Datasets:

librarian-bots

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model_cards_with_metadata_test

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EleutherAI/gpt-neo-125m

EleutherAI

transformers

text-generation

--- language: - en tags: - text generation - pytorch - causal-lm license: mit datasets: - EleutherAI/pile --- # GPT-Neo 125M ## Model Description GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model. ## Training data GPT-Neo 125M was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. ## Training procedure This model was trained on the Pile for 300 billion tokens over 572,300 steps. It was trained as a masked autoregressive language model, using cross-entropy loss. ## Intended Use and Limitations 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 model is best at what it was pretrained for however, which is generating texts from a prompt. ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-125M') >>> generator("EleutherAI has", do_sample=True, min_length=20) [{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}] ``` ### Limitations and Biases GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. ## Eval results TBD ### Down-Stream Applications TBD ### BibTeX entry and citation info To cite this model, use ```bibtex @software{gpt-neo, author = {Black, Sid and Leo, Gao and Wang, Phil and Leahy, Connor and Biderman, Stella}, title = {{GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow}}, month = mar, year = 2021, note = {{If you use this software, please cite it using these metadata.}}, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.5297715}, url = {https://doi.org/10.5281/zenodo.5297715} } @article{gao2020pile, title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neo-125m) | Metric | Value | |-----------------------|---------------------------| | Avg. | 25.79 | | ARC (25-shot) | 22.95 | | HellaSwag (10-shot) | 30.26 | | MMLU (5-shot) | 25.97 | | TruthfulQA (0-shot) | 45.58 | | Winogrande (5-shot) | 51.78 | | GSM8K (5-shot) | 0.3 | | DROP (3-shot) | 3.69 |

vikp/surya_rec

vikp

transformers

--- license: cc-by-nc-sa-4.0 --- Text recognition (ocr) model for [surya](https://github.com/VikParuchuri/surya). See repo for details.

emilianJR/chilloutmix_NiPrunedFp32Fix

emilianJR

diffusers

text-to-image

--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Diffuser model for this SD checkpoint: https://civitai.com/models/6424/chilloutmix **emilianJR/chilloutmix_NiPrunedFp32Fix** is the HuggingFace diffuser that you can use with **diffusers.StableDiffusionPipeline()**. Examples | Examples | Examples ---- | ---- | ----  |  |   |  |  ------- ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "emilianJR/chilloutmix_NiPrunedFp32Fix" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "YOUR PROMPT" image = pipe(prompt).images[0] image.save("image.png") ``` ## License 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: [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

facebook/esmfold_v1

facebook

transformers

--- license: mit --- # ESMFold ESMFold is a state-of-the-art end-to-end protein folding model based on an ESM-2 backbone. It does not require any lookup or MSA step, and therefore does not require any external databases to be present in order to make predictions. As a result, inference time is very significantly faster than AlphaFold2. For details on the model architecture and training, please refer to the [accompanying paper](https://www.science.org/doi/10.1126/science.ade2574). If you're interested in using ESMFold in practice, please check out the associated [tutorial notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_folding.ipynb).

jonatasgrosman/wav2vec2-large-xlsr-53-finnish

jonatasgrosman

transformers

automatic-speech-recognition

--- language: fi 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 Finnish by Jonatas Grosman results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice fi type: common_voice args: fi metrics: - name: Test WER type: wer value: 41.60 - name: Test CER type: cer value: 8.23 --- # Fine-tuned XLSR-53 large model for speech recognition in Finnish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Finnish using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10). 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-finnish") 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 = "fi" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-finnish" SAMPLES = 5 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 | | ------------- | ------------- | | MYSTEERIMIES OLI OPPINUT MORAALINSA TARUISTA, ELOKUVISTA JA PELEISTÄ. | MYSTEERIMIES OLI OPPINUT MORALINSA TARUISTA ELOKUVISTA JA PELEISTÄ | | ÄÄNESTIN MIETINNÖN PUOLESTA! | ÄÄNESTIN MIETINNÖN PUOLESTA | | VAIN TUNTIA AIKAISEMMIN OLIMME MIEHENI KANSSA TUNTENEET SUURINTA ILOA. | PAIN TUNTIA AIKAISEMMIN OLIN MIEHENI KANSSA TUNTENEET SUURINTA ILAA | | ENSIMMÄISELLE MIEHELLE SAI KOLME LASTA. | ENSIMMÄISELLE MIEHELLE SAI KOLME LASTA | | ÄÄNESTIN MIETINNÖN PUOLESTA, SILLÄ POHJIMMILTAAN SIINÄ VASTUSTETAAN TÄTÄ SUUNTAUSTA. | ÄÄNESTIN MIETINNÖN PUOLESTA SILLÄ POHJIMMILTAAN SIINÄ VASTOTTETAAN TÄTÄ SUUNTAUSTA | | TÄHDENLENTOJENKO VARALTA MINÄ SEN OLISIN TÄNNE KUSKANNUT? | TÄHDEN LENTOJENKO VARALTA MINÄ SEN OLISIN TÄNNE KUSKANNUT | | SIITÄ SE TULEE. | SIITA SE TULEE | | NIIN, KUULUU KIROUS, JA KAUHEA KARJAISU. | NIIN KUULUU KIROUS JA KAUHEA KARJAISU | | ARKIT KUN OVAT NÄES ELEMENTTIRAKENTEISIA. | ARKIT KUN OVAT MÄISS' ELÄMÄTTEROKENTEISIÄ | | JÄIN ALUKSEN SISÄÄN, MUTTA KUULIN OVEN LÄPI, ETTÄ ULKOPUOLELLA ALKOI TAPAHTUA. | JAKALOKSEHÄN SISÄL MUTTA KUULIN OVENLAPI ETTÄ ULKA KUOLLALLA ALKOI TAPAHTUA | ## Evaluation The model can be evaluated as follows on the Finnish 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 = "fi" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-finnish" 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 2021-04-21). 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 | | ------------- | ------------- | ------------- | | aapot/wav2vec2-large-xlsr-53-finnish | **32.51%** | **5.34%** | | Tommi/wav2vec2-large-xlsr-53-finnish | 35.22% | 5.81% | | vasilis/wav2vec2-large-xlsr-53-finnish | 38.24% | 6.49% | | jonatasgrosman/wav2vec2-large-xlsr-53-finnish | 41.60% | 8.23% | | birgermoell/wav2vec2-large-xlsr-finnish | 53.51% | 9.18% | ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-finnish, title={Fine-tuned {XLSR}-53 large model for speech recognition in {F}innish}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-finnish}}, year={2021} } ```

sentence-transformers/sentence-t5-large

sentence-transformers

sentence-transformers

sentence-similarity

--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity pipeline_tag: sentence-similarity --- # sentence-transformers/sentence-t5-large This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks. This model was converted from the Tensorflow model [st5-large-1](https://tfhub.dev/google/sentence-t5/st5-large/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results. The model uses only the encoder from a T5-large model. The weights are stored in FP16. ## 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/sentence-t5-large') embeddings = model.encode(sentences) print(embeddings) ``` The model requires sentence-transformers version 2.2.0 or newer. ## 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/sentence-t5-large) ## Citing & Authors If you find this model helpful, please cite the respective publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)

h-e-l-l-o/email-spam-classification-merged

h-e-l-l-o

transformers

text-classification

--- datasets: - legacy107/spamming-email-classification language: - en metrics: - accuracy library_name: transformers ---

microsoft/Phi-3-mini-128k-instruct

microsoft

transformers

text-generation

--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE language: - en pipeline_tag: text-generation tags: - nlp - code widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- ## Model Summary The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support. After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures. When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai) + [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) | | Short Context | Long Context | | ------- | ------------- | ------------ | | Mini | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)| | Small | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)| | Medium | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)| | Vision | | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)| ## Intended Uses **Primary use cases** The model is intended for commercial and research use in English. The model provides uses for applications which require: 1) Memory/compute constrained environments 2) Latency bound scenarios 3) Strong reasoning (especially code, math and logic) Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. **Use case considerations** Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. ## How to Use Phi-3 Mini-128K-Instruct has been integrated in the development version (4.41.0.dev0) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following: * When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function. * Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source. The current `transformers` version can be verified with: `pip list | grep transformers`. ### Tokenizer Phi-3 Mini-128K-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size. ### Chat Format Given the nature of the training data, the Phi-3 Mini-128K-Instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follow: ```markdown <|user|>\nQuestion<|end|>\n<|assistant|> ``` For example: ```markdown <|user|> How to explain Internet for a medieval knight?<|end|> <|assistant|> ``` where the model generates the text after `<|assistant|>`. In case of few-shots prompt, the prompt can be formatted as the following: ```markdown <|user|> I am going to Paris, what should I see?<|end|> <|assistant|> Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|> <|user|> What is so great about #1?<|end|> <|assistant|> ``` ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline torch.random.manual_seed(0) model = AutoModelForCausalLM.from_pretrained( "microsoft/Phi-3-mini-128k-instruct", device_map="cuda", torch_dtype="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct") messages = [ {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, ] pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, ) generation_args = { "max_new_tokens": 500, "return_full_text": False, "temperature": 0.0, "do_sample": False, } output = pipe(messages, **generation_args) print(output[0]['generated_text']) ``` *Some applications/frameworks might not include a BOS token (`<s>`) at the start of the conversation. Please ensure that it is included since it provides more reliable results.* ## Responsible AI Considerations Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. ## Training ### Model * Architecture: Phi-3 Mini-128K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines. * Inputs: Text. It is best suited for prompts using chat format. * Context length: 128K tokens * GPUs: 512 H100-80G * Training time: 7 days * Training data: 3.3T tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models. ### Datasets Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of 1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; 2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); 3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. ### Fine-tuning A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided [here](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/sample_finetune.py). ## Benchmarks We report the results for Phi-3-Mini-128K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Phi-2, Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5. All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation. As is now standard, we use few-shot prompts to evaluate the models, at temperature 0. The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3. More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model. The number of k–shot examples is listed per-benchmark. | | Phi-3-Mini-128K-In<br>3.8b | Phi-3-Small<br>7b (preview) | Phi-3-Medium<br>14b (preview) | Phi-2<br>2.7b | Mistral<br>7b | Gemma<br>7b | Llama-3-In<br>8b | Mixtral<br>8x7b | GPT-3.5<br>version 1106 | |---|---|---|---|---|---|---|---|---|---| | MMLU <br>5-Shot | 68.1 | 75.3 | 78.2 | 56.3 | 61.7 | 63.6 | 66.5 | 68.4 | 71.4 | | HellaSwag <br> 5-Shot | 74.5 | 78.7 | 83.2 | 53.6 | 58.5 | 49.8 | 71.1 | 70.4 | 78.8 | | ANLI <br> 7-Shot | 52.8 | 55.0 | 58.7 | 42.5 | 47.1 | 48.7 | 57.3 | 55.2 | 58.1 | | GSM-8K <br> 0-Shot; CoT | 83.6 | 86.4 | 90.8 | 61.1 | 46.4 | 59.8 | 77.4 | 64.7 | 78.1 | | MedQA <br> 2-Shot | 55.3 | 58.2 | 69.8 | 40.9 | 49.6 | 50.0 | 60.5 | 62.2 | 63.4 | | AGIEval <br> 0-Shot | 36.9 | 45.0 | 49.7 | 29.8 | 35.1 | 42.1 | 42.0 | 45.2 | 48.4 | | TriviaQA <br> 5-Shot | 57.1 | 59.1 | 73.3 | 45.2 | 72.3 | 75.2 | 67.7 | 82.2 | 85.8 | | Arc-C <br> 10-Shot | 84.0 | 90.7 | 91.9 | 75.9 | 78.6 | 78.3 | 82.8 | 87.3 | 87.4 | | Arc-E <br> 10-Shot | 95.2 | 97.1 | 98.0 | 88.5 | 90.6 | 91.4 | 93.4 | 95.6 | 96.3 | | PIQA <br> 5-Shot | 83.6 | 87.8 | 88.2 | 60.2 | 77.7 | 78.1 | 75.7 | 86.0 | 86.6 | | SociQA <br> 5-Shot | 76.1 | 79.0 | 79.4 | 68.3 | 74.6 | 65.5 | 73.9 | 75.9 | 68.3 | | BigBench-Hard <br> 0-Shot | 71.5 | 75.0 | 82.5 | 59.4 | 57.3 | 59.6 | 51.5 | 69.7 | 68.32 | | WinoGrande <br> 5-Shot | 72.5 | 82.5 | 81.2 | 54.7 | 54.2 | 55.6 | 65.0 | 62.0 | 68.8 | | OpenBookQA <br> 10-Shot | 80.6 | 88.4 | 86.6 | 73.6 | 79.8 | 78.6 | 82.6 | 85.8 | 86.0 | | BoolQ <br> 0-Shot | 78.7 | 82.9 | 86.5 | -- | 72.2 | 66.0 | 80.9 | 77.6 | 79.1 | | CommonSenseQA <br> 10-Shot | 78.0 | 80.3 | 82.6 | 69.3 | 72.6 | 76.2 | 79 | 78.1 | 79.6 | | TruthfulQA <br> 10-Shot | 63.2 | 68.1 | 74.8 | -- | 52.1 | 53.0 | 63.2 | 60.1 | 85.8 | | HumanEval <br> 0-Shot | 57.9 | 59.1 | 54.7 | 47.0 | 28.0 | 34.1 | 60.4| 37.8 | 62.2 | | MBPP <br> 3-Shot | 62.5 | 71.4 | 73.7 | 60.6 | 50.8 | 51.5 | 67.7 | 60.2 | 77.8 | ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [DeepSpeed](https://github.com/microsoft/DeepSpeed) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 If you want to run the model on: * NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager" * Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [128K](https://aka.ms/phi3-mini-128k-instruct-onnx) ## Cross Platform Support ONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-128K-Instruct ONNX model [here](https://aka.ms/phi3-mini-128k-instruct-onnx). Optimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. Along with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile. Here are some of the optimized configurations we have added: 1. ONNX models for int4 DML: Quantized to int4 via AWQ 2. ONNX model for fp16 CUDA 3. ONNX model for int4 CUDA: Quantized to int4 via RTN 4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-128k/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

google/electra-small-discriminator

google

transformers

--- language: en thumbnail: https://huggingface.co/front/thumbnails/google.png license: apache-2.0 --- ## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators **ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. For a detailed description and experimental results, please refer to our paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). This repository contains code to pre-train ELECTRA, including small ELECTRA models on a single GPU. It also supports fine-tuning ELECTRA on downstream tasks including classification tasks (e.g,. [GLUE](https://gluebenchmark.com/)), QA tasks (e.g., [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)), and sequence tagging tasks (e.g., [text chunking](https://www.clips.uantwerpen.be/conll2000/chunking/)). ## How to use the discriminator in `transformers` ```python from transformers import ElectraForPreTraining, ElectraTokenizerFast import torch discriminator = ElectraForPreTraining.from_pretrained("google/electra-small-discriminator") tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-small-discriminator") sentence = "The quick brown fox jumps over the lazy dog" fake_sentence = "The quick brown fox fake over the lazy dog" fake_tokens = tokenizer.tokenize(fake_sentence) fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt") discriminator_outputs = discriminator(fake_inputs) predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2) [print("%7s" % token, end="") for token in fake_tokens] [print("%7s" % int(prediction), end="") for prediction in predictions.squeeze().tolist()] ```

openai/whisper-large-v2

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 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). Compared to the Whisper large model, the large-v2 model is trained for 2.5x more epochs with added regularization for improved performance. **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-large-v2") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2") >>> 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-large-v2") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2") >>> 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-large-v2") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2") >>> 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 Large 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-large-v2") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2").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"])) 3.0003583080317572 ``` ## 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-large-v2", >>> 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} } ```

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 

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} } ```

microsoft/deberta-v3-small

microsoft

transformers

fill-mask

--- language: en tags: - deberta - deberta-v3 - fill-mask thumbnail: https://huggingface.co/front/thumbnails/microsoft.png license: mit --- ## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543). Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates. The DeBERTa V3 small model comes with 6 layers and a hidden size of 768. It has **44M** backbone parameters with a vocabulary containing 128K tokens which introduces 98M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2. #### Fine-tuning on NLU tasks We present the dev results on SQuAD 2.0 and MNLI tasks. | Model |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)| |-------------------|----------|-------------------|-----------|----------| | RoBERTa-base |50 |86 | 83.7/80.5 | 87.6/- | | XLNet-base |32 |92 | -/80.2 | 86.8/- | | ELECTRA-base |30 |86 | -/80.5 | 88.8/ | | DeBERTa-base |50 |100 | 86.2/83.1| 88.8/88.5| | DeBERTa-v3-large|128|304 | 91.5/89.0 | 91.8/91.9 | | DeBERTa-v3-base |128|86 | 88.4/85.4 | 90.6/90.7| | **DeBERTa-v3-small** |128|**44** | **82.8/80.4** | **88.3/87.7**| | DeBERTa-v3-small+SiFT|128|22 | -/- | 88.8/88.5| #### Fine-tuning with HF transformers ```bash #!/bin/bash cd transformers/examples/pytorch/text-classification/ pip install datasets export TASK_NAME=mnli output_dir="ds_results" num_gpus=8 batch_size=8 python -m torch.distributed.launch --nproc_per_node=${num_gpus} \ run_glue.py \ --model_name_or_path microsoft/deberta-v3-small \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --evaluation_strategy steps \ --max_seq_length 256 \ --warmup_steps 1500 \ --per_device_train_batch_size ${batch_size} \ --learning_rate 4.5e-5 \ --num_train_epochs 3 \ --output_dir $output_dir \ --overwrite_output_dir \ --logging_steps 1000 \ --logging_dir $output_dir ``` ### Citation If you find DeBERTa useful for your work, please cite the following papers: ``` latex @misc{he2021debertav3, title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing}, author={Pengcheng He and Jianfeng Gao and Weizhu Chen}, year={2021}, eprint={2111.09543}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` latex @inproceedings{ he2021deberta, title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION}, author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=XPZIaotutsD} } ```

timm/nfnet_l0.ra2_in1k

timm

timm

image-classification

--- license: apache-2.0 library_name: timm tags: - image-classification - timm datasets: - imagenet-1k --- # Model card for nfnet_l0.ra2_in1k A NFNet-Lite (Lightweight NFNet) image classification model. Trained in `timm` by Ross Wightman. Normalization Free Networks are (pre-activation) ResNet-like models without any normalization layers. Instead of Batch Normalization or alternatives, they use Scaled Weight Standardization and specifically placed scalar gains in residual path and at non-linearities based on signal propagation analysis. Lightweight NFNets are `timm` specific variants that reduce the SE and bottleneck ratio from 0.5 -> 0.25 (reducing widths) and use a smaller group size while maintaining the same depth. SiLU activations used instead of GELU. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 35.1 - GMACs: 4.4 - Activations (M): 10.5 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - High-Performance Large-Scale Image Recognition Without Normalization: https://arxiv.org/abs/2102.06171 - Characterizing signal propagation to close the performance gap in unnormalized ResNets: https://arxiv.org/abs/2101.08692 - **Original:** https://github.com/huggingface/pytorch-image-models - **Dataset:** ImageNet-1k ## 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('nfnet_l0.ra2_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( 'nfnet_l0.ra2_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, 64, 112, 112]) # torch.Size([1, 256, 56, 56]) # torch.Size([1, 512, 28, 28]) # torch.Size([1, 1536, 14, 14]) # torch.Size([1, 2304, 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( 'nfnet_l0.ra2_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, 2304, 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 @article{brock2021high, author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan}, title={High-Performance Large-Scale Image Recognition Without Normalization}, journal={arXiv preprint arXiv:2102.06171}, year={2021} } ``` ```bibtex @inproceedings{brock2021characterizing, author={Andrew Brock and Soham De and Samuel L. Smith}, title={Characterizing signal propagation to close the performance gap in unnormalized ResNets}, booktitle={9th International Conference on Learning Representations, {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}} } ```

humarin/chatgpt_paraphraser_on_T5_base

humarin

transformers

text2text-generation

--- license: openrail datasets: - humarin/chatgpt-paraphrases language: - en library_name: transformers inference: parameters: num_beams: 5 num_beam_groups: 5 num_return_sequences: 5 repetition_penalty: 10.01 diversity_penalty: 3.01 no_repeat_ngram_size: 2 temperature: 0.7 max_length: 128 widget: - text: What are the best places to see in New York? example_title: New York tourist attractions - text: When should I go to the doctor? example_title: Doctor's time - text: >- Rammstein's album Mutter was recorded in the south of France in May and June 2000, and mixed in Stockholm in October of that year. example_title: Rammstein's album Mutter pipeline_tag: text2text-generation --- <a href="https://www.patreon.com/HumarinAI"> <img src="https://cdn-uploads.huggingface.co/production/uploads/60e85cdf977b59e2fda5c46c/4tcUfRObyMrxirxTfvdXg.png" alt="Descriptive Text of Image" style="width: 60%; height: auto;"> </a> Join the Humarin community on [Patreon](https://patreon.com/HumarinAI?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink) and help us conduct research to release more exciting models! This model was trained on our [ChatGPT paraphrase dataset](https://huggingface.co/datasets/humarin/chatgpt-paraphrases). This dataset is based on the [Quora paraphrase question](https://www.kaggle.com/competitions/quora-question-pairs), texts from the [SQUAD 2.0](https://huggingface.co/datasets/squad_v2) and the [CNN news dataset](https://huggingface.co/datasets/cnn_dailymail). This model is based on the T5-base model. We used "transfer learning" to get our model to generate paraphrases as well as ChatGPT. Now we can say that this is one of the best paraphrases of the Hugging Face. [Kaggle](https://www.kaggle.com/datasets/vladimirvorobevv/chatgpt-paraphrases) link [Author's 1 LinkedIn](https://www.linkedin.com/in/vladimir-vorobev/) link [Author's 2 LinkedIn](https://www.linkedin.com/in/makual/) link ## Deploying example ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM device = "cuda" tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base") model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base").to(device) def paraphrase( question, num_beams=5, num_beam_groups=5, num_return_sequences=5, repetition_penalty=10.0, diversity_penalty=3.0, no_repeat_ngram_size=2, temperature=0.7, max_length=128 ): input_ids = tokenizer( f'paraphrase: {question}', return_tensors="pt", padding="longest", max_length=max_length, truncation=True, ).input_ids.to(device) outputs = model.generate( input_ids, temperature=temperature, repetition_penalty=repetition_penalty, num_return_sequences=num_return_sequences, no_repeat_ngram_size=no_repeat_ngram_size, num_beams=num_beams, num_beam_groups=num_beam_groups, max_length=max_length, diversity_penalty=diversity_penalty ) res = tokenizer.batch_decode(outputs, skip_special_tokens=True) return res ``` ## Usage examples **Input:** ```python text = 'What are the best places to see in New York?' paraphrase(text) ``` **Output:** ```python ['What are some must-see places in New York?', 'Can you suggest some must-see spots in New York?', 'Where should one go to experience the best NYC has to offer?', 'Which places should I visit in New York?', 'What are the top destinations to explore in New York?'] ``` **Input:** ```python text = "Rammstein's album Mutter was recorded in the south of France in May and June 2000, and mixed in Stockholm in October of that year." paraphrase(text) ``` **Output:** ```python ['In May and June 2000, Rammstein travelled to the south of France to record his album Mutter, which was mixed in Stockholm in October of that year.', 'The album Mutter by Rammstein was recorded in the south of France during May and June 2000, with mixing taking place in Stockholm in October of that year.', 'The album Mutter by Rammstein was recorded in the south of France during May and June 2000, with mixing taking place in Stockholm in October of that year. It', 'Mutter, the album released by Rammstein, was recorded in southern France during May and June 2000, with mixing taking place between October and September.', 'In May and June 2000, Rammstein recorded his album Mutter in the south of France, with the mix being made at Stockholm during October.'] ``` ## Train parameters ```python epochs = 5 batch_size = 64 max_length = 128 lr = 5e-5 batches_qty = 196465 betas = (0.9, 0.999) eps = 1e-08 ``` ### BibTeX entry and citation info ```bibtex @inproceedings{chatgpt_paraphraser, author={Vladimir Vorobev, Maxim Kuznetsov}, title={A paraphrasing model based on ChatGPT paraphrases}, year={2023} } ```

