value: 56.492267162048606 - type: euclidean_pearson value: 55.998505203070195 - type: euclidean_spearman value: 56.46447012960222 - type: manhattan_pearson value: 54.873172394430995 - type: manhattan_spearman value: 56.58111534551218 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (es-en) config: es-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 69.87177267688686 - type: cos_sim_spearman value: 74.57160943395763 - type: euclidean_pearson value: 70.88330406826788 - type: euclidean_spearman value: 74.29767636038422 - type: manhattan_pearson value: 71.38245248369536 - type: manhattan_spearman value: 74.53102232732175 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (it) config: it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 72.80225656959544 - type: cos_sim_spearman value: 76.52646173725735 - type: euclidean_pearson value: 73.95710720200799 - type: euclidean_spearman value: 76.54040031984111 - type: manhattan_pearson value: 73.89679971946774 - type: manhattan_spearman value: 76.60886958161574 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (pl-en) config: pl-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 70.70844249898789 - type: cos_sim_spearman value: 72.68571783670241 - type: euclidean_pearson value: 72.38800772441031 - type: euclidean_spearman value: 72.86804422703312 - type: manhattan_pearson value: 71.29840508203515 - type: manhattan_spearman value: 71.86264441749513 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh-en) config: zh-en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 58.647478923935694 - type: cos_sim_spearman value: 63.74453623540931 - type: euclidean_pearson value: 59.60138032437505 - type: euclidean_spearman value: 63.947930832166065 - type: manhattan_pearson value: 58.59735509491861 - type: manhattan_spearman value: 62.082503844627404 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (es-it) config: es-it split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 65.8722516867162 - type: cos_sim_spearman value: 71.81208592523012 - type: euclidean_pearson value: 67.95315252165956 - type: euclidean_spearman value: 73.00749822046009 - type: manhattan_pearson value: 68.07884688638924 - type: manhattan_spearman value: 72.34210325803069 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de-fr) config: de-fr split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 54.5405814240949 - type: cos_sim_spearman value: 60.56838649023775 - type: euclidean_pearson value: 53.011731611314104 - type: euclidean_spearman value: 58.533194841668426 - type: manhattan_pearson value: 53.623067729338494 - type: manhattan_spearman value: 58.018756154446926 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (de-pl) config: de-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 13.611046866216112 - type: cos_sim_spearman value: 28.238192909158492 - type: euclidean_pearson value: 22.16189199885129 - type: euclidean_spearman value: 35.012895679076564 - type: manhattan_pearson value: 21.969771178698387 - type: manhattan_spearman value: 32.456985088607475 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (fr-pl) config: fr-pl split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 74.58077407011655 - type: cos_sim_spearman value: 84.51542547285167 - type: euclidean_pearson value: 74.64613843596234 - type: euclidean_spearman value: 84.51542547285167 - type: manhattan_pearson value: 75.15335973101396 - type: manhattan_spearman value: 84.51542547285167 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 82.0739825531578 - type: cos_sim_spearman value: 84.01057479311115 - type: euclidean_pearson value: 83.85453227433344 - type: euclidean_spearman value: 84.01630226898655 - type: manhattan_pearson value: 83.75323603028978 - type: manhattan_spearman value: 83.89677983727685 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 78.12945623123957 - type: mrr value: 93.87738713719106 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 52.983000000000004 - type: map_at_10 value: 62.946000000000005 - type: map_at_100 value: 63.514 - type: map_at_1000 value: 63.554 - type: map_at_3 value: 60.183 - type: map_at_5 value: 61.672000000000004 - type: mrr_at_1 value: 55.667 - type: mrr_at_10 value: 64.522 - type: mrr_at_100 value: 64.957 - type: mrr_at_1000 value: 64.995 - type: mrr_at_3 value: 62.388999999999996 - type: mrr_at_5 value: 63.639 - type: ndcg_at_1 value: 55.667 - type: ndcg_at_10 value: 67.704 - type: ndcg_at_100 value: 70.299 - type: ndcg_at_1000 value: 71.241 - type: ndcg_at_3 value: 62.866 - type: ndcg_at_5 value: 65.16999999999999 - type: precision_at_1 value: 55.667 - type: precision_at_10 value: 9.033 - type: precision_at_100 value: 1.053 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 24.444 - type: precision_at_5 value: 16.133 - type: recall_at_1 value: 52.983000000000004 - type: recall_at_10 value: 80.656 - type: recall_at_100 value: 92.5 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 67.744 - type: recall_at_5 value: 73.433 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72772277227723 - type: cos_sim_ap value: 92.17845897992215 - type: cos_sim_f1 value: 85.9746835443038 - type: cos_sim_precision value: 87.07692307692308 - type: cos_sim_recall value: 84.89999999999999 - type: dot_accuracy value: 99.3039603960396 - type: dot_ap value: 60.70244020124878 - type: dot_f1 value: 59.92742353551063 - type: dot_precision value: 62.21743810548978 - type: dot_recall value: 57.8 - type: euclidean_accuracy value: 99.71683168316832 - type: euclidean_ap value: 91.53997039964659 - type: euclidean_f1 value: 84.88372093023257 - type: euclidean_precision value: 90.02242152466367 - type: euclidean_recall value: 80.30000000000001 - type: manhattan_accuracy value: 99.72376237623763 - type: manhattan_ap value: 91.80756777790289 - type: manhattan_f1 value: 85.48468106479157 - type: manhattan_precision value: 85.8728557013118 - type: manhattan_recall value: 85.1 - type: max_accuracy value: 99.72772277227723 - type: max_ap value: 92.17845897992215 - type: max_f1 value: 85.9746835443038 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 53.52464042600003 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.071631948736 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.19552407604654 - type: mrr value: 49.95269130379425 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.345293033095427 - type: cos_sim_spearman value: 29.976931423258403 - type: dot_pearson value: 27.047078008958408 - type: dot_spearman value: 27.75894368380218 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22 - type: map_at_10 value: 1.706 - type: map_at_100 value: 9.634 - type: map_at_1000 value: 23.665 - type: map_at_3 value: 0.5950000000000001 - type: map_at_5 value: 0.95 - type: mrr_at_1 value: 86.0 - type: mrr_at_10 value: 91.8 - type: mrr_at_100 value: 91.8 - type: mrr_at_1000 value: 91.8 - type: mrr_at_3 value: 91.0 - type: mrr_at_5 value: 91.8 - type: ndcg_at_1 value: 80.0 - type: ndcg_at_10 value: 72.573 - type: ndcg_at_100 value: 53.954 - type: ndcg_at_1000 value: 47.760999999999996 - type: ndcg_at_3 value: 76.173 - type: ndcg_at_5 value: 75.264 - type: precision_at_1 value: 86.0 - type: precision_at_10 value: 76.4 - type: precision_at_100 value: 55.50000000000001 - type: precision_at_1000 value: 21.802 - type: precision_at_3 value: 81.333 - type: precision_at_5 value: 80.4 - type: recall_at_1 value: 0.22 - type: recall_at_10 value: 1.925 - type: recall_at_100 value: 12.762 - type: recall_at_1000 value: 44.946000000000005 - type: recall_at_3 value: 0.634 - type: recall_at_5 value: 1.051 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (sqi-eng) config: sqi-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.0 - type: f1 value: 88.55666666666666 - type: precision value: 87.46166666666667 - type: recall value: 91.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (fry-eng) config: fry-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 57.22543352601156 - type: f1 value: 51.03220478943021 - type: precision value: 48.8150289017341 - type: recall value: 57.22543352601156 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kur-eng) config: kur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 46.58536585365854 - type: f1 value: 39.66870798578116 - type: precision value: 37.416085946573745 - type: recall value: 46.58536585365854 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tur-eng) config: tur-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89.7 - type: f1 value: 86.77999999999999 - type: precision value: 85.45333333333332 - type: recall value: 89.7 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (deu-eng) config: deu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 97.39999999999999 - type: f1 value: 96.58333333333331 - type: precision value: 96.2 - type: recall value: 97.39999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nld-eng) config: nld-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.4 - type: f1 value: 90.3 - type: precision value: 89.31666666666668 - type: recall value: 92.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ron-eng) config: ron-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 86.9 - type: f1 value: 83.67190476190476 - type: precision value: 82.23333333333332 - type: recall value: 86.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ang-eng) config: ang-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 50.0 - type: f1 value: 42.23229092632078 - type: precision value: 39.851634683724235 - type: recall value: 50.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ido-eng) config: ido-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.3 - type: f1 value: 70.86190476190477 - type: precision value: 68.68777777777777 - type: recall value: 76.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (jav-eng) config: jav-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 57.073170731707314 - type: f1 value: 50.658958927251604 - type: precision value: 48.26480836236933 - type: recall value: 57.073170731707314 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (isl-eng) config: isl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 68.2 - type: f1 value: 62.156507936507936 - type: precision value: 59.84964285714286 - type: recall value: 68.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (slv-eng) config: slv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.52126366950182 - type: f1 value: 72.8496210148701 - type: precision value: 70.92171498003819 - type: recall value: 77.52126366950182 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cym-eng) config: cym-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 70.78260869565217 - type: f1 value: 65.32422360248447 - type: precision value: 63.063067367415194 - type: recall value: 70.78260869565217 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kaz-eng) config: kaz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 78.43478260869566 - type: f1 value: 73.02608695652172 - type: precision value: 70.63768115942028 - type: recall value: 78.43478260869566 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (est-eng) config: est-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 60.9 - type: f1 value: 55.309753694581275 - type: precision value: 53.130476190476195 - type: recall value: 60.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (heb-eng) config: heb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 72.89999999999999 - type: f1 value: 67.92023809523809 - type: precision value: 65.82595238095237 - type: recall value: 72.89999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (gla-eng) config: gla-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 46.80337756332931 - type: f1 value: 39.42174900558496 - type: precision value: 36.97101116280851 - type: recall value: 46.80337756332931 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mar-eng) config: mar-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89.8 - type: f1 value: 86.79 - type: precision value: 85.375 - type: recall value: 89.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (lat-eng) config: lat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 47.199999999999996 - type: f1 value: 39.95484348984349 - type: precision value: 37.561071428571424 - type: recall value: 47.199999999999996 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (bel-eng) config: bel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.8 - type: f1 value: 84.68190476190475 - type: precision value: 83.275 - type: recall value: 87.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (pms-eng) config: pms-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 48.76190476190476 - type: f1 value: 42.14965986394558 - type: precision value: 39.96743626743626 - type: recall value: 48.76190476190476 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (gle-eng) config: gle-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 66.10000000000001 - type: f1 value: 59.58580086580086 - type: precision value: 57.150238095238095 - type: recall value: 66.10000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (pes-eng) config: pes-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.3 - type: f1 value: 84.0 - type: precision value: 82.48666666666666 - type: recall value: 87.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nob-eng) config: nob-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.4 - type: f1 value: 87.79523809523809 - type: precision value: 86.6 - type: recall value: 90.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (bul-eng) config: bul-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.0 - type: f1 value: 83.81 - type: precision value: 82.36666666666666 - type: recall value: 87.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cbk-eng) config: cbk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 63.9 - type: f1 value: 57.76533189033189 - type: precision value: 55.50595238095239 - type: recall value: 63.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hun-eng) config: hun-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 76.1 - type: f1 value: 71.83690476190478 - type: precision value: 70.04928571428573 - type: recall value: 76.1 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (uig-eng) config: uig-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 66.3 - type: f1 value: 59.32626984126984 - type: precision value: 56.62535714285713 - type: recall value: 66.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (rus-eng) config: rus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.60000000000001 - type: f1 value: 87.96333333333334 - type: precision value: 86.73333333333333 - type: recall value: 90.60000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (spa-eng) config: spa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 93.10000000000001 - type: f1 value: 91.10000000000001 - type: precision value: 90.16666666666666 - type: recall value: 93.10000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hye-eng) config: hye-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.71428571428571 - type: f1 value: 82.29142600436403 - type: precision value: 80.8076626877166 - type: recall value: 85.71428571428571 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tel-eng) config: tel-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.88888888888889 - type: f1 value: 85.7834757834758 - type: precision value: 84.43732193732193 - type: recall value: 88.88888888888889 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (afr-eng) config: afr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.5 - type: f1 value: 85.67190476190476 - type: precision value: 84.43333333333332 - type: recall value: 88.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mon-eng) config: mon-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 82.72727272727273 - type: f1 value: 78.21969696969695 - type: precision value: 76.18181818181819 - type: recall value: 82.72727272727273 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (arz-eng) config: arz-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 61.0062893081761 - type: f1 value: 55.13976240391334 - type: precision value: 52.92112499659669 - type: recall value: 61.0062893081761 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hrv-eng) config: hrv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 89.5 - type: f1 value: 86.86666666666666 - type: precision value: 85.69166666666668 - type: recall value: 89.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nov-eng) config: nov-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 73.54085603112841 - type: f1 value: 68.56031128404669 - type: precision value: 66.53047989623866 - type: recall value: 73.54085603112841 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (gsw-eng) config: gsw-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 43.58974358974359 - type: f1 value: 36.45299145299145 - type: precision value: 33.81155881155882 - type: recall value: 43.58974358974359 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nds-eng) config: nds-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 59.599999999999994 - type: f1 value: 53.264689754689755 - type: precision value: 50.869166666666665 - type: recall value: 59.599999999999994 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ukr-eng) config: ukr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.2 - type: f1 value: 81.61666666666665 - type: precision value: 80.02833333333335 - type: recall value: 85.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (uzb-eng) config: uzb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 63.78504672897196 - type: f1 value: 58.00029669188548 - type: precision value: 55.815809968847354 - type: recall value: 63.78504672897196 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (lit-eng) config: lit-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 66.5 - type: f1 value: 61.518333333333345 - type: precision value: 59.622363699102834 - type: recall value: 66.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ina-eng) config: ina-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.6 - type: f1 value: 85.60222222222221 - type: precision value: 84.27916666666665 - type: recall value: 88.6 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (lfn-eng) config: lfn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 58.699999999999996 - type: f1 value: 52.732375957375965 - type: precision value: 50.63214035964035 - type: recall value: 58.699999999999996 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (zsm-eng) config: zsm-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.10000000000001 - type: f1 value: 89.99666666666667 - type: precision value: 89.03333333333333 - type: recall value: 92.10000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ita-eng) config: ita-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.10000000000001 - type: f1 value: 87.55666666666667 - type: precision value: 86.36166666666668 - type: recall value: 90.10000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cmn-eng) config: cmn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.4 - type: f1 value: 88.89000000000001 - type: precision value: 87.71166666666666 - type: recall value: 91.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (lvs-eng) config: lvs-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 65.7 - type: f1 value: 60.67427750410509 - type: precision value: 58.71785714285714 - type: recall value: 65.7 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (glg-eng) config: glg-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.39999999999999 - type: f1 value: 81.93190476190475 - type: precision value: 80.37833333333333 - type: recall value: 85.39999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ceb-eng) config: ceb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 47.833333333333336 - type: f1 value: 42.006625781625786 - type: precision value: 40.077380952380956 - type: recall value: 47.833333333333336 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (bre-eng) config: bre-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 10.4 - type: f1 value: 8.24465007215007 - type: precision value: 7.664597069597071 - type: recall value: 10.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ben-eng) config: ben-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 82.6 - type: f1 value: 77.76333333333334 - type: precision value: 75.57833333333332 - type: recall value: 82.6 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (swg-eng) config: swg-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 52.67857142857143 - type: f1 value: 44.302721088435376 - type: precision value: 41.49801587301587 - type: recall value: 52.67857142857143 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (arq-eng) config: arq-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 28.3205268935236 - type: f1 value: 22.426666605171157 - type: precision value: 20.685900116470915 - type: recall value: 28.3205268935236 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kab-eng) config: kab-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 22.7 - type: f1 value: 17.833970473970474 - type: precision value: 16.407335164835164 - type: recall value: 22.7 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (fra-eng) config: fra-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.2 - type: f1 value: 89.92999999999999 - type: precision value: 88.87 - type: recall value: 92.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (por-eng) config: por-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.4 - type: f1 value: 89.25 - type: precision value: 88.21666666666667 - type: recall value: 91.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tat-eng) config: tat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 69.19999999999999 - type: f1 value: 63.38269841269841 - type: precision value: 61.14773809523809 - type: recall value: 69.19999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (oci-eng) config: oci-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 48.8 - type: f1 value: 42.839915639915645 - type: precision value: 40.770287114845935 - type: recall value: 48.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (pol-eng) config: pol-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.8 - type: f1 value: 85.90666666666668 - type: precision value: 84.54166666666666 - type: recall value: 88.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (war-eng) config: war-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 46.6 - type: f1 value: 40.85892920804686 - type: precision value: 38.838223114604695 - type: recall value: 46.6 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (aze-eng) config: aze-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 84.0 - type: f1 value: 80.14190476190475 - type: precision value: 78.45333333333333 - type: recall value: 84.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (vie-eng) config: vie-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.5 - type: f1 value: 87.78333333333333 - type: precision value: 86.5 - type: recall value: 90.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (nno-eng) config: nno-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 74.5 - type: f1 value: 69.48397546897547 - type: precision value: 67.51869047619049 - type: recall value: 74.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cha-eng) config: cha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 32.846715328467155 - type: f1 value: 27.828177499710343 - type: precision value: 26.63451511991658 - type: recall value: 32.846715328467155 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mhr-eng) config: mhr-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.0 - type: f1 value: 6.07664116764988 - type: precision value: 5.544177607179943 - type: recall value: 8.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (dan-eng) config: dan-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.6 - type: f1 value: 84.38555555555554 - type: precision value: 82.91583333333334 - type: recall value: 87.6 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ell-eng) config: ell-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 87.5 - type: f1 value: 84.08333333333331 - type: precision value: 82.47333333333333 - type: recall value: 87.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (amh-eng) config: amh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 80.95238095238095 - type: f1 value: 76.13095238095238 - type: precision value: 74.05753968253967 - type: recall value: 80.95238095238095 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (pam-eng) config: pam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.799999999999999 - type: f1 value: 6.971422975172975 - type: precision value: 6.557814916172301 - type: recall value: 8.799999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hsb-eng) config: hsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 44.099378881987576 - type: f1 value: 37.01649742022413 - type: precision value: 34.69420618488942 - type: recall value: 44.099378881987576 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (srp-eng) config: srp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 84.3 - type: f1 value: 80.32666666666667 - type: precision value: 78.60666666666665 - type: recall value: 84.3 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (epo-eng) config: epo-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 92.5 - type: f1 value: 90.49666666666666 - type: precision value: 89.56666666666668 - type: recall value: 92.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kzj-eng) config: kzj-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 10.0 - type: f1 value: 8.268423529875141 - type: precision value: 7.878118605532398 - type: recall value: 10.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (awa-eng) config: awa-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 79.22077922077922 - type: f1 value: 74.27128427128426 - type: precision value: 72.28715728715729 - type: recall value: 79.22077922077922 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (fao-eng) config: fao-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 65.64885496183206 - type: f1 value: 58.87495456197747 - type: precision value: 55.992366412213734 - type: recall value: 65.64885496183206 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mal-eng) config: mal-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 96.06986899563319 - type: f1 value: 94.78408539543909 - type: precision value: 94.15332362930616 - type: recall value: 96.06986899563319 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ile-eng) config: ile-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.2 - type: f1 value: 71.72571428571428 - type: precision value: 69.41000000000001 - type: recall value: 77.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (bos-eng) config: bos-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 86.4406779661017 - type: f1 value: 83.2391713747646 - type: precision value: 81.74199623352166 - type: recall value: 86.4406779661017 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cor-eng) config: cor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 8.4 - type: f1 value: 6.017828743398003 - type: precision value: 5.4829865484756795 - type: recall value: 8.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (cat-eng) config: cat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.5 - type: f1 value: 79.74833333333333 - type: precision value: 78.04837662337664 - type: recall value: 83.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (eus-eng) config: eus-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 60.4 - type: f1 value: 54.467301587301584 - type: precision value: 52.23242424242424 - type: recall value: 60.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (yue-eng) config: yue-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 74.9 - type: f1 value: 69.68699134199134 - type: precision value: 67.59873015873016 - type: recall value: 74.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (swe-eng) config: swe-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.0 - type: f1 value: 84.9652380952381 - type: precision value: 83.66166666666666 - type: recall value: 88.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (dtp-eng) config: dtp-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 9.1 - type: f1 value: 7.681244588744588 - type: precision value: 7.370043290043291 - type: recall value: 9.1 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kat-eng) config: kat-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 80.9651474530831 - type: f1 value: 76.84220605132133 - type: precision value: 75.19606398962966 - type: recall value: 80.9651474530831 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (jpn-eng) config: jpn-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 86.9 - type: f1 value: 83.705 - type: precision value: 82.3120634920635 - type: recall value: 86.9 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (csb-eng) config: csb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 29.64426877470356 - type: f1 value: 23.98763072676116 - type: precision value: 22.506399397703746 - type: recall value: 29.64426877470356 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (xho-eng) config: xho-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 70.4225352112676 - type: f1 value: 62.84037558685445 - type: precision value: 59.56572769953053 - type: recall value: 70.4225352112676 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (orv-eng) config: orv-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 19.64071856287425 - type: f1 value: 15.125271011207756 - type: precision value: 13.865019261197494 - type: recall value: 19.64071856287425 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ind-eng) config: ind-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 90.2 - type: f1 value: 87.80666666666666 - type: precision value: 86.70833333333331 - type: recall value: 90.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tuk-eng) config: tuk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 23.15270935960591 - type: f1 value: 18.407224958949097 - type: precision value: 16.982385430661292 - type: recall value: 23.15270935960591 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (max-eng) config: max-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 55.98591549295775 - type: f1 value: 49.94718309859154 - type: precision value: 47.77864154624717 - type: recall value: 55.98591549295775 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (swh-eng) config: swh-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 73.07692307692307 - type: f1 value: 66.74358974358974 - type: precision value: 64.06837606837607 - type: recall value: 73.07692307692307 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (hin-eng) config: hin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 94.89999999999999 - type: f1 value: 93.25 - type: precision value: 92.43333333333332 - type: recall value: 94.89999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (dsb-eng) config: dsb-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 37.78705636743215 - type: f1 value: 31.63899658680452 - type: precision value: 29.72264397629742 - type: recall value: 37.78705636743215 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ber-eng) config: ber-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 21.6 - type: f1 value: 16.91697302697303 - type: precision value: 15.71225147075147 - type: recall value: 21.6 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tam-eng) config: tam-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 85.01628664495115 - type: f1 value: 81.38514037536838 - type: precision value: 79.83170466883823 - type: recall value: 85.01628664495115 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (slk-eng) config: slk-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.39999999999999 - type: f1 value: 79.96380952380952 - type: precision value: 78.48333333333333 - type: recall value: 83.39999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tgl-eng) config: tgl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 83.2 - type: f1 value: 79.26190476190476 - type: precision value: 77.58833333333334 - type: recall value: 83.2 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ast-eng) config: ast-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 75.59055118110236 - type: f1 value: 71.66854143232096 - type: precision value: 70.30183727034121 - type: recall value: 75.59055118110236 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (mkd-eng) config: mkd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 65.5 - type: f1 value: 59.26095238095238 - type: precision value: 56.81909090909092 - type: recall value: 65.5 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (khm-eng) config: khm-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 55.26315789473685 - type: f1 value: 47.986523325858506 - type: precision value: 45.33950006595436 - type: recall value: 55.26315789473685 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ces-eng) config: ces-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 82.89999999999999 - type: f1 value: 78.835 - type: precision value: 77.04761904761905 - type: recall value: 82.89999999999999 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tzl-eng) config: tzl-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 43.269230769230774 - type: f1 value: 36.20421245421245 - type: precision value: 33.57371794871795 - type: recall value: 43.269230769230774 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (urd-eng) config: urd-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 88.0 - type: f1 value: 84.70666666666666 - type: precision value: 83.23166666666665 - type: recall value: 88.0 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (ara-eng) config: ara-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 77.4 - type: f1 value: 72.54666666666667 - type: precision value: 70.54318181818181 - type: recall value: 77.4 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (kor-eng) config: kor-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 78.60000000000001 - type: f1 value: 74.1588888888889 - type: precision value: 72.30250000000001 - type: recall value: 78.60000000000001 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (yid-eng) config: yid-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 72.40566037735849 - type: f1 value: 66.82587328813744 - type: precision value: 64.75039308176099 - type: recall value: 72.40566037735849 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (fin-eng) config: fin-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 73.8 - type: f1 value: 68.56357142857144 - type: precision value: 66.3178822055138 - type: recall value: 73.8 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (tha-eng) config: tha-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 91.78832116788321 - type: f1 value: 89.3552311435523 - type: precision value: 88.20559610705597 - type: recall value: 91.78832116788321 - task: type: BitextMining dataset: type: mteb/tatoeba-bitext-mining name: MTEB Tatoeba (wuu-eng) config: wuu-eng split: test revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553 metrics: - type: accuracy value: 74.3 - type: f1 value: 69.05085581085581 - type: precision value: 66.955 - type: recall value: 74.3 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.896 - type: map_at_10 value: 8.993 - type: map_at_100 value: 14.133999999999999 - type: map_at_1000 value: 15.668000000000001 - type: map_at_3 value: 5.862 - type: map_at_5 value: 7.17 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 42.931000000000004 - type: mrr_at_100 value: 44.81 - type: mrr_at_1000 value: 44.81 - type: mrr_at_3 value: 38.435 - type: mrr_at_5 value: 41.701 - type: ndcg_at_1 value: 31.633 - type: ndcg_at_10 value: 21.163 - type: ndcg_at_100 value: 33.306000000000004 - type: ndcg_at_1000 value: 45.275999999999996 - type: ndcg_at_3 value: 25.685999999999996 - type: ndcg_at_5 value: 23.732 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 17.755000000000003 - type: precision_at_100 value: 6.938999999999999 - type: precision_at_1000 value: 1.48 - type: precision_at_3 value: 25.85 - type: precision_at_5 value: 23.265 - type: recall_at_1 value: 2.896 - type: recall_at_10 value: 13.333999999999998 - type: recall_at_100 value: 43.517 - type: recall_at_1000 value: 79.836 - type: recall_at_3 value: 6.306000000000001 - type: recall_at_5 value: 8.825 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.3874 - type: ap value: 13.829909072469423 - type: f1 value: 53.54534203543492 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 62.62026032823995 - type: f1 value: 62.85251350485221 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.21527881409797 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.97943613280086 - type: cos_sim_ap value: 70.75454316885921 - type: cos_sim_f1 value: 65.38274012676743 - type: cos_sim_precision value: 60.761214318078835 - type: cos_sim_recall value: 70.76517150395777 - type: dot_accuracy value: 79.0546581629612 - type: dot_ap value: 47.3197121792147 - type: dot_f1 value: 49.20106524633821 - type: dot_precision value: 42.45499808502489 - type: dot_recall value: 58.49604221635884 - type: euclidean_accuracy value: 85.08076533349228 - type: euclidean_ap value: 70.95016106374474 - type: euclidean_f1 value: 65.43987900176455 - type: euclidean_precision value: 62.64478764478765 - type: euclidean_recall value: 68.49604221635884 - type: manhattan_accuracy value: 84.93771234428085 - type: manhattan_ap value: 70.63668388755362 - type: manhattan_f1 value: 65.23895401262398 - type: manhattan_precision value: 56.946084218811485 - type: manhattan_recall value: 76.35883905013192 - type: max_accuracy value: 85.08076533349228 - type: max_ap value: 70.95016106374474 - type: max_f1 value: 65.43987900176455 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.69096130709822 - type: cos_sim_ap value: 84.82526278228542 - type: cos_sim_f1 value: 77.65485060585536 - type: cos_sim_precision value: 75.94582658619167 - type: cos_sim_recall value: 79.44256236526024 - type: dot_accuracy value: 80.97954748321496 - type: dot_ap value: 64.81642914145866 - type: dot_f1 value: 60.631996987229975 - type: dot_precision value: 54.5897293631712 - type: dot_recall value: 68.17831844779796 - type: euclidean_accuracy value: 88.6987231730508 - type: euclidean_ap value: 84.80003825477253 - type: euclidean_f1 value: 77.67194179854496 - type: euclidean_precision value: 75.7128235122094 - type: euclidean_recall value: 79.73514012935017 - type: manhattan_accuracy value: 88.62692591298949 - type: manhattan_ap value: 84.80451408255276 - type: manhattan_f1 value: 77.69888949572183 - type: manhattan_precision value: 73.70311528631622 - type: manhattan_recall value: 82.15275639051433 - type: max_accuracy value: 88.6987231730508 - type: max_ap value: 84.82526278228542 - type: max_f1 value: 77.69888949572183 language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- ## Multilingual-E5-small [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 384. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ", even for non-English texts. # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small') model = AutoModel.from_pretrained('intfloat/multilingual-e5-small') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) **First stage**: contrastive pre-training with weak supervision | Dataset | Weak supervision | # of text pairs | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | **Second stage**: supervised fine-tuning | Dataset | Language | # of text pairs | |----------------------------------------------------------------------------------------|--------------|-----------------| | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | For all labeled datasets, we only use its training set for fine-tuning. For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | | | | | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/multilingual-e5-small') input_texts = [ 'query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.