thenlper/gte-large

thenlper

sentence-transformers

sentence-similarity

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.62686567164178 - type: ap value: 34.46944126809772 - type: f1 value: 66.23684353950857 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.51805 - type: ap value: 89.49842783330848 - type: f1 value: 92.51112169431808 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.074 - type: f1 value: 48.44785682572955 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.077 - type: map_at_10 value: 48.153 - type: map_at_100 value: 48.963 - type: map_at_1000 value: 48.966 - type: map_at_3 value: 43.184 - type: map_at_5 value: 46.072 - type: mrr_at_1 value: 33.073 - type: mrr_at_10 value: 48.54 - type: mrr_at_100 value: 49.335 - type: mrr_at_1000 value: 49.338 - type: mrr_at_3 value: 43.563 - type: mrr_at_5 value: 46.383 - type: ndcg_at_1 value: 32.077 - type: ndcg_at_10 value: 57.158 - type: ndcg_at_100 value: 60.324999999999996 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 46.934 - type: ndcg_at_5 value: 52.158 - type: precision_at_1 value: 32.077 - type: precision_at_10 value: 8.591999999999999 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.275000000000002 - type: precision_at_5 value: 14.111 - type: recall_at_1 value: 32.077 - type: recall_at_10 value: 85.917 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 57.824 - type: recall_at_5 value: 70.555 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.619246083417295 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.3574067664688 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.06359661829253 - type: mrr value: 76.15596007562766 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 90.25407547368691 - type: cos_sim_spearman value: 88.65081514968477 - type: euclidean_pearson value: 88.14857116664494 - type: euclidean_spearman value: 88.50683596540692 - type: manhattan_pearson value: 87.9654797992225 - type: manhattan_spearman value: 88.21164851646908 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.05844155844157 - type: f1 value: 86.01555597681825 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.10510519739522 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.84689960264385 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.800000000000004 - type: map_at_10 value: 44.857 - type: map_at_100 value: 46.512 - type: map_at_1000 value: 46.635 - type: map_at_3 value: 41.062 - type: map_at_5 value: 43.126 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 50.879 - type: mrr_at_100 value: 51.605000000000004 - type: mrr_at_1000 value: 51.641000000000005 - type: mrr_at_3 value: 48.14 - type: mrr_at_5 value: 49.835 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 51.819 - type: ndcg_at_100 value: 57.318999999999996 - type: ndcg_at_1000 value: 58.955999999999996 - type: ndcg_at_3 value: 46.409 - type: ndcg_at_5 value: 48.825 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 10.072000000000001 - type: precision_at_100 value: 1.625 - type: precision_at_1000 value: 0.21 - type: precision_at_3 value: 22.556 - type: precision_at_5 value: 16.309 - type: recall_at_1 value: 32.800000000000004 - type: recall_at_10 value: 65.078 - type: recall_at_100 value: 87.491 - type: recall_at_1000 value: 97.514 - type: recall_at_3 value: 49.561 - type: recall_at_5 value: 56.135999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.614 - type: map_at_10 value: 43.578 - type: map_at_100 value: 44.897 - type: map_at_1000 value: 45.023 - type: map_at_3 value: 40.282000000000004 - type: map_at_5 value: 42.117 - type: mrr_at_1 value: 40.510000000000005 - type: mrr_at_10 value: 49.428 - type: mrr_at_100 value: 50.068999999999996 - type: mrr_at_1000 value: 50.111000000000004 - type: mrr_at_3 value: 47.176 - type: mrr_at_5 value: 48.583999999999996 - type: ndcg_at_1 value: 40.510000000000005 - type: ndcg_at_10 value: 49.478 - type: ndcg_at_100 value: 53.852 - type: ndcg_at_1000 value: 55.782 - type: ndcg_at_3 value: 45.091 - type: ndcg_at_5 value: 47.19 - type: precision_at_1 value: 40.510000000000005 - type: precision_at_10 value: 9.363000000000001 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 21.741 - type: precision_at_5 value: 15.465000000000002 - type: recall_at_1 value: 32.614 - type: recall_at_10 value: 59.782000000000004 - type: recall_at_100 value: 78.012 - type: recall_at_1000 value: 90.319 - type: recall_at_3 value: 46.825 - type: recall_at_5 value: 52.688 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.266000000000005 - type: map_at_10 value: 53.756 - type: map_at_100 value: 54.809 - type: map_at_1000 value: 54.855 - type: map_at_3 value: 50.073 - type: map_at_5 value: 52.293 - type: mrr_at_1 value: 46.332 - type: mrr_at_10 value: 57.116 - type: mrr_at_100 value: 57.767 - type: mrr_at_1000 value: 57.791000000000004 - type: mrr_at_3 value: 54.461999999999996 - type: mrr_at_5 value: 56.092 - type: ndcg_at_1 value: 46.332 - type: ndcg_at_10 value: 60.092 - type: ndcg_at_100 value: 64.034 - type: ndcg_at_1000 value: 64.937 - type: ndcg_at_3 value: 54.071000000000005 - type: ndcg_at_5 value: 57.254000000000005 - type: precision_at_1 value: 46.332 - type: precision_at_10 value: 9.799 - type: precision_at_100 value: 1.278 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.368000000000002 - type: precision_at_5 value: 16.89 - type: recall_at_1 value: 40.266000000000005 - type: recall_at_10 value: 75.41499999999999 - type: recall_at_100 value: 92.01700000000001 - type: recall_at_1000 value: 98.379 - type: recall_at_3 value: 59.476 - type: recall_at_5 value: 67.297 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.589 - type: map_at_10 value: 37.755 - type: map_at_100 value: 38.881 - type: map_at_1000 value: 38.954 - type: map_at_3 value: 34.759 - type: map_at_5 value: 36.544 - type: mrr_at_1 value: 30.734 - type: mrr_at_10 value: 39.742 - type: mrr_at_100 value: 40.774 - type: mrr_at_1000 value: 40.824 - type: mrr_at_3 value: 37.137 - type: mrr_at_5 value: 38.719 - type: ndcg_at_1 value: 30.734 - type: ndcg_at_10 value: 42.978 - type: ndcg_at_100 value: 48.309000000000005 - type: ndcg_at_1000 value: 50.068 - type: ndcg_at_3 value: 37.361 - type: ndcg_at_5 value: 40.268 - type: precision_at_1 value: 30.734 - type: precision_at_10 value: 6.565 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.744 - type: precision_at_5 value: 11.096 - type: recall_at_1 value: 28.589 - type: recall_at_10 value: 57.126999999999995 - type: recall_at_100 value: 81.051 - type: recall_at_1000 value: 94.027 - type: recall_at_3 value: 42.045 - type: recall_at_5 value: 49.019 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.5 - type: map_at_10 value: 27.950999999999997 - type: map_at_100 value: 29.186 - type: map_at_1000 value: 29.298000000000002 - type: map_at_3 value: 25.141000000000002 - type: map_at_5 value: 26.848 - type: mrr_at_1 value: 22.637 - type: mrr_at_10 value: 32.572 - type: mrr_at_100 value: 33.472 - type: mrr_at_1000 value: 33.533 - type: mrr_at_3 value: 29.747 - type: mrr_at_5 value: 31.482 - type: ndcg_at_1 value: 22.637 - type: ndcg_at_10 value: 33.73 - type: ndcg_at_100 value: 39.568 - type: ndcg_at_1000 value: 42.201 - type: ndcg_at_3 value: 28.505999999999997 - type: ndcg_at_5 value: 31.255 - type: precision_at_1 value: 22.637 - type: precision_at_10 value: 6.281000000000001 - type: precision_at_100 value: 1.073 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 13.847000000000001 - type: precision_at_5 value: 10.224 - type: recall_at_1 value: 18.5 - type: recall_at_10 value: 46.744 - type: recall_at_100 value: 72.072 - type: recall_at_1000 value: 91.03999999999999 - type: recall_at_3 value: 32.551 - type: recall_at_5 value: 39.533 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.602 - type: map_at_10 value: 42.18 - type: map_at_100 value: 43.6 - type: map_at_1000 value: 43.704 - type: map_at_3 value: 38.413000000000004 - type: map_at_5 value: 40.626 - type: mrr_at_1 value: 37.344 - type: mrr_at_10 value: 47.638000000000005 - type: mrr_at_100 value: 48.485 - type: mrr_at_1000 value: 48.52 - type: mrr_at_3 value: 44.867000000000004 - type: mrr_at_5 value: 46.566 - type: ndcg_at_1 value: 37.344 - type: ndcg_at_10 value: 48.632 - type: ndcg_at_100 value: 54.215 - type: ndcg_at_1000 value: 55.981 - type: ndcg_at_3 value: 42.681999999999995 - type: ndcg_at_5 value: 45.732 - type: precision_at_1 value: 37.344 - type: precision_at_10 value: 8.932 - type: precision_at_100 value: 1.376 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 20.276 - type: precision_at_5 value: 14.726 - type: recall_at_1 value: 30.602 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 85.12100000000001 - type: recall_at_1000 value: 96.439 - type: recall_at_3 value: 45.848 - type: recall_at_5 value: 53.615 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.952 - type: map_at_10 value: 35.177 - type: map_at_100 value: 36.59 - type: map_at_1000 value: 36.703 - type: map_at_3 value: 31.261 - type: map_at_5 value: 33.222 - type: mrr_at_1 value: 29.337999999999997 - type: mrr_at_10 value: 40.152 - type: mrr_at_100 value: 40.963 - type: mrr_at_1000 value: 41.016999999999996 - type: mrr_at_3 value: 36.91 - type: mrr_at_5 value: 38.685 - type: ndcg_at_1 value: 29.337999999999997 - type: ndcg_at_10 value: 41.994 - type: ndcg_at_100 value: 47.587 - type: ndcg_at_1000 value: 49.791000000000004 - type: ndcg_at_3 value: 35.27 - type: ndcg_at_5 value: 38.042 - type: precision_at_1 value: 29.337999999999997 - type: precision_at_10 value: 8.276 - type: precision_at_100 value: 1.276 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 17.161 - type: precision_at_5 value: 12.671 - type: recall_at_1 value: 23.952 - type: recall_at_10 value: 57.267 - type: recall_at_100 value: 80.886 - type: recall_at_1000 value: 95.611 - type: recall_at_3 value: 38.622 - type: recall_at_5 value: 45.811 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.092083333333335 - type: map_at_10 value: 37.2925 - type: map_at_100 value: 38.57041666666666 - type: map_at_1000 value: 38.68141666666667 - type: map_at_3 value: 34.080000000000005 - type: map_at_5 value: 35.89958333333333 - type: mrr_at_1 value: 31.94758333333333 - type: mrr_at_10 value: 41.51049999999999 - type: mrr_at_100 value: 42.36099999999999 - type: mrr_at_1000 value: 42.4125 - type: mrr_at_3 value: 38.849583333333335 - type: mrr_at_5 value: 40.448249999999994 - type: ndcg_at_1 value: 31.94758333333333 - type: ndcg_at_10 value: 43.17633333333333 - type: ndcg_at_100 value: 48.45241666666668 - type: ndcg_at_1000 value: 50.513999999999996 - type: ndcg_at_3 value: 37.75216666666667 - type: ndcg_at_5 value: 40.393833333333326 - type: precision_at_1 value: 31.94758333333333 - type: precision_at_10 value: 7.688916666666666 - type: precision_at_100 value: 1.2250833333333333 - type: precision_at_1000 value: 0.1595 - type: precision_at_3 value: 17.465999999999998 - type: precision_at_5 value: 12.548083333333333 - type: recall_at_1 value: 27.092083333333335 - type: recall_at_10 value: 56.286583333333326 - type: recall_at_100 value: 79.09033333333333 - type: recall_at_1000 value: 93.27483333333335 - type: recall_at_3 value: 41.35325 - type: recall_at_5 value: 48.072750000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.825 - type: map_at_10 value: 33.723 - type: map_at_100 value: 34.74 - type: map_at_1000 value: 34.824 - type: map_at_3 value: 31.369000000000003 - type: map_at_5 value: 32.533 - type: mrr_at_1 value: 29.293999999999997 - type: mrr_at_10 value: 36.84 - type: mrr_at_100 value: 37.681 - type: mrr_at_1000 value: 37.742 - type: mrr_at_3 value: 34.79 - type: mrr_at_5 value: 35.872 - type: ndcg_at_1 value: 29.293999999999997 - type: ndcg_at_10 value: 38.385999999999996 - type: ndcg_at_100 value: 43.327 - type: ndcg_at_1000 value: 45.53 - type: ndcg_at_3 value: 33.985 - type: ndcg_at_5 value: 35.817 - type: precision_at_1 value: 29.293999999999997 - type: precision_at_10 value: 6.12 - type: precision_at_100 value: 0.9329999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 14.621999999999998 - type: precision_at_5 value: 10.030999999999999 - type: recall_at_1 value: 25.825 - type: recall_at_10 value: 49.647000000000006 - type: recall_at_100 value: 72.32300000000001 - type: recall_at_1000 value: 88.62400000000001 - type: recall_at_3 value: 37.366 - type: recall_at_5 value: 41.957 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.139 - type: map_at_10 value: 26.107000000000003 - type: map_at_100 value: 27.406999999999996 - type: map_at_1000 value: 27.535999999999998 - type: map_at_3 value: 23.445 - type: map_at_5 value: 24.916 - type: mrr_at_1 value: 21.817 - type: mrr_at_10 value: 29.99 - type: mrr_at_100 value: 31.052000000000003 - type: mrr_at_1000 value: 31.128 - type: mrr_at_3 value: 27.627000000000002 - type: mrr_at_5 value: 29.005 - type: ndcg_at_1 value: 21.817 - type: ndcg_at_10 value: 31.135 - type: ndcg_at_100 value: 37.108000000000004 - type: ndcg_at_1000 value: 39.965 - type: ndcg_at_3 value: 26.439 - type: ndcg_at_5 value: 28.655 - type: precision_at_1 value: 21.817 - type: precision_at_10 value: 5.757000000000001 - type: precision_at_100 value: 1.036 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 12.537 - type: precision_at_5 value: 9.229 - type: recall_at_1 value: 18.139 - type: recall_at_10 value: 42.272999999999996 - type: recall_at_100 value: 68.657 - type: recall_at_1000 value: 88.93799999999999 - type: recall_at_3 value: 29.266 - type: recall_at_5 value: 34.892 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.755000000000003 - type: map_at_10 value: 37.384 - type: map_at_100 value: 38.56 - type: map_at_1000 value: 38.655 - type: map_at_3 value: 34.214 - type: map_at_5 value: 35.96 - type: mrr_at_1 value: 32.369 - type: mrr_at_10 value: 41.625 - type: mrr_at_100 value: 42.449 - type: mrr_at_1000 value: 42.502 - type: mrr_at_3 value: 38.899 - type: mrr_at_5 value: 40.489999999999995 - type: ndcg_at_1 value: 32.369 - type: ndcg_at_10 value: 43.287 - type: ndcg_at_100 value: 48.504999999999995 - type: ndcg_at_1000 value: 50.552 - type: ndcg_at_3 value: 37.549 - type: ndcg_at_5 value: 40.204 - type: precision_at_1 value: 32.369 - type: precision_at_10 value: 7.425 - type: precision_at_100 value: 1.134 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.102 - type: precision_at_5 value: 12.107999999999999 - type: recall_at_1 value: 27.755000000000003 - type: recall_at_10 value: 57.071000000000005 - type: recall_at_100 value: 79.456 - type: recall_at_1000 value: 93.54299999999999 - type: recall_at_3 value: 41.298 - type: recall_at_5 value: 48.037 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.855 - type: map_at_10 value: 34.53 - type: map_at_100 value: 36.167 - type: map_at_1000 value: 36.394999999999996 - type: map_at_3 value: 31.037 - type: map_at_5 value: 33.119 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.763999999999996 - type: mrr_at_100 value: 40.77 - type: mrr_at_1000 value: 40.826 - type: mrr_at_3 value: 36.495 - type: mrr_at_5 value: 38.561 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 40.942 - type: ndcg_at_100 value: 47.07 - type: ndcg_at_1000 value: 49.363 - type: ndcg_at_3 value: 35.038000000000004 - type: ndcg_at_5 value: 38.161 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 7.983999999999999 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 16.206 - type: precision_at_5 value: 12.253 - type: recall_at_1 value: 24.855 - type: recall_at_10 value: 53.291999999999994 - type: recall_at_100 value: 80.283 - type: recall_at_1000 value: 94.309 - type: recall_at_3 value: 37.257 - type: recall_at_5 value: 45.282 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.208 - type: map_at_10 value: 30.512 - type: map_at_100 value: 31.496000000000002 - type: map_at_1000 value: 31.595000000000002 - type: map_at_3 value: 27.904 - type: map_at_5 value: 29.491 - type: mrr_at_1 value: 22.736 - type: mrr_at_10 value: 32.379999999999995 - type: mrr_at_100 value: 33.245000000000005 - type: mrr_at_1000 value: 33.315 - type: mrr_at_3 value: 29.945 - type: mrr_at_5 value: 31.488 - type: ndcg_at_1 value: 22.736 - type: ndcg_at_10 value: 35.643 - type: ndcg_at_100 value: 40.535 - type: ndcg_at_1000 value: 43.042 - type: ndcg_at_3 value: 30.625000000000004 - type: ndcg_at_5 value: 33.323 - type: precision_at_1 value: 22.736 - type: precision_at_10 value: 5.6930000000000005 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 13.431999999999999 - type: precision_at_5 value: 9.575 - type: recall_at_1 value: 21.208 - type: recall_at_10 value: 49.47 - type: recall_at_100 value: 71.71499999999999 - type: recall_at_1000 value: 90.55499999999999 - type: recall_at_3 value: 36.124 - type: recall_at_5 value: 42.606 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 11.363 - type: map_at_10 value: 20.312 - type: map_at_100 value: 22.225 - type: map_at_1000 value: 22.411 - type: map_at_3 value: 16.68 - type: map_at_5 value: 18.608 - type: mrr_at_1 value: 25.537 - type: mrr_at_10 value: 37.933 - type: mrr_at_100 value: 38.875 - type: mrr_at_1000 value: 38.911 - type: mrr_at_3 value: 34.387 - type: mrr_at_5 value: 36.51 - type: ndcg_at_1 value: 25.537 - type: ndcg_at_10 value: 28.82 - type: ndcg_at_100 value: 36.341 - type: ndcg_at_1000 value: 39.615 - type: ndcg_at_3 value: 23.01 - type: ndcg_at_5 value: 25.269000000000002 - type: precision_at_1 value: 25.537 - type: precision_at_10 value: 9.153 - type: precision_at_100 value: 1.7319999999999998 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 17.22 - type: precision_at_5 value: 13.629 - type: recall_at_1 value: 11.363 - type: recall_at_10 value: 35.382999999999996 - type: recall_at_100 value: 61.367000000000004 - type: recall_at_1000 value: 79.699 - type: recall_at_3 value: 21.495 - type: recall_at_5 value: 27.42 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.65 - type: map_at_10 value: 20.742 - type: map_at_100 value: 29.614 - type: map_at_1000 value: 31.373 - type: map_at_3 value: 14.667 - type: map_at_5 value: 17.186 - type: mrr_at_1 value: 69.75 - type: mrr_at_10 value: 76.762 - type: mrr_at_100 value: 77.171 - type: mrr_at_1000 value: 77.179 - type: mrr_at_3 value: 75.125 - type: mrr_at_5 value: 76.287 - type: ndcg_at_1 value: 57.62500000000001 - type: ndcg_at_10 value: 42.370999999999995 - type: ndcg_at_100 value: 47.897 - type: ndcg_at_1000 value: 55.393 - type: ndcg_at_3 value: 46.317 - type: ndcg_at_5 value: 43.906 - type: precision_at_1 value: 69.75 - type: precision_at_10 value: 33.95 - type: precision_at_100 value: 10.885 - type: precision_at_1000 value: 2.2239999999999998 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42.3 - type: recall_at_1 value: 9.65 - type: recall_at_10 value: 26.117 - type: recall_at_100 value: 55.084 - type: recall_at_1000 value: 78.62400000000001 - type: recall_at_3 value: 15.823 - type: recall_at_5 value: 19.652 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.885 - type: f1 value: 42.99567641346983 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 70.97 - type: map_at_10 value: 80.34599999999999 - type: map_at_100 value: 80.571 - type: map_at_1000 value: 80.584 - type: map_at_3 value: 79.279 - type: map_at_5 value: 79.94 - type: mrr_at_1 value: 76.613 - type: mrr_at_10 value: 85.15700000000001 - type: mrr_at_100 value: 85.249 - type: mrr_at_1000 value: 85.252 - type: mrr_at_3 value: 84.33800000000001 - type: mrr_at_5 value: 84.89 - type: ndcg_at_1 value: 76.613 - type: ndcg_at_10 value: 84.53399999999999 - type: ndcg_at_100 value: 85.359 - type: ndcg_at_1000 value: 85.607 - type: ndcg_at_3 value: 82.76599999999999 - type: ndcg_at_5 value: 83.736 - type: precision_at_1 value: 76.613 - type: precision_at_10 value: 10.206 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 31.913000000000004 - type: precision_at_5 value: 19.769000000000002 - type: recall_at_1 value: 70.97 - type: recall_at_10 value: 92.674 - type: recall_at_100 value: 95.985 - type: recall_at_1000 value: 97.57000000000001 - type: recall_at_3 value: 87.742 - type: recall_at_5 value: 90.28 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.494 - type: map_at_10 value: 36.491 - type: map_at_100 value: 38.550000000000004 - type: map_at_1000 value: 38.726 - type: map_at_3 value: 31.807000000000002 - type: map_at_5 value: 34.299 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.146 - type: mrr_at_100 value: 54.013999999999996 - type: mrr_at_1000 value: 54.044000000000004 - type: mrr_at_3 value: 50.952 - type: mrr_at_5 value: 52.124 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 44.499 - type: ndcg_at_100 value: 51.629000000000005 - type: ndcg_at_1000 value: 54.367 - type: ndcg_at_3 value: 40.900999999999996 - type: ndcg_at_5 value: 41.737 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.346 - type: precision_at_100 value: 1.974 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 27.366 - type: precision_at_5 value: 19.846 - type: recall_at_1 value: 22.494 - type: recall_at_10 value: 51.156 - type: recall_at_100 value: 77.11200000000001 - type: recall_at_1000 value: 93.44 - type: recall_at_3 value: 36.574 - type: recall_at_5 value: 42.361 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.568999999999996 - type: map_at_10 value: 58.485 - type: map_at_100 value: 59.358999999999995 - type: map_at_1000 value: 59.429 - type: map_at_3 value: 55.217000000000006 - type: map_at_5 value: 57.236 - type: mrr_at_1 value: 77.137 - type: mrr_at_10 value: 82.829 - type: mrr_at_100 value: 83.04599999999999 - type: mrr_at_1000 value: 83.05399999999999 - type: mrr_at_3 value: 81.904 - type: mrr_at_5 value: 82.50800000000001 - type: ndcg_at_1 value: 77.137 - type: ndcg_at_10 value: 67.156 - type: ndcg_at_100 value: 70.298 - type: ndcg_at_1000 value: 71.65700000000001 - type: ndcg_at_3 value: 62.535 - type: ndcg_at_5 value: 65.095 - type: precision_at_1 value: 77.137 - type: precision_at_10 value: 13.911999999999999 - type: precision_at_100 value: 1.6389999999999998 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 39.572 - type: precision_at_5 value: 25.766 - type: recall_at_1 value: 38.568999999999996 - type: recall_at_10 value: 69.56099999999999 - type: recall_at_100 value: 81.931 - type: recall_at_1000 value: 90.91799999999999 - type: recall_at_3 value: 59.358999999999995 - type: recall_at_5 value: 64.416 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.45600000000002 - type: ap value: 84.09725115338568 - type: f1 value: 88.41874909080512 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.404999999999998 - type: map_at_10 value: 33.921 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.164 - type: map_at_3 value: 30.043999999999997 - type: map_at_5 value: 32.327 - type: mrr_at_1 value: 21.977 - type: mrr_at_10 value: 34.505 - type: mrr_at_100 value: 35.638999999999996 - type: mrr_at_1000 value: 35.68 - type: mrr_at_3 value: 30.703999999999997 - type: mrr_at_5 value: 32.96 - type: ndcg_at_1 value: 21.963 - type: ndcg_at_10 value: 40.859 - type: ndcg_at_100 value: 46.614 - type: ndcg_at_1000 value: 47.789 - type: ndcg_at_3 value: 33.007999999999996 - type: ndcg_at_5 value: 37.084 - type: precision_at_1 value: 21.963 - type: precision_at_10 value: 6.493 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.155000000000001 - type: precision_at_5 value: 10.544 - type: recall_at_1 value: 21.404999999999998 - type: recall_at_10 value: 62.175000000000004 - type: recall_at_100 value: 88.786 - type: recall_at_1000 value: 97.738 - type: recall_at_3 value: 40.925 - type: recall_at_5 value: 50.722 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.50661194710442 - type: f1 value: 93.30311193153668 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 73.24669402644778 - type: f1 value: 54.23122108002977 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.61936785474109 - type: f1 value: 70.52644941025565 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.76529926025555 - type: f1 value: 77.26872729322514 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.39450293021839 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.757796879839294 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.62512146657428 - type: mrr value: 33.84624322066173 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.462 - type: map_at_10 value: 14.947 - type: map_at_100 value: 19.344 - type: map_at_1000 value: 20.933 - type: map_at_3 value: 10.761999999999999 - type: map_at_5 value: 12.744 - type: mrr_at_1 value: 47.988 - type: mrr_at_10 value: 57.365 - type: mrr_at_100 value: 57.931 - type: mrr_at_1000 value: 57.96 - type: mrr_at_3 value: 54.85 - type: mrr_at_5 value: 56.569 - type: ndcg_at_1 value: 46.129999999999995 - type: ndcg_at_10 value: 38.173 - type: ndcg_at_100 value: 35.983 - type: ndcg_at_1000 value: 44.507000000000005 - type: ndcg_at_3 value: 42.495 - type: ndcg_at_5 value: 41.019 - type: precision_at_1 value: 47.678 - type: precision_at_10 value: 28.731 - type: precision_at_100 value: 9.232 - type: precision_at_1000 value: 2.202 - type: precision_at_3 value: 39.628 - type: precision_at_5 value: 35.851 - type: recall_at_1 value: 6.462 - type: recall_at_10 value: 18.968 - type: recall_at_100 value: 37.131 - type: recall_at_1000 value: 67.956 - type: recall_at_3 value: 11.905000000000001 - type: recall_at_5 value: 15.097 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 30.335 - type: map_at_10 value: 46.611999999999995 - type: map_at_100 value: 47.632000000000005 - type: map_at_1000 value: 47.661 - type: map_at_3 value: 41.876999999999995 - type: map_at_5 value: 44.799 - type: mrr_at_1 value: 34.125 - type: mrr_at_10 value: 49.01 - type: mrr_at_100 value: 49.75 - type: mrr_at_1000 value: 49.768 - type: mrr_at_3 value: 45.153 - type: mrr_at_5 value: 47.589999999999996 - type: ndcg_at_1 value: 34.125 - type: ndcg_at_10 value: 54.777 - type: ndcg_at_100 value: 58.914 - type: ndcg_at_1000 value: 59.521 - type: ndcg_at_3 value: 46.015 - type: ndcg_at_5 value: 50.861000000000004 - type: precision_at_1 value: 34.125 - type: precision_at_10 value: 9.166 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 21.147 - type: precision_at_5 value: 15.469 - type: recall_at_1 value: 30.335 - type: recall_at_10 value: 77.194 - type: recall_at_100 value: 94.812 - type: recall_at_1000 value: 99.247 - type: recall_at_3 value: 54.681000000000004 - type: recall_at_5 value: 65.86800000000001 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.62 - type: map_at_10 value: 84.536 - type: map_at_100 value: 85.167 - type: map_at_1000 value: 85.184 - type: map_at_3 value: 81.607 - type: map_at_5 value: 83.423 - type: mrr_at_1 value: 81.36 - type: mrr_at_10 value: 87.506 - type: mrr_at_100 value: 87.601 - type: mrr_at_1000 value: 87.601 - type: mrr_at_3 value: 86.503 - type: mrr_at_5 value: 87.179 - type: ndcg_at_1 value: 81.36 - type: ndcg_at_10 value: 88.319 - type: ndcg_at_100 value: 89.517 - type: ndcg_at_1000 value: 89.60900000000001 - type: ndcg_at_3 value: 85.423 - type: ndcg_at_5 value: 86.976 - type: precision_at_1 value: 81.36 - type: precision_at_10 value: 13.415 - type: precision_at_100 value: 1.529 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.342999999999996 - type: precision_at_5 value: 24.534 - type: recall_at_1 value: 70.62 - type: recall_at_10 value: 95.57600000000001 - type: recall_at_100 value: 99.624 - type: recall_at_1000 value: 99.991 - type: recall_at_3 value: 87.22 - type: recall_at_5 value: 91.654 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.826438478212744 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.24027467551447 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.997999999999999 - type: map_at_10 value: 14.267 - type: map_at_100 value: 16.843 - type: map_at_1000 value: 17.229 - type: map_at_3 value: 9.834 - type: map_at_5 value: 11.92 - type: mrr_at_1 value: 24.7 - type: mrr_at_10 value: 37.685 - type: mrr_at_100 value: 38.704 - type: mrr_at_1000 value: 38.747 - type: mrr_at_3 value: 34.150000000000006 - type: mrr_at_5 value: 36.075 - type: ndcg_at_1 value: 24.7 - type: ndcg_at_10 value: 23.44 - type: ndcg_at_100 value: 32.617000000000004 - type: ndcg_at_1000 value: 38.628 - type: ndcg_at_3 value: 21.747 - type: ndcg_at_5 value: 19.076 - type: precision_at_1 value: 24.7 - type: precision_at_10 value: 12.47 - type: precision_at_100 value: 2.564 - type: precision_at_1000 value: 0.4 - type: precision_at_3 value: 20.767 - type: precision_at_5 value: 17.06 - type: recall_at_1 value: 4.997999999999999 - type: recall_at_10 value: 25.3 - type: recall_at_100 value: 52.048 - type: recall_at_1000 value: 81.093 - type: recall_at_3 value: 12.642999999999999 - type: recall_at_5 value: 17.312 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.44942006292234 - type: cos_sim_spearman value: 79.80930790660699 - type: euclidean_pearson value: 82.93400777494863 - type: euclidean_spearman value: 80.04664991110705 - type: manhattan_pearson value: 82.93551681854949 - type: manhattan_spearman value: 80.03156736837379 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.63574059135726 - type: cos_sim_spearman value: 76.80552915288186 - type: euclidean_pearson value: 82.46368529820518 - type: euclidean_spearman value: 76.60338474719275 - type: manhattan_pearson value: 82.4558617035968 - type: manhattan_spearman value: 76.57936082895705 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 86.24116811084211 - type: cos_sim_spearman value: 88.10998662068769 - type: euclidean_pearson value: 87.04961732352689 - type: euclidean_spearman value: 88.12543945864087 - type: manhattan_pearson value: 86.9905224528854 - type: manhattan_spearman value: 88.07827944705546 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.74847296555048 - type: cos_sim_spearman value: 82.66200957916445 - type: euclidean_pearson value: 84.48132256004965 - type: euclidean_spearman value: 82.67915286000596 - type: manhattan_pearson value: 84.44950477268334 - type: manhattan_spearman value: 82.63327639173352 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.23056258027053 - type: cos_sim_spearman value: 88.92791680286955 - type: euclidean_pearson value: 88.13819235461933 - type: euclidean_spearman value: 88.87294661361716 - type: manhattan_pearson value: 88.14212133687899 - type: manhattan_spearman value: 88.88551854529777 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.64179522732887 - type: cos_sim_spearman value: 84.25028809903114 - type: euclidean_pearson value: 83.40175015236979 - type: euclidean_spearman value: 84.23369296429406 - type: manhattan_pearson value: 83.43768174261321 - type: manhattan_spearman value: 84.27855229214734 - 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: 88.20378955494732 - type: cos_sim_spearman value: 88.46863559173111 - type: euclidean_pearson value: 88.8249295811663 - type: euclidean_spearman value: 88.6312737724905 - type: manhattan_pearson value: 88.87744466378827 - type: manhattan_spearman value: 88.82908423767314 - 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: 69.91342028796086 - type: cos_sim_spearman value: 69.71495021867864 - type: euclidean_pearson value: 70.65334330405646 - type: euclidean_spearman value: 69.4321253472211 - type: manhattan_pearson value: 70.59743494727465 - type: manhattan_spearman value: 69.11695509297482 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.42451709766952 - type: cos_sim_spearman value: 86.07166710670508 - type: euclidean_pearson value: 86.12711421258899 - type: euclidean_spearman value: 86.05232086925126 - type: manhattan_pearson value: 86.15591089932126 - type: manhattan_spearman value: 86.0890128623439 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.1976344717285 - type: mrr value: 96.3703145075694 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 59.511 - type: map_at_10 value: 69.724 - type: map_at_100 value: 70.208 - type: map_at_1000 value: 70.22800000000001 - type: map_at_3 value: 66.986 - type: map_at_5 value: 68.529 - type: mrr_at_1 value: 62.333000000000006 - type: mrr_at_10 value: 70.55 - type: mrr_at_100 value: 70.985 - type: mrr_at_1000 value: 71.004 - type: mrr_at_3 value: 68.611 - type: mrr_at_5 value: 69.728 - type: ndcg_at_1 value: 62.333000000000006 - type: ndcg_at_10 value: 74.265 - type: ndcg_at_100 value: 76.361 - type: ndcg_at_1000 value: 76.82900000000001 - type: ndcg_at_3 value: 69.772 - type: ndcg_at_5 value: 71.94800000000001 - type: precision_at_1 value: 62.333000000000006 - type: precision_at_10 value: 9.9 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.444000000000003 - type: precision_at_5 value: 18 - type: recall_at_1 value: 59.511 - type: recall_at_10 value: 87.156 - type: recall_at_100 value: 96.5 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 75.2 - type: recall_at_5 value: 80.661 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81683168316832 - type: cos_sim_ap value: 95.74716566563774 - type: cos_sim_f1 value: 90.64238745574103 - type: cos_sim_precision value: 91.7093142272262 - type: cos_sim_recall value: 89.60000000000001 - type: dot_accuracy value: 99.69405940594059 - type: dot_ap value: 91.09013507754594 - type: dot_f1 value: 84.54227113556779 - type: dot_precision value: 84.58458458458459 - type: dot_recall value: 84.5 - type: euclidean_accuracy value: 99.81782178217821 - type: euclidean_ap value: 95.6324301072609 - type: euclidean_f1 value: 90.58341862845445 - type: euclidean_precision value: 92.76729559748428 - type: euclidean_recall value: 88.5 - type: manhattan_accuracy value: 99.81980198019802 - type: manhattan_ap value: 95.68510494437183 - type: manhattan_f1 value: 90.58945191313342 - type: manhattan_precision value: 93.79014989293361 - type: manhattan_recall value: 87.6 - type: max_accuracy value: 99.81980198019802 - type: max_ap value: 95.74716566563774 - type: max_f1 value: 90.64238745574103 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 67.63761899427078 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.572473369697235 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.63000245208579 - type: mrr value: 54.504193722943725 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.300791939416545 - type: cos_sim_spearman value: 31.662904057924123 - type: dot_pearson value: 26.21198530758316 - type: dot_spearman value: 27.006921548904263 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.197 - type: map_at_10 value: 1.752 - type: map_at_100 value: 10.795 - type: map_at_1000 value: 27.18 - type: map_at_3 value: 0.5890000000000001 - type: map_at_5 value: 0.938 - type: mrr_at_1 value: 74 - type: mrr_at_10 value: 85.833 - type: mrr_at_100 value: 85.833 - type: mrr_at_1000 value: 85.833 - type: mrr_at_3 value: 85.333 - type: mrr_at_5 value: 85.833 - type: ndcg_at_1 value: 69 - type: ndcg_at_10 value: 70.22 - type: ndcg_at_100 value: 55.785 - type: ndcg_at_1000 value: 52.93600000000001 - type: ndcg_at_3 value: 72.084 - type: ndcg_at_5 value: 71.184 - type: precision_at_1 value: 74 - type: precision_at_10 value: 75.2 - type: precision_at_100 value: 57.3 - type: precision_at_1000 value: 23.302 - type: precision_at_3 value: 77.333 - type: precision_at_5 value: 75.6 - type: recall_at_1 value: 0.197 - type: recall_at_10 value: 2.019 - type: recall_at_100 value: 14.257 - type: recall_at_1000 value: 50.922 - type: recall_at_3 value: 0.642 - type: recall_at_5 value: 1.043 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.803 - type: map_at_10 value: 10.407 - type: map_at_100 value: 16.948 - type: map_at_1000 value: 18.424 - type: map_at_3 value: 5.405 - type: map_at_5 value: 6.908 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 50.221000000000004 - type: mrr_at_100 value: 51.388 - type: mrr_at_1000 value: 51.402 - type: mrr_at_3 value: 47.278999999999996 - type: mrr_at_5 value: 49.626 - type: ndcg_at_1 value: 34.694 - type: ndcg_at_10 value: 25.507 - type: ndcg_at_100 value: 38.296 - type: ndcg_at_1000 value: 49.492000000000004 - type: ndcg_at_3 value: 29.006999999999998 - type: ndcg_at_5 value: 25.979000000000003 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 22.041 - type: precision_at_100 value: 8.02 - type: precision_at_1000 value: 1.567 - type: precision_at_3 value: 28.571 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.803 - type: recall_at_10 value: 16.378 - type: recall_at_100 value: 50.489 - type: recall_at_1000 value: 85.013 - type: recall_at_3 value: 6.505 - type: recall_at_5 value: 9.243 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.55579999999999 - type: ap value: 14.206982753316227 - type: f1 value: 54.372142814964285 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 56.57611771363893 - type: f1 value: 56.924172639063144 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.82304915719759 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.92716218632653 - type: cos_sim_ap value: 73.73359122546046 - type: cos_sim_f1 value: 68.42559487116262 - type: cos_sim_precision value: 64.22124508215691 - type: cos_sim_recall value: 73.21899736147758 - type: dot_accuracy value: 80.38981939560112 - type: dot_ap value: 54.61060862444974 - type: dot_f1 value: 53.45710627400769 - type: dot_precision value: 44.87638839125761 - type: dot_recall value: 66.09498680738787 - type: euclidean_accuracy value: 86.02849138701794 - type: euclidean_ap value: 73.95673761922404 - type: euclidean_f1 value: 68.6783042394015 - type: euclidean_precision value: 65.1063829787234 - type: euclidean_recall value: 72.66490765171504 - type: manhattan_accuracy value: 85.9808070572808 - type: manhattan_ap value: 73.9050720058029 - type: manhattan_f1 value: 68.57560618983794 - type: manhattan_precision value: 63.70839936608558 - type: manhattan_recall value: 74.24802110817942 - type: max_accuracy value: 86.02849138701794 - type: max_ap value: 73.95673761922404 - type: max_f1 value: 68.6783042394015 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.72783017037295 - type: cos_sim_ap value: 85.52705223340233 - type: cos_sim_f1 value: 77.91659078492079 - type: cos_sim_precision value: 73.93378032764221 - type: cos_sim_recall value: 82.35294117647058 - type: dot_accuracy value: 85.41739434159972 - type: dot_ap value: 77.17734818118443 - type: dot_f1 value: 71.63473589973144 - type: dot_precision value: 66.96123719622415 - type: dot_recall value: 77.00954727440714 - type: euclidean_accuracy value: 88.68125897465751 - type: euclidean_ap value: 85.47712213906692 - type: euclidean_f1 value: 77.81419950830664 - type: euclidean_precision value: 75.37162649733006 - type: euclidean_recall value: 80.42038805050817 - type: manhattan_accuracy value: 88.67349710870494 - type: manhattan_ap value: 85.46506475241955 - type: manhattan_f1 value: 77.87259084890393 - type: manhattan_precision value: 74.54929577464789 - type: manhattan_recall value: 81.50600554357868 - type: max_accuracy value: 88.72783017037295 - type: max_ap value: 85.52705223340233 - type: max_f1 value: 77.91659078492079 language: - en license: mit --- # gte-large 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-large") model = AutoModel.from_pretrained("thenlper/gte-large") # 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} } ```

Lykon/DreamShaper

Lykon

diffusers

text-to-image

--- language: - en license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - art - artistic - diffusers - anime inference: false --- # Dream Shaper ## Official Repository Read more about this model here: https://civitai.com/models/4384/dreamshaper Also please support by giving 5 stars and a heart, which will notify new updates. Please consider supporting me on Patreon or buy me a coffee - https://www.patreon.com/Lykon275 - https://snipfeed.co/lykon You can run this model on: - https://huggingface.co/spaces/Lykon/DreamShaper-webui - Mage.space, sinkin.ai and more

openai-community/gpt2-xl

openai-community

transformers

text-generation

--- language: en license: mit --- # GPT-2 XL ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) ## Model Details **Model Description:** GPT-2 XL is the **1.5B 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:** [GPT-2](https://huggingface.co/gpt2), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-Large](https://huggingface.co/gpt2-large) - **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) - [OpenAI GPT-2 1.5B Release Blog Post](https://openai.com/blog/gpt-2-1-5b-release/) - 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-xl') set_seed(42) generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) ``` 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-xl') model = GPT2Model.from_pretrained('gpt2-xl') 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-xl') model = TFGPT2Model.from_pretrained('gpt2-xl') 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.** #### Biases 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-xl') set_seed(42) generator("The man worked as a", max_length=10, num_return_sequences=5) set_seed(42) generator("The woman worked as a", max_length=10, num_return_sequences=5) ``` 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. #### Risks and Limitations When they released the 1.5B parameter model, OpenAI wrote in a [blog post](https://openai.com/blog/gpt-2-1-5b-release/): > GPT-2 can be fine-tuned for misuse. Our partners at the Middlebury Institute of International Studies’ Center on Terrorism, Extremism, and Counterterrorism (CTEC) found that extremist groups can use GPT-2 for misuse, specifically by fine-tuning GPT-2 models on four ideological positions: white supremacy, Marxism, jihadist Islamism, and anarchism. CTEC demonstrated that it’s possible to create models that can generate synthetic propaganda for these ideologies. They also show that, despite having low detection accuracy on synthetic outputs, ML-based detection methods can give experts reasonable suspicion that an actor is generating synthetic text. The blog post further discusses the risks, limitations, and biases 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) | | | 8.63 | 63.24 | 93.30 | 89.05 | 18.34 | 35.76 | 0.93 | 0.98 | 17.48 | 42.16 | ## 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). The hardware type and hours used are based on information provided by one of the model authors on [Reddit](https://bit.ly/2Tw1x4L). - **Hardware Type:** 32 TPUv3 chips - **Hours used:** 168 - **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, 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.

openai/whisper-tiny.en

openai

transformers

automatic-speech-recognition

--- language: - en 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-tiny.en 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: 8.4372112320138 - 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: 14.857607503498355 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 This checkpoint is an *English-only* model, meaning it can be used for English speech recognition. Multilingual speech recognition or speech translation is possible through use of a multilingual checkpoint. 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) ## Transcription ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") >>> # 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|><|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`. ## Evaluation This code snippet shows how to evaluate Whisper tiny.en 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-tiny.en") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en").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"])) 5.655609406528749 ``` ## 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-tiny.en", >>> 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} } ```