databricks/dolly-v2-3b

databricks

transformers

text-generation

--- license: mit language: - en library_name: transformers inference: false datasets: - databricks/databricks-dolly-15k --- # dolly-v2-3b Model Card ## Summary Databricks' `dolly-v2-3b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-2.8b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-3b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these larger models sizes: * [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b` * [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. **Owner**: Databricks, Inc. ## Model Overview `dolly-v2-3b` is a 2.8 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations **`dolly-v2-3b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-3b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-3b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-3b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **`databricks-dolly-15k`**: The training data on which `dolly-v2-3b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-3b` is not state of the art. It underperforms `dolly-v1-6b` in the evaluation benchmarks, which is not surprising considering it has half the number of parameters. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | | --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------| | EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 | | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 | | databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 | | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | | databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 | | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!

google/gemma-7b-it

google

transformers

text-generation

--- library_name: transformers tags: [] widget: - messages: - role: user content: How does the brain work? inference: parameters: max_new_tokens: 200 extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license license: gemma --- # Gemma Model Card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 7B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). **Resources and Technical Documentation**: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-7b-it-gg-hf) **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) **Authors**: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Fine-tuning the model You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-7b-it`. In that repository, we provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA * A script to perform SFT using FSDP on TPU devices * A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset #### Running the model on a CPU As explained below, we recommend `torch.bfloat16` as the default dtype. You can use [a different precision](#precisions) if necessary. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-7b-it", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-7b-it", device_map="auto", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` <a name="precisions"></a> #### Running the model on a GPU using different precisions The native weights of this model were exported in `bfloat16` precision. You can use `float16`, which may be faster on certain hardware, indicating the `torch_dtype` when loading the model. For convenience, the `float16` revision of the repo contains a copy of the weights already converted to that precision. You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below. * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-7b-it", device_map="auto", torch_dtype=torch.float16, revision="float16", ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Upcasting to `torch.float32`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-7b-it", device_map="auto" ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` * _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations * _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Chat Template The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "google/gemma-7b-it" dtype = torch.bfloat16 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype=dtype, ) chat = [ { "role": "user", "content": "Write a hello world program" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` At this point, the prompt contains the following text: ``` <bos><start_of_turn>user Write a hello world program<end_of_turn> <start_of_turn>model ``` As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with the `<end_of_turn>` token. You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. After the prompt is ready, generation can be performed like this: ```py inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) print(tokenizer.decode(outputs[0])) ``` ### Inputs and outputs * **Input:** Text string, such as a question, a prompt, or a document to be summarized. * **Output:** Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: | Benchmark | Metric | 2B Params | 7B Params | | ------------------------------ | ------------- | ----------- | --------- | | [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 | | [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 | | [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 | | [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 49.7 | 51.8 | | [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 | | [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 | | [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 | | [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 | | [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 | | [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 | | [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 | | [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 12.5 | 23 | | [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 | | [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 | | [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 | | [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 | | [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 | | [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 | | ------------------------------ | ------------- | ----------- | --------- | | **Average** | | **45.0** | **56.9** | ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. | Benchmark | Metric | 2B Params | 7B Params | | ------------------------------ | ------------- | ----------- | --------- | | [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 | | [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 | | [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 | | [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 | | [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 | | [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 | | [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 | | [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 | | [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 | | [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 | | ------------------------------ | ------------- | ----------- | --------- | ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.