PixArt-alpha/PixArt-XL-2-1024-MS

PixArt-alpha

diffusers

text-to-image

--- license: openrail++ tags: - text-to-image - Pixart-α --- <p align="center"> <img src="asset/logo.png" height=120> </p> <div style="display:flex;justify-content: center"> <a href="https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha"><img src="https://img.shields.io/static/v1?label=Demo&message=Huggingface&color=yellow"></a> &ensp; <a href="https://pixart-alpha.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages"></a> &ensp; <a href="https://arxiv.org/abs/2310.00426"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv&color=red&logo=arxiv"></a> &ensp; <a href="https://colab.research.google.com/drive/1jZ5UZXk7tcpTfVwnX33dDuefNMcnW9ME?usp=sharing"><img src="https://img.shields.io/static/v1?label=Free%20Trial&message=Google%20Colab&logo=google&color=orange"></a> &ensp; <a href="https://github.com/orgs/PixArt-alpha/discussions"><img src="https://img.shields.io/static/v1?label=Discussion&message=Github&color=green&logo=github"></a> &ensp; </div> # 🐱 Pixart-α Model Card  ## Model  [Pixart-α](https://arxiv.org/abs/2310.00426) consists of pure transformer blocks for latent diffusion: It can directly generate 1024px images from text prompts within a single sampling process. Source code is available at https://github.com/PixArt-alpha/PixArt-alpha. ### Model Description - **Developed by:** Pixart-α - **Model type:** Diffusion-Transformer-based text-to-image generative model - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Transformer Latent Diffusion Model](https://arxiv.org/abs/2310.00426) that uses one fixed, pretrained text encoders ([T5]( https://huggingface.co/DeepFloyd/t5-v1_1-xxl)) and one latent feature encoder ([VAE](https://arxiv.org/abs/2112.10752)). - **Resources for more information:** Check out our [GitHub Repository](https://github.com/PixArt-alpha/PixArt-alpha) and the [Pixart-α report on arXiv](https://arxiv.org/abs/2310.00426). ### Model Sources For research purposes, we recommend our `generative-models` Github repository (https://github.com/PixArt-alpha/PixArt-alpha), which is more suitable for both training and inference and for which most advanced diffusion sampler like [SA-Solver](https://arxiv.org/abs/2309.05019) will be added over time. [Hugging Face](https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha) provides free Pixart-α inference. - **Repository:** https://github.com/PixArt-alpha/PixArt-alpha - **Demo:** https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha # 🔥🔥🔥 Why PixArt-α? ## Training Efficiency PixArt-α only takes 10.8% of Stable Diffusion v1.5's training time (675 vs. 6,250 A100 GPU days), saving nearly $300,000 ($26,000 vs. $320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%.  | Method | Type | #Params | #Images | A100 GPU days | |-----------|------|---------|---------|---------------| | DALL·E | Diff | 12.0B | 1.54B | | | GLIDE | Diff | 5.0B | 5.94B | | | LDM | Diff | 1.4B | 0.27B | | | DALL·E 2 | Diff | 6.5B | 5.63B | 41,66 | | SDv1.5 | Diff | 0.9B | 3.16B | 6,250 | | GigaGAN | GAN | 0.9B | 0.98B | 4,783 | | Imagen | Diff | 3.0B | 15.36B | 7,132 | | RAPHAEL | Diff | 3.0B | 5.0B | 60,000 | | PixArt-α | Diff | 0.6B | 0.025B | 675 | ## Evaluation  The chart above evaluates user preference for Pixart-α over SDXL 0.9, Stable Diffusion 2, DALLE-2 and DeepFloyd. The Pixart-α base model performs comparable or even better than the existing state-of-the-art models. ### 🧨 Diffusers Make sure to upgrade diffusers to >= 0.22.0: ``` pip install -U diffusers --upgrade ``` In addition make sure to install `transformers`, `safetensors`, `sentencepiece`, and `accelerate`: ``` pip install transformers accelerate safetensors sentencepiece ``` To just use the base model, you can run: ```py from diffusers import PixArtAlphaPipeline import torch pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16) pipe = pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" images = pipe(prompt=prompt).images[0] ``` When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline: ```py pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True) ``` If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload` instead of `.to("cuda")`: ```diff - pipe.to("cuda") + pipe.enable_model_cpu_offload() ``` For more information on how to use Pixart-α with `diffusers`, please have a look at [the Pixart-α Docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pixart). ### Free Google Colab You can use Google Colab to generate images from PixArt-α free of charge. Click [here](https://colab.research.google.com/drive/1jZ5UZXk7tcpTfVwnX33dDuefNMcnW9ME?usp=sharing) to try. ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. Excluded uses are described below. ### 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. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - fingers, .etc in general may not be generated properly. - The autoencoding part of the model is lossy. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.

openai/whisper-tiny

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-tiny 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: 7.54 - 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: 17.15 - 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: 141 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-tiny") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") >>> 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-tiny") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") >>> 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-tiny") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") >>> 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 Tiny 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-tiny") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny").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"])) 7.547098647858638 ``` ## 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-tiny", >>> 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} } ```

sentence-transformers/msmarco-bert-base-dot-v5

sentence-transformers

sentence-transformers

sentence-similarity

--- language: - en library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- # msmarco-bert-base-dot-v5 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500K (query, answer) pairs from the [MS MARCO dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking/). For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html) ## 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, util query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] #Load the model model = SentenceTransformer('sentence-transformers/msmarco-bert-base-dot-v5') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores print("Query:", query) for doc, score in doc_score_pairs: print(score, doc) ``` ## 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 correct 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.last_hidden_state 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) #Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input, return_dict=True) # Perform pooling embeddings = mean_pooling(model_output, encoded_input['attention_mask']) return embeddings # Sentences we want sentence embeddings for query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-bert-base-dot-v5") model = AutoModel.from_pretrained("sentence-transformers/msmarco-bert-base-dot-v5") #Encode query and docs query_emb = encode(query) doc_emb = encode(docs) #Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores print("Query:", query) for doc, score in doc_score_pairs: print(score, doc) ``` ## Technical Details In the following some technical details how this model must be used: | Setting | Value | | --- | :---: | | Dimensions | 768 | | Max Sequence Length | 512 | | Produces normalized embeddings | No | | Pooling-Method | Mean pooling | | Suitable score functions | dot-product (e.g. `util.dot_score`) | ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=msmarco-bert-base-base-dot-v5) ## Training See `train_script.py` in this repository for the used training script. The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 7858 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MarginMSELoss.MarginMSELoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 30, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 1e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: bert-base-uncased (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", } ```

m-a-p/music2vec-v1

m-a-p

transformers

feature-extraction

--- license: cc-by-nc-4.0 inference: false tags: - music --- # Introduction to our series work The development log of our Music Audio Pre-training (m-a-p) model family: - 17/03/2023: we release two advanced music understanding models, [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) and [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset [MERT-v0-public](https://huggingface.co/m-a-p/MERT-v0-public) - 29/12/2022: a music understanding model [MERT-v0](https://huggingface.co/m-a-p/MERT-v0) trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model [music2vec](https://huggingface.co/m-a-p/music2vec-v1) trained with **BYOL** paradigm. Here is a table for quick model pick-up: | Name | Pre-train Paradigm | Training Data (hour) | Pre-train Context (second) | Model Size | Transformer Layer-Dimension | Feature Rate | Sample Rate | Release Date | | ------------------------------------------------------------ | ------------------ | -------------------- | ---------------------------- | ---------- | --------------------------- | ------------ | ----------- | ------------ | | [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) | MLM | 160K | 5 | 330M | 24-1024 | 75 Hz | 24K Hz | 17/03/2023 | | [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) | MLM | 20K | 5 | 95M | 12-768 | 75 Hz | 24K Hz | 17/03/2023 | | [MERT-v0-public](https://huggingface.co/m-a-p/MERT-v0-public) | MLM | 900 | 5 | 95M | 12-768 | 50 Hz | 16K Hz | 14/03/2023 | | [MERT-v0](https://huggingface.co/m-a-p/MERT-v0) | MLM | 1000 | 5 | 95 M | 12-768 | 50 Hz | 16K Hz | 29/12/2022 | | [music2vec-v1](https://huggingface.co/m-a-p/music2vec-v1) | BYOL | 1000 | 30 | 95 M | 12-768 | 50 Hz | 16K Hz | 30/10/2022 | ## Explanation The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with. # Introduction to Music2Vec **Music2Vec** is accepted as 2-page abstract in Late Breaking Demos (LBD) at the ISMIR 2022. It is a completely unsupervised model trained on 1000 hour music audios. We release the **crop5s** version base model as music2vec-v1. Our base model is SOTA-comparable on multiple MIR tasks even under probing settings, while keeping fine-tunable on a single 2080Ti. Larger models trained with more data are on the way~ For a more recent pretrained model with better performance, please refer to [m-a-p/MERT-v0](https://huggingface.co/m-a-p/MERT-v0). # Model Architecture Music2Vec Framework. During pre-training, the student model aims to reconstruct the masked music audio by taking the contextualized representations provided by the teacher model as prediction targets.  # Performance Comparison With 95M parameters and relatively small training data (1k hr), our base Music2Vec representation achieves comparable performance to the SOTA Jukebox-5B representation. Note that our base model size is **<2%** of Jukebox-5B.  # Model Usage ```python from transformers import Wav2Vec2Processor, Data2VecAudioModel import torch from torch import nn from datasets import load_dataset # load demo audio and set processor dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") dataset = dataset.sort("id") sampling_rate = dataset.features["audio"].sampling_rate processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h") # loading our model weights model = Data2VecAudioModel.from_pretrained("m-a-p/music2vec-v1") # audio file is decoded on the fly inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs, output_hidden_states=True) # take a look at the output shape, there are 13 layers of representation # each layer performs differently in different downstream tasks, you should choose empirically all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze() print(all_layer_hidden_states.shape) # [13 layer, 292 timestep, 768 feature_dim] # for utterance level classification tasks, you can simply reduce the representation in time time_reduced_hidden_states = all_layer_hidden_states.mean(-2) print(time_reduced_hidden_states.shape) # [13, 768] # you can even use a learnable weighted average representation aggregator = nn.Conv1d(in_channels=13, out_channels=1, kernel_size=1) weighted_avg_hidden_states = aggregator(time_reduced_hidden_states).squeeze() print(weighted_avg_hidden_states.shape) # [768] ``` Our model is based on the [data2vec audio model](https://huggingface.co/docs/transformers/model_doc/data2vec#transformers.Data2VecAudioModel). # Citation The paper can be found at [ISMIR](https://ismir2022program.ismir.net/lbd_410.html). ```shell @article{li2022map, title={MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning}, author={Li, Yizhi and Yuan, Ruibin and Zhang, Ge and Ma, Yinghao and Lin, Chenghua and Chen, Xingran and Ragni, Anton and Yin, Hanzhi and Hu, Zhijie and He, Haoyu and others}, journal={arXiv preprint arXiv:2212.02508}, year={2022} } ```

Helsinki-NLP/opus-mt-it-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-it-en * source languages: it * target languages: en * OPUS readme: [it-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/it-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/it-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/it-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/it-en/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009.it.en | 35.3 | 0.600 | | newstest2009.it.en | 34.0 | 0.594 | | Tatoeba.it.en | 70.9 | 0.808 |

superb/wav2vec2-base-superb-sid

superb

transformers

audio-classification

--- language: en datasets: - superb tags: - speech - audio - wav2vec2 - audio-classification 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 license: apache-2.0 --- # Wav2Vec2-Base for Speaker Identification ## Model description This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1). The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) ## Task and dataset description Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) dataset is adopted For the original model's training and evaluation instructions refer to the [S3PRL downstream task README](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sid-speaker-identification). ## Usage examples You can use the model via the Audio Classification pipeline: ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("anton-l/superb_demo", "si", split="test") classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-sid") labels = classifier(dataset[0]["file"], top_k=5) ``` Or use the model directly: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor def map_to_array(example): speech, _ = librosa.load(example["file"], sr=16000, mono=True) example["speech"] = speech return example # load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "si", split="test") dataset = dataset.map(map_to_array) model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-sid") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-sid") # compute attention masks and normalize the waveform if needed inputs = feature_extractor(dataset[:2]["speech"], sampling_rate=16000, padding=True, return_tensors="pt") logits = model(**inputs).logits predicted_ids = torch.argmax(logits, dim=-1) labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()] ``` ## Eval results The evaluation metric is accuracy. | | **s3prl** | **transformers** | |--------|-----------|------------------| |**test**| `0.7518` | `0.7518` | ### BibTeX entry and citation info ```bibtex @article{yang2021superb, title={SUPERB: Speech processing Universal PERformance Benchmark}, author={Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y and Liu, Andy T and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and others}, journal={arXiv preprint arXiv:2105.01051}, year={2021} } ```

facebook/dino-vitb16

facebook

transformers

image-feature-extraction

--- license: apache-2.0 tags: - dino - vision datasets: - imagenet-1k --- # Vision Transformer (base-sized model, patch size 16) trained using DINO Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin and first released in [this repository](https://github.com/facebookresearch/dino). Disclaimer: The team releasing DINO did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not include any fine-tuned heads. 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 images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python from transformers import ViTImageProcessor, ViTModel from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) processor = ViTImageProcessor.from_pretrained('facebook/dino-vitb16') model = ViTModel.from_pretrained('facebook/dino-vitb16') inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2104-14294, author = {Mathilde Caron and Hugo Touvron and Ishan Misra and Herv{\'{e}} J{\'{e}}gou and Julien Mairal and Piotr Bojanowski and Armand Joulin}, title = {Emerging Properties in Self-Supervised Vision Transformers}, journal = {CoRR}, volume = {abs/2104.14294}, year = {2021}, url = {https://arxiv.org/abs/2104.14294}, archivePrefix = {arXiv}, eprint = {2104.14294}, timestamp = {Tue, 04 May 2021 15:12:43 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-14294.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

microsoft/speecht5_hifigan

microsoft

transformers

--- license: mit tags: - audio --- # SpeechT5 HiFi-GAN Vocoder This is the HiFi-GAN vocoder for use with the SpeechT5 text-to-speech and voice conversion models. SpeechT5 was first released in [this repository](https://github.com/microsoft/SpeechT5/), [original weights](https://huggingface.co/mechanicalsea/speecht5-tts). The license used is [MIT](https://github.com/microsoft/SpeechT5/blob/main/LICENSE). Disclaimer: The team releasing SpeechT5 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Citation **BibTeX:** ```bibtex @inproceedings{ao-etal-2022-speecht5, title = {{S}peech{T}5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing}, author = {Ao, Junyi and Wang, Rui and Zhou, Long and Wang, Chengyi and Ren, Shuo and Wu, Yu and Liu, Shujie and Ko, Tom and Li, Qing and Zhang, Yu and Wei, Zhihua and Qian, Yao and Li, Jinyu and Wei, Furu}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = {May}, year = {2022}, pages={5723--5738}, } ```

fxmarty/tiny-llama-fast-tokenizer

fxmarty

transformers

text-generation

Note: this model has random weights and is useful only for testing purposes.

MMG/xlm-roberta-large-ner-spanish

MMG

transformers

token-classification

--- language: - es datasets: - CoNLL-2002 widget: - text: "Las oficinas de MMG están en Las Rozas." --- # xlm-roberta-large-ner-spanish This model is a XLM-Roberta-large model fine-tuned for Named Entity Recognition (NER) over the Spanish portion of the CoNLL-2002 dataset. Evaluating it over the test subset of this dataset, we get a F1-score of 89.17, being one of the best NER for Spanish available at the moment.

echarlaix/tiny-random-mistral

echarlaix

transformers

text-generation

--- license: apache-2.0 ---

facebook/esm2_t12_35M_UR50D

facebook

transformers

fill-mask

--- license: mit widget: - text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG" --- ## ESM-2 ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the [accompanying paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in some demo notebooks ([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb), [TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb)) which demonstrate how to fine-tune ESM-2 models on your tasks of interest. Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train: | Checkpoint name | Num layers | Num parameters | |------------------------------|----|----------| | [esm2_t48_15B_UR50D](https://huggingface.co/facebook/esm2_t48_15B_UR50D) | 48 | 15B | | [esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) | 36 | 3B | | [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) | 33 | 650M | | [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) | 30 | 150M | | [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) | 12 | 35M | | [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) | 6 | 8M |

kingabzpro/wav2vec2-large-xls-r-300m-Urdu

kingabzpro

transformers

automatic-speech-recognition

--- language: - ur license: apache-2.0 tags: - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 metrics: - wer base_model: facebook/wav2vec2-xls-r-300m model-index: - name: wav2vec2-large-xls-r-300m-Urdu results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: ur metrics: - type: wer value: 39.89 name: Test WER - type: cer value: 16.7 name: Test CER --- --- <!-- 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. --> # wav2vec2-large-xls-r-300m-Urdu This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9889 - Wer: 0.5607 - Cer: 0.2370 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-300m-Urdu --dataset mozilla-foundation/common_voice_8_0 --config ur --split test ``` ### Inference With LM ```python from datasets import load_dataset, Audio from transformers import pipeline model = "kingabzpro/wav2vec2-large-xls-r-300m-Urdu" data = load_dataset("mozilla-foundation/common_voice_8_0", "ur", split="test", streaming=True, use_auth_token=True) sample_iter = iter(data.cast_column("path", Audio(sampling_rate=16_000))) sample = next(sample_iter) asr = pipeline("automatic-speech-recognition", model=model) prediction = asr(sample["path"]["array"], chunk_length_s=5, stride_length_s=1) prediction # => {'text': 'اب یہ ونگین لمحاتانکھار دلمیں میںفوث کریلیا اجائ'} ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 3.6398 | 30.77 | 400 | 3.3517 | 1.0 | 1.0 | | 2.9225 | 61.54 | 800 | 2.5123 | 1.0 | 0.8310 | | 1.2568 | 92.31 | 1200 | 0.9699 | 0.6273 | 0.2575 | | 0.8974 | 123.08 | 1600 | 0.9715 | 0.5888 | 0.2457 | | 0.7151 | 153.85 | 2000 | 0.9984 | 0.5588 | 0.2353 | | 0.6416 | 184.62 | 2400 | 0.9889 | 0.5607 | 0.2370 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 ### Eval results on Common Voice 8 "test" (WER): | Without LM | With LM (run `./eval.py`) | |---|---| | 52.03 | 39.89 |

stabilityai/stable-video-diffusion-img2vid-xt-1-1

stabilityai

diffusers

image-to-video

--- pipeline_tag: image-to-video license: other license_name: stable-video-diffusion-1-1-nc-community license_link: LICENSE extra_gated_prompt: >- STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT Dated: February 2, 2024 By clicking “I Accept” below or by using or distributing any portion or element of the Models, Software, Software Products or Derivative Works, you agree to the terms of this License. If you do not agree to this License, then you do not have any rights to use the Software Products or Derivative Works through this License, and you must immediately cease using the Software Products or Derivative Works. If you are agreeing to be bound by the terms of this License on behalf of your employer or other entity, you represent and warrant to Stability AI that you have full legal authority to bind your employer or such entity to this License. If you do not have the requisite authority, you may not accept the License or access the Software Products or Derivative Works on behalf of your employer or other entity. "Agreement" means this Stable Non-Commercial Research Community License Agreement. “AUP” means the Stability AI Acceptable Use Policy available at https://stability.ai/use-policy, as may be updated from time to time. "Derivative Work(s)” means (a) any derivative work of the Software Products as recognized by U.S. copyright laws and (b) any modifications to a Model, and any other model created which is based on or derived from the Model or the Model’s output. For clarity, Derivative Works do not include the output of any Model. “Documentation” means any specifications, manuals, documentation, and other written information provided by Stability AI related to the Software. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Software Products and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Stability AI 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 any Software Products or Derivative Works. Sections 2-4 shall survive the termination of this Agreement. 6. Governing Law. This Agreement will be governed by and construed in accordance with the laws of the United States and the State of California without regard to choice of law principles. extra_gated_description: Stable Video Diffusion 1.1 License Agreement extra_gated_button_content: Submit extra_gated_fields: Name: text Company Name (if applicable): text Email: text Other Comments: text By clicking here, you accept the License agreement, and will use the Software Products and Derivative Works for non-commercial or research purposes only: checkbox By clicking here, you agree to sharing with Stability AI the information contained within this form and that Stability AI can contact you for the purposes of marketing our products and services: checkbox --- # Stable Video Diffusion 1.1 Image-to-Video Model Card <!-- Provide a quick summary of what the model is/does. -->  Stable Video Diffusion (SVD) 1.1 Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it. Please note: For commercial use, please refer to https://stability.ai/membership. ## Model Details ### Model Description (SVD 1.1) Image-to-Video is a latent diffusion model trained to generate short video clips from an image conditioning. This model was trained to generate 25 frames at resolution 1024x576 given a context frame of the same size, finetuned from [SVD Image-to-Video [25 frames]](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt). Fine tuning was performed with fixed conditioning at 6FPS and Motion Bucket Id 127 to improve the consistency of outputs without the need to adjust hyper parameters. These conditions are still adjustable and have not been removed. Performance outside of the fixed conditioning settings may vary compared to SVD 1.0. - **Developed by:** Stability AI - **Funded by:** Stability AI - **Model type:** Generative image-to-video model - **Finetuned from model:** SVD Image-to-Video [25 frames] ### Model Sources For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference). - **Repository:** https://github.com/Stability-AI/generative-models - **Paper:** https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets ## Uses ### Direct Use The model is intended for both non-commercial and commercial usage. You can use this model for non-commercial or research purposes under the following [license](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1/blob/main/LICENSE). Possible research areas and tasks include - Research on generative models. - 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. For commercial use, please refer to https://stability.ai/membership. Excluded uses are described below. ### 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. The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy). ## Limitations and Bias ### Limitations - The generated videos are rather short (<= 4sec), and the model does not achieve perfect photorealism. - The model may generate videos without motion, or very slow camera pans. - The model cannot be controlled through text. - The model cannot render legible text. - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy. ### Recommendations The model is intended for both non-commercial and commercial usage. ## How to Get Started with the Model Check out https://github.com/Stability-AI/generative-models

microsoft/swinv2-tiny-patch4-window16-256

microsoft

transformers

image-classification

--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k 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 --- # Swin Transformer v2 (tiny-sized model) Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). Disclaimer: The team releasing Swin Transformer v2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally. Swin Transformer v2 adds 3 main improvements: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) a log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) a self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.  [Source](https://paperswithcode.com/method/swin-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swinv2) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window16-256") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window16-256") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swinv2.html#). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2111-09883, author = {Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo}, title = {Swin Transformer {V2:} Scaling Up Capacity and Resolution}, journal = {CoRR}, volume = {abs/2111.09883}, year = {2021}, url = {https://arxiv.org/abs/2111.09883}, eprinttype = {arXiv}, eprint = {2111.09883}, timestamp = {Thu, 02 Dec 2021 15:54:22 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-09883.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

OpenAssistant/oasst-rm-2-pythia-6.9b-epoch-1

OpenAssistant

transformers

--- license: apache-2.0 --- # Pythia 6.9B Based Reward Model - base model: [andreaskoepf/pythia-6.9b-gpt4all-pretrain](https://huggingface.co/andreaskoepf/pythia-6.9b-gpt4all-pretrain) - wandb: https://wandb.ai/open-assistant/reward-model/runs/5xld9wmd - checkpoint: 3500 steps Compute was generously provided by [Stability AI](https://stability.ai/) ### How to use ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer # install open assistant model_training module (e.g. run `pip install -e .` in `model/` directory of open-assistant repository) import model_training.models.reward_model # noqa: F401 (registers reward model for AutoModel loading) model_name = "OpenAssistant/oasst-rm-2-pythia-6.9b-epoch-1" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) input_text = "<|prompter|>Hi how are you?<|endoftext|><|assistant|>Hi, I am Open-Assistant a large open-source language model trained by LAION AI. How can I help you today?<|endoftext|>" inputs = tokenizer(input_text, return_tensors="pt") score = model(**inputs).logits[0].cpu().detach() print(score) ``` ### Datasets ``` datasets: - oasst_export: lang: "en,es,de,fr" input_file_path: 2023-03-27_oasst_research_ready_synth.jsonl.gz val_split: 0.1 - anthropic_rlhf: fraction: 0.1 max_val_set: 1000 - shp: max_val_set: 1000 - hellaswag: fraction: 0.5 max_val_set: 1000 - webgpt: val_split: 0.05 max_val_set: 1000 - hf_summary_pairs: fraction: 0.1 max_val_set: 250 ```

moka-ai/m3e-base

moka-ai

sentence-transformers

--- language: - zh - en tags: - embedding - text-embedding library_name: sentence-transformers --- # 🅜 M3E Models [m3e-small](https://huggingface.co/moka-ai/m3e-small) | [m3e-base](https://huggingface.co/moka-ai/m3e-base) M3E 是 Moka Massive Mixed Embedding 的缩写 - Moka，此模型由 MokaAI 训练，开源和评测，训练脚本使用 [uniem](https://github.com/wangyuxinwhy/uniem/blob/main/scripts/train_m3e.py) ，评测 BenchMark 使用 [MTEB-zh](https://github.com/wangyuxinwhy/uniem/tree/main/mteb-zh) - Massive，此模型通过**千万级** (2200w+) 的中文句对数据集进行训练 - Mixed，此模型支持中英双语的同质文本相似度计算，异质文本检索等功能，未来还会支持代码检索 - Embedding，此模型是文本嵌入模型，可以将自然语言转换成稠密的向量 ## 🆕 更新说明 - 2023.06.24，添加微调 M3E 的教程 [notebook](https://github.com/wangyuxinwhy/uniem/blob/main/examples/finetune.ipynb)，几行代码，更佳适配！<a target="_blank" href="https://colab.research.google.com/github/wangyuxinwhy/uniem/blob/main/examples/finetune.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> - 2023.06.14，添加了三个中文开源文本嵌入模型到评测中，包括 UER, ErLangShen, DMetaSoul - 2023.06.08，添加检索任务的评测结果，在 T2Ranking 1W 中文数据集上，m3e-base 在 ndcg@10 上达到了 0.8004，超过了 openai-ada-002 的 0.7786 - 2023.06.07，添加文本分类任务的评测结果，在 6 种文本分类数据集上，m3e-base 在 accuracy 上达到了 0.6157，超过了 openai-ada-002 的 0.5956 ## ⚖️ 模型对比 | | 参数数量 | 维度 | 中文 | 英文 | s2s | s2p | s2c | 开源 | 兼容性 | s2s Acc | s2p ndcg@10 | | --------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | ---- | ---------- | ------------ | -------- | | m3e-small | 24M | 512 | 是 | 否 | 是 | 否 | 否 | 是 | 优 | 0.5834 | 0.7262 | | m3e-base | 110M | 768 | 是 | 是 | 是 | 是 | 否 | 是 | 优 | **0.6157** | **0.8004** | | text2vec | 110M | 768 | 是 | 否 | 是 | 否 | 否 | 是 | 优 | 0.5755 | 0.6346 | | openai-ada-002 | 未知 | 1536 | 是 | 是 | 是 | 是 | 是 | 否 | 优 | 0.5956 | 0.7786 | 说明： - s2s, 即 sentence to sentence ，代表了同质文本之间的嵌入能力，适用任务：文本相似度，重复问题检测，文本分类等 - s2p, 即 sentence to passage ，代表了异质文本之间的嵌入能力，适用任务：文本检索，GPT 记忆模块等 - s2c, 即 sentence to code ，代表了自然语言和程序语言之间的嵌入能力，适用任务：代码检索 - 兼容性，代表了模型在开源社区中各种项目被支持的程度，由于 m3e 和 text2vec 都可以直接通过 sentence-transformers 直接使用，所以和 openai 在社区的支持度上相当 - ACC & ndcg@10，详情见下方的评测 Tips: - 使用场景主要是中文，少量英文的情况，建议使用 m3e 系列的模型 - 多语言使用场景，并且不介意数据隐私的话，我建议使用 openai text-embedding-ada-002 - 代码检索场景，推荐使用 openai text-embedding-ada-002 - 文本检索场景，请使用具备文本检索能力的模型，只在 S2S 上训练的文本嵌入模型，没有办法完成文本检索任务 ## 🔧 使用 M3E 您需要先安装 sentence-transformers ```bash pip install -U sentence-transformers ``` 安装完成后，您可以使用以下代码来使用 M3E Models ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('moka-ai/m3e-base') #Our sentences we like to encode sentences = [ '* Moka 此文本嵌入模型由 MokaAI 训练并开源，训练脚本使用 uniem', '* Massive 此文本嵌入模型通过**千万级**的中文句对数据集进行训练', '* Mixed 此文本嵌入模型支持中英双语的同质文本相似度计算，异质文本检索等功能，未来还会支持代码检索，ALL in one' ] #Sentences are encoded by calling model.encode() embeddings = model.encode(sentences) #Print the embeddings for sentence, embedding in zip(sentences, embeddings): print("Sentence:", sentence) print("Embedding:", embedding) print("") ``` M3E 系列的所有模型在设计的时候就考虑到完全兼容 [sentence-transformers](https://www.sbert.net/) ，所以你可以通过**替换名称字符串**的方式在所有支持 sentence-transformers 的项目中**无缝**使用 M3E Models，比如 [chroma](https://docs.trychroma.com/getting-started), [guidance](https://github.com/microsoft/guidance), [semantic-kernel](https://github.com/microsoft/semantic-kernel) 。 ## 🎨 微调模型 `uniem` 提供了非常易用的 finetune 接口，几行代码，即刻适配！ ```python from datasets import load_dataset from uniem.finetuner import FineTuner dataset = load_dataset('shibing624/nli_zh', 'STS-B') # 指定训练的模型为 m3e-small finetuner = FineTuner.from_pretrained('moka-ai/m3e-small', dataset=dataset) finetuner.run(epochs=1) ``` 详见 [uniem 微调教程](https://github.com/wangyuxinwhy/uniem/blob/main/examples/finetune.ipynb) <a target="_blank" href="https://colab.research.google.com/github/wangyuxinwhy/uniem/blob/main/examples/finetune.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> ## ➿ 训练方案 M3E 使用 in-batch 负采样的对比学习的方式在句对数据集进行训练，为了保证 in-batch 负采样的效果，我们使用 A100 80G 来最大化 batch-size，并在共计 2200W+ 的句对数据集上训练了 1 epoch。训练脚本使用 [uniem](https://github.com/wangyuxinwhy/uniem/blob/main/scripts/train_m3e.py)，您可以在这里查看具体细节。 ## 🌟 特性 - 中文训练集，M3E 在大规模句对数据集上的训练，包含中文百科，金融，医疗，法律，新闻，学术等多个领域共计 2200W 句对样本，数据集详见 [M3E 数据集](#M3E数据集) - 英文训练集，M3E 使用 MEDI 145W 英文三元组数据集进行训练，数据集详见 [MEDI 数据集](https://drive.google.com/file/d/1vZ5c2oJNonGOvXzppNg5mHz24O6jcc52/view)，此数据集由 [instructor team](https://github.com/HKUNLP/instructor-embedding) 提供 - 指令数据集，M3E 使用了 300W + 的指令微调数据集，这使得 M3E 对文本编码的时候可以遵从指令，这部分的工作主要被启发于 [instructor-embedding](https://github.com/HKUNLP/instructor-embedding) - 基础模型，M3E 使用 hfl 实验室的 [Roberta](https://huggingface.co/hfl/chinese-roberta-wwm-ext) 系列模型进行训练，目前提供 small 和 base 两个版本，大家则需选用 - ALL IN ONE，M3E 旨在提供一个 ALL IN ONE 的文本嵌入模型，不仅支持同质句子相似度判断，还支持异质文本检索，你只需要一个模型就可以覆盖全部的应用场景，未来还会支持代码检索 ## 💯 MTEB-zh 评测 - 评测模型，[text2vec](https://github.com/shibing624/text2vec), m3e-base, m3e-small, openai text-embedding-ada-002, [DMetaSoul](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2), [UER](https://huggingface.co/uer/sbert-base-chinese-nli), [ErLangShen](https://huggingface.co/IDEA-CCNL/Erlangshen-SimCSE-110M-Chinese) - 评测脚本，具体参考 [MTEB-zh] (https://github.com/wangyuxinwhy/uniem/blob/main/mteb-zh) ### 文本分类 - 数据集选择，选择开源在 HuggingFace 上的 6 种文本分类数据集，包括新闻、电商评论、股票评论、长文本等 - 评测方式，使用 MTEB 的方式进行评测，报告 Accuracy。 | | text2vec | m3e-small | m3e-base | openai | DMetaSoul | uer | erlangshen | | ----------------- | -------- | --------- | -------- | ------ | ----------- | ------- | ----------- | | TNews | 0.43 | 0.4443 | **0.4827** | 0.4594 | 0.3084 | 0.3539 | 0.4361 | | JDIphone | 0.8214 | 0.8293 | **0.8533** | 0.746 | 0.7972 | 0.8283 | 0.8356 | | GubaEastmony | 0.7472 | 0.712 | 0.7621 | 0.7574 | 0.735 | 0.7534 | **0.7787** | | TYQSentiment | 0.6099 | 0.6596 | **0.7188** | 0.68 | 0.6437 | 0.6662 | 0.6444 | | StockComSentiment | 0.4307 | 0.4291 | 0.4363 | **0.4819** | 0.4309 | 0.4555 | 0.4482 | | IFlyTek | 0.414 | 0.4263 | 0.4409 | **0.4486** | 0.3969 | 0.3762 | 0.4241 | | Average | 0.5755 | 0.5834 | **0.6157** | 0.5956 | 0.552016667 | 0.57225 | 0.594516667 | ### 检索排序 #### T2Ranking 1W - 数据集选择，使用 [T2Ranking](https://github.com/THUIR/T2Ranking/tree/main) 数据集，由于 T2Ranking 的数据集太大，openai 评测起来的时间成本和 api 费用有些高，所以我们只选择了 T2Ranking 中的前 10000 篇文章 - 评测方式，使用 MTEB 的方式进行评测，报告 map@1, map@10, mrr@1, mrr@10, ndcg@1, ndcg@10 - 注意！从实验结果和训练方式来看，除了 M3E 模型和 openai 模型外，其余模型都没有做检索任务的训练，所以结果仅供参考。 | | text2vec | openai-ada-002 | m3e-small | m3e-base | DMetaSoul | uer | erlangshen | | ------- | -------- | -------------- | --------- | -------- | --------- | ------- | ---------- | | map@1 | 0.4684 | 0.6133 | 0.5574 | **0.626** | 0.25203 | 0.08647 | 0.25394 | | map@10 | 0.5877 | 0.7423 | 0.6878 | **0.7656** | 0.33312 | 0.13008 | 0.34714 | | mrr@1 | 0.5345 | 0.6931 | 0.6324 | **0.7047** | 0.29258 | 0.10067 | 0.29447 | | mrr@10 | 0.6217 | 0.7668 | 0.712 | **0.7841** | 0.36287 | 0.14516 | 0.3751 | | ndcg@1 | 0.5207 | 0.6764 | 0.6159 | **0.6881** | 0.28358 | 0.09748 | 0.28578 | | ndcg@10 | 0.6346 | 0.7786 | 0.7262 | **0.8004** | 0.37468 | 0.15783 | 0.39329 | #### T2Ranking - 数据集选择，使用 T2Ranking，刨除 openai-ada-002 模型后，我们对剩余的三个模型，进行 T2Ranking 10W 和 T2Ranking 50W 的评测。（T2Ranking 评测太耗内存了... 128G 都不行） - 评测方式，使用 MTEB 的方式进行评测，报告 ndcg@10 | | text2vec | m3e-small | m3e-base | | ------- | -------- | --------- | -------- | | t2r-1w | 0.6346 | 0.72621 | **0.8004** | | t2r-10w | 0.44644 | 0.5251 | **0.6263** | | t2r-50w | 0.33482 | 0.38626 | **0.47364** | 说明： - 检索排序对于 text2vec 并不公平，因为 text2vec 在训练的时候没有使用过检索相关的数据集，所以没有办法很好的完成检索任务也是正常的。 ## 📂 M3E数据集 如果您想要使用这些数据集，你可以在 [uniem process_zh_datasets](https://github.com/wangyuxinwhy/uniem/blob/main/scripts/process_zh_datasets.py) 中找到加载 huggingface 数据集的脚本，非 huggingface 数据集需要您根据下方提供的链接自行下载和处理。 | 数据集名称 | 领域 | 数量 | 任务类型 | Prompt | 质量 | 数据提供者 | 说明 | 是否开源/研究使用 | 是否商用 | 脚本 | Done | URL | 是否同质 | | -------------------- | ---- | --------- | ----------------- | ------ | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----------------- | -------- | ---- | ---- | ------------------------------------------------------------ | -------- | | cmrc2018 | 百科 | 14,363 | 问答 | 问答 | 优 | Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu | https://github.com/ymcui/cmrc2018/blob/master/README_CN.md 专家标注的基于维基百科的中文阅读理解数据集，将问题和上下文视为正例 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/cmrc2018 | 否 | | belle_2m | 百科 | 2,000,000 | 指令微调 | 无 | 优 | LianjiaTech/BELLE | belle 的指令微调数据集，使用 self instruct 方法基于 gpt3.5 生成 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/BelleGroup/train_2M_CN | 否 | | firefily | 百科 | 1,649,399 | 指令微调 | 无 | 优 | YeungNLP | Firefly（流萤） 是一个开源的中文对话式大语言模型，使用指令微调（Instruction Tuning）在中文数据集上进行调优。使用了词表裁剪、ZeRO等技术，有效降低显存消耗和提高训练效率。 在训练中，我们使用了更小的模型参数量，以及更少的计算资源。 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M | 否 | | alpaca_gpt4 | 百科 | 48,818 | 指令微调 | 无 | 优 | Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao | 本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/alpaca-zh | 否 | | zhihu_kol | 百科 | 1,006,218 | 问答 | 问答 | 优 | wangrui6 | 知乎问答 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/wangrui6/Zhihu-KOL | 否 | | hc3_chinese | 百科 | 39,781 | 问答 | 问答 | 良 | Hello-SimpleAI | 问答数据，包括人工回答和 GPT 回答 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/Hello-SimpleAI/HC3-Chinese | 否 | | amazon_reviews_multi | 电商 | 210,000 | 问答 文本分类 | 摘要 | 优 | 亚马逊 | 亚马逊产品评论数据集 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/amazon_reviews_multi/viewer/zh/train?row=8 | 否 | | mlqa | 百科 | 85,853 | 问答 | 问答 | 良 | patrickvonplaten | 一个用于评估跨语言问答性能的基准数据集 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/mlqa/viewer/mlqa-translate-train.zh/train?p=2 | 否 | | xlsum | 新闻 | 93,404 | 摘要 | 摘要 | 良 | BUET CSE NLP Group | BBC的专业注释文章摘要对 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/csebuetnlp/xlsum/viewer/chinese_simplified/train?row=259 | 否 | | ocnli | 口语 | 17,726 | 自然语言推理 | 推理 | 良 | Thomas Wolf | 自然语言推理数据集 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/clue/viewer/ocnli | 是 | | BQ | 金融 | 60,000 | 文本分类 | 相似 | 良 | Intelligent Computing Research Center, Harbin Institute of Technology(Shenzhen) | http://icrc.hitsz.edu.cn/info/1037/1162.htm BQ 语料库包含来自网上银行自定义服务日志的 120，000 个问题对。它分为三部分：100，000 对用于训练，10，000 对用于验证，10，000 对用于测试。 数据提供者： 哈尔滨工业大学（深圳）智能计算研究中心 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/nli_zh/viewer/BQ | 是 | | lcqmc | 口语 | 149,226 | 文本分类 | 相似 | 良 | Ming Xu | 哈工大文本匹配数据集，LCQMC 是哈尔滨工业大学在自然语言处理国际顶会 COLING2018 构建的问题语义匹配数据集，其目标是判断两个问题的语义是否相同 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/shibing624/nli_zh/viewer/LCQMC/train | 是 | | paws-x | 百科 | 23,576 | 文本分类 | 相似 | 优 | Bhavitvya Malik | PAWS Wiki中的示例 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/paws-x/viewer/zh/train | 是 | | wiki_atomic_edit | 百科 | 1,213,780 | 平行语义 | 相似 | 优 | abhishek thakur | 基于中文维基百科的编辑记录收集的数据集 | 未说明 | 未说明 | 是 | 是 | https://huggingface.co/datasets/wiki_atomic_edits | 是 | | chatmed_consult | 医药 | 549,326 | 问答 | 问答 | 优 | Wei Zhu | 真实世界的医学相关的问题，使用 gpt3.5 进行回答 | 是 | 否 | 是 | 是 | https://huggingface.co/datasets/michaelwzhu/ChatMed_Consult_Dataset | 否 | | webqa | 百科 | 42,216 | 问答 | 问答 | 优 | suolyer | 百度于2016年开源的数据集，数据来自于百度知道；格式为一个问题多篇意思基本一致的文章，分为人为标注以及浏览器检索；数据整体质量中，因为混合了很多检索而来的文章 | 是 | 未说明 | 是 | 是 | https://huggingface.co/datasets/suolyer/webqa/viewer/suolyer--webqa/train?p=3 | 否 | | dureader_robust | 百科 | 65,937 | 机器阅读理解 问答 | 问答 | 优 | 百度 | DuReader robust旨在利用真实应用中的数据样本来衡量阅读理解模型的鲁棒性，评测模型的过敏感性、过稳定性以及泛化能力，是首个中文阅读理解鲁棒性数据集。 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/PaddlePaddle/dureader_robust/viewer/plain_text/train?row=96 | 否 | | csl | 学术 | 395,927 | 语料 | 摘要 | 优 | Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao and Hui Zhang | 提供首个中文科学文献数据集（CSL），包含 396,209 篇中文核心期刊论文元信息 （标题、摘要、关键词、学科、门类）。CSL 数据集可以作为预训练语料，也可以构建许多NLP任务，例如文本摘要（标题预测）、 关键词生成和文本分类等。 | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/neuclir/csl | 否 | | miracl-corpus | 百科 | 4,934,368 | 语料 | 摘要 | 优 | MIRACL | The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., \n\n in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage. | 是 | 是 | 是 | 是 | https://huggingface.co/datasets/miracl/miracl-corpus | 否 | | lawzhidao | 法律 | 36,368 | 问答 | 问答 | 优 | 和鲸社区-Ustinian | 百度知道清洗后的法律问答 | 是 | 是 | 否 | 是 | https://www.heywhale.com/mw/dataset/5e953ca8e7ec38002d02fca7/content | 否 | | CINLID | 成语 | 34,746 | 平行语义 | 相似 | 优 | 高长宽 | 中文成语语义推理数据集（Chinese Idioms Natural Language Inference Dataset）收集了106832条由人工撰写的成语对（含少量歇后语、俗语等短文本），通过人工标注的方式进行平衡分类，标签为entailment、contradiction和neutral，支持自然语言推理（NLI）的任务。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=39 | 是 | | DuSQL | SQL | 25,003 | NL2SQL | SQL | 优 | 百度 | DuSQL是一个面向实际应用的数据集，包含200个数据库，覆盖了164个领域，问题覆盖了匹配、计算、推理等实际应用中常见形式。该数据集更贴近真实应用场景，要求模型领域无关、问题无关，且具备计算推理等能力。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=13 | 否 | | Zhuiyi-NL2SQL | SQL | 45,918 | NL2SQL | SQL | 优 | 追一科技 刘云峰 | NL2SQL是一个多领域的简单数据集，其主要包含匹配类型问题。该数据集主要验证模型的泛化能力，其要求模型具有较强的领域泛化能力、问题泛化能力。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=12 | 否 | | Cspider | SQL | 7,785 | NL2SQL | SQL | 优 | 西湖大学 张岳 | CSpider是一个多语言数据集，其问题以中文表达，数据库以英文存储，这种双语模式在实际应用中也非常常见，尤其是数据库引擎对中文支持不好的情况下。该数据集要求模型领域无关、问题无关，且能够实现多语言匹配。 | 是 | 否 | 否 | 是 | https://www.luge.ai/#/luge/dataDetail?id=11 | 否 | | news2016zh | 新闻 | 2,507,549 | 语料 | 摘要 | 良 | Bright Xu | 包含了250万篇新闻。新闻来源涵盖了6.3万个媒体，含标题、关键词、描述、正文。 | 是 | 是 | 否 | 是 | https://github.com/brightmart/nlp_chinese_corpus | 否 | | baike2018qa | 百科 | 1,470,142 | 问答 | 问答 | 良 | Bright Xu | 含有150万个预先过滤过的、高质量问题和答案，每个问题属于一个类别。总共有492个类别，其中频率达到或超过10次的类别有434个。 | 是 | 是 | 否 | 是 | https://github.com/brightmart/nlp_chinese_corpus | 否 | | webtext2019zh | 百科 | 4,258,310 | 问答 | 问答 | 优 | Bright Xu | 含有410万个预先过滤过的、高质量问题和回复。每个问题属于一个【话题】，总共有2.8万个各式话题，话题包罗万象。 | 是 | 是 | 否 | 是 | https://github.com/brightmart/nlp_chinese_corpus | 否 | | SimCLUE | 百科 | 775,593 | 平行语义 | 相似 | 良 | 数据集合，请在 simCLUE 中查看 | 整合了中文领域绝大多数可用的开源的语义相似度和自然语言推理的数据集，并重新做了数据拆分和整理。 | 是 | 否 | 否 | 是 | https://github.com/CLUEbenchmark/SimCLUE | 是 | | Chinese-SQuAD | 新闻 | 76,449 | 机器阅读理解 | 问答 | 优 | junzeng-pluto | 中文机器阅读理解数据集，通过机器翻译加人工校正的方式从原始Squad转换而来 | 是 | 否 | 否 | 是 | https://github.com/pluto-junzeng/ChineseSquad | 否 | ## 🗓️ 计划表 - [x] 完成 MTEB 中文评测 BenchMark, [MTEB-zh](https://github.com/wangyuxinwhy/uniem/tree/main/mteb-zh) - [x] 完成 Large 模型的训练和开源 - [x] 完成 Finetuner ，允许更优雅的微调 - [ ] 完成支持代码检索的模型 - [ ] 对 M3E 数据集进行清洗，保留高质量的部分，组成 m3e-hq，并在 huggingface 上开源 - [ ] 在 m3e-hq 的数据集上补充 hard negative 的样本及相似度分数，组成 m3e-hq-with-score，并在 huggingface 上开源 - [ ] 在 m3e-hq-with-score 上通过 [cosent loss](https://github.com/wangyuxinwhy/uniem/blob/main/uniem/criteria.py#LL24C39-L24C39) loss 进行训练并开源模型，CoSent 原理参考这篇[博客](https://kexue.fm/archives/8847) - [ ] 开源商用版本的 M3E models ## 🙏 致谢 感谢开源社区提供的中文语料，感谢所有在此工作中提供帮助的人们，希望中文社区越来越好，共勉！ ## 📜 License M3E models 使用的数据集中包括大量非商用的数据集，所以 M3E models 也是非商用的，仅供研究使用。不过我们已经在 M3E 数据集上标识了商用和非商用的数据集，您可以根据自己的需求自行训练。 ## Citation Please cite this model using the following format: ``` @software {Moka Massive Mixed Embedding, author = {Wang Yuxin,Sun Qingxuan,He sicheng}, title = {M3E: Moka Massive Mixed Embedding Model}, 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

neulab/codebert-python

neulab

transformers

fill-mask

This is a `microsoft/codebert-base-mlm` model, trained for 1,000,000 steps (with `batch_size=32`) on **Python** code from the `codeparrot/github-code-clean` dataset, on the masked-language-modeling task. It is intended to be used in CodeBERTScore: [https://github.com/neulab/code-bert-score](https://github.com/neulab/code-bert-score), but can be used for any other model or task. For more information, see: [https://github.com/neulab/code-bert-score](https://github.com/neulab/code-bert-score) ## Citation If you use this model for research, please cite: ``` @article{zhou2023codebertscore, url = {https://arxiv.org/abs/2302.05527}, author = {Zhou, Shuyan and Alon, Uri and Agarwal, Sumit and Neubig, Graham}, title = {CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code}, publisher = {arXiv}, year = {2023}, } ```