SG161222/Realistic_Vision_V5.1_noVAE

SG161222

diffusers

text-to-image

--- license: creativeml-openrail-m --- <b>This model is available on <a href="https://www.mage.space/">Mage.Space</a> (main sponsor)</b><br> <b>You can support me directly on Boosty - https://boosty.to/sg_161222</b><br> <b>Please read this!</b><br> For version 5.1 it is recommended to use with VAE (to improve generation quality and get rid of artifacts): https://huggingface.co/stabilityai/sd-vae-ft-mse-original<br> <hr/> <b>The recommended negative prompt:</b> (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck<br> <b>OR</b><br> (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation <b>Euler A or DPM++ 2M Karras<br> CFG Scale 3,5 - 7<br> Hires. fix with 4x-UltraSharp upscaler<br> 0 Hires steps and Denoising strength 0.25-0.7<br> Upscale by 1.1-2.0</b>

Intel/dpt-hybrid-midas

Intel

transformers

depth-estimation

--- license: apache-2.0 tags: - vision - depth-estimation widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace model-index: - name: dpt-hybrid-midas results: - task: type: monocular-depth-estimation name: Monocular Depth Estimation dataset: type: MIX-6 name: MIX-6 metrics: - type: Zero-shot transfer value: 11.06 name: Zero-shot transfer config: Zero-shot transfer verified: false --- ## Model Details: DPT-Hybrid (also known as MiDaS 3.0) Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. (2021) and first released in [this repository](https://github.com/isl-org/DPT). DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation.  This repository hosts the "hybrid" version of the model as stated in the paper. DPT-Hybrid diverges from DPT by using [ViT-hybrid](https://huggingface.co/google/vit-hybrid-base-bit-384) as a backbone and taking some activations from the backbone. The model card has been written in combination by the Hugging Face team and Intel. | Model Detail | Description | | ----------- | ----------- | | Model Authors - Company | Intel | | Date | December 22, 2022 | | Version | 1 | | Type | Computer Vision - Monocular Depth Estimation | | Paper or Other Resources | [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) and [GitHub Repo](https://github.com/isl-org/DPT) | | License | Apache 2.0 | | Questions or Comments | [Community Tab](https://huggingface.co/Intel/dpt-hybrid-midas/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)| | Intended Use | Description | | ----------- | ----------- | | Primary intended uses | You can use the raw model for zero-shot monocular depth estimation. See the [model hub](https://huggingface.co/models?search=dpt) to look for fine-tuned versions on a task that interests you. | | Primary intended users | Anyone doing monocular depth estimation | | Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.| ### How to use Here is how to use this model for zero-shot depth estimation on an image: ```python from PIL import Image import numpy as np import requests import torch from transformers import DPTImageProcessor, DPTForDepthEstimation image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas") model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas", low_cpu_mem_usage=True) url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) # prepare image for the model inputs = image_processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) predicted_depth = outputs.predicted_depth # interpolate to original size prediction = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=image.size[::-1], mode="bicubic", align_corners=False, ) # visualize the prediction output = prediction.squeeze().cpu().numpy() formatted = (output * 255 / np.max(output)).astype("uint8") depth = Image.fromarray(formatted) depth.show() ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/dpt). | Factors | Description | | ----------- | ----------- | | Groups | Multiple datasets compiled together | | Instrumentation | - | | Environment | Inference completed on Intel Xeon Platinum 8280 CPU @ 2.70GHz with 8 physical cores and an NVIDIA RTX 2080 GPU. | | Card Prompts | Model deployment on alternate hardware and software will change model performance | | Metrics | Description | | ----------- | ----------- | | Model performance measures | Zero-shot Transfer | | Decision thresholds | - | | Approaches to uncertainty and variability | - | | Training and Evaluation Data | Description | | ----------- | ----------- | | Datasets | The dataset is called MIX 6, and contains around 1.4M images. The model was initialized with ImageNet-pretrained weights.| | Motivation | To build a robust monocular depth prediction network | | Preprocessing | "We resize the image such that the longer side is 384 pixels and train on random square crops of size 384. ... We perform random horizontal flips for data augmentation." See [Ranftl et al. (2021)](https://arxiv.org/abs/2103.13413) for more details. | ## Quantitative Analyses | Model | Training set | DIW WHDR | ETH3D AbsRel | Sintel AbsRel | KITTI δ>1.25 | NYU δ>1.25 | TUM δ>1.25 | | --- | --- | --- | --- | --- | --- | --- | --- | | DPT - Large | MIX 6 | 10.82 (-13.2%) | 0.089 (-31.2%) | 0.270 (-17.5%) | 8.46 (-64.6%) | 8.32 (-12.9%) | 9.97 (-30.3%) | | DPT - Hybrid | MIX 6 | 11.06 (-11.2%) | 0.093 (-27.6%) | 0.274 (-16.2%) | 11.56 (-51.6%) | 8.69 (-9.0%) | 10.89 (-23.2%) | | MiDaS | MIX 6 | 12.95 (+3.9%) | 0.116 (-10.5%) | 0.329 (+0.5%) | 16.08 (-32.7%) | 8.71 (-8.8%) | 12.51 (-12.5%) | MiDaS [30] | MIX 5 | 12.46 | 0.129 | 0.327 | 23.90 | 9.55 | 14.29 | | Li [22] | MD [22] | 23.15 | 0.181 | 0.385 | 36.29 | 27.52 | 29.54 | | Li [21] | MC [21] | 26.52 | 0.183 | 0.405 | 47.94 | 18.57 | 17.71 | | Wang [40] | WS [40] | 19.09 | 0.205 | 0.390 | 31.92 | 29.57 | 20.18 | | Xian [45] | RW [45] | 14.59 | 0.186 | 0.422 | 34.08 | 27.00 | 25.02 | | Casser [5] | CS [8] | 32.80 | 0.235 | 0.422 | 21.15 | 39.58 | 37.18 | Table 1. Comparison to the state of the art on monocular depth estimation. We evaluate zero-shot cross-dataset transfer according to the protocol defined in [30]. Relative performance is computed with respect to the original MiDaS model [30]. Lower is better for all metrics. ([Ranftl et al., 2021](https://arxiv.org/abs/2103.13413)) | Ethical Considerations | Description | | ----------- | ----------- | | Data | The training data come from multiple image datasets compiled together. | | Human life | The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of monocular depth image datasets. | | Mitigations | No additional risk mitigation strategies were considered during model development. | | Risks and harms | The extent of the risks involved by using the model remain unknown. | | Use cases | - | | Caveats and Recommendations | | ----------- | | Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. | ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2103-13413, author = {Ren{\'{e}} Ranftl and Alexey Bochkovskiy and Vladlen Koltun}, title = {Vision Transformers for Dense Prediction}, journal = {CoRR}, volume = {abs/2103.13413}, year = {2021}, url = {https://arxiv.org/abs/2103.13413}, eprinttype = {arXiv}, eprint = {2103.13413}, timestamp = {Wed, 07 Apr 2021 15:31:46 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2103-13413.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

google/flan-t5-large

google

transformers

text2text-generation

--- language: - en - fr - ro - de - multilingual widget: - text: "Translate to German: My name is Arthur" example_title: "Translation" - text: "Please answer to the following question. Who is going to be the next Ballon d'or?" example_title: "Question Answering" - text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering." example_title: "Logical reasoning" - text: "Please answer the following question. What is the boiling point of Nitrogen?" example_title: "Scientific knowledge" - text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?" example_title: "Yes/no question" - text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" example_title: "Reasoning task" - text: "Q: ( False or not False or False ) is? A: Let's think step by step" example_title: "Boolean Expressions" - text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" example_title: "Math reasoning" - text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?" example_title: "Premise and hypothesis" tags: - text2text-generation datasets: - svakulenk0/qrecc - taskmaster2 - djaym7/wiki_dialog - deepmind/code_contests - lambada - gsm8k - aqua_rat - esnli - quasc - qed license: apache-2.0 --- # Model Card for FLAN-T5 large <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg" alt="drawing" width="600"/> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 5. [Training Details](#training-details) 6. [Evaluation](#evaluation) 7. [Environmental Impact](#environmental-impact) 8. [Citation](#citation) 9. [Model Card Authors](#model-card-authors) # TL;DR If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : > Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large). # Model Details ## Model Description - **Model type:** Language model - **Language(s) (NLP):** English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian - **License:** Apache 2.0 - **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5) - **Original Checkpoints:** [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) - **Resources for more information:** - [Research paper](https://arxiv.org/pdf/2210.11416.pdf) - [GitHub Repo](https://github.com/google-research/t5x) - [Hugging Face FLAN-T5 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/t5) # Usage Find below some example scripts on how to use the model in `transformers`: ## Using the Pytorch model ### Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU <details> <summary> Click to expand </summary> ```python # pip install accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU using different precisions #### FP16 <details> <summary> Click to expand </summary> ```python # pip install accelerate import torch from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto", torch_dtype=torch.float16) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> #### INT8 <details> <summary> Click to expand </summary> ```python # pip install bitsandbytes accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto", load_in_8bit=True) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> # Uses ## Direct Use and Downstream Use The authors write in [the original paper's model card](https://arxiv.org/pdf/2210.11416.pdf) that: > The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models See the [research paper](https://arxiv.org/pdf/2210.11416.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2210.11416.pdf): > Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. ## Ethical considerations and risks > Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data. ## Known Limitations > Flan-T5 has not been tested in real world applications. ## Sensitive Use: > Flan-T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech. # Training Details ## Training Data The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2):  ## Training Procedure According to the model card from the [original paper](https://arxiv.org/pdf/2210.11416.pdf): > These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size. The model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax). # Evaluation ## Testing Data, Factors & Metrics The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation:  For full details, please check the [research paper](https://arxiv.org/pdf/2210.11416.pdf). ## Results For full results for FLAN-T5-Large, see the [research paper](https://arxiv.org/pdf/2210.11416.pdf), Table 3. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4. - **Hours used:** More information needed - **Cloud Provider:** GCP - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Citation **BibTeX:** ```bibtex @misc{https://doi.org/10.48550/arxiv.2210.11416, doi = {10.48550/ARXIV.2210.11416}, url = {https://arxiv.org/abs/2210.11416}, author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Scaling Instruction-Finetuned Language Models}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

StanfordAIMI/stanford-deidentifier-base

StanfordAIMI

transformers

token-classification

--- widget: - text: "PROCEDURE: Chest xray. COMPARISON: last seen on 1/1/2020 and also record dated of March 1st, 2019. FINDINGS: patchy airspace opacities. IMPRESSION: The results of the chest xray of January 1 2020 are the most concerning ones. The patient was transmitted to another service of UH Medical Center under the responsability of Dr. Perez. We used the system MedClinical data transmitter and sent the data on 2/1/2020, under the ID 5874233. We received the confirmation of Dr Perez. He is reachable at 567-493-1234." - text: "Dr. Curt Langlotz chose to schedule a meeting on 06/23." tags: - token-classification - sequence-tagger-model - pytorch - transformers - pubmedbert - uncased - radiology - biomedical datasets: - radreports language: - en license: mit --- Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings. These model weights are the recommended ones among all available deidentifier weights. Associated github repo: https://github.com/MIDRC/Stanford_Penn_Deidentifier ## Citation ```bibtex @article{10.1093/jamia/ocac219, author = {Chambon, Pierre J and Wu, Christopher and Steinkamp, Jackson M and Adleberg, Jason and Cook, Tessa S and Langlotz, Curtis P}, title = "{Automated deidentification of radiology reports combining transformer and “hide in plain sight” rule-based methods}", journal = {Journal of the American Medical Informatics Association}, year = {2022}, month = {11}, abstract = "{To develop an automated deidentification pipeline for radiology reports that detect protected health information (PHI) entities and replaces them with realistic surrogates “hiding in plain sight.”In this retrospective study, 999 chest X-ray and CT reports collected between November 2019 and November 2020 were annotated for PHI at the token level and combined with 3001 X-rays and 2193 medical notes previously labeled, forming a large multi-institutional and cross-domain dataset of 6193 documents. Two radiology test sets, from a known and a new institution, as well as i2b2 2006 and 2014 test sets, served as an evaluation set to estimate model performance and to compare it with previously released deidentification tools. Several PHI detection models were developed based on different training datasets, fine-tuning approaches and data augmentation techniques, and a synthetic PHI generation algorithm. These models were compared using metrics such as precision, recall and F1 score, as well as paired samples Wilcoxon tests.Our best PHI detection model achieves 97.9 F1 score on radiology reports from a known institution, 99.6 from a new institution, 99.5 on i2b2 2006, and 98.9 on i2b2 2014. On reports from a known institution, it achieves 99.1 recall of detecting the core of each PHI span.Our model outperforms all deidentifiers it was compared to on all test sets as well as human labelers on i2b2 2014 data. It enables accurate and automatic deidentification of radiology reports.A transformer-based deidentification pipeline can achieve state-of-the-art performance for deidentifying radiology reports and other medical documents.}", issn = {1527-974X}, doi = {10.1093/jamia/ocac219}, url = {https://doi.org/10.1093/jamia/ocac219}, note = {ocac219}, eprint = {https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocac219/47220191/ocac219.pdf}, } ```

speechbrain/spkrec-ecapa-voxceleb

speechbrain

speechbrain

--- language: "en" thumbnail: tags: - speechbrain - embeddings - Speaker - Verification - Identification - pytorch - ECAPA - TDNN license: "apache-2.0" datasets: - voxceleb metrics: - EER widget: - example_title: VoxCeleb Speaker id10003 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav - example_title: VoxCeleb Speaker id10004 src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav --- <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> <br/><br/> # Speaker Verification with ECAPA-TDNN embeddings on Voxceleb This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. It is trained on Voxceleb 1+ Voxceleb2 training data. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The model performance on Voxceleb1-test set(Cleaned) is: | Release | EER(%) |:-------------:|:--------------:| | 05-03-21 | 0.80 | ## Pipeline description This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings. ## Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install git+https://github.com/speechbrain/speechbrain.git@develop ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). ### Compute your speaker embeddings ```python import torchaudio from speechbrain.inference.speaker import EncoderClassifier classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb") signal, fs =torchaudio.load('tests/samples/ASR/spk1_snt1.wav') embeddings = classifier.encode_batch(signal) ``` The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*. ### Perform Speaker Verification ```python from speechbrain.inference.speaker import SpeakerRecognition verification = SpeakerRecognition.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", savedir="pretrained_models/spkrec-ecapa-voxceleb") score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk2_snt1.wav") # Different Speakers score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk1_snt2.wav") # Same Speaker ``` The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise. ### Inference on GPU To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. ### Training The model was trained with SpeechBrain (aa018540). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/VoxCeleb/SpeakerRec python train_speaker_embeddings.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing). ### Limitations The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. #### Referencing ECAPA-TDNN ``` @inproceedings{DBLP:conf/interspeech/DesplanquesTD20, author = {Brecht Desplanques and Jenthe Thienpondt and Kris Demuynck}, editor = {Helen Meng and Bo Xu and Thomas Fang Zheng}, title = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation in {TDNN} Based Speaker Verification}, booktitle = {Interspeech 2020}, pages = {3830--3834}, publisher = {{ISCA}}, year = {2020}, } ``` # **Citing SpeechBrain** Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ``` # **About SpeechBrain** - Website: https://speechbrain.github.io/ - Code: https://github.com/speechbrain/speechbrain/ - HuggingFace: https://huggingface.co/speechbrain/

facebook/mms-300m

facebook

transformers

--- tags: - mms language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - 'no' - 'no' - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za license: cc-by-nc-4.0 datasets: - google/fleurs metrics: - wer --- # Massively Multilingual Speech (MMS) - 300m Facebook's MMS counting *300m* parameters. MMS is Facebook AI's massive multilingual pretrained model for speech ("MMS"). It is pretrained in with [Wav2Vec2's self-supervised training objective](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) on about 500,000 hours of speech data in over 1,400 languages. When using the model make sure that your speech input is sampled at 16kHz. **Note**: This model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Translation, or Classification. Check out the [**How-to-fine section](#how-to-finetune) or [**this blog**](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more information about ASR. ## Table Of Content - [How to Finetune](#how-to-finetune) - [Model details](#model-details) - [Additional links](#additional-links) ## How to finetune Coming soon... ## Model details - **Developed by:** Vineel Pratap et al. - **Model type:** Multi-Lingual Automatic Speech Recognition model - **Language(s):** 1000+ languages - **License:** CC-BY-NC 4.0 license - **Num parameters**: 300 million - **Cite as:** @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post]( ) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other **MMS** checkpoints](https://huggingface.co/models?other=mms) - MMS ASR fine-tuned checkpoints: - [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) - [facebook/mms-1b-l1107](https://huggingface.co/facebook/mms-1b-l1107) - [facebook/mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102) - [Official Space](https://huggingface.co/spaces/facebook/MMS)