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

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

fxmarty/pix2struct-tiny-random

fxmarty

transformers

image-to-text

--- license: mit pipeline_tag: image-to-text ---

openai-community/roberta-large-openai-detector

openai-community

transformers

text-classification

--- language: en license: mit tags: - exbert datasets: - bookcorpus - wikipedia --- # RoBERTa Large OpenAI Detector ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) - [How To Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details **Model Description:** RoBERTa large OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa large model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as OpenAI released the weights of the [largest GPT-2 model](https://huggingface.co/gpt2-xl), the 1.5B parameter version. - **Developed by:** OpenAI, see [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) and [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for full author list - **Model Type:** Fine-tuned transformer-based language model - **Language(s):** English - **License:** MIT - **Related Models:** [RoBERTa large](https://huggingface.co/roberta-large), [GPT-XL (1.5B parameter version)](https://huggingface.co/gpt2-xl), [GPT-Large (the 774M parameter version)](https://huggingface.co/gpt2-large), [GPT-Medium (the 355M parameter version)](https://huggingface.co/gpt2-medium) and [GPT-2 (the 124M parameter version)](https://huggingface.co/gpt2) - **Resources for more information:** - [Research Paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) (see, in particular, the section beginning on page 12 about Automated ML-based detection). - [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) - [OpenAI Blog Post](https://openai.com/blog/gpt-2-1-5b-release/) - [Explore the detector model here](https://huggingface.co/openai-detector ) ## Uses #### Direct Use The model is a classifier that can be used to detect text generated by GPT-2 models. #### Downstream Use The model's developers have stated that they developed and released the model to help with research related to synthetic text generation, so the model could potentially be used for downstream tasks related to synthetic text generation. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further discussion. #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model developers discuss the risk of adversaries using the model to better evade detection in their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), suggesting that using the model for evading detection or for supporting efforts to evade detection would be a misuse of the model. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. #### Risks and Limitations In their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), the model developers discuss the risk that the model may be used by bad actors to develop capabilities for evading detection, though one purpose of releasing the model is to help improve detection research. In a related [blog post](https://openai.com/blog/gpt-2-1-5b-release/), the model developers also discuss the limitations of automated methods for detecting synthetic text and the need to pair automated detection tools with other, non-automated approaches. They write: > We conducted in-house detection research and developed a detection model that has detection rates of ~95% for detecting 1.5B GPT-2-generated text. We believe this is not high enough accuracy for standalone detection and needs to be paired with metadata-based approaches, human judgment, and public education to be more effective. The model developers also [report](https://openai.com/blog/gpt-2-1-5b-release/) finding that classifying content from larger models is more difficult, suggesting that detection with automated tools like this model will be increasingly difficult as model sizes increase. The authors find that training detector models on the outputs of larger models can improve accuracy and robustness. #### Bias 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)). Predictions generated by RoBERTa large and GPT-2 1.5B (which this model is built/fine-tuned on) can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups (see the [RoBERTa large](https://huggingface.co/roberta-large) and [GPT-2 XL](https://huggingface.co/gpt2-xl) model cards for more information). The developers of this model discuss these issues further in their [paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf). ## Training #### Training Data The model is a sequence classifier based on RoBERTa large (see the [RoBERTa large model card](https://huggingface.co/roberta-large) for more details on the RoBERTa large training data) and then fine-tuned using the outputs of the 1.5B GPT-2 model (available [here](https://github.com/openai/gpt-2-output-dataset)). #### Training Procedure The model developers write that: > We based a sequence classifier on RoBERTaLARGE (355 million parameters) and fine-tuned it to classify the outputs from the 1.5B GPT-2 model versus WebText, the dataset we used to train the GPT-2 model. They later state: > To develop a robust detector model that can accurately classify generated texts regardless of the sampling method, we performed an analysis of the model’s transfer performance. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the training procedure. ## Evaluation The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf). #### Testing Data, Factors and Metrics The model is intended to be used for detecting text generated by GPT-2 models, so the model developers test the model on text datasets, measuring accuracy by: > testing 510-token test examples comprised of 5,000 samples from the WebText dataset and 5,000 samples generated by a GPT-2 model, which were not used during the training. #### Results The model developers [find](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf): > Our classifier is able to detect 1.5 billion parameter GPT-2-generated text with approximately 95% accuracy...The model’s accuracy depends on sampling methods used when generating outputs, like temperature, Top-K, and nucleus sampling ([Holtzman et al., 2019](https://arxiv.org/abs/1904.09751). Nucleus sampling outputs proved most difficult to correctly classify, but a detector trained using nucleus sampling transfers well across other sampling methods. As seen in Figure 1 [in the paper], we found consistently high accuracy when trained on nucleus sampling. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), Figure 1 (on page 14) and Figure 2 (on page 16) for full results. ## 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 The model developers write that: See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the modeling architecture and training details. ## Citation Information ```bibtex @article{solaiman2019release, title={Release strategies and the social impacts of language models}, author={Solaiman, Irene and Brundage, Miles and Clark, Jack and Askell, Amanda and Herbert-Voss, Ariel and Wu, Jeff and Radford, Alec and Krueger, Gretchen and Kim, Jong Wook and Kreps, Sarah and others}, journal={arXiv preprint arXiv:1908.09203}, year={2019} } ``` APA: - Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., ... & Wang, J. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203. https://huggingface.co/papers/1908.09203 ## Model Card Authors This model card was written by the team at Hugging Face. ## How to Get Started with the Model More information needed

dccuchile/bert-base-spanish-wwm-uncased

dccuchile

transformers

fill-mask

--- language: - es tags: - masked-lm --- # BETO: Spanish BERT BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with [Multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) as well as other (not BERT-based) models. ## Download | | | | | |-|:--------:|:-----:|:----:| |BETO uncased|[tensorflow_weights](https://users.dcc.uchile.cl/~jperez/beto/uncased_2M/tensorflow_weights.tar.gz) | [pytorch_weights](https://users.dcc.uchile.cl/~jperez/beto/uncased_2M/pytorch_weights.tar.gz) | [vocab](./config/uncased_2M/vocab.txt), [config](./config/uncased_2M/config.json) | |BETO cased| [tensorflow_weights](https://users.dcc.uchile.cl/~jperez/beto/cased_2M/tensorflow_weights.tar.gz) | [pytorch_weights](https://users.dcc.uchile.cl/~jperez/beto/cased_2M/pytorch_weights.tar.gz) | [vocab](./config/cased_2M/vocab.txt), [config](./config/cased_2M/config.json) | All models use a vocabulary of about 31k BPE subwords constructed using SentencePiece and were trained for 2M steps. ## Benchmarks The following table shows some BETO results in the Spanish version of every task. We compare BETO (cased and uncased) with the Best Multilingual BERT results that we found in the literature (as of October 2019). The table also shows some alternative methods for the same tasks (not necessarily BERT-based methods). References for all methods can be found [here](#references). |Task | BETO-cased | BETO-uncased | Best Multilingual BERT | Other results | |-------|--------------:|--------------:|--------------------------:|-------------------------------:| |[POS](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1827) | **98.97** | 98.44 | 97.10 [2] | 98.91 [6], 96.71 [3] | |[NER-C](https://www.kaggle.com/nltkdata/conll-corpora) | [**88.43**](https://github.com/gchaperon/beto-benchmarks/blob/master/conll2002/dev_results_beto-cased_conll2002.txt) | 82.67 | 87.38 [2] | 87.18 [3] | |[MLDoc](https://github.com/facebookresearch/MLDoc) | [95.60](https://github.com/gchaperon/beto-benchmarks/blob/master/MLDoc/dev_results_beto-cased_mldoc.txt) | [**96.12**](https://github.com/gchaperon/beto-benchmarks/blob/master/MLDoc/dev_results_beto-uncased_mldoc.txt) | 95.70 [2] | 88.75 [4] | |[PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx) | 89.05 | 89.55 | 90.70 [8] | |[XNLI](https://github.com/facebookresearch/XNLI) | **82.01** | 80.15 | 78.50 [2] | 80.80 [5], 77.80 [1], 73.15 [4]| ## Example of use For further details on how to use BETO you can visit the [🤗Huggingface Transformers library](https://github.com/huggingface/transformers), starting by the [Quickstart section](https://huggingface.co/transformers/quickstart.html). BETO models can be accessed simply as [`'dccuchile/bert-base-spanish-wwm-cased'`](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) and [`'dccuchile/bert-base-spanish-wwm-uncased'`](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) by using the Transformers library. An example on how to download and use the models in this page can be found in [this colab notebook](https://colab.research.google.com/drive/1pYOYsCU59GBOwztkWCw5PTsqBiJbRy4S?usp=sharing). (We will soon add a more detailed step-by-step tutorial in Spanish for newcommers 😉) ## Acknowledgments We thank [Adereso](https://www.adere.so/) for kindly providing support for traininig BETO-uncased, and the [Millennium Institute for Foundational Research on Data](https://imfd.cl/en/) that provided support for training BETO-cased. Also thanks to Google for helping us with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program. ## Citation [Spanish Pre-Trained BERT Model and Evaluation Data](https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf) To cite this resource in a publication please use the following: ``` @inproceedings{CaneteCFP2020, title={Spanish Pre-Trained BERT Model and Evaluation Data}, author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge}, booktitle={PML4DC at ICLR 2020}, year={2020} } ``` ## License Disclaimer The license CC BY 4.0 best describes our intentions for our work. However we are not sure that all the datasets used to train BETO have licenses compatible with CC BY 4.0 (specially for commercial use). Please use at your own discretion and verify that the licenses of the original text resources match your needs. ## References * [1] [Original Multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) * [2] [Multilingual BERT on "Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT"](https://arxiv.org/pdf/1904.09077.pdf) * [3] [Multilingual BERT on "How Multilingual is Multilingual BERT?"](https://arxiv.org/pdf/1906.01502.pdf) * [4] [LASER](https://arxiv.org/abs/1812.10464) * [5] [XLM (MLM+TLM)](https://arxiv.org/pdf/1901.07291.pdf) * [6] [UDPipe on "75 Languages, 1 Model: Parsing Universal Dependencies Universally"](https://arxiv.org/pdf/1904.02099.pdf) * [7] [Multilingual BERT on "Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation"](https://arxiv.org/pdf/1906.01569.pdf) * [8] [Multilingual BERT on "PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification"](https://arxiv.org/abs/1908.11828)

microsoft/Phi-3-vision-128k-instruct

microsoft

transformers

text-generation

--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE language: - multilingual pipeline_tag: text-generation tags: - nlp - code - vision inference: parameters: temperature: 0.7 widget: - messages: - role: user content: <|image_1|>Can you describe what you see in the image? --- ## Model Summary The Phi-3-Vision-128K-Instruct is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 model family, and the multimodal version comes with 128K context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/try-phi3vision) + [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) | | Short Context | Long Context | | ------- | ------------- | ------------ | | Mini | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)| | Small | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)| | Medium | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)| | Vision | | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)| ## Intended Uses **Primary use cases** The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications with visual and text input capabilities which require 1) memory/compute constrained environments; 2) latency bound scenarios; 3) general image understanding; 4) OCR; 5) chart and table understanding. Our model is designed to accelerate research on efficient language and multimodal models, for use as a building block for generative AI powered features. **Use case considerations** Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. ## How to Use Phi-3-Vision-128K-Instruct has been integrated in the development version (4.40.2) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following: * When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function. * Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source. The current `transformers` version can be verified with: `pip list | grep transformers`. Examples of required packages: ``` flash_attn==2.5.8 numpy==1.24.4 Pillow==10.3.0 Requests==2.31.0 torch==2.3.0 torchvision==0.18.0 transformers==4.40.2 ``` Phi-3-Vision-128K-Instruct is also available in [Azure AI Studio](https://aka.ms/phi3-azure-ai). ### Chat Format Given the nature of the training data, the Phi-3-Vision-128K-Instruct model is best suited for a single image input wih prompts using the chat format as follows. You can provide the prompt as a single image with a generic template as follow: ```markdown <|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n ``` where the model generates the text after `<|assistant|>` . In case of multi-turn conversation, the prompt can be formatted as follows: ```markdown <|user|>\n<|image_1|>\n{prompt_1}<|end|>\n<|assistant|>\n{response_1}<|end|>\n<|user|>\n{prompt_2}<|end|>\n<|assistant|>\n ``` ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python from PIL import Image import requests from transformers import AutoModelForCausalLM from transformers import AutoProcessor model_id = "microsoft/Phi-3-vision-128k-instruct" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2') # use _attn_implementation='eager' to disable flash attention processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) messages = [ {"role": "user", "content": "<|image_1|>\nWhat is shown in this image?"}, {"role": "assistant", "content": "The chart displays the percentage of respondents who agree with various statements about their preparedness for meetings. It shows five categories: 'Having clear and pre-defined goals for meetings', 'Knowing where to find the information I need for a meeting', 'Understanding my exact role and responsibilities when I'm invited', 'Having tools to manage admin tasks like note-taking or summarization', and 'Having more focus time to sufficiently prepare for meetings'. Each category has an associated bar indicating the level of agreement, measured on a scale from 0% to 100%."}, {"role": "user", "content": "Provide insightful questions to spark discussion."} ] url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png" image = Image.open(requests.get(url, stream=True).raw) prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0") generation_args = { "max_new_tokens": 500, "temperature": 0.0, "do_sample": False, } generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) # remove input tokens generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:] response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(response) ``` Additional basic examples are provided [here](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/sample_inference.py). ## Responsible AI Considerations Like other models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: The Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. + Identification of individuals: models with vision capabilities may have the potential to uniquely identify individuals in images. Safety post-training steers the model to refuse such requests, but developers should consider and implement, as appropriate, additional mitigations or user consent flows as required in their respective jurisdiction, (e.g., building measures to blur faces in image inputs before processing. ## Training ### Model * Architecture: Phi-3-Vision-128K-Instruct has 4.2B parameters and contains image encoder, connector, projector, and Phi-3 Mini language model. * Inputs: Text and Image. It’s best suited for prompts using the chat format. * Context length: 128K tokens * GPUs: 512 H100-80G * Training time: 1.5 days * Training data: 500B vision and text tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline text dataset with cutoff date Mar 15, 2024. Future versions of the tuned models may be released as we improve models. * Release Type: Open weight release * Release dates: The model weight is released on May 21, 2024. ### Datasets Our training data includes a wide variety of sources, and is a combination of 1) publicly available documents filtered rigorously for quality, selected high-quality educational data and code; 2) selected high-quality image-text interleave; 3) newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.), newly created image data, e.g., chart/table/diagram/slides; 4) high quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. The data collection process involved sourcing information from publicly available documents, with a meticulous approach to filtering out undesirable documents and images. To safeguard privacy, we carefully filtered various image and text data sources to remove or scrub any potentially personal data from the training data. More details can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report). ## Benchmarks To understand the capabilities, we compare Phi-3-Vision-128K-Instruct with a set of models over a variety of zero-shot benchmarks using our internal benchmark platform. |Benchmark|Phi-3 Vision-128K-In|LlaVA-1.6 Vicuna-7B|QWEN-VL Chat|Llama3-Llava-Next-8B|Claude-3 Haiku|Gemini 1.0 Pro V|GPT-4V-Turbo| |---------|---------------------|------------------|------------|--------------------|--------------|----------------|------------| |MMMU|40.4|34.2|39.0|36.4|40.7|42.0|55.5|  |MMBench|80.5|76.3|75.8|79.4|62.4|80.0|86.1| |ScienceQA|90.8|70.6|67.2|73.7|72.0|79.7|75.7| |MathVista|44.5|31.5|29.4|34.8|33.2|35.0|47.5| |InterGPS|38.1|20.5|22.3|24.6|32.1|28.6|41.0| |AI2D|76.7|63.1|59.8|66.9|60.3|62.8|74.7| |ChartQA|81.4|55.0|50.9|65.8|59.3|58.0|62.3| |TextVQA|70.9|64.6|59.4|55.7|62.7|64.7|68.1| |POPE|85.8|87.2|82.6|87.0|74.4|84.2|83.7| ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3-Vision-128K model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

jonathandinu/face-parsing

jonathandinu

transformers

image-segmentation

--- language: en library_name: transformers tags: - vision - image-segmentation - nvidia/mit-b5 - transformers.js - onnx datasets: - celebamaskhq --- # Face Parsing  [Semantic segmentation](https://huggingface.co/docs/transformers/tasks/semantic_segmentation) model fine-tuned from [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) with [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ) for face parsing. For additional options, see the Transformers [Segformer docs](https://huggingface.co/docs/transformers/model_doc/segformer). > ONNX model for web inference contributed by [Xenova](https://huggingface.co/Xenova). ## Usage in Python Exhaustive list of labels can be extracted from [config.json](https://huggingface.co/jonathandinu/face-parsing/blob/65972ac96180b397f86fda0980bbe68e6ee01b8f/config.json#L30). | id | label | note | | :-: | :--------- | :---------------- | | 0 | background | | | 1 | skin | | | 2 | nose | | | 3 | eye_g | eyeglasses | | 4 | l_eye | left eye | | 5 | r_eye | right eye | | 6 | l_brow | left eyebrow | | 7 | r_brow | right eyebrow | | 8 | l_ear | left ear | | 9 | r_ear | right ear | | 10 | mouth | area between lips | | 11 | u_lip | upper lip | | 12 | l_lip | lower lip | | 13 | hair | | | 14 | hat | | | 15 | ear_r | earring | | 16 | neck_l | necklace | | 17 | neck | | | 18 | cloth | clothing | ```python import torch from torch import nn from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation from PIL import Image import matplotlib.pyplot as plt import requests # convenience expression for automatically determining device device = ( "cuda" # Device for NVIDIA or AMD GPUs if torch.cuda.is_available() else "mps" # Device for Apple Silicon (Metal Performance Shaders) if torch.backends.mps.is_available() else "cpu" ) # load models image_processor = SegformerImageProcessor.from_pretrained("jonathandinu/face-parsing") model = SegformerForSemanticSegmentation.from_pretrained("jonathandinu/face-parsing") model.to(device) # expects a PIL.Image or torch.Tensor url = "https://images.unsplash.com/photo-1539571696357-5a69c17a67c6" image = Image.open(requests.get(url, stream=True).raw) # run inference on image inputs = image_processor(images=image, return_tensors="pt").to(device) outputs = model(**inputs) logits = outputs.logits # shape (batch_size, num_labels, ~height/4, ~width/4) # resize output to match input image dimensions upsampled_logits = nn.functional.interpolate(logits, size=image.size[::-1], # H x W mode='bilinear', align_corners=False) # get label masks labels = upsampled_logits.argmax(dim=1)[0] # move to CPU to visualize in matplotlib labels_viz = labels.cpu().numpy() plt.imshow(labels_viz) plt.show() ``` ## Usage in the browser (Transformers.js) ```js import { pipeline, env, } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.14.0"; // important to prevent errors since the model files are likely remote on HF hub env.allowLocalModels = false; // instantiate image segmentation pipeline with pretrained face parsing model model = await pipeline("image-segmentation", "jonathandinu/face-parsing"); // async inference since it could take a few seconds const output = await model(url); // each label is a separate mask object // [ // { score: null, label: 'background', mask: transformers.js RawImage { ... }} // { score: null, label: 'hair', mask: transformers.js RawImage { ... }} // ... // ] for (const m of output) { print(`Found ${m.label}`); m.mask.save(`${m.label}.png`); } ``` ### p5.js Since [p5.js](https://p5js.org/) uses an animation loop abstraction, we need to take care loading the model and making predictions. ```js // ... // asynchronously load transformers.js and instantiate model async function preload() { // load transformers.js library with a dynamic import const { pipeline, env } = await import( "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.14.0" ); // important to prevent errors since the model files are remote on HF hub env.allowLocalModels = false; // instantiate image segmentation pipeline with pretrained face parsing model model = await pipeline("image-segmentation", "jonathandinu/face-parsing"); print("face-parsing model loaded"); } // ... ``` [full p5.js example](https://editor.p5js.org/jonathan.ai/sketches/wZn15Dvgh) ### Model Description - **Developed by:** [Jonathan Dinu](https://twitter.com/jonathandinu) - **Model type:** Transformer-based semantic segmentation image model - **License:** non-commercial research and educational purposes - **Resources for more information:** Transformers docs on [Segformer](https://huggingface.co/docs/transformers/model_doc/segformer) and/or the [original research paper](https://arxiv.org/abs/2105.15203). ## Limitations and Bias ### Bias While the capabilities of computer vision models are impressive, they can also reinforce or exacerbate social biases. The [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ) dataset used for fine-tuning is large but not necessarily perfectly diverse or representative. Also, they are images of.... just celebrities.