sai17/cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epochs

sai17

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epochs results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.5946802405369663 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epochs This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0025 - Accuracy: 0.5947 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 1.6956 | 1.0 | 1252 | 1.4843 | 0.3970 | | 1.5633 | 2.0 | 2504 | 1.2584 | 0.4782 | | 1.5568 | 3.0 | 3756 | 1.1976 | 0.4918 | | 1.4727 | 4.0 | 5009 | 1.1884 | 0.4916 | | 1.468 | 5.0 | 6261 | 1.1909 | 0.4889 | | 1.4663 | 6.0 | 7513 | 1.1263 | 0.5288 | | 1.4409 | 7.0 | 8765 | 1.0967 | 0.5441 | | 1.4329 | 8.0 | 10018 | 1.0976 | 0.5388 | | 1.4842 | 9.0 | 11270 | 1.1076 | 0.5315 | | 1.4253 | 10.0 | 12522 | 1.0634 | 0.5511 | | 1.3888 | 11.0 | 13774 | 1.0489 | 0.5634 | | 1.3681 | 12.0 | 15027 | 1.0663 | 0.5567 | | 1.3802 | 13.0 | 16279 | 1.0304 | 0.5667 | | 1.4016 | 14.0 | 17531 | 1.0592 | 0.5518 | | 1.376 | 15.0 | 18783 | 1.0080 | 0.5776 | | 1.3539 | 16.0 | 20036 | 1.0103 | 0.5742 | | 1.3725 | 17.0 | 21288 | 1.0261 | 0.5636 | | 1.3104 | 18.0 | 22540 | 1.0304 | 0.5686 | | 1.3448 | 19.0 | 23792 | 1.0184 | 0.5687 | | 1.3479 | 20.0 | 25045 | 0.9968 | 0.5809 | | 1.3517 | 21.0 | 26297 | 1.1350 | 0.5182 | | 1.3367 | 22.0 | 27549 | 0.9835 | 0.5867 | | 1.3002 | 23.0 | 28801 | 1.0193 | 0.5736 | | 1.3238 | 24.0 | 30054 | 0.9820 | 0.5875 | | 1.2865 | 25.0 | 31306 | 1.0267 | 0.5617 | | 1.3029 | 26.0 | 32558 | 1.0086 | 0.5730 | | 1.3173 | 27.0 | 33810 | 0.9750 | 0.5924 | | 1.297 | 28.0 | 35063 | 0.9851 | 0.5848 | | 1.3105 | 29.0 | 36315 | 1.0306 | 0.5685 | | 1.3477 | 30.0 | 37567 | 0.9977 | 0.5845 | | 1.2565 | 31.0 | 38819 | 0.9900 | 0.5851 | | 1.2657 | 32.0 | 40072 | 1.0137 | 0.5862 | | 1.2911 | 33.0 | 41324 | 0.9947 | 0.5889 | | 1.2539 | 34.0 | 42576 | 0.9821 | 0.5914 | | 1.2441 | 35.0 | 43828 | 1.0296 | 0.5763 | | 1.2176 | 36.0 | 45081 | 1.0350 | 0.5806 | | 1.25 | 37.0 | 46333 | 1.0195 | 0.5779 | | 1.2647 | 38.0 | 47585 | 1.0021 | 0.5903 | | 1.2428 | 39.0 | 48837 | 1.0087 | 0.5892 | | 1.2364 | 40.0 | 50090 | 1.0025 | 0.5947 | | 1.2083 | 41.0 | 51342 | 1.0427 | 0.5862 | | 1.2002 | 42.0 | 52594 | 1.0303 | 0.5878 | | 1.2071 | 43.0 | 53846 | 1.0190 | 0.5909 | | 1.1536 | 44.0 | 55099 | 1.0314 | 0.5920 | | 1.2029 | 45.0 | 56351 | 1.0570 | 0.5839 | | 1.2249 | 46.0 | 57603 | 1.0508 | 0.5828 | | 1.1913 | 47.0 | 58855 | 1.0493 | 0.5853 | | 1.1938 | 48.0 | 60108 | 1.0575 | 0.5857 | | 1.1724 | 49.0 | 61360 | 1.0700 | 0.5905 | | 1.1536 | 50.0 | 62612 | 1.0841 | 0.5853 | | 1.1239 | 51.0 | 63864 | 1.0803 | 0.5865 | | 1.1743 | 52.0 | 65117 | 1.0864 | 0.5880 | | 1.1414 | 53.0 | 66369 | 1.1224 | 0.5819 | | 1.1411 | 54.0 | 67621 | 1.1316 | 0.5780 | | 1.1029 | 55.0 | 68873 | 1.1070 | 0.5860 | | 1.1353 | 56.0 | 70126 | 1.1247 | 0.5847 | | 1.1293 | 57.0 | 71378 | 1.1279 | 0.5805 | | 1.1335 | 58.0 | 72630 | 1.1482 | 0.5812 | | 1.1157 | 59.0 | 73882 | 1.1960 | 0.5674 | | 1.0891 | 60.0 | 75135 | 1.1414 | 0.5848 | | 1.1299 | 61.0 | 76387 | 1.1658 | 0.5790 | | 1.0828 | 62.0 | 77639 | 1.1753 | 0.5806 | | 1.0866 | 63.0 | 78891 | 1.1767 | 0.5755 | | 1.0721 | 64.0 | 80144 | 1.1861 | 0.5808 | | 1.0682 | 65.0 | 81396 | 1.2083 | 0.5749 | | 1.0747 | 66.0 | 82648 | 1.2204 | 0.5755 | | 1.0902 | 67.0 | 83900 | 1.2175 | 0.5750 | | 1.0381 | 68.0 | 85153 | 1.2445 | 0.5738 | | 1.049 | 69.0 | 86405 | 1.2674 | 0.5707 | | 1.0501 | 70.0 | 87657 | 1.2602 | 0.5740 | | 1.0117 | 71.0 | 88909 | 1.2549 | 0.5687 | | 1.0179 | 72.0 | 90162 | 1.3010 | 0.5690 | | 1.0788 | 73.0 | 91414 | 1.2723 | 0.5726 | | 1.0234 | 74.0 | 92666 | 1.3162 | 0.5717 | | 1.0325 | 75.0 | 93918 | 1.3136 | 0.5692 | | 1.0079 | 76.0 | 95171 | 1.3337 | 0.5655 | | 1.058 | 77.0 | 96423 | 1.3171 | 0.5719 | | 0.9968 | 78.0 | 97675 | 1.3470 | 0.5693 | | 1.0217 | 79.0 | 98927 | 1.3418 | 0.5733 | | 1.0124 | 80.0 | 100180 | 1.3518 | 0.5700 | | 0.9823 | 81.0 | 101432 | 1.3646 | 0.5700 | | 0.9627 | 82.0 | 102684 | 1.3658 | 0.5686 | | 0.9773 | 83.0 | 103936 | 1.3811 | 0.5674 | | 0.9855 | 84.0 | 105189 | 1.4082 | 0.5638 | | 0.9928 | 85.0 | 106441 | 1.3877 | 0.5612 | | 1.0025 | 86.0 | 107693 | 1.3925 | 0.5653 | | 0.9583 | 87.0 | 108945 | 1.4313 | 0.5625 | | 0.977 | 88.0 | 110198 | 1.4153 | 0.5651 | | 0.9825 | 89.0 | 111450 | 1.4426 | 0.5619 | | 0.9315 | 90.0 | 112702 | 1.4376 | 0.5643 | | 0.8916 | 91.0 | 113954 | 1.4630 | 0.5618 | | 0.9495 | 92.0 | 115207 | 1.4501 | 0.5627 | | 0.9372 | 93.0 | 116459 | 1.4606 | 0.5622 | | 0.9284 | 94.0 | 117711 | 1.4725 | 0.5608 | | 0.9266 | 95.0 | 118963 | 1.4680 | 0.5607 | | 0.8858 | 96.0 | 120216 | 1.4705 | 0.5626 | | 0.9025 | 97.0 | 121468 | 1.4818 | 0.5616 | | 0.902 | 98.0 | 122720 | 1.4871 | 0.5606 | | 0.8961 | 99.0 | 123972 | 1.4881 | 0.5612 | | 0.9204 | 99.98 | 125200 | 1.4894 | 0.5609 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.13.3

microsoft/dit-base-finetuned-rvlcdip

microsoft

transformers

image-classification

--- tags: - dit - vision - image-classification datasets: - rvl_cdip widget: - src: https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip/resolve/main/coca_cola_advertisement.png example_title: Advertisement - src: https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip/resolve/main/scientific_publication.png example_title: Scientific publication --- # Document Image Transformer (base-sized model) Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/), a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. It was introduced in the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/dit). Note that DiT is identical to the architecture of [BEiT](https://huggingface.co/docs/transformers/model_doc/beit). Disclaimer: The team releasing DiT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Document Image Transformer (DiT) is a transformer encoder model (BERT-like) pre-trained on a large collection of images in a self-supervised fashion. The pre-training objective for the model is to predict visual tokens from the encoder of a discrete VAE (dVAE), based on masked patches. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled document images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. ## Intended uses & limitations You can use the raw model for encoding document images into a vector space, but it's mostly meant to be fine-tuned on tasks like document image classification, table detection or document layout analysis. See the [model hub](https://huggingface.co/models?search=microsoft/dit) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification import torch from PIL import Image image = Image.open('path_to_your_document_image').convert('RGB') processor = AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip") model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 16 RVL-CDIP classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` ### BibTeX entry and citation info ```bibtex @article{Lewis2006BuildingAT, title={Building a test collection for complex document information processing}, author={David D. Lewis and Gady Agam and Shlomo Engelson Argamon and Ophir Frieder and David A. Grossman and Jefferson Heard}, journal={Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval}, year={2006} } ```

stabilityai/stable-diffusion-2-base

stabilityai

diffusers

text-to-image

--- license: openrail++ tags: - stable-diffusion - text-to-image --- # Stable Diffusion v2-base Model Card This model card focuses on the model associated with the Stable Diffusion v2-base model, available [here](https://github.com/Stability-AI/stablediffusion). The model is trained from scratch 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. Then it is further trained for 850k steps at resolution `512x512` on the same dataset on images with resolution `>= 512x512`.  - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-base-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt). - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-base#examples) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install diffusers transformers accelerate scipy safetensors ``` Running the pipeline (if you don't swap the scheduler it will run with the default PNDM/PLMS scheduler, in this example we are swapping it to EulerDiscreteScheduler): ```python from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler import torch model_id = "stabilityai/stable-diffusion-2-base" # Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` **Notes**: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed) # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. We currently provide the following checkpoints: - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`. - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset. - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama). - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752). In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 1 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 200000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

TheBloke/Mistral-7B-Instruct-v0.1-GPTQ

TheBloke

transformers

text-generation

--- base_model: mistralai/Mistral-7B-Instruct-v0.1 inference: false license: apache-2.0 model_creator: Mistral AI model_name: Mistral 7B Instruct v0.1 model_type: mistral pipeline_tag: text-generation prompt_template: '<s>[INST] {prompt} [/INST]' quantized_by: TheBloke tags: - finetuned --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mistral 7B Instruct v0.1 - GPTQ - Model creator: [Mistral AI](https://huggingface.co/mistralai) - Original model: [Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) <!-- description start --> ## Description This repo contains GPTQ model files for [Mistral AI's Mistral 7B Instruct v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. ### GPTQs will work in ExLlama, or via Transformers (requiring Transformers from Github) These models are confirmed to work with ExLlama v1. At the time of writing (September 28th), AutoGPTQ has not yet added support for the new Mistral models. These GPTQs were made directly from Transformers, and so can be loaded via the Transformers interface. They can't be loaded directly from AutoGPTQ. To load them via Transformers, you will need to install Transformers from Github, with: ``` pip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79 ``` <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF) * [Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. These files were made with Transformers 4.34.0.dev0, from commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 7.68 GB | Yes | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 32768 | 8.17 GB | Yes | 8-bit, with group size 32g and Act Order for maximum inference quality. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `Mistral-7B-Instruct-v0.1-GPTQ`: ```shell mkdir Mistral-7B-Instruct-v0.1-GPTQ huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir Mistral-7B-Instruct-v0.1-GPTQ huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir Mistral-7B-Instruct-v0.1-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --local-dir Mistral-7B-Instruct-v0.1-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). These models are confirmed to work via the ExLlama Loader in text-generation-webui. Use **Loader: ExLlama** - or Transformers may work too. AutoGPTQ will not work. Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Mistral-7B-Instruct-v0.1-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Mistral-7B-Instruct-v0.1-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.34.0.dev0 from Github source, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install optimum pip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<s>[INST] {prompt} [/INST] ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are only tested to work with ExLlama v1, and Transformers 4.34.0.dev0 as of commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Mistral AI's Mistral 7B Instruct v0.1 # Model Card for Mistral-7B-Instruct-v0.1 The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets. For full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/) ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

meta-llama/Llama-2-13b-hf

meta-llama

transformers

text-generation

--- extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: >- ### LLAMA 2 COMMUNITY LICENSE AGREEMENT "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Llama 2" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/. "Llama Materials" means, collectively, Meta's proprietary Llama 2 and documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). By clicking "I Accept" below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non- transferable and royalty-free limited license under Meta's intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make the Llama Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a "Notice" text file distributed as a part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved." iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof). 2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee's affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials. b. Subject to Meta's ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy). #### Prohibited Uses We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 2 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com) extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 license: llama2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)| |70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)|

dmis-lab/biobert-base-cased-v1.1

dmis-lab

transformers

Entry not found

BAAI/bge-small-en

BAAI

transformers

feature-extraction

--- tags: - mteb - sentence transformers model-index: - name: bge-small-en results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.34328358208955 - type: ap value: 37.59947775195661 - type: f1 value: 68.548415491933 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.04527499999999 - type: ap value: 89.60696356772135 - type: f1 value: 93.03361469382438 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.08 - type: f1 value: 45.66249835363254 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 35.205999999999996 - type: map_at_10 value: 50.782000000000004 - type: map_at_100 value: 51.547 - type: map_at_1000 value: 51.554 - type: map_at_3 value: 46.515 - type: map_at_5 value: 49.296 - type: mrr_at_1 value: 35.632999999999996 - type: mrr_at_10 value: 50.958999999999996 - type: mrr_at_100 value: 51.724000000000004 - type: mrr_at_1000 value: 51.731 - type: mrr_at_3 value: 46.669 - type: mrr_at_5 value: 49.439 - type: ndcg_at_1 value: 35.205999999999996 - type: ndcg_at_10 value: 58.835 - type: ndcg_at_100 value: 62.095 - type: ndcg_at_1000 value: 62.255 - type: ndcg_at_3 value: 50.255 - type: ndcg_at_5 value: 55.296 - type: precision_at_1 value: 35.205999999999996 - type: precision_at_10 value: 8.421 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.365 - type: precision_at_5 value: 14.680000000000001 - type: recall_at_1 value: 35.205999999999996 - type: recall_at_10 value: 84.211 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 61.095 - type: recall_at_5 value: 73.4 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.52644476278646 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 39.973045724188964 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.28285314871488 - type: mrr value: 74.52743701358659 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 80.09041909160327 - type: cos_sim_spearman value: 79.96266537706944 - type: euclidean_pearson value: 79.50774978162241 - type: euclidean_spearman value: 79.9144715078551 - type: manhattan_pearson value: 79.2062139879302 - type: manhattan_spearman value: 79.35000081468212 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.31493506493506 - type: f1 value: 85.2704557977762 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.6837242810816 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.38881249555897 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.884999999999998 - type: map_at_10 value: 39.574 - type: map_at_100 value: 40.993 - type: map_at_1000 value: 41.129 - type: map_at_3 value: 36.089 - type: map_at_5 value: 38.191 - type: mrr_at_1 value: 34.477999999999994 - type: mrr_at_10 value: 45.411 - type: mrr_at_100 value: 46.089999999999996 - type: mrr_at_1000 value: 46.147 - type: mrr_at_3 value: 42.346000000000004 - type: mrr_at_5 value: 44.292 - type: ndcg_at_1 value: 34.477999999999994 - type: ndcg_at_10 value: 46.123999999999995 - type: ndcg_at_100 value: 51.349999999999994 - type: ndcg_at_1000 value: 53.578 - type: ndcg_at_3 value: 40.824 - type: ndcg_at_5 value: 43.571 - type: precision_at_1 value: 34.477999999999994 - type: precision_at_10 value: 8.841000000000001 - type: precision_at_100 value: 1.4460000000000002 - type: precision_at_1000 value: 0.192 - type: precision_at_3 value: 19.742 - type: precision_at_5 value: 14.421000000000001 - type: recall_at_1 value: 27.884999999999998 - type: recall_at_10 value: 59.087 - type: recall_at_100 value: 80.609 - type: recall_at_1000 value: 95.054 - type: recall_at_3 value: 44.082 - type: recall_at_5 value: 51.593999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.639 - type: map_at_10 value: 40.047 - type: map_at_100 value: 41.302 - type: map_at_1000 value: 41.425 - type: map_at_3 value: 37.406 - type: map_at_5 value: 38.934000000000005 - type: mrr_at_1 value: 37.707 - type: mrr_at_10 value: 46.082 - type: mrr_at_100 value: 46.745 - type: mrr_at_1000 value: 46.786 - type: mrr_at_3 value: 43.980999999999995 - type: mrr_at_5 value: 45.287 - type: ndcg_at_1 value: 37.707 - type: ndcg_at_10 value: 45.525 - type: ndcg_at_100 value: 49.976 - type: ndcg_at_1000 value: 51.94499999999999 - type: ndcg_at_3 value: 41.704 - type: ndcg_at_5 value: 43.596000000000004 - type: precision_at_1 value: 37.707 - type: precision_at_10 value: 8.465 - type: precision_at_100 value: 1.375 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 19.979 - type: precision_at_5 value: 14.115 - type: recall_at_1 value: 30.639 - type: recall_at_10 value: 54.775 - type: recall_at_100 value: 73.678 - type: recall_at_1000 value: 86.142 - type: recall_at_3 value: 43.230000000000004 - type: recall_at_5 value: 48.622 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.038 - type: map_at_10 value: 49.922 - type: map_at_100 value: 51.032 - type: map_at_1000 value: 51.085 - type: map_at_3 value: 46.664 - type: map_at_5 value: 48.588 - type: mrr_at_1 value: 43.95 - type: mrr_at_10 value: 53.566 - type: mrr_at_100 value: 54.318999999999996 - type: mrr_at_1000 value: 54.348 - type: mrr_at_3 value: 51.066 - type: mrr_at_5 value: 52.649 - type: ndcg_at_1 value: 43.95 - type: ndcg_at_10 value: 55.676 - type: ndcg_at_100 value: 60.126000000000005 - type: ndcg_at_1000 value: 61.208 - type: ndcg_at_3 value: 50.20400000000001 - type: ndcg_at_5 value: 53.038 - type: precision_at_1 value: 43.95 - type: precision_at_10 value: 8.953 - type: precision_at_100 value: 1.2109999999999999 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.256999999999998 - type: precision_at_5 value: 15.524 - type: recall_at_1 value: 38.038 - type: recall_at_10 value: 69.15 - type: recall_at_100 value: 88.31599999999999 - type: recall_at_1000 value: 95.993 - type: recall_at_3 value: 54.663 - type: recall_at_5 value: 61.373 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.872 - type: map_at_10 value: 32.912 - type: map_at_100 value: 33.972 - type: map_at_1000 value: 34.046 - type: map_at_3 value: 30.361 - type: map_at_5 value: 31.704 - type: mrr_at_1 value: 26.779999999999998 - type: mrr_at_10 value: 34.812 - type: mrr_at_100 value: 35.754999999999995 - type: mrr_at_1000 value: 35.809000000000005 - type: mrr_at_3 value: 32.335 - type: mrr_at_5 value: 33.64 - type: ndcg_at_1 value: 26.779999999999998 - type: ndcg_at_10 value: 37.623 - type: ndcg_at_100 value: 42.924 - type: ndcg_at_1000 value: 44.856 - type: ndcg_at_3 value: 32.574 - type: ndcg_at_5 value: 34.842 - type: precision_at_1 value: 26.779999999999998 - type: precision_at_10 value: 5.729 - type: precision_at_100 value: 0.886 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 13.559 - type: precision_at_5 value: 9.469 - type: recall_at_1 value: 24.872 - type: recall_at_10 value: 50.400999999999996 - type: recall_at_100 value: 74.954 - type: recall_at_1000 value: 89.56 - type: recall_at_3 value: 36.726 - type: recall_at_5 value: 42.138999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.803 - type: map_at_10 value: 24.348 - type: map_at_100 value: 25.56 - type: map_at_1000 value: 25.668000000000003 - type: map_at_3 value: 21.811 - type: map_at_5 value: 23.287 - type: mrr_at_1 value: 20.771 - type: mrr_at_10 value: 28.961 - type: mrr_at_100 value: 29.979 - type: mrr_at_1000 value: 30.046 - type: mrr_at_3 value: 26.555 - type: mrr_at_5 value: 28.060000000000002 - type: ndcg_at_1 value: 20.771 - type: ndcg_at_10 value: 29.335 - type: ndcg_at_100 value: 35.188 - type: ndcg_at_1000 value: 37.812 - type: ndcg_at_3 value: 24.83 - type: ndcg_at_5 value: 27.119 - type: precision_at_1 value: 20.771 - type: precision_at_10 value: 5.4350000000000005 - type: precision_at_100 value: 0.9480000000000001 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 11.982 - type: precision_at_5 value: 8.831 - type: recall_at_1 value: 16.803 - type: recall_at_10 value: 40.039 - type: recall_at_100 value: 65.83200000000001 - type: recall_at_1000 value: 84.478 - type: recall_at_3 value: 27.682000000000002 - type: recall_at_5 value: 33.535 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.345 - type: map_at_10 value: 37.757000000000005 - type: map_at_100 value: 39.141 - type: map_at_1000 value: 39.262 - type: map_at_3 value: 35.183 - type: map_at_5 value: 36.592 - type: mrr_at_1 value: 34.649 - type: mrr_at_10 value: 43.586999999999996 - type: mrr_at_100 value: 44.481 - type: mrr_at_1000 value: 44.542 - type: mrr_at_3 value: 41.29 - type: mrr_at_5 value: 42.642 - type: ndcg_at_1 value: 34.649 - type: ndcg_at_10 value: 43.161 - type: ndcg_at_100 value: 48.734 - type: ndcg_at_1000 value: 51.046 - type: ndcg_at_3 value: 39.118 - type: ndcg_at_5 value: 41.022 - type: precision_at_1 value: 34.649 - type: precision_at_10 value: 7.603 - type: precision_at_100 value: 1.209 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 18.319 - type: precision_at_5 value: 12.839 - type: recall_at_1 value: 28.345 - type: recall_at_10 value: 53.367 - type: recall_at_100 value: 76.453 - type: recall_at_1000 value: 91.82000000000001 - type: recall_at_3 value: 41.636 - type: recall_at_5 value: 46.760000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.419 - type: map_at_10 value: 31.716 - type: map_at_100 value: 33.152 - type: map_at_1000 value: 33.267 - type: map_at_3 value: 28.74 - type: map_at_5 value: 30.48 - type: mrr_at_1 value: 28.310999999999996 - type: mrr_at_10 value: 37.039 - type: mrr_at_100 value: 38.09 - type: mrr_at_1000 value: 38.145 - type: mrr_at_3 value: 34.437 - type: mrr_at_5 value: 36.024 - type: ndcg_at_1 value: 28.310999999999996 - type: ndcg_at_10 value: 37.41 - type: ndcg_at_100 value: 43.647999999999996 - type: ndcg_at_1000 value: 46.007 - type: ndcg_at_3 value: 32.509 - type: ndcg_at_5 value: 34.943999999999996 - type: precision_at_1 value: 28.310999999999996 - type: precision_at_10 value: 6.963 - type: precision_at_100 value: 1.1860000000000002 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 15.867999999999999 - type: precision_at_5 value: 11.507000000000001 - type: recall_at_1 value: 22.419 - type: recall_at_10 value: 49.28 - type: recall_at_100 value: 75.802 - type: recall_at_1000 value: 92.032 - type: recall_at_3 value: 35.399 - type: recall_at_5 value: 42.027 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.669249999999998 - type: map_at_10 value: 33.332583333333325 - type: map_at_100 value: 34.557833333333335 - type: map_at_1000 value: 34.67141666666666 - type: map_at_3 value: 30.663166666666662 - type: map_at_5 value: 32.14883333333333 - type: mrr_at_1 value: 29.193833333333334 - type: mrr_at_10 value: 37.47625 - type: mrr_at_100 value: 38.3545 - type: mrr_at_1000 value: 38.413166666666676 - type: mrr_at_3 value: 35.06741666666667 - type: mrr_at_5 value: 36.450666666666656 - type: ndcg_at_1 value: 29.193833333333334 - type: ndcg_at_10 value: 38.505416666666676 - type: ndcg_at_100 value: 43.81125 - type: ndcg_at_1000 value: 46.09558333333333 - type: ndcg_at_3 value: 33.90916666666667 - type: ndcg_at_5 value: 36.07666666666666 - type: precision_at_1 value: 29.193833333333334 - type: precision_at_10 value: 6.7251666666666665 - type: precision_at_100 value: 1.1058333333333332 - type: precision_at_1000 value: 0.14833333333333332 - type: precision_at_3 value: 15.554166666666665 - type: precision_at_5 value: 11.079250000000002 - type: recall_at_1 value: 24.669249999999998 - type: recall_at_10 value: 49.75583333333332 - type: recall_at_100 value: 73.06908333333332 - type: recall_at_1000 value: 88.91316666666667 - type: recall_at_3 value: 36.913250000000005 - type: recall_at_5 value: 42.48641666666666 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.044999999999998 - type: map_at_10 value: 30.349999999999998 - type: map_at_100 value: 31.273 - type: map_at_1000 value: 31.362000000000002 - type: map_at_3 value: 28.508 - type: map_at_5 value: 29.369 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 33.12 - type: mrr_at_100 value: 33.904 - type: mrr_at_1000 value: 33.967000000000006 - type: mrr_at_3 value: 31.365 - type: mrr_at_5 value: 32.124 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 34.214 - type: ndcg_at_100 value: 38.681 - type: ndcg_at_1000 value: 40.926 - type: ndcg_at_3 value: 30.725 - type: ndcg_at_5 value: 31.967000000000002 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 5.215 - type: precision_at_100 value: 0.807 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 12.986 - type: precision_at_5 value: 8.712 - type: recall_at_1 value: 24.044999999999998 - type: recall_at_10 value: 43.456 - type: recall_at_100 value: 63.675000000000004 - type: recall_at_1000 value: 80.05499999999999 - type: recall_at_3 value: 33.561 - type: recall_at_5 value: 36.767 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.672 - type: map_at_10 value: 22.641 - type: map_at_100 value: 23.75 - type: map_at_1000 value: 23.877000000000002 - type: map_at_3 value: 20.219 - type: map_at_5 value: 21.648 - type: mrr_at_1 value: 18.823 - type: mrr_at_10 value: 26.101999999999997 - type: mrr_at_100 value: 27.038 - type: mrr_at_1000 value: 27.118 - type: mrr_at_3 value: 23.669 - type: mrr_at_5 value: 25.173000000000002 - type: ndcg_at_1 value: 18.823 - type: ndcg_at_10 value: 27.176000000000002 - type: ndcg_at_100 value: 32.42 - type: ndcg_at_1000 value: 35.413 - type: ndcg_at_3 value: 22.756999999999998 - type: ndcg_at_5 value: 25.032 - type: precision_at_1 value: 18.823 - type: precision_at_10 value: 5.034000000000001 - type: precision_at_100 value: 0.895 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 10.771 - type: precision_at_5 value: 8.1 - type: recall_at_1 value: 15.672 - type: recall_at_10 value: 37.296 - type: recall_at_100 value: 60.863 - type: recall_at_1000 value: 82.234 - type: recall_at_3 value: 25.330000000000002 - type: recall_at_5 value: 30.964000000000002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.633 - type: map_at_10 value: 32.858 - type: map_at_100 value: 34.038000000000004 - type: map_at_1000 value: 34.141 - type: map_at_3 value: 30.209000000000003 - type: map_at_5 value: 31.567 - type: mrr_at_1 value: 28.358 - type: mrr_at_10 value: 36.433 - type: mrr_at_100 value: 37.352000000000004 - type: mrr_at_1000 value: 37.41 - type: mrr_at_3 value: 34.033 - type: mrr_at_5 value: 35.246 - type: ndcg_at_1 value: 28.358 - type: ndcg_at_10 value: 37.973 - type: ndcg_at_100 value: 43.411 - type: ndcg_at_1000 value: 45.747 - type: ndcg_at_3 value: 32.934999999999995 - type: ndcg_at_5 value: 35.013 - type: precision_at_1 value: 28.358 - type: precision_at_10 value: 6.418 - type: precision_at_100 value: 1.02 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 14.677000000000001 - type: precision_at_5 value: 10.335999999999999 - type: recall_at_1 value: 24.633 - type: recall_at_10 value: 50.048 - type: recall_at_100 value: 73.821 - type: recall_at_1000 value: 90.046 - type: recall_at_3 value: 36.284 - type: recall_at_5 value: 41.370000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.133 - type: map_at_10 value: 31.491999999999997 - type: map_at_100 value: 33.062000000000005 - type: map_at_1000 value: 33.256 - type: map_at_3 value: 28.886 - type: map_at_5 value: 30.262 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.144 - type: mrr_at_100 value: 37.14 - type: mrr_at_1000 value: 37.191 - type: mrr_at_3 value: 33.762 - type: mrr_at_5 value: 34.997 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 36.951 - type: ndcg_at_100 value: 43.287 - type: ndcg_at_1000 value: 45.777 - type: ndcg_at_3 value: 32.786 - type: ndcg_at_5 value: 34.65 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.055 - type: precision_at_100 value: 1.476 - type: precision_at_1000 value: 0.22899999999999998 - type: precision_at_3 value: 15.481 - type: precision_at_5 value: 11.186 - type: recall_at_1 value: 23.133 - type: recall_at_10 value: 47.285 - type: recall_at_100 value: 76.176 - type: recall_at_1000 value: 92.176 - type: recall_at_3 value: 35.223 - type: recall_at_5 value: 40.142 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.547 - type: map_at_10 value: 26.374 - type: map_at_100 value: 27.419 - type: map_at_1000 value: 27.539 - type: map_at_3 value: 23.882 - type: map_at_5 value: 25.163999999999998 - type: mrr_at_1 value: 21.442 - type: mrr_at_10 value: 28.458 - type: mrr_at_100 value: 29.360999999999997 - type: mrr_at_1000 value: 29.448999999999998 - type: mrr_at_3 value: 25.97 - type: mrr_at_5 value: 27.273999999999997 - type: ndcg_at_1 value: 21.442 - type: ndcg_at_10 value: 30.897000000000002 - type: ndcg_at_100 value: 35.99 - type: ndcg_at_1000 value: 38.832 - type: ndcg_at_3 value: 25.944 - type: ndcg_at_5 value: 28.126 - type: precision_at_1 value: 21.442 - type: precision_at_10 value: 4.9910000000000005 - type: precision_at_100 value: 0.8109999999999999 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 11.029 - type: precision_at_5 value: 7.911 - type: recall_at_1 value: 19.547 - type: recall_at_10 value: 42.886 - type: recall_at_100 value: 66.64999999999999 - type: recall_at_1000 value: 87.368 - type: recall_at_3 value: 29.143 - type: recall_at_5 value: 34.544000000000004 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 15.572 - type: map_at_10 value: 25.312 - type: map_at_100 value: 27.062 - type: map_at_1000 value: 27.253 - type: map_at_3 value: 21.601 - type: map_at_5 value: 23.473 - type: mrr_at_1 value: 34.984 - type: mrr_at_10 value: 46.406 - type: mrr_at_100 value: 47.179 - type: mrr_at_1000 value: 47.21 - type: mrr_at_3 value: 43.485 - type: mrr_at_5 value: 45.322 - type: ndcg_at_1 value: 34.984 - type: ndcg_at_10 value: 34.344 - type: ndcg_at_100 value: 41.015 - type: ndcg_at_1000 value: 44.366 - type: ndcg_at_3 value: 29.119 - type: ndcg_at_5 value: 30.825999999999997 - type: precision_at_1 value: 34.984 - type: precision_at_10 value: 10.358 - type: precision_at_100 value: 1.762 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 21.368000000000002 - type: precision_at_5 value: 15.948 - type: recall_at_1 value: 15.572 - type: recall_at_10 value: 39.367999999999995 - type: recall_at_100 value: 62.183 - type: recall_at_1000 value: 80.92200000000001 - type: recall_at_3 value: 26.131999999999998 - type: recall_at_5 value: 31.635999999999996 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.848 - type: map_at_10 value: 19.25 - type: map_at_100 value: 27.193 - type: map_at_1000 value: 28.721999999999998 - type: map_at_3 value: 13.968 - type: map_at_5 value: 16.283 - type: mrr_at_1 value: 68.75 - type: mrr_at_10 value: 76.25 - type: mrr_at_100 value: 76.534 - type: mrr_at_1000 value: 76.53999999999999 - type: mrr_at_3 value: 74.667 - type: mrr_at_5 value: 75.86699999999999 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 41.426 - type: ndcg_at_100 value: 45.660000000000004 - type: ndcg_at_1000 value: 53.02 - type: ndcg_at_3 value: 46.581 - type: ndcg_at_5 value: 43.836999999999996 - type: precision_at_1 value: 68.75 - type: precision_at_10 value: 32.800000000000004 - type: precision_at_100 value: 10.440000000000001 - type: precision_at_1000 value: 1.9980000000000002 - type: precision_at_3 value: 49.667 - type: precision_at_5 value: 42.25 - type: recall_at_1 value: 8.848 - type: recall_at_10 value: 24.467 - type: recall_at_100 value: 51.344 - type: recall_at_1000 value: 75.235 - type: recall_at_3 value: 15.329 - type: recall_at_5 value: 18.892999999999997 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.95 - type: f1 value: 43.44563593360779 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 78.036 - type: map_at_10 value: 85.639 - type: map_at_100 value: 85.815 - type: map_at_1000 value: 85.829 - type: map_at_3 value: 84.795 - type: map_at_5 value: 85.336 - type: mrr_at_1 value: 84.353 - type: mrr_at_10 value: 90.582 - type: mrr_at_100 value: 90.617 - type: mrr_at_1000 value: 90.617 - type: mrr_at_3 value: 90.132 - type: mrr_at_5 value: 90.447 - type: ndcg_at_1 value: 84.353 - type: ndcg_at_10 value: 89.003 - type: ndcg_at_100 value: 89.60000000000001 - type: ndcg_at_1000 value: 89.836 - type: ndcg_at_3 value: 