Crosstyan/BPModel

Crosstyan

diffusers

text-to-image

--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - safetensors inference: true thumbnail: https://s2.loli.net/2023/05/31/bl27yWANrT3asoG.png widget: - text: >- 1girl with blonde two side up disheveled hair red eyes in black serafuku red ribbon, upper body, simple background, grey background, collarbone example_title: example 1girl datasets: - Crosstyan/BPDataset library_name: diffusers --- # BPModel  ## Update **2023-01-02:** I wasted more GPU hours to train it a little bit more overfitting. Check out [bp_mk3.safetensors](bp_mk3.safetensors) and [bp_mk5.safetensors](bp_mk5.safetensors). Prepare yourself own VAE! Update your WebUI if you can't load [safetensors](https://github.com/huggingface/safetensors). Adds lots of samples in `images` folder! **2023-01-06:** Checkout [NMFSAN](NMFSAN/README.md) for a new model trained with custom embeddings. ## Introduction  BPModel is an experimental Stable Diffusion model based on [ACertainty](https://huggingface.co/JosephusCheung/ACertainty) from [Joseph Cheung](https://huggingface.co/JosephusCheung). Why is the Model even existing? There are loads of Stable Diffusion model out there, especially anime style models. Well, is there any models trained with resolution base resolution (`base_res`) 768 even 1024 before? Don't think so. Here it is, the BPModel, a Stable Diffusion model you may love or hate. Trained with 5k high quality images that suit my taste (not necessary yours unfortunately) from [Sankaku Complex](https://chan.sankakucomplex.com) with annotations. The dataset is public in [Crosstyan/BPDataset](https://huggingface.co/datasets/Crosstyan/BPDataset) for the sake of full disclosure . Pure combination of tags may not be the optimal way to describe the image, but I don't need to do extra work. And no, I won't feed any AI generated image to the model even it might outlaw the model from being used in some countries. The training of a high resolution model requires a significant amount of GPU hours and can be costly. In this particular case, 10 V100 GPU hours were spent on training 30 epochs with a resolution of 512, while 60 V100 GPU hours were spent on training 30 epochs with a resolution of 768. An additional 100 V100 GPU hours were also spent on training a model with a resolution of 1024, although **ONLY** 10 epochs were run. The results of the training on the 1024 resolution model did not show a significant improvement compared to the 768 resolution model, and the resource demands, achieving a batch size of 1 on a V100 with 32G VRAM, were high. However, training on the 768 resolution did yield better results than training on the 512 resolution, and it is worth considering as an option. It is worth noting that Stable Diffusion 2.x also chose to train on a 768 resolution model. However, it may be more efficient to start with training on a 512 resolution model due to the slower training process and the need for additional prior knowledge to speed up the training process when working with a 768 resolution. [Mikubill/naifu-diffusion](https://github.com/Mikubill/naifu-diffusion) is used as training script and I also recommend to checkout [CCRcmcpe/scal-sdt](https://github.com/CCRcmcpe/scal-sdt). The configuration for 1024 and 768 resolution with aspect ratio bucket is presented here. ```yaml # 768 arb: enabled: true debug: false base_res: [768, 768] max_size: [1152, 768] divisible: 64 max_ar_error: 4 min_dim: 512 dim_limit: 1792 # 1024 arb: enabled: true debug: false base_res: [1024, 1024] max_size: [1536, 1024] divisible: 64 max_ar_error: 4 min_dim: 960 dim_limit: 2389 ``` ## Limitation  The limitation described in [SCAL-SDT Wiki](https://github.com/CCRcmcpe/scal-sdt/wiki#what-you-should-expect) is still applied. > SD cannot generate human body properly, like generating 6 fingers on one hand. BPModel can generate [more proper kitty cat](https://twitter.com/crosstyan/status/1606026536246685696) (if you know what I mean) than other anime model, but it's still not perfect. As results presented in [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://arxiv.org/abs/2212.03860), the copy and paste effect is still observed. Anything v3™ has been proven to be the most popular anime model in the community, but it's not perfect either as described in [JosephusCheung/ACertainThing](https://huggingface.co/JosephusCheung/ACertainThing) > It does not always stay true to your prompts; it adds irrelevant details, and sometimes these details are highly homogenized. BPModel, which has been fine-tuned on a relatively small dataset, is prone to overfit inherently. This is not surprising given the size of the dataset, but the strong prior knowledge of ACertainty (full Danbooru) and Stable Diffusion (LAION) helps to minimize the impact of overfitting. However I believe it would perform better than some artist style DreamBooth model which only train with a few hundred images or even less. I also oppose changing style by merging model since You could apply different style by training with proper captions and prompting. Besides some of images in my dataset have the artist name in the caption, however some artist name will be misinterpreted by CLIP when tokenizing. For example, *as109* will be tokenized as `[as, 1, 0, 9]` and *fuzichoco* will become `[fu, z, ic, hoco]`. Romanized Japanese suffers from the problem a lot and I don't have a good solution to fix it other than changing the artist name in the caption, which is time consuming and you can't promise the token you choose is unique enough. [Remember the sks?](https://www.reddit.com/r/StableDiffusion/comments/yju5ks/from_one_of_the_original_dreambooth_authors_stop/) Language drift problem is still exist. There's nothing I can do unless I can find a way to generate better caption or caption the image manually. [OFA](https://github.com/OFA-Sys/OFA) combined with [convnext-tagger](https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger) could provide a better result for SFW content. However fine tune is necessary for NSFW content, which I don't think anyone would like to do. (Could Unstable Diffusion give us surprise?) ## Cherry Picked Samples Here're some **cherry picked** samples. I were using [xformers](https://github.com/facebookresearch/xformers) when generating these sample and it might yield slight different result even with the same seed (welcome to the non deterministic field). "`Upscale latent space image when doing hires. fix`" is enabled also.  ```txt by (fkey:1) (shion:0.4) [sketch:0.75] (closed mouth expressionless:1) cat ears nekomimi 1girl, wearing a white sailor uniform with a short skirt and white pantyhose standing on the deck of a yacht, cowboy shot, and the sun setting behind her in the background, light particle, bokeh Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, worst quality, low quality, normal quality, lipstick, 2koma, 3koma, dutch angle, blush, from behind Steps: 28, Sampler: Euler a, CFG scale: 12, Seed: 4236324744, Size: 960x1600, Model hash: 855959a4, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ```  ```txt 1girl in black serafuku standing in a field solo, food, fruit, lemon, bubble, planet, moon, orange \(fruit\), lemon slice, leaf, fish, orange slice, by (tabi:1.25), spot color, looking at viewer, closeup cowboy shot Negative prompt: (bad:0.81), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:0.81), (speech:0.81), (worst:0.81), (blush:0.9), 2koma, 3koma, 4koma, collage, lipstick Steps: 18, Sampler: DDIM, CFG scale: 7, Seed: 2017390109, Size: 768x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 1, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ```  ```txt [sketch:0.75] [(oil painting:0.5)::0.75] by (fuzichoco:0.8) shion (fkey:0.9), fang solo cat ears nekomimi girl with multicolor streaked messy hair blue [black|blue] long hair bangs blue eyes in blue sailor collar school uniform serafuku short sleeves hand on own cheek hand on own face sitting, upper body, strawberry sweets ice cream food fruit spoon orange parfait Negative prompt: (bad:0.98), (normal:0.98), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:1), (speech:0.81), (worst:0.81), 2koma, 3koma, 4koma, collage, lipstick Steps: 40, Sampler: Euler a, CFG scale: 8, Seed: 910302581, Size: 960x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 2, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ```  ```txt (best:0.7), highly detailed,1girl,upper body,beautiful detailed eyes, medium_breasts, long hair,grey hair, grey eyes, curly hair, bangs,empty eyes,expressionless,twintails, beautiful detailed sky, beautiful detailed water, [cinematic lighting:0.6], upper body, school uniform,black ribbon,light smile Negative prompt: (bad:0.98), (normal:0.98), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:1), (speech:0.81), (worst:0.81), 2koma, 3koma, 4koma, collage, lipstick Steps: 40, Sampler: Euler, CFG scale: 8.5, Seed: 2311603025, Size: 960x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 3, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ``` *I don't think other model can do that.*  ```txt by [shion (fkey:0.9):momoko \(momopoco\):0.15], fang solo cat ears nekomimi girl with multicolor streaked messy hair blue [black|blue] long hair bangs blue eyes in blue sailor collar school uniform serafuku short sleeves hand on own cheek (middle finger:1.1) sitting, upper body, strawberry sweets ice cream food fruit spoon orange parfait Negative prompt: (bad:0.98), (normal:0.98), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:1), (speech:0.81), (worst:0.81), 2koma, 3koma, 4koma, collage, lipstick Steps: 40, Sampler: Euler a, CFG scale: 8, Seed: 2496891010, Size: 960x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 1, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ```  ```txt by [shion (fkey:0.9):momoko \(momopoco\):0.55], closed mouth fang solo cat ears nekomimi girl with multicolor streaked messy hair blue [black|blue] long hair bangs blue eyes in blue sailor collar school uniform serafuku short sleeves (middle finger:1.1) sitting, upper body, strawberry sweets ice cream food fruit spoon orange parfait Negative prompt: (bad:0.98), (normal:0.98), (comic:0.81), (cropped:0.81), (error:0.81), (extra:0.81), (low:0.81), (lowres:1), (speech:0.81), (worst:0.81), 2koma, 3koma, 4koma, collage, lipstick, (chibi:0.8) Steps: 40, Sampler: Euler a, CFG scale: 8, Seed: 2668993375, Size: 960x1600, Model hash: fed5b383, Batch size: 4, Batch pos: 3, Denoising strength: 0.7, Clip skip: 2, ENSD: 31337, First pass size: 0x0 ``` more samples can be found in [images](images/00976-3769766671_20221226155509.png) folder. ## Usage The [`bp_1024_e10.ckpt`](bp_1024_e10.ckpt) doesn't include any VAE and you should using other popular VAE in the community when using with [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or you would see the LaTeNt SpAcE! Use [`bp_1024_with_vae_te.ckpt`](bp_1024_with_vae_te.ckpt) if you don't have VAE and text encoder with you, still EMA weight is not included and it's fp16. If you want to continue training, use [`bp_1024_e10_ema.ckpt`](bp_1024_e10_ema.ckpt) which is the ema unet weight and with fp32 precision. For better performance, it is strongly recommended to use Clip skip (CLIP stop at last layers) 2. It's also recommended to use turn on "`Upscale latent space image when doing hires. fix`" in the settings of [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) which adds intricate details when using `Highres. fix`. ## About the Model Name I asked the [chatGPT](https://openai.com/blog/chatgpt/) what the proper explanation of abbreviation BP could be. ```txt Here are a few more ideas for creative interpretations of the abbreviation "BP": - Brightest Point - This could refer to a moment of exceptional brilliance or clarity. - Brainpower - the abbreviation refers to something that requires a lot of mental effort or intelligence to accomplish. - Bespoke Partition - A custom made section that separates two distinct areas. - Bukkake Picchi - A Japanese style of rice dish. - Bokeh Picker - A traditional Japanese photography technique that involves selecting points of light from a digital image. - Bipolarity - Two distinct and opposing elements or perspectives. Note that "BP" is often used as an abbreviation for "blood pressure," so it is important to context to determine the most appropriate interpretation of the abbreviation. ``` Personally, I would call it "Big Pot". ## License 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. The authors 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/blob/main/license.txt)

timm/vit_base_patch16_224.augreg2_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_base_patch16_224.augreg2_in21k_ft_in1k A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k by paper authors and (re) fine-tuned on ImageNet-1k with additional augmentation and regularization by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 86.6 - GMACs: 16.9 - Activations (M): 16.5 - 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_base_patch16_224.augreg2_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_base_patch16_224.augreg2_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, 768) 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}} } ```

vikp/surya_det2

vikp

transformers

Entry not found

MaziyarPanahi/WizardLM-70B-V1.0

MaziyarPanahi

transformers

text-generation

--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - wizardlm - llama - finetuned --- <!-- original-model-card start --> # Original model card: WizardLM's WizardLM 70B V1.0 ## WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions <p align="center"> 🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> </p> ## Unofficial Video Introductions Thanks to the enthusiastic friends, their video introductions are more lively and interesting. 1. [NEW WizardLM 70b 🔥 Giant Model...Insane Performance](https://www.youtube.com/watch?v=WdpiIXrO4_o) 2. [GET WizardLM NOW! 7B LLM KING That Can Beat ChatGPT! I'm IMPRESSED!](https://www.youtube.com/watch?v=SaJ8wyKMBds) 3. [WizardLM: Enhancing Large Language Models to Follow Complex Instructions](https://www.youtube.com/watch?v=I6sER-qivYk) 4. [WizardCoder AI Is The NEW ChatGPT's Coding TWIN!](https://www.youtube.com/watch?v=XjsyHrmd3Xo) ## News - 🔥🔥🔥[2023/08/26] We released **WizardCoder-Python-34B-V1.0** , which achieves the **73.2 pass@1** and surpasses **GPT4 (2023/03/15)**, **ChatGPT-3.5**, and **Claude2** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder). - [2023/06/16] We released **WizardCoder-15B-V1.0** , which surpasses **Claude-Plus (+6.8)**, **Bard (+15.3)** and **InstructCodeT5+ (+22.3)** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder). | Model | Checkpoint | Paper | HumanEval | MBPP | Demo | License | | ----- |------| ---- |------|-------| ----- | ----- | | WizardCoder-Python-34B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 73.2 | 61.2 | [Demo](http://47.103.63.15:50085/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> | | WizardCoder-15B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 59.8 |50.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> | | WizardCoder-Python-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 64.0 | 55.6 | -- | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> | | WizardCoder-Python-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 55.5 | 51.6 | [Demo](http://47.103.63.15:50088/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> | | WizardCoder-3B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 34.8 |37.4 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> | | WizardCoder-1B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 23.8 |28.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> | - 🔥 [08/11/2023] We release **WizardMath** Models. - 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**. - 🔥 Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM. - 🔥 Our **WizardMath-70B-V1.0** model achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM. | Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License| | ----- |------| ---- |------|-------| ----- | ----- | | WizardMath-70B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> | | WizardMath-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> | | WizardMath-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **54.9** | **10.7** | [Demo](http://47.103.63.15:50080/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>| <font size=4> | ^Model | ^Checkpoint | ^Paper |^MT-Bench | ^AlpacaEval | ^GSM8k | ^HumanEval | ^License| | ----- |------| ---- |------|-------| ----- | ----- | ----- | | ^{**WizardLM-70B-V1.0**} | ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-70B-V1.0" target="_blank">HF Link</a>}|^{📃**Coming Soon**}| ^**7.78** | ^**92.91%** |^**77.6%** | ^{**50.6 pass@1**}|^{<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a>} | | ^{WizardLM-13B-V1.2} | ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a>}| | ^7.06 | ^89.17% |^55.3% | ^{36.6 pass@1}|^{<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a>} | | ^{WizardLM-13B-V1.1} |^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a>} | | ^6.76 |^86.32% | | ^{25.0 pass@1}| ^{Non-commercial}| | ^{WizardLM-30B-V1.0} | ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a>} | | ^7.01 | | | ^{37.8 pass@1}| ^{Non-commercial} | | ^{WizardLM-13B-V1.0} | ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a>} | | ^6.35 | ^75.31% | | ^{24.0 pass@1} | ^{Non-commercial}| | ^{WizardLM-7B-V1.0}| ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a>} |^{📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a>}| | | |^{19.1 pass@1}|^{Non-commercial}| </font> - 🔥🔥🔥 [08/09/2023] We released **WizardLM-70B-V1.0** model. **Github Repo**: https://github.com/nlpxucan/WizardLM **Twitter**: https://twitter.com/WizardLM_AI/status/1689270108747976704 **Discord**: https://discord.gg/bpmeZD7V ❗<b>Note for model system prompts usage:</b> <b>WizardLM</b> adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>USER: Who are you? ASSISTANT: I am WizardLM.</s>...... ``` ## Inference WizardLM Demo Script We provide the inference WizardLM demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo). Please cite the paper if you use the data or code from WizardLM. ``` @article{xu2023wizardlm, title={Wizardlm: Empowering large language models to follow complex instructions}, author={Xu, Can and Sun, Qingfeng and Zheng, Kai and Geng, Xiubo and Zhao, Pu and Feng, Jiazhan and Tao, Chongyang and Jiang, Daxin}, journal={arXiv preprint arXiv:2304.12244}, year={2023} } ``` ❗<b>To commen concern about dataset:</b> Recently, there have been clear changes in the open-source policy and regulations of our overall organization's code, data, and models. Despite this, we have still worked hard to obtain opening the weights of the model first, but the data involves stricter auditing and is in review with our legal team . Our researchers have no authority to publicly release them without authorization. Thank you for your understanding. <!-- original-model-card end -->

jonatasgrosman/wav2vec2-large-xlsr-53-greek

jonatasgrosman

transformers

automatic-speech-recognition

--- language: el 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 Greek by Jonatas Grosman results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice el type: common_voice args: el metrics: - name: Test WER type: wer value: 11.62 - name: Test CER type: cer value: 3.36 --- # Fine-tuned XLSR-53 large model for speech recognition in Greek Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Greek using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10). 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-greek") 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 = "el" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-greek" SAMPLES = 5 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 | | ------------- | ------------- | | ΤΟ ΒΑΣΙΛΌΠΟΥΛΟ, ΠΟΥ ΜΟΙΆΖΕΙ ΛΕΟΝΤΑΡΆΚΙ ΚΑΙ ΑΕΤΟΥΔΆΚΙ | ΤΟ ΒΑΣΙΛΌΠΟΥΛΟ ΠΟΥ ΜΙΑΣΕ ΛΙΟΝΤΑΡΑΚΉ ΚΑΙ ΑΪΤΟΥΔΆΚΙ | | ΣΥΝΆΜΑ ΞΕΠΡΌΒΑΛΑΝ ΑΠΌ ΜΈΣΑ ΑΠΌ ΤΑ ΔΈΝΤΡΑ, ΔΕΞΙΆ, ΑΡΜΑΤΩΜΈΝΟΙ ΚΑΒΑΛΑΡΈΟΙ. | ΣΥΝΆΜΑ ΚΑΙ ΤΡΌΒΑΛΑΝ ΑΠΌ ΜΈΣΑ ΑΠΌ ΤΑ ΔΈΝΤΡΑ ΔΕΞΙΆ ΑΡΜΑΤΩΜΈΝΟΙ ΚΑΒΑΛΑΡΈΟΙ | | ΤΑ ΣΥΣΚΕΥΑΣΜΈΝΑ ΒΙΟΛΟΓΙΚΆ ΛΑΧΑΝΙΚΆ ΔΕΝ ΠΕΡΙΈΧΟΥΝ ΣΥΝΤΗΡΗΤΙΚΆ ΚΑΙ ΟΡΜΌΝΕΣ | ΤΑ ΣΥΣΚΕΦΑΣΜΈΝΑ ΒΙΟΛΟΓΙΚΆ ΛΑΧΑΝΙΚΆ ΔΕΝ ΠΕΡΙΈΧΟΥΝ ΣΙΔΗΡΗΤΙΚΆ ΚΑΙ ΟΡΜΌΝΕΣ | | ΑΚΟΛΟΥΘΉΣΕΤΕ ΜΕ! | ΑΚΟΛΟΥΘΉΣΤΕ ΜΕ | | ΚΑΙ ΠΟΎ ΜΠΟΡΏ ΝΑ ΤΟΝ ΒΡΩ; | Ε ΠΟΎ ΜΠΟΡΏ ΝΑ ΤΙ ΕΒΡΩ | | ΝΑΙ! ΑΠΟΚΡΊΘΗΚΕ ΤΟ ΠΑΙΔΊ | ΝΑΙ ΑΠΟΚΡΊΘΗΚΕ ΤΟ ΠΑΙΔΊ | | ΤΟ ΠΑΛΆΤΙ ΜΟΥ ΤΟ ΠΡΟΜΉΘΕΥΕ. | ΤΟ ΠΑΛΆΤΙ ΜΟΥ ΤΟ ΠΡΟΜΉΘΕΥΕ | | ΉΛΘΕ ΜΉΝΥΜΑ ΑΠΌ ΤΟ ΘΕΊΟ ΒΑΣΙΛΙΆ; | ΉΛΘΑ ΜΕΊΝΕΙ ΜΕ ΑΠΌ ΤΟ ΘΕΊΟ ΒΑΣΊΛΙΑ | | ΠΑΡΑΚΆΤΩ, ΈΝΑ ΡΥΆΚΙ ΜΟΥΡΜΟΎΡΙΖΕ ΓΛΥΚΆ, ΚΥΛΏΝΤΑΣ ΤΑ ΚΡΥΣΤΑΛΛΈΝΙΑ ΝΕΡΆ ΤΟΥ ΑΝΆΜΕΣΑ ΣΤΑ ΠΥΚΝΆ ΧΑΜΌΔΕΝΤΡΑ. | ΠΑΡΑΚΆΤΩ ΈΝΑ ΡΥΆΚΙ ΜΟΥΡΜΟΎΡΙΖΕ ΓΛΥΚΆ ΚΥΛΏΝΤΑΣ ΤΑ ΚΡΥΣΤΑΛΛΈΝΙΑ ΝΕΡΆ ΤΟΥ ΑΝΆΜΕΣΑ ΣΤΑ ΠΥΚΡΆ ΧΑΜΌΔΕΝΤΡΑ | | ΠΡΆΓΜΑΤΙ, ΕΊΝΑΙ ΑΣΤΕΊΟ ΝΑ ΠΆΡΕΙ Ο ΔΙΆΒΟΛΟΣ | ΠΡΆΓΜΑΤΗ ΕΊΝΑΙ ΑΣΤΕΊΟ ΝΑ ΠΆΡΕΙ Ο ΔΙΆΒΟΛΟΣ | ## Evaluation The model can be evaluated as follows on the Greek 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 = "el" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-greek" 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 2021-04-22). 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 | | ------------- | ------------- | ------------- | | lighteternal/wav2vec2-large-xlsr-53-greek | **10.13%** | **2.66%** | | jonatasgrosman/wav2vec2-large-xlsr-53-greek | 11.62% | 3.36% | | vasilis/wav2vec2-large-xlsr-53-greek | 19.09% | 5.88% | | PereLluis13/wav2vec2-large-xlsr-53-greek | 20.16% | 5.71% | ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-greek, title={Fine-tuned {XLSR}-53 large model for speech recognition in {G}reek}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-greek}}, year={2021} } ```

microsoft/trocr-small-handwritten

microsoft

transformers

image-to-text

--- tags: - trocr - image-to-text widget: - src: https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg example_title: Note 1 - src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU example_title: Note 2 - src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRNYtTuSBpZPV_nkBYPMFwVVD9asZOPgHww4epu9EqWgDmXW--sE2o8og40ZfDGo87j5w&usqp=CAU example_title: Note 3 --- # TrOCR (small-sized model, fine-tuned on IAM) TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr). ## Model description The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of DeiT, while the text decoder was initialized from the weights of UniLM. 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. Next, the Transformer text decoder autoregressively generates tokens. ## Intended uses & limitations You can use the raw model for optical character recognition (OCR) on single text-line images. See the [model hub](https://huggingface.co/models?search=microsoft/trocr) 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 TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image import requests # load image from the IAM database url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' image = Image.open(requests.get(url, stream=True).raw).convert("RGB") processor = TrOCRProcessor.from_pretrained('microsoft/trocr-small-handwritten') model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-small-handwritten') pixel_values = processor(images=image, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### BibTeX entry and citation info ```bibtex @misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