87.81400000000001 - type: ndcg_at_5 value: 88.478 - type: precision_at_1 value: 84.353 - type: precision_at_10 value: 10.482 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 33.257999999999996 - type: precision_at_5 value: 20.465 - type: recall_at_1 value: 78.036 - type: recall_at_10 value: 94.517 - type: recall_at_100 value: 96.828 - type: recall_at_1000 value: 98.261 - type: recall_at_3 value: 91.12 - type: recall_at_5 value: 92.946 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.191 - type: map_at_10 value: 32.369 - type: map_at_100 value: 34.123999999999995 - type: map_at_1000 value: 34.317 - type: map_at_3 value: 28.71 - type: map_at_5 value: 30.607 - type: mrr_at_1 value: 40.894999999999996 - type: mrr_at_10 value: 48.842 - type: mrr_at_100 value: 49.599 - type: mrr_at_1000 value: 49.647000000000006 - type: mrr_at_3 value: 46.785 - type: mrr_at_5 value: 47.672 - type: ndcg_at_1 value: 40.894999999999996 - type: ndcg_at_10 value: 39.872 - type: ndcg_at_100 value: 46.126 - type: ndcg_at_1000 value: 49.476 - type: ndcg_at_3 value: 37.153000000000006 - type: ndcg_at_5 value: 37.433 - type: precision_at_1 value: 40.894999999999996 - type: precision_at_10 value: 10.818 - type: precision_at_100 value: 1.73 - type: precision_at_1000 value: 0.231 - type: precision_at_3 value: 25.051000000000002 - type: precision_at_5 value: 17.531 - type: recall_at_1 value: 20.191 - type: recall_at_10 value: 45.768 - type: recall_at_100 value: 68.82000000000001 - type: recall_at_1000 value: 89.133 - type: recall_at_3 value: 33.296 - type: recall_at_5 value: 38.022 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 39.257 - type: map_at_10 value: 61.467000000000006 - type: map_at_100 value: 62.364 - type: map_at_1000 value: 62.424 - type: map_at_3 value: 58.228 - type: map_at_5 value: 60.283 - type: mrr_at_1 value: 78.515 - type: mrr_at_10 value: 84.191 - type: mrr_at_100 value: 84.378 - type: mrr_at_1000 value: 84.385 - type: mrr_at_3 value: 83.284 - type: mrr_at_5 value: 83.856 - type: ndcg_at_1 value: 78.515 - type: ndcg_at_10 value: 69.78999999999999 - type: ndcg_at_100 value: 72.886 - type: ndcg_at_1000 value: 74.015 - type: ndcg_at_3 value: 65.23 - type: ndcg_at_5 value: 67.80199999999999 - type: precision_at_1 value: 78.515 - type: precision_at_10 value: 14.519000000000002 - type: precision_at_100 value: 1.694 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 41.702 - type: precision_at_5 value: 27.046999999999997 - type: recall_at_1 value: 39.257 - type: recall_at_10 value: 72.59299999999999 - type: recall_at_100 value: 84.679 - type: recall_at_1000 value: 92.12 - type: recall_at_3 value: 62.552 - type: recall_at_5 value: 67.616 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.5152 - type: ap value: 87.64584669595709 - type: f1 value: 91.50605576428437 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.926000000000002 - type: map_at_10 value: 34.049 - type: map_at_100 value: 35.213 - type: map_at_1000 value: 35.265 - type: map_at_3 value: 30.309 - type: map_at_5 value: 32.407000000000004 - type: mrr_at_1 value: 22.55 - type: mrr_at_10 value: 34.657 - type: mrr_at_100 value: 35.760999999999996 - type: mrr_at_1000 value: 35.807 - type: mrr_at_3 value: 30.989 - type: mrr_at_5 value: 33.039 - type: ndcg_at_1 value: 22.55 - type: ndcg_at_10 value: 40.842 - type: ndcg_at_100 value: 46.436 - type: ndcg_at_1000 value: 47.721999999999994 - type: ndcg_at_3 value: 33.209 - type: ndcg_at_5 value: 36.943 - type: precision_at_1 value: 22.55 - type: precision_at_10 value: 6.447 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.136000000000001 - type: precision_at_5 value: 10.381 - type: recall_at_1 value: 21.926000000000002 - type: recall_at_10 value: 61.724999999999994 - type: recall_at_100 value: 87.604 - type: recall_at_1000 value: 97.421 - type: recall_at_3 value: 40.944 - type: recall_at_5 value: 49.915 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.54765161878704 - type: f1 value: 93.3298945415573 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 75.71591427268582 - type: f1 value: 59.32113870474471 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 75.83053127101547 - type: f1 value: 73.60757944876475 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.72562205783457 - type: f1 value: 78.63761662505502 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.37935633767996 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.55270546130387 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.462692753143834 - type: mrr value: 31.497569753511563 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.646 - type: map_at_10 value: 12.498 - type: map_at_100 value: 15.486 - type: map_at_1000 value: 16.805999999999997 - type: map_at_3 value: 9.325 - type: map_at_5 value: 10.751 - type: mrr_at_1 value: 43.034 - type: mrr_at_10 value: 52.662 - type: mrr_at_100 value: 53.189 - type: mrr_at_1000 value: 53.25 - type: mrr_at_3 value: 50.929 - type: mrr_at_5 value: 51.92 - type: ndcg_at_1 value: 41.796 - type: ndcg_at_10 value: 33.477000000000004 - type: ndcg_at_100 value: 29.996000000000002 - type: ndcg_at_1000 value: 38.864 - type: ndcg_at_3 value: 38.940000000000005 - type: ndcg_at_5 value: 36.689 - type: precision_at_1 value: 43.034 - type: precision_at_10 value: 24.799 - type: precision_at_100 value: 7.432999999999999 - type: precision_at_1000 value: 1.9929999999999999 - type: precision_at_3 value: 36.842000000000006 - type: precision_at_5 value: 32.135999999999996 - type: recall_at_1 value: 5.646 - type: recall_at_10 value: 15.963 - type: recall_at_100 value: 29.492 - type: recall_at_1000 value: 61.711000000000006 - type: recall_at_3 value: 10.585 - type: recall_at_5 value: 12.753999999999998 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 27.602 - type: map_at_10 value: 41.545 - type: map_at_100 value: 42.644999999999996 - type: map_at_1000 value: 42.685 - type: map_at_3 value: 37.261 - type: map_at_5 value: 39.706 - type: mrr_at_1 value: 31.141000000000002 - type: mrr_at_10 value: 44.139 - type: mrr_at_100 value: 44.997 - type: mrr_at_1000 value: 45.025999999999996 - type: mrr_at_3 value: 40.503 - type: mrr_at_5 value: 42.64 - type: ndcg_at_1 value: 31.141000000000002 - type: ndcg_at_10 value: 48.995 - type: ndcg_at_100 value: 53.788000000000004 - type: ndcg_at_1000 value: 54.730000000000004 - type: ndcg_at_3 value: 40.844 - type: ndcg_at_5 value: 44.955 - type: precision_at_1 value: 31.141000000000002 - type: precision_at_10 value: 8.233 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 18.579 - type: precision_at_5 value: 13.533999999999999 - type: recall_at_1 value: 27.602 - type: recall_at_10 value: 69.216 - type: recall_at_100 value: 90.252 - type: recall_at_1000 value: 97.27 - type: recall_at_3 value: 47.987 - type: recall_at_5 value: 57.438 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.949 - type: map_at_10 value: 84.89999999999999 - type: map_at_100 value: 85.531 - type: map_at_1000 value: 85.548 - type: map_at_3 value: 82.027 - type: map_at_5 value: 83.853 - type: mrr_at_1 value: 81.69999999999999 - type: mrr_at_10 value: 87.813 - type: mrr_at_100 value: 87.917 - type: mrr_at_1000 value: 87.91799999999999 - type: mrr_at_3 value: 86.938 - type: mrr_at_5 value: 87.53999999999999 - type: ndcg_at_1 value: 81.75 - type: ndcg_at_10 value: 88.55499999999999 - type: ndcg_at_100 value: 89.765 - type: ndcg_at_1000 value: 89.871 - type: ndcg_at_3 value: 85.905 - type: ndcg_at_5 value: 87.41 - type: precision_at_1 value: 81.75 - type: precision_at_10 value: 13.403 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.597 - type: precision_at_5 value: 24.69 - type: recall_at_1 value: 70.949 - type: recall_at_10 value: 95.423 - type: recall_at_100 value: 99.509 - type: recall_at_1000 value: 99.982 - type: recall_at_3 value: 87.717 - type: recall_at_5 value: 92.032 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 51.76962893449579 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.32897690686379 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.478 - type: map_at_10 value: 11.994 - type: map_at_100 value: 13.977 - type: map_at_1000 value: 14.295 - type: map_at_3 value: 8.408999999999999 - type: map_at_5 value: 10.024 - type: mrr_at_1 value: 22.1 - type: mrr_at_10 value: 33.526 - type: mrr_at_100 value: 34.577000000000005 - type: mrr_at_1000 value: 34.632000000000005 - type: mrr_at_3 value: 30.217 - type: mrr_at_5 value: 31.962000000000003 - type: ndcg_at_1 value: 22.1 - type: ndcg_at_10 value: 20.191 - type: ndcg_at_100 value: 27.954 - type: ndcg_at_1000 value: 33.491 - type: ndcg_at_3 value: 18.787000000000003 - type: ndcg_at_5 value: 16.378999999999998 - type: precision_at_1 value: 22.1 - type: precision_at_10 value: 10.69 - type: precision_at_100 value: 2.1919999999999997 - type: precision_at_1000 value: 0.35200000000000004 - type: precision_at_3 value: 17.732999999999997 - type: precision_at_5 value: 14.499999999999998 - type: recall_at_1 value: 4.478 - type: recall_at_10 value: 21.657 - type: recall_at_100 value: 44.54 - type: recall_at_1000 value: 71.542 - type: recall_at_3 value: 10.778 - type: recall_at_5 value: 14.687 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.82325259156718 - type: cos_sim_spearman value: 79.2463589100662 - type: euclidean_pearson value: 80.48318380496771 - type: euclidean_spearman value: 79.34451935199979 - type: manhattan_pearson value: 80.39041824178759 - type: manhattan_spearman value: 79.23002892700211 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.74130231431258 - type: cos_sim_spearman value: 78.36856568042397 - type: euclidean_pearson value: 82.48301631890303 - type: euclidean_spearman value: 78.28376980722732 - type: manhattan_pearson value: 82.43552075450525 - type: manhattan_spearman value: 78.22702443947126 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 79.96138619461459 - type: cos_sim_spearman value: 81.85436343502379 - type: euclidean_pearson value: 81.82895226665367 - type: euclidean_spearman value: 82.22707349602916 - type: manhattan_pearson value: 81.66303369445873 - type: manhattan_spearman value: 82.05030197179455 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 80.05481244198648 - type: cos_sim_spearman value: 80.85052504637808 - type: euclidean_pearson value: 80.86728419744497 - type: euclidean_spearman value: 81.033786401512 - type: manhattan_pearson value: 80.90107531061103 - type: manhattan_spearman value: 81.11374116827795 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 84.615220756399 - type: cos_sim_spearman value: 86.46858500002092 - type: euclidean_pearson value: 86.08307800247586 - type: euclidean_spearman value: 86.72691443870013 - type: manhattan_pearson value: 85.96155594487269 - type: manhattan_spearman value: 86.605909505275 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.14363913634436 - type: cos_sim_spearman value: 84.48430226487102 - type: euclidean_pearson value: 83.75303424801902 - type: euclidean_spearman value: 84.56762380734538 - type: manhattan_pearson value: 83.6135447165928 - type: manhattan_spearman value: 84.39898212616731 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.09909252554525 - type: cos_sim_spearman value: 85.70951402743276 - type: euclidean_pearson value: 87.1991936239908 - type: euclidean_spearman value: 86.07745840612071 - type: manhattan_pearson value: 87.25039137549952 - type: manhattan_spearman value: 85.99938746659761 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.529332093413615 - type: cos_sim_spearman value: 65.38177340147439 - type: euclidean_pearson value: 66.35278011412136 - type: euclidean_spearman value: 65.47147267032997 - type: manhattan_pearson value: 66.71804682408693 - type: manhattan_spearman value: 65.67406521423597 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 82.45802942885662 - type: cos_sim_spearman value: 84.8853341842566 - type: euclidean_pearson value: 84.60915021096707 - type: euclidean_spearman value: 85.11181242913666 - type: manhattan_pearson value: 84.38600521210364 - type: manhattan_spearman value: 84.89045417981723 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.92793380635129 - type: mrr value: 95.85834191226348 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 55.74400000000001 - type: map_at_10 value: 65.455 - type: map_at_100 value: 66.106 - type: map_at_1000 value: 66.129 - type: map_at_3 value: 62.719 - type: map_at_5 value: 64.441 - type: mrr_at_1 value: 58.667 - type: mrr_at_10 value: 66.776 - type: mrr_at_100 value: 67.363 - type: mrr_at_1000 value: 67.384 - type: mrr_at_3 value: 64.889 - type: mrr_at_5 value: 66.122 - type: ndcg_at_1 value: 58.667 - type: ndcg_at_10 value: 69.904 - type: ndcg_at_100 value: 72.807 - type: ndcg_at_1000 value: 73.423 - type: ndcg_at_3 value: 65.405 - type: ndcg_at_5 value: 67.86999999999999 - type: precision_at_1 value: 58.667 - type: precision_at_10 value: 9.3 - type: precision_at_100 value: 1.08 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.444 - type: precision_at_5 value: 17 - type: recall_at_1 value: 55.74400000000001 - type: recall_at_10 value: 82.122 - type: recall_at_100 value: 95.167 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 70.14399999999999 - type: recall_at_5 value: 76.417 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.86534653465347 - type: cos_sim_ap value: 96.54142419791388 - type: cos_sim_f1 value: 93.07535641547861 - type: cos_sim_precision value: 94.81327800829875 - type: cos_sim_recall value: 91.4 - type: dot_accuracy value: 99.86435643564356 - type: dot_ap value: 96.53682260449868 - type: dot_f1 value: 92.98515104966718 - type: dot_precision value: 95.27806925498426 - type: dot_recall value: 90.8 - type: euclidean_accuracy value: 99.86336633663366 - type: euclidean_ap value: 96.5228676185697 - type: euclidean_f1 value: 92.9735234215886 - type: euclidean_precision value: 94.70954356846472 - type: euclidean_recall value: 91.3 - type: manhattan_accuracy value: 99.85841584158416 - type: manhattan_ap value: 96.50392760934032 - type: manhattan_f1 value: 92.84642321160581 - type: manhattan_precision value: 92.8928928928929 - type: manhattan_recall value: 92.80000000000001 - type: max_accuracy value: 99.86534653465347 - type: max_ap value: 96.54142419791388 - type: max_f1 value: 93.07535641547861 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 61.08285408766616 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.640675309010604 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.20333913710715 - type: mrr value: 54.088813555725324 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.79465221925075 - type: cos_sim_spearman value: 30.530816059163634 - type: dot_pearson value: 31.364837244718043 - type: dot_spearman value: 30.79726823684003 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22599999999999998 - type: map_at_10 value: 1.735 - type: map_at_100 value: 8.978 - type: map_at_1000 value: 20.851 - type: map_at_3 value: 0.613 - type: map_at_5 value: 0.964 - type: mrr_at_1 value: 88 - type: mrr_at_10 value: 92.867 - type: mrr_at_100 value: 92.867 - type: mrr_at_1000 value: 92.867 - type: mrr_at_3 value: 92.667 - type: mrr_at_5 value: 92.667 - type: ndcg_at_1 value: 82 - type: ndcg_at_10 value: 73.164 - type: ndcg_at_100 value: 51.878 - type: ndcg_at_1000 value: 44.864 - type: ndcg_at_3 value: 79.184 - type: ndcg_at_5 value: 76.39 - type: precision_at_1 value: 88 - type: precision_at_10 value: 76.2 - type: precision_at_100 value: 52.459999999999994 - type: precision_at_1000 value: 19.692 - type: precision_at_3 value: 82.667 - type: precision_at_5 value: 80 - type: recall_at_1 value: 0.22599999999999998 - type: recall_at_10 value: 1.942 - type: recall_at_100 value: 12.342 - type: recall_at_1000 value: 41.42 - type: recall_at_3 value: 0.637 - type: recall_at_5 value: 1.034 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.567 - type: map_at_10 value: 13.116 - type: map_at_100 value: 19.39 - type: map_at_1000 value: 20.988 - type: map_at_3 value: 7.109 - type: map_at_5 value: 9.950000000000001 - type: mrr_at_1 value: 42.857 - type: mrr_at_10 value: 57.404999999999994 - type: mrr_at_100 value: 58.021 - type: mrr_at_1000 value: 58.021 - type: mrr_at_3 value: 54.762 - type: mrr_at_5 value: 56.19 - type: ndcg_at_1 value: 38.775999999999996 - type: ndcg_at_10 value: 30.359 - type: ndcg_at_100 value: 41.284 - type: ndcg_at_1000 value: 52.30200000000001 - type: ndcg_at_3 value: 36.744 - type: ndcg_at_5 value: 34.326 - type: precision_at_1 value: 42.857 - type: precision_at_10 value: 26.122 - type: precision_at_100 value: 8.082 - type: precision_at_1000 value: 1.559 - type: precision_at_3 value: 40.136 - type: precision_at_5 value: 35.510000000000005 - type: recall_at_1 value: 3.567 - type: recall_at_10 value: 19.045 - type: recall_at_100 value: 49.979 - type: recall_at_1000 value: 84.206 - type: recall_at_3 value: 8.52 - type: recall_at_5 value: 13.103000000000002 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 68.8394 - type: ap value: 13.454399712443099 - type: f1 value: 53.04963076364322 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.546123372948514 - type: f1 value: 60.86952793277713 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.10042955060234 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.03308100375514 - type: cos_sim_ap value: 71.08284605869684 - type: cos_sim_f1 value: 65.42539436255494 - type: cos_sim_precision value: 64.14807302231237 - type: cos_sim_recall value: 66.75461741424802 - type: dot_accuracy value: 84.68736961316088 - type: dot_ap value: 69.20524036530992 - type: dot_f1 value: 63.54893953365829 - type: dot_precision value: 63.45698500394633 - type: dot_recall value: 63.641160949868066 - type: euclidean_accuracy value: 85.07480479227513 - type: euclidean_ap value: 71.14592761009864 - type: euclidean_f1 value: 65.43814432989691 - type: euclidean_precision value: 63.95465994962216 - type: euclidean_recall value: 66.99208443271768 - type: manhattan_accuracy value: 85.06288370984085 - type: manhattan_ap value: 71.07289742593868 - type: manhattan_f1 value: 65.37585421412301 - type: manhattan_precision value: 62.816147859922175 - type: manhattan_recall value: 68.15303430079156 - type: max_accuracy value: 85.07480479227513 - type: max_ap value: 71.14592761009864 - type: max_f1 value: 65.43814432989691 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.79058485659952 - type: cos_sim_ap value: 83.7183187008759 - type: cos_sim_f1 value: 75.86921142180798 - type: cos_sim_precision value: 73.00683371298405 - type: cos_sim_recall value: 78.96519864490298 - type: dot_accuracy value: 87.0085768618776 - type: dot_ap value: 81.87467488474279 - type: dot_f1 value: 74.04188363990559 - type: dot_precision value: 72.10507114191901 - type: dot_recall value: 76.08561749307053 - type: euclidean_accuracy value: 87.8332751193387 - type: euclidean_ap value: 83.83585648120315 - type: euclidean_f1 value: 76.02582177042369 - type: euclidean_precision value: 73.36388371759989 - type: euclidean_recall value: 78.88820449645827 - type: manhattan_accuracy value: 87.87208444910156 - type: manhattan_ap value: 83.8101950642973 - type: manhattan_f1 value: 75.90454195535027 - type: manhattan_precision value: 72.44419564761039 - type: manhattan_recall value: 79.71204188481676 - type: max_accuracy value: 87.87208444910156 - type: max_ap value: 83.83585648120315 - type: max_f1 value: 76.02582177042369 license: mit language: - en --- **Recommend switching to newest [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5), which has more reasonable similarity distribution and same method of usage.** <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focus on retrieval-augmented LLMs, consisting of following projects currently: - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding), [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## News - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [LM-Cocktail](https://huggingface.co/Shitao) | English | | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

siebert/sentiment-roberta-large-english

siebert

transformers

text-classification

--- language: "en" tags: - sentiment - twitter - reviews - siebert --- ## SiEBERT - English-Language Sentiment Classification # Overview This model ("SiEBERT", prefix for "Sentiment in English") is a fine-tuned checkpoint of [RoBERTa-large](https://huggingface.co/roberta-large) ([Liu et al. 2019](https://arxiv.org/pdf/1907.11692.pdf)). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). Consequently, it outperforms models trained on only one type of text (e.g., movie reviews from the popular SST-2 benchmark) when used on new data as shown below. # Predictions on a data set If you want to predict sentiment for your own data, we provide an example script via [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb). You can load your data to a Google Drive and run the script for free on a Colab GPU. Set-up only takes a few minutes. We suggest that you manually label a subset of your data to evaluate performance for your use case. For performance benchmark values across various sentiment analysis contexts, please refer to our paper ([Hartmann et al. 2022](https://www.sciencedirect.com/science/article/pii/S0167811622000477?via%3Dihub)). [](https://colab.research.google.com/github/chrsiebert/sentiment-roberta-large-english/blob/main/sentiment_roberta_prediction_example.ipynb) # Use in a Hugging Face pipeline The easiest way to use the model for single predictions is Hugging Face's [sentiment analysis pipeline](https://huggingface.co/transformers/quicktour.html#getting-started-on-a-task-with-a-pipeline), which only needs a couple lines of code as shown in the following example: ``` from transformers import pipeline sentiment_analysis = pipeline("sentiment-analysis",model="siebert/sentiment-roberta-large-english") print(sentiment_analysis("I love this!")) ``` [](https://colab.research.google.com/github/chrsiebert/sentiment-roberta-large-english/blob/main/sentiment_roberta_pipeline.ipynb) # Use for further fine-tuning The model can also be used as a starting point for further fine-tuning of RoBERTa on your specific data. Please refer to Hugging Face's [documentation](https://huggingface.co/docs/transformers/training) for further details and example code. # Performance To evaluate the performance of our general-purpose sentiment analysis model, we set aside an evaluation set from each data set, which was not used for training. On average, our model outperforms a [DistilBERT-based model](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) (which is solely fine-tuned on the popular SST-2 data set) by more than 15 percentage points (78.1 vs. 93.2 percent, see table below). As a robustness check, we evaluate the model in a leave-one-out manner (training on 14 data sets, evaluating on the one left out), which decreases model performance by only about 3 percentage points on average and underscores its generalizability. Model performance is given as evaluation set accuracy in percent. |Dataset|DistilBERT SST-2|This model| |---|---|---| |McAuley and Leskovec (2013) (Reviews)|84.7|98.0| |McAuley and Leskovec (2013) (Review Titles)|65.5|87.0| |Yelp Academic Dataset|84.8|96.5| |Maas et al. (2011)|80.6|96.0| |Kaggle|87.2|96.0| |Pang and Lee (2005)|89.7|91.0| |Nakov et al. (2013)|70.1|88.5| |Shamma (2009)|76.0|87.0| |Blitzer et al. (2007) (Books)|83.0|92.5| |Blitzer et al. (2007) (DVDs)|84.5|92.5| |Blitzer et al. (2007) (Electronics)|74.5|95.0| |Blitzer et al. (2007) (Kitchen devices)|80.0|98.5| |Pang et al. (2002)|73.5|95.5| |Speriosu et al. (2011)|71.5|85.5| |Hartmann et al. (2019)|65.5|98.0| |**Average**|**78.1**|**93.2**| # Fine-tuning hyperparameters - learning_rate = 2e-5 - num_train_epochs = 3.0 - warmump_steps = 500 - weight_decay = 0.01 Other values were left at their defaults as listed [here](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments). # Citation and contact Please cite [this paper](https://www.sciencedirect.com/science/article/pii/S0167811622000477) (Published in the [IJRM](https://www.journals.elsevier.com/international-journal-of-research-in-marketing)) when you use our model. Feel free to reach out to [christian.siebert@uni-hamburg.de](mailto:christian.siebert@uni-hamburg.de) with any questions or feedback you may have. ``` @article{hartmann2023, title = {More than a Feeling: Accuracy and Application of Sentiment Analysis}, journal = {International Journal of Research in Marketing}, volume = {40}, number = {1}, pages = {75-87}, year = {2023}, doi = {https://doi.org/10.1016/j.ijresmar.2022.05.005}, url = {https://www.sciencedirect.com/science/article/pii/S0167811622000477}, author = {Jochen Hartmann and Mark Heitmann and Christian Siebert and Christina Schamp}, } ```

runwayml/stable-diffusion-inpainting

runwayml

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: false library_name: diffusers extra_gated_prompt: |- One more step before getting this model. This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well. extra_gated_fields: I have read the License and agree with its terms: checkbox --- Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask. The **Stable-Diffusion-Inpainting** was initialized with the weights of the [Stable-Diffusion-v-1-2](https://steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original). First 595k steps regular training, then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning to improve classifier-free [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. [](https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb) :-------------------------:|:-------------------------:| ## Examples: You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion). ### Diffusers ```python from diffusers import StableDiffusionInpaintPipeline pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", revision="fp16", torch_dtype=torch.float16, ) prompt = "Face of a yellow cat, high resolution, sitting on a park bench" #image and mask_image should be PIL images. #The mask structure is white for inpainting and black for keeping as is image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0] image.save("./yellow_cat_on_park_bench.png") ``` **How it works:** `image` | `mask_image` :-------------------------:|:-------------------------:| <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="300"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="300"/> `prompt` | `Output` :-------------------------:|:-------------------------:| <span style="position: relative;bottom: 150px;">Face of a yellow cat, high resolution, sitting on a park bench</span> | <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/test.png" alt="drawing" width="300"/> ### Original GitHub Repository 1. Download the weights [sd-v1-5-inpainting.ckpt](https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt) 2. Follow instructions [here](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion). ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/runwayml/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide six checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`, `sd-v1-4.ckpt`, `sd-v1-5.ckpt` and `sd-v1-5-inpainting.ckpt` which were trained as follows, - `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. 515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - `sd-v1-4.ckpt`: Resumed from stable-diffusion-v1-2.225,000 steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - `sd-v1-5.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. - `sd-v1-5-inpaint.ckpt`: Resumed from sd-v1-2.ckpt. 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. Then 440k steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything. - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Inpainting Evaluation To assess the performance of the inpainting model, we used the same evaluation protocol as in our [LDM paper](https://arxiv.org/abs/2112.10752). Since the Stable Diffusion Inpainting Model acccepts a text input, we simply used a fixed prompt of `photograph of a beautiful empty scene, highest quality settings`. | Model | FID | LPIPS | |-----------------------------|------|------------------| | Stable Diffusion Inpainting | 1.00 | 0.141 (+- 0.082) | | Latent Diffusion Inpainting | 1.50 | 0.137 (+- 0.080) | | CoModGAN | 1.82 | 0.15 | | LaMa | 2.21 | 0.134 (+- 0.080) | ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

facebook/sam-vit-huge

facebook

transformers

mask-generation

--- license: apache-2.0 tags: - vision --- # Model Card for Segment Anything Model (SAM) - ViT Huge (ViT-H) version <p> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-architecture.png" alt="Model architecture"> <em> Detailed architecture of Segment Anything Model (SAM).</em> </p> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Citation](#citation) # TL;DR [Link to original repository](https://github.com/facebookresearch/segment-anything) | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-beancans.png" alt="Snow" width="600" height="600"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-dog-masks.png" alt="Forest" width="600" height="600"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car-seg.png" alt="Mountains" width="600" height="600"> | |---------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------| The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. The abstract of the paper states: > We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at [https://segment-anything.com](https://segment-anything.com) to foster research into foundation models for computer vision. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the original [SAM model card](https://github.com/facebookresearch/segment-anything). # Model Details The SAM model is made up of 3 modules: - The `VisionEncoder`: a VIT based image encoder. It computes the image embeddings using attention on patches of the image. Relative Positional Embedding is used. - The `PromptEncoder`: generates embeddings for points and bounding boxes - The `MaskDecoder`: a two-ways transformer which performs cross attention between the image embedding and the point embeddings (->) and between the point embeddings and the image embeddings. The outputs are fed - The `Neck`: predicts the output masks based on the contextualized masks produced by the `MaskDecoder`. # Usage ## Prompted-Mask-Generation ```python from PIL import Image import requests from transformers import SamModel, SamProcessor model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png" raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") input_points = [[[450, 600]]] # 2D localization of a window ``` ```python inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to("cuda") outputs = model(**inputs) masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()) scores = outputs.iou_scores ``` Among other arguments to generate masks, you can pass 2D locations on the approximate position of your object of interest, a bounding box wrapping the object of interest (the format should be x, y coordinate of the top right and bottom left point of the bounding box), a segmentation mask. At this time of writing, passing a text as input is not supported by the official model according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844). For more details, refer to this notebook, which shows a walk throught of how to use the model, with a visual example! ## Automatic-Mask-Generation The model can be used for generating segmentation masks in a "zero-shot" fashion, given an input image. The model is automatically prompt with a grid of `1024` points which are all fed to the model. The pipeline is made for automatic mask generation. The following snippet demonstrates how easy you can run it (on any device! Simply feed the appropriate `points_per_batch` argument) ```python from transformers import pipeline generator = pipeline("mask-generation", device = 0, points_per_batch = 256) image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png" outputs = generator(image_url, points_per_batch = 256) ``` Now to display the image: ```python import matplotlib.pyplot as plt from PIL import Image import numpy as np def show_mask(mask, ax, random_color=False): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6]) h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) ax.imshow(mask_image) plt.imshow(np.array(raw_image)) ax = plt.gca() for mask in outputs["masks"]: show_mask(mask, ax=ax, random_color=True) plt.axis("off") plt.show() ``` This should give you the following  # Citation If you use this model, please use the following BibTeX entry. ``` @article{kirillov2023segany, title={Segment Anything}, author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross}, journal={arXiv:2304.02643}, year={2023} } ```

NousResearch/Llama-2-7b-chat-hf

NousResearch

transformers

text-generation

--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

coqui/XTTS-v2

coqui

coqui

text-to-speech

--- license: other license_name: coqui-public-model-license license_link: https://coqui.ai/cpml library_name: coqui pipeline_tag: text-to-speech widget: - text: "Once when I was six years old I saw a magnificent picture" --- # ⓍTTS ⓍTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 6-second audio clip. There is no need for an excessive amount of training data that spans countless hours. This is the same or similar model to what powers [Coqui Studio](https://coqui.ai/) and [Coqui API](https://docs.coqui.ai/docs). ### Features - Supports 17 languages. - Voice cloning with just a 6-second audio clip. - Emotion and style transfer by cloning. - Cross-language voice cloning. - Multi-lingual speech generation. - 24khz sampling rate. ### Updates over XTTS-v1 - 2 new languages; Hungarian and Korean - Architectural improvements for speaker conditioning. - Enables the use of multiple speaker references and interpolation between speakers. - Stability improvements. - Better prosody and audio quality across the board. ### Languages XTTS-v2 supports 17 languages: **English (en), Spanish (es), French (fr), German (de), Italian (it), Portuguese (pt), Polish (pl), Turkish (tr), Russian (ru), Dutch (nl), Czech (cs), Arabic (ar), Chinese (zh-cn), Japanese (ja), Hungarian (hu), Korean (ko) Hindi (hi)**. Stay tuned as we continue to add support for more languages. If you have any language requests, feel free to reach out! ### Code The [code-base](https://github.com/coqui-ai/TTS) supports inference and [fine-tuning](https://tts.readthedocs.io/en/latest/models/xtts.html#training). ### Demo Spaces - [XTTS Space](https://huggingface.co/spaces/coqui/xtts) : You can see how model performs on supported languages, and try with your own reference or microphone input - [XTTS Voice Chat with Mistral or Zephyr](https://huggingface.co/spaces/coqui/voice-chat-with-mistral) : You can experience streaming voice chat with Mistral 7B Instruct or Zephyr 7B Beta | | | | ------------------------------- | --------------------------------------- | | 🐸💬 **CoquiTTS** | [coqui/TTS on Github](https://github.com/coqui-ai/TTS)| | 💼 **Documentation** | [ReadTheDocs](https://tts.readthedocs.io/en/latest/) | 👩‍💻 **Questions** | [GitHub Discussions](https://github.com/coqui-ai/TTS/discussions) | | 🗯 **Community** | [Discord](https://discord.gg/5eXr5seRrv) | ### License This model is licensed under [Coqui Public Model License](https://coqui.ai/cpml). There's a lot that goes into a license for generative models, and you can read more of [the origin story of CPML here](https://coqui.ai/blog/tts/cpml). ### Contact Come and join in our 🐸Community. We're active on [Discord](https://discord.gg/fBC58unbKE) and [Twitter](https://twitter.com/coqui_ai). You can also mail us at info@coqui.ai. Using 🐸TTS API: ```python from TTS.api import TTS tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True) # generate speech by cloning a voice using default settings tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", file_path="output.wav", speaker_wav="/path/to/target/speaker.wav", language="en") ``` Using 🐸TTS Command line: ```console tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \ --text "Bugün okula gitmek istemiyorum." \ --speaker_wav /path/to/target/speaker.wav \ --language_idx tr \ --use_cuda true ``` Using the model directly: ```python from TTS.tts.configs.xtts_config import XttsConfig from TTS.tts.models.xtts import Xtts config = XttsConfig() config.load_json("/path/to/xtts/config.json") model = Xtts.init_from_config(config) model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", eval=True) model.cuda() outputs = model.synthesize( "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", config, speaker_wav="/data/TTS-public/_refclips/3.wav", gpt_cond_len=3, language="en", ) ```

openai-community/gpt2-medium

openai-community

transformers

text-generation

--- language: en license: mit --- # GPT-2 Medium ## Model Details **Model Description:** GPT-2 Medium is the **355M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - **Developed by:** OpenAI, see [associated research paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [GitHub repo](https://github.com/openai/gpt-2) for model developers. - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** [Modified MIT License](https://github.com/openai/gpt-2/blob/master/LICENSE) - **Related Models:** [GPT2](https://huggingface.co/gpt2), [GPT2-Large](https://huggingface.co/gpt2-large) and [GPT2-XL](https://huggingface.co/gpt2-xl) - **Resources for more information:** - [Research Paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) - [OpenAI Blog Post](https://openai.com/blog/better-language-models/) - [GitHub Repo](https://github.com/openai/gpt-2) - [OpenAI Model Card for GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md) - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large ## How to Get Started with the Model Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2-medium') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, I'm a language. I'm a compiler, I'm a parser, I'm a server process. I"}, {'generated_text': "Hello, I'm a language model, and I'd like to join an existing team. What can I do to get started?\n\nI'd"}, {'generated_text': "Hello, I'm a language model, why does my code get created? Can't I just copy it? But why did my code get created when"}, {'generated_text': "Hello, I'm a language model, a functional language...\n\nI'm a functional language. Is it hard? A little, yes. But"}, {'generated_text': "Hello, I'm a language model, not an object model.\n\nIn a nutshell, I need to give me objects from which I can get"}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium') model = GPT2Model.from_pretrained('gpt2-medium') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium') model = TFGPT2Model.from_pretrained('gpt2-medium') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Uses #### Direct Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > The primary intended users of these models are AI researchers and practitioners. > > We primarily imagine these language models will be used by researchers to better understand the behaviors, capabilities, biases, and constraints of large-scale generative language models. #### Downstream Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Here are some secondary use cases we believe are likely: > > - Writing assistance: Grammar assistance, autocompletion (for normal prose or code) > - Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art. > - Entertainment: Creation of games, chat bots, and amusing generations. #### Misuse and Out-of-scope Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2-medium') >>> set_seed(42) >>> generator("The man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The man worked as a security guard in a military'}, {'generated_text': 'The man worked as a salesman in Mexico and eventually'}, {'generated_text': 'The man worked as a supervisor at the department for'}, {'generated_text': 'The man worked as a cleaner for the same corporation'}, {'generated_text': 'The man worked as a barman and was involved'}] >>> set_seed(42) >>> generator("The woman worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The woman worked as a social worker in a children'}, {'generated_text': 'The woman worked as a marketing manager, and her'}, {'generated_text': 'The woman worked as a customer service agent in a'}, {'generated_text': 'The woman worked as a cleaner for the same corporation'}, {'generated_text': 'The woman worked as a barista and was involved'}] ``` This bias will also affect all fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. ## Training #### Training Data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). #### Training Procedure The model is pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. ## Evaluation The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). #### Testing Data, Factors and Metrics The model authors write in the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) that: > Since our model operates on a byte level and does not require lossy pre-processing or tokenization, we can evaluate it on any language model benchmark. Results on language modeling datasets are commonly reported in a quantity which is a scaled or ex- ponentiated version of the average negative log probability per canonical prediction unit - usually a character, a byte, or a word. We evaluate the same quantity by computing the log-probability of a dataset according to a WebText LM and dividing by the number of canonical units. For many of these datasets, WebText LMs would be tested significantly out- of-distribution, having to predict aggressively standardized text, tokenization artifacts such as disconnected punctuation and contractions, shuffled sentences, and even the string <UNK> which is extremely rare in WebText - occurring only 26 times in 40 billion bytes. We report our main results...using invertible de-tokenizers which remove as many of these tokenization / pre-processing artifacts as possible. Since these de-tokenizers are invertible, we can still calculate the log probability of a dataset and they can be thought of as a simple form of domain adaptation. #### Results The model achieves the following results without any fine-tuning (zero-shot): | Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW | |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| | (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) | | | 15.60 | 55.48 | 92.35 | 87.1 | 22.76 | 47.33 | 1.01 | 1.06 | 26.37 | 55.72 | ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Unknown - **Hours used:** Unknown - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown ## Technical Specifications See the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details. ## Citation Information ```bibtex @article{radford2019language, title={Language models are unsupervised multitask learners}, author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others}, journal={OpenAI blog}, volume={1}, number={8}, pages={9}, year={2019} } ``` ## Model Card Authors This model card was written by the Hugging Face team.

Joe99/FinetuneBERTClsFAQ

Joe99

transformers

text-classification

Entry not found

sshleifer/distilbart-cnn-12-6

sshleifer

transformers

summarization

--- language: en tags: - summarization license: apache-2.0 datasets: - cnn_dailymail - xsum thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png --- ### Usage This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information. ### Metrics for DistilBART models | Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | |:---------------------------|------------:|----------------------:|----------:|----------:|----------:| | distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 | | distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 | | distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 | | distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 | | bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 | | distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 | | bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 | | distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 | | distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 | | distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 |

facebook/wav2vec2-base

facebook

transformers

--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # Wav2Vec2-Base [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. [Paper](https://arxiv.org/abs/2006.11477) Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli **Abstract** We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model.

Helsinki-NLP/opus-mt-ar-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-ar-en * source languages: ar * target languages: en * OPUS readme: [ar-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ar-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/ar-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ar-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ar-en/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.ar.en | 49.4 | 0.661 |

nlpie/compact-biobert

nlpie

transformers

fill-mask

--- title: README emoji: 🏃 colorFrom: gray colorTo: purple sdk: static pinned: false license: mit --- # Model Description CompactBioBERT is a distilled version of the [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2?text=The+goal+of+life+is+%5BMASK%5D.) model which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset. # Distillation Procedure This model has the same overall architecture as [DistilBioBERT](https://huggingface.co/nlpie/distil-biobert) with the difference that here we combine the distillation approaches of DistilBioBERT and [TinyBioBERT](https://huggingface.co/nlpie/tiny-biobert). We utilise the same initialisation technique as in [DistilBioBERT](https://huggingface.co/nlpie/distil-biobert), and apply a layer-to-layer distillation with three major components, namely, MLM, layer, and output distillation. # Initialisation Following [DistilBERT](https://huggingface.co/distilbert-base-uncased?text=The+goal+of+life+is+%5BMASK%5D.), we initialise the student model by taking weights from every other layer of the teacher. # Architecture In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 28996. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million parameters. # Citation If you use this model, please consider citing the following paper: ```bibtex @article{rohanian2023effectiveness, title={On the effectiveness of compact biomedical transformers}, author={Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A}, journal={Bioinformatics}, volume={39}, number={3}, pages={btad103}, year={2023}, publisher={Oxford University Press} } ```

mattmdjaga/segformer_b2_clothes

mattmdjaga

transformers

image-segmentation

--- license: mit tags: - vision - image-segmentation widget: - src: https://images.unsplash.com/photo-1643310325061-2beef64926a5?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8cmFjb29uc3xlbnwwfHwwfHw%3D&w=1000&q=80 example_title: Person - src: https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg example_title: Person datasets: - mattmdjaga/human_parsing_dataset --- # Segformer B2 fine-tuned for clothes segmentation SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation but can also be used for human segmentation. The dataset on hugging face is called "mattmdjaga/human_parsing_dataset". **NEW** - **[Training code](https://github.com/mattmdjaga/segformer_b2_clothes)**. Right now it only contains the pure code with some comments, but soon I'll add a colab notebook version and a blog post with it to make it more friendly. ```python from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation from PIL import Image import requests import matplotlib.pyplot as plt import torch.nn as nn processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes") model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes") url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits.cpu() upsampled_logits = nn.functional.interpolate( logits, size=image.size[::-1], mode="bilinear", align_corners=False, ) pred_seg = upsampled_logits.argmax(dim=1)[0] plt.imshow(pred_seg) ``` Labels: 0: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 16: "Bag", 17: "Scarf" ### Evaluation | Label Index | Label Name | Category Accuracy | Category IoU | |:-------------:|:----------------:|:-----------------:|:------------:| | 0 | Background | 0.99 | 0.99 | | 1 | Hat | 0.73 | 0.68 | | 2 | Hair | 0.91 | 0.82 | | 3 | Sunglasses | 0.73 | 0.63 | | 4 | Upper-clothes | 0.87 | 0.78 | | 5 | Skirt | 0.76 | 0.65 | | 6 | Pants | 0.90 | 0.84 | | 7 | Dress | 0.74 | 0.55 | | 8 | Belt | 0.35 | 0.30 | | 9 | Left-shoe | 0.74 | 0.58 | | 10 | Right-shoe | 0.75 | 0.60 | | 11 | Face | 0.92 | 0.85 | | 12 | Left-leg | 0.90 | 0.82 | | 13 | Right-leg | 0.90 | 0.81 | | 14 | Left-arm | 0.86 | 0.74 | | 15 | Right-arm | 0.82 | 0.73 | | 16 | Bag | 0.91 | 0.84 | | 17 | Scarf | 0.63 | 0.29 | Overall Evaluation Metrics: - Evaluation Loss: 0.15 - Mean Accuracy: 0.80 - Mean IoU: 0.69 ### License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2105-15203, author = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo}, title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, journal = {CoRR}, volume = {abs/2105.15203}, year = {2021}, url = {https://arxiv.org/abs/2105.15203}, eprinttype = {arXiv}, eprint = {2105.15203}, timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

lucadiliello/BLEURT-20-D12

lucadiliello

transformers

text-classification

This model is based on a custom Transformer model that can be installed with: ```bash pip install git+https://github.com/lucadiliello/bleurt-pytorch.git ``` Now load the model and make predictions with: ```python import torch from bleurt_pytorch import BleurtConfig, BleurtForSequenceClassification, BleurtTokenizer config = BleurtConfig.from_pretrained('lucadiliello/BLEURT-20-D12') model = BleurtForSequenceClassification.from_pretrained('lucadiliello/BLEURT-20-D12') tokenizer = BleurtTokenizer.from_pretrained('lucadiliello/BLEURT-20-D12') references = ["a bird chirps by the window", "this is a random sentence"] candidates = ["a bird chirps by the window", "this looks like a random sentence"] model.eval() with torch.no_grad(): inputs = tokenizer(references, candidates, padding='longest', return_tensors='pt') res = model(**inputs).logits.flatten().tolist() print(res) # [0.9604414105415344, 0.8080050349235535] ``` Take a look at this [repository](https://github.com/lucadiliello/bleurt-pytorch) for the definition of `BleurtConfig`, `BleurtForSequenceClassification` and `BleurtTokenizer` in PyTorch.

jonatasgrosman/wav2vec2-large-xlsr-53-japanese

jonatasgrosman

transformers

automatic-speech-recognition

--- language: ja datasets: - common_voice metrics: - wer - cer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Japanese by Jonatas Grosman results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ja type: common_voice args: ja metrics: - name: Test WER type: wer value: 81.80 - name: Test CER type: cer value: 20.16 --- # Fine-tuned XLSR-53 large model for speech recognition in Japanese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyong/css10) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint ## Usage The model can be used directly (without a language model) as follows... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-japanese") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "ja" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) for i, predicted_sentence in enumerate(predicted_sentences): print("-" * 100) print("Reference:", test_dataset[i]["sentence"]) print("Prediction:", predicted_sentence) ``` | Reference | Prediction | | ------------- | ------------- | | 祖母は、おおむね機嫌よく、サイコロをころがしている。 | 人母は重にきね起くさいがしている | | 財布をなくしたので、交番へ行きます。 | 財布をなく手端ので勾番へ行きます | | 飲み屋のおやじ、旅館の主人、医者をはじめ、交際のある人にきいてまわったら、みんな、私より収入が多いはずなのに、税金は安い。 | ノ宮屋のお親じ旅館の主に医者をはじめ交際のアル人トに聞いて回ったらみんな私より収入が多いはなうに税金は安い | | 新しい靴をはいて出かけます。 | だらしい靴をはいて出かけます | | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表現することがある | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表弁することがある | | 松井さんはサッカーより野球のほうが上手です。 | 松井さんはサッカーより野球のほうが上手です | | 新しいお皿を使います。 | 新しいお皿を使います | | 結婚以来三年半ぶりの東京も、旧友とのお酒も、夜行列車も、駅で寝て、朝を待つのも久しぶりだ。 | 結婚ル二来三年半降りの東京も吸とのお酒も野越者も駅で寝て朝を待つの久しぶりた | | これまで、少年野球、ママさんバレーなど、地域スポーツを支え、市民に密着してきたのは、無数のボランティアだった。 | これまで少年野球<unk>三バレーなど地域スポーツを支え市民に満着してきたのは娘数のボランティアだった | | 靴を脱いで、スリッパをはきます。 | 靴を脱いでスイパーをはきます | ## Evaluation The model can be evaluated as follows on the Japanese test data of Common Voice. ```python import torch import re import librosa from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "ja" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese" DEVICE = "cuda" CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"] test_dataset = load_dataset("common_voice", LANG_ID, split="test") wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) model.to(DEVICE) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): with warnings.catch_warnings(): warnings.simplefilter("ignore") speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) predictions = [x.upper() for x in result["pred_strings"]] references = [x.upper() for x in result["sentence"]] print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") ``` **Test Result**: In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-10). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used. | Model | WER | CER | | ------------- | ------------- | ------------- | | jonatasgrosman/wav2vec2-large-xlsr-53-japanese | **81.80%** | **20.16%** | | vumichien/wav2vec2-large-xlsr-japanese | 1108.86% | 23.40% | | qqhann/w2v_hf_jsut_xlsr53 | 1012.18% | 70.77% | ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-japanese, title={Fine-tuned {XLSR}-53 large model for speech recognition in {J}apanese}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese}}, year={2021} } ```

openai/whisper-base

openai

transformers

automatic-speech-recognition

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - no - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition - hf-asr-leaderboard widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac model-index: - name: whisper-base results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: 5.008769117619326 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (other) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 12.84936273212057 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: language: hi metrics: - name: Test WER type: wer value: 131 pipeline_tag: automatic-speech-recognition license: apache-2.0 --- # Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need for fine-tuning. Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper). **Disclaimer**: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the *same* language as the audio. For speech translation, the model predicts transcriptions to a *different* language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: | Size | Parameters | English-only | Multilingual | |----------|------------|------------------------------------------------------|-----------------------------------------------------| | tiny | 39 M | [✓](https://huggingface.co/openai/whisper-tiny.en) | [✓](https://huggingface.co/openai/whisper-tiny) | | base | 74 M | [✓](https://huggingface.co/openai/whisper-base.en) | [✓](https://huggingface.co/openai/whisper-base) | | small | 244 M | [✓](https://huggingface.co/openai/whisper-small.en) | [✓](https://huggingface.co/openai/whisper-small) | | medium | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) | | large | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) | | large-v2 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v2) | # Usage To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the `<|startoftranscript|>` token 2. The second token is the language token (e.g. `<|en|>` for English) 3. The third token is the "task token". It can take one of two values: `<|transcribe|>` for speech recognition or `<|translate|>` for speech translation 4. In addition, a `<|notimestamps|>` token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: ``` <|startoftranscript|> <|en|> <|transcribe|> <|notimestamps|> ``` Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: ```python model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe") ``` Which forces the model to predict in English under the task of speech recognition. ## Transcription ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-base") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base") >>> model.config.forced_decoder_ids = None >>> # load dummy dataset and read audio files >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) ['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'] ``` The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-base") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids) ['<|startoftranscript|><|fr|><|transcribe|><|notimestamps|> Un vrai travail intéressant va enfin être mené sur ce sujet.<|endoftext|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Un vrai travail intéressant va enfin être mené sur ce sujet.'] ``` ## Translation Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-base") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' A very interesting work, we will finally be given on this subject.'] ``` ## Evaluation This code snippet shows how to evaluate Whisper Base on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr): ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import load >>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") >>> processor = WhisperProcessor.from_pretrained("openai/whisper-base") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base").to("cuda") >>> def map_to_pred(batch): >>> audio = batch["audio"] >>> input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features >>> batch["reference"] = processor.tokenizer._normalize(batch['text']) >>> >>> with torch.no_grad(): >>> predicted_ids = model.generate(input_features.to("cuda"))[0] >>> transcription = processor.decode(predicted_ids) >>> batch["prediction"] = processor.tokenizer._normalize(transcription) >>> return batch >>> result = librispeech_test_clean.map(map_to_pred) >>> wer = load("wer") >>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"])) 5.082316555716899 ``` ## Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from datasets import load_dataset >>> device = "cuda:0" if torch.cuda.is_available() else "cpu" >>> pipe = pipeline( >>> "automatic-speech-recognition", >>> model="openai/whisper-base", >>> chunk_length_s=30, >>> device=device, >>> ) >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> prediction = pipe(sample.copy(), batch_size=8)["text"] " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." >>> # we can also return timestamps for the predictions >>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"] [{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.', 'timestamp': (0.0, 5.44)}] ``` Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm. ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through *fine-tuning*. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```

BAAI/bge-reranker-large

BAAI

transformers

text-classification

--- license: mit language: - en - zh tags: - mteb model-index: - name: bge-reranker-base results: - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 81.27206722525007 - type: mrr value: 84.14238095238095 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 84.10369934291236 - type: mrr value: 86.79376984126984 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 35.4600511272538 - type: mrr value: 34.602380952380946 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 67.27728847727172 - type: mrr value: 77.1315192743764 --- **We have updated the [new reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), supporting larger lengths, more languages, and achieving better performance.** <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> **More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).** [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Embedding Model**: [Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/visual), [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - **Reranker Model**: [llm rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 3/18/2024: Release new [rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation. - 3/18/2024: Release [Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/visual), equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text data. - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. Refer to this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) for the fine-tuning for reranker </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` #### Usage reranker with the ONNX files ```python from optimum.onnxruntime import ORTModelForSequenceClassification # type: ignore import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base') model_ort = ORTModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base', file_name="onnx/model.onnx") # Sentences we want sentence embeddings for pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] # Tokenize sentences encoded_input = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt') scores_ort = model_ort(**encoded_input, return_dict=True).logits.view(-1, ).float() # Compute token embeddings with torch.inference_mode(): scores = model_ort(**encoded_input, return_dict=True).logits.view(-1, ).float() # scores and scores_ort are identical ``` #### Usage reranker with infinity Its also possible to deploy the onnx/torch files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs query='what is a panda?' docs = ['The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear', "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs(model_name_or_path = "BAAI/bge-reranker-base", device="cpu", engine="torch" # or engine="optimum" for onnx )) async def main(): async with engine: ranking, usage = await engine.rerank(query=query, docs=docs) print(list(zip(ranking, docs))) asyncio.run(main()) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.