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 |

flair/ner-french

flair

flair

token-classification

--- tags: - flair - token-classification - sequence-tagger-model language: fr datasets: - conll2003 widget: - text: "George Washington est allé à Washington" --- ## French NER in Flair (default model) This is the standard 4-class NER model for French that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **90,61** (WikiNER) 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-french") # make example sentence sentence = Sentence("George Washington est allé à 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 (0.7394)] Span [6]: "Washington" [− Labels: LOC (0.9161)] ``` So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington est allé à 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 WIKINER_FRENCH from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. get the corpus corpus: Corpus = WIKINER_FRENCH() # 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('fr'), # contextual string embeddings, forward FlairEmbeddings('fr-forward'), # contextual string embeddings, backward FlairEmbeddings('fr-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-french', 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/).

avsolatorio/GIST-all-MiniLM-L6-v2

avsolatorio

sentence-transformers

sentence-similarity

--- language: - en library_name: sentence-transformers license: mit pipeline_tag: sentence-similarity tags: - feature-extraction - mteb - sentence-similarity - sentence-transformers model-index: - name: GIST-all-MiniLM-L6-v2 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.8955223880597 - type: ap value: 35.447605103320775 - type: f1 value: 66.82951715365854 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 87.19474999999998 - type: ap value: 83.09577890808514 - type: f1 value: 87.13833121762009 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 42.556000000000004 - type: f1 value: 42.236256693772276 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 26.884999999999998 - type: map_at_10 value: 42.364000000000004 - type: map_at_100 value: 43.382 - type: map_at_1000 value: 43.391000000000005 - type: map_at_3 value: 37.162 - type: map_at_5 value: 40.139 - type: mrr_at_1 value: 26.884999999999998 - type: mrr_at_10 value: 42.193999999999996 - type: mrr_at_100 value: 43.211 - type: mrr_at_1000 value: 43.221 - type: mrr_at_3 value: 36.949 - type: mrr_at_5 value: 40.004 - type: ndcg_at_1 value: 26.884999999999998 - type: ndcg_at_10 value: 51.254999999999995 - type: ndcg_at_100 value: 55.481 - type: ndcg_at_1000 value: 55.68300000000001 - type: ndcg_at_3 value: 40.565 - type: ndcg_at_5 value: 45.882 - type: precision_at_1 value: 26.884999999999998 - type: precision_at_10 value: 7.9799999999999995 - type: precision_at_100 value: 0.98 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 16.808999999999997 - type: precision_at_5 value: 12.645999999999999 - type: recall_at_1 value: 26.884999999999998 - type: recall_at_10 value: 79.801 - type: recall_at_100 value: 98.009 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 50.427 - type: recall_at_5 value: 63.229 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.31044837358167 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 35.44751738734691 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.96517580629869 - type: mrr value: 76.30051004704744 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 83.97262600499639 - type: cos_sim_spearman value: 81.25787561220484 - type: euclidean_pearson value: 64.96260261677082 - type: euclidean_spearman value: 64.17616109254686 - type: manhattan_pearson value: 65.05620628102835 - type: manhattan_spearman value: 64.71171546419122 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.2435064935065 - type: f1 value: 84.2334859253828 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.38358435972693 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 31.093619653843124 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 35.016999999999996 - type: map_at_10 value: 47.019 - type: map_at_100 value: 48.634 - type: map_at_1000 value: 48.757 - type: map_at_3 value: 43.372 - type: map_at_5 value: 45.314 - type: mrr_at_1 value: 43.491 - type: mrr_at_10 value: 53.284 - type: mrr_at_100 value: 54.038 - type: mrr_at_1000 value: 54.071000000000005 - type: mrr_at_3 value: 51.001 - type: mrr_at_5 value: 52.282 - type: ndcg_at_1 value: 43.491 - type: ndcg_at_10 value: 53.498999999999995 - type: ndcg_at_100 value: 58.733999999999995 - type: ndcg_at_1000 value: 60.307 - type: ndcg_at_3 value: 48.841 - type: ndcg_at_5 value: 50.76199999999999 - type: precision_at_1 value: 43.491 - type: precision_at_10 value: 10.315000000000001 - type: precision_at_100 value: 1.6209999999999998 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 23.462 - type: precision_at_5 value: 16.652 - type: recall_at_1 value: 35.016999999999996 - type: recall_at_10 value: 64.92 - type: recall_at_100 value: 86.605 - type: recall_at_1000 value: 96.174 - type: recall_at_3 value: 50.99 - type: recall_at_5 value: 56.93 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.866 - type: map_at_10 value: 40.438 - type: map_at_100 value: 41.77 - type: map_at_1000 value: 41.913 - type: map_at_3 value: 37.634 - type: map_at_5 value: 39.226 - type: mrr_at_1 value: 37.834 - type: mrr_at_10 value: 46.765 - type: mrr_at_100 value: 47.410000000000004 - type: mrr_at_1000 value: 47.461 - type: mrr_at_3 value: 44.735 - type: mrr_at_5 value: 46.028000000000006 - type: ndcg_at_1 value: 37.834 - type: ndcg_at_10 value: 46.303 - type: ndcg_at_100 value: 50.879 - type: ndcg_at_1000 value: 53.112 - type: ndcg_at_3 value: 42.601 - type: ndcg_at_5 value: 44.384 - type: precision_at_1 value: 37.834 - type: precision_at_10 value: 8.898 - type: precision_at_100 value: 1.4409999999999998 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 20.977 - type: precision_at_5 value: 14.841 - type: recall_at_1 value: 29.866 - type: recall_at_10 value: 56.06100000000001 - type: recall_at_100 value: 75.809 - type: recall_at_1000 value: 89.875 - type: recall_at_3 value: 44.707 - type: recall_at_5 value: 49.846000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.985 - type: map_at_10 value: 51.165000000000006 - type: map_at_100 value: 52.17 - type: map_at_1000 value: 52.229000000000006 - type: map_at_3 value: 48.089999999999996 - type: map_at_5 value: 49.762 - type: mrr_at_1 value: 44.577 - type: mrr_at_10 value: 54.493 - type: mrr_at_100 value: 55.137 - type: mrr_at_1000 value: 55.167 - type: mrr_at_3 value: 52.079 - type: mrr_at_5 value: 53.518 - type: ndcg_at_1 value: 44.577 - type: ndcg_at_10 value: 56.825 - type: ndcg_at_100 value: 60.842 - type: ndcg_at_1000 value: 62.015 - type: ndcg_at_3 value: 51.699 - type: ndcg_at_5 value: 54.11 - type: precision_at_1 value: 44.577 - type: precision_at_10 value: 9.11 - type: precision_at_100 value: 1.206 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 23.156 - type: precision_at_5 value: 15.737000000000002 - type: recall_at_1 value: 38.985 - type: recall_at_10 value: 70.164 - type: recall_at_100 value: 87.708 - type: recall_at_1000 value: 95.979 - type: recall_at_3 value: 56.285 - type: recall_at_5 value: 62.303 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.137 - type: map_at_10 value: 36.729 - type: map_at_100 value: 37.851 - type: map_at_1000 value: 37.932 - type: map_at_3 value: 34.074 - type: map_at_5 value: 35.398 - type: mrr_at_1 value: 30.621 - type: mrr_at_10 value: 39.007 - type: mrr_at_100 value: 39.961 - type: mrr_at_1000 value: 40.02 - type: mrr_at_3 value: 36.591 - type: mrr_at_5 value: 37.806 - type: ndcg_at_1 value: 30.621 - type: ndcg_at_10 value: 41.772 - type: ndcg_at_100 value: 47.181 - type: ndcg_at_1000 value: 49.053999999999995 - type: ndcg_at_3 value: 36.577 - type: ndcg_at_5 value: 38.777 - type: precision_at_1 value: 30.621 - type: precision_at_10 value: 6.372999999999999 - type: precision_at_100 value: 0.955 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.367 - type: precision_at_5 value: 10.531 - type: recall_at_1 value: 28.137 - type: recall_at_10 value: 55.162 - type: recall_at_100 value: 79.931 - type: recall_at_1000 value: 93.67 - type: recall_at_3 value: 41.057 - type: recall_at_5 value: 46.327 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.798 - type: map_at_10 value: 25.267 - type: map_at_100 value: 26.579000000000004 - type: map_at_1000 value: 26.697 - type: map_at_3 value: 22.456 - type: map_at_5 value: 23.912 - type: mrr_at_1 value: 20.771 - type: mrr_at_10 value: 29.843999999999998 - type: mrr_at_100 value: 30.849 - type: mrr_at_1000 value: 30.916 - type: mrr_at_3 value: 27.156000000000002 - type: mrr_at_5 value: 28.518 - type: ndcg_at_1 value: 20.771 - type: ndcg_at_10 value: 30.792 - type: ndcg_at_100 value: 36.945 - type: ndcg_at_1000 value: 39.619 - type: ndcg_at_3 value: 25.52 - type: ndcg_at_5 value: 27.776 - type: precision_at_1 value: 20.771 - type: precision_at_10 value: 5.734 - type: precision_at_100 value: 1.031 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 12.148 - type: precision_at_5 value: 9.055 - type: recall_at_1 value: 16.798 - type: recall_at_10 value: 43.332 - type: recall_at_100 value: 70.016 - type: recall_at_1000 value: 88.90400000000001 - type: recall_at_3 value: 28.842000000000002 - type: recall_at_5 value: 34.37 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.180000000000003 - type: map_at_10 value: 41.78 - type: map_at_100 value: 43.102000000000004 - type: map_at_1000 value: 43.222 - type: map_at_3 value: 38.505 - type: map_at_5 value: 40.443 - type: mrr_at_1 value: 37.824999999999996 - type: mrr_at_10 value: 47.481 - type: mrr_at_100 value: 48.268 - type: mrr_at_1000 value: 48.313 - type: mrr_at_3 value: 44.946999999999996 - type: mrr_at_5 value: 46.492 - type: ndcg_at_1 value: 37.824999999999996 - type: ndcg_at_10 value: 47.827 - type: ndcg_at_100 value: 53.407000000000004 - type: ndcg_at_1000 value: 55.321 - type: ndcg_at_3 value: 42.815 - type: ndcg_at_5 value: 45.363 - type: precision_at_1 value: 37.824999999999996 - type: precision_at_10 value: 8.652999999999999 - type: precision_at_100 value: 1.354 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 20.372 - type: precision_at_5 value: 14.591000000000001 - type: recall_at_1 value: 31.180000000000003 - type: recall_at_10 value: 59.894000000000005 - type: recall_at_100 value: 83.722 - type: recall_at_1000 value: 95.705 - type: recall_at_3 value: 45.824 - type: recall_at_5 value: 52.349999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.66 - type: map_at_10 value: 34.141 - type: map_at_100 value: 35.478 - type: map_at_1000 value: 35.594 - type: map_at_3 value: 30.446 - type: map_at_5 value: 32.583 - type: mrr_at_1 value: 29.909000000000002 - type: mrr_at_10 value: 38.949 - type: mrr_at_100 value: 39.803 - type: mrr_at_1000 value: 39.867999999999995 - type: mrr_at_3 value: 35.921 - type: mrr_at_5 value: 37.753 - type: ndcg_at_1 value: 29.909000000000002 - type: ndcg_at_10 value: 40.012 - type: ndcg_at_100 value: 45.707 - type: ndcg_at_1000 value: 48.15 - type: ndcg_at_3 value: 34.015 - type: ndcg_at_5 value: 37.002 - type: precision_at_1 value: 29.909000000000002 - type: precision_at_10 value: 7.693999999999999 - type: precision_at_100 value: 1.2229999999999999 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 16.323999999999998 - type: precision_at_5 value: 12.306000000000001 - type: recall_at_1 value: 24.66 - type: recall_at_10 value: 52.478 - type: recall_at_100 value: 77.051 - type: recall_at_1000 value: 93.872 - type: recall_at_3 value: 36.382999999999996 - type: recall_at_5 value: 43.903999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.768416666666667 - type: map_at_10 value: 36.2485 - type: map_at_100 value: 37.520833333333336 - type: map_at_1000 value: 37.64033333333334 - type: map_at_3 value: 33.25791666666667 - type: map_at_5 value: 34.877250000000004 - type: mrr_at_1 value: 31.65408333333334 - type: mrr_at_10 value: 40.43866666666667 - type: mrr_at_100 value: 41.301249999999996 - type: mrr_at_1000 value: 41.357499999999995 - type: mrr_at_3 value: 37.938916666666664 - type: mrr_at_5 value: 39.35183333333334 - type: ndcg_at_1 value: 31.65408333333334 - type: ndcg_at_10 value: 41.76983333333334 - type: ndcg_at_100 value: 47.138 - type: ndcg_at_1000 value: 49.33816666666667 - type: ndcg_at_3 value: 36.76683333333333 - type: ndcg_at_5 value: 39.04441666666666 - type: precision_at_1 value: 31.65408333333334 - type: precision_at_10 value: 7.396249999999998 - type: precision_at_100 value: 1.1974166666666666 - type: precision_at_1000 value: 0.15791666666666668 - type: precision_at_3 value: 16.955583333333333 - type: precision_at_5 value: 12.09925 - type: recall_at_1 value: 26.768416666666667 - type: recall_at_10 value: 53.82366666666667 - type: recall_at_100 value: 77.39600000000002 - type: recall_at_1000 value: 92.46300000000001 - type: recall_at_3 value: 39.90166666666667 - type: recall_at_5 value: 45.754000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.369 - type: map_at_10 value: 32.025 - type: map_at_100 value: 33.08 - type: map_at_1000 value: 33.169 - type: map_at_3 value: 29.589 - type: map_at_5 value: 30.894 - type: mrr_at_1 value: 27.301 - type: mrr_at_10 value: 34.64 - type: mrr_at_100 value: 35.556 - type: mrr_at_1000 value: 35.616 - type: mrr_at_3 value: 32.515 - type: mrr_at_5 value: 33.666000000000004 - type: ndcg_at_1 value: 27.301 - type: ndcg_at_10 value: 36.386 - type: ndcg_at_100 value: 41.598 - type: ndcg_at_1000 value: 43.864999999999995 - type: ndcg_at_3 value: 32.07 - type: ndcg_at_5 value: 34.028999999999996 - type: precision_at_1 value: 27.301 - type: precision_at_10 value: 5.782 - type: precision_at_100 value: 0.923 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 13.804 - type: precision_at_5 value: 9.693 - type: recall_at_1 value: 24.369 - type: recall_at_10 value: 47.026 - type: recall_at_100 value: 70.76400000000001 - type: recall_at_1000 value: 87.705 - type: recall_at_3 value: 35.366 - type: recall_at_5 value: 40.077 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.878 - type: map_at_10 value: 25.582 - type: map_at_100 value: 26.848 - type: map_at_1000 value: 26.985 - type: map_at_3 value: 22.997 - type: map_at_5 value: 24.487000000000002 - type: mrr_at_1 value: 22.023 - type: mrr_at_10 value: 29.615000000000002 - type: mrr_at_100 value: 30.656 - type: mrr_at_1000 value: 30.737 - type: mrr_at_3 value: 27.322999999999997 - type: mrr_at_5 value: 28.665000000000003 - type: ndcg_at_1 value: 22.023 - type: ndcg_at_10 value: 30.476999999999997 - type: ndcg_at_100 value: 36.258 - type: ndcg_at_1000 value: 39.287 - type: ndcg_at_3 value: 25.995 - type: ndcg_at_5 value: 28.174 - type: precision_at_1 value: 22.023 - type: precision_at_10 value: 5.657 - type: precision_at_100 value: 1.01 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 12.491 - type: precision_at_5 value: 9.112 - type: recall_at_1 value: 17.878 - type: recall_at_10 value: 41.155 - type: recall_at_100 value: 66.62599999999999 - type: recall_at_1000 value: 88.08200000000001 - type: recall_at_3 value: 28.505000000000003 - type: recall_at_5 value: 34.284 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.369999999999997 - type: map_at_10 value: 36.115 - type: map_at_100 value: 37.346000000000004 - type: map_at_1000 value: 37.449 - type: map_at_3 value: 32.976 - type: map_at_5 value: 34.782000000000004 - type: mrr_at_1 value: 30.784 - type: mrr_at_10 value: 40.014 - type: mrr_at_100 value: 40.913 - type: mrr_at_1000 value: 40.967999999999996 - type: mrr_at_3 value: 37.205 - type: mrr_at_5 value: 38.995999999999995 - type: ndcg_at_1 value: 30.784 - type: ndcg_at_10 value: 41.797000000000004 - type: ndcg_at_100 value: 47.355000000000004 - type: ndcg_at_1000 value: 49.535000000000004 - type: ndcg_at_3 value: 36.29 - type: ndcg_at_5 value: 39.051 - type: precision_at_1 value: 30.784 - type: precision_at_10 value: 7.164 - type: precision_at_100 value: 1.122 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 16.636 - type: precision_at_5 value: 11.996 - type: recall_at_1 value: 26.369999999999997 - type: recall_at_10 value: 55.010000000000005 - type: recall_at_100 value: 79.105 - type: recall_at_1000 value: 94.053 - type: recall_at_3 value: 40.139 - type: recall_at_5 value: 47.089 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.421 - type: map_at_10 value: 35.253 - type: map_at_100 value: 36.97 - type: map_at_1000 value: 37.195 - type: map_at_3 value: 32.068000000000005 - type: map_at_5 value: 33.763 - type: mrr_at_1 value: 31.423000000000002 - type: mrr_at_10 value: 39.995999999999995 - type: mrr_at_100 value: 40.977999999999994 - type: mrr_at_1000 value: 41.024 - type: mrr_at_3 value: 36.989 - type: mrr_at_5 value: 38.629999999999995 - type: ndcg_at_1 value: 31.423000000000002 - type: ndcg_at_10 value: 41.382000000000005 - type: ndcg_at_100 value: 47.532000000000004 - type: ndcg_at_1000 value: 49.829 - type: ndcg_at_3 value: 35.809000000000005 - type: ndcg_at_5 value: 38.308 - type: precision_at_1 value: 31.423000000000002 - type: precision_at_10 value: 7.885000000000001 - type: precision_at_100 value: 1.609 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 16.469 - type: precision_at_5 value: 12.174 - type: recall_at_1 value: 26.421 - type: recall_at_10 value: 53.618 - type: recall_at_100 value: 80.456 - type: recall_at_1000 value: 94.505 - type: recall_at_3 value: 37.894 - type: recall_at_5 value: 44.352999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.54 - type: map_at_10 value: 29.468 - type: map_at_100 value: 30.422 - type: map_at_1000 value: 30.542 - type: map_at_3 value: 26.888 - type: map_at_5 value: 27.962999999999997 - type: mrr_at_1 value: 23.29 - type: mrr_at_10 value: 31.176 - type: mrr_at_100 value: 32.046 - type: mrr_at_1000 value: 32.129000000000005 - type: mrr_at_3 value: 28.804999999999996 - type: mrr_at_5 value: 29.868 - type: ndcg_at_1 value: 23.29 - type: ndcg_at_10 value: 34.166000000000004 - type: ndcg_at_100 value: 39.217999999999996 - type: ndcg_at_1000 value: 41.964 - type: ndcg_at_3 value: 28.970000000000002 - type: ndcg_at_5 value: 30.797 - type: precision_at_1 value: 23.29 - type: precision_at_10 value: 5.489999999999999 - type: precision_at_100 value: 0.874 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 12.261 - type: precision_at_5 value: 8.503 - type: recall_at_1 value: 21.54 - type: recall_at_10 value: 47.064 - type: recall_at_100 value: 70.959 - type: recall_at_1000 value: 91.032 - type: recall_at_3 value: 32.828 - type: recall_at_5 value: 37.214999999999996 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.102 - type: map_at_10 value: 17.469 - type: map_at_100 value: 19.244 - type: map_at_1000 value: 19.435 - type: map_at_3 value: 14.257 - type: map_at_5 value: 16.028000000000002 - type: mrr_at_1 value: 22.866 - type: mrr_at_10 value: 33.535 - type: mrr_at_100 value: 34.583999999999996 - type: mrr_at_1000 value: 34.622 - type: mrr_at_3 value: 29.946 - type: mrr_at_5 value: 32.157000000000004 - type: ndcg_at_1 value: 22.866 - type: ndcg_at_10 value: 25.16 - type: ndcg_at_100 value: 32.347 - type: ndcg_at_1000 value: 35.821 - type: ndcg_at_3 value: 19.816 - type: ndcg_at_5 value: 22.026 - type: precision_at_1 value: 22.866 - type: precision_at_10 value: 8.072 - type: precision_at_100 value: 1.5709999999999997 - type: precision_at_1000 value: 0.22200000000000003 - type: precision_at_3 value: 14.701 - type: precision_at_5 value: 11.960999999999999 - type: recall_at_1 value: 10.102 - type: recall_at_10 value: 31.086000000000002 - type: recall_at_100 value: 55.896 - type: recall_at_1000 value: 75.375 - type: recall_at_3 value: 18.343999999999998 - type: recall_at_5 value: 24.102 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 7.961 - type: map_at_10 value: 16.058 - type: map_at_100 value: 21.878 - type: map_at_1000 value: 23.156 - type: map_at_3 value: 12.206999999999999 - type: map_at_5 value: 13.747000000000002 - type: mrr_at_1 value: 60.5 - type: mrr_at_10 value: 68.488 - type: mrr_at_100 value: 69.02199999999999 - type: mrr_at_1000 value: 69.03200000000001 - type: mrr_at_3 value: 66.792 - type: mrr_at_5 value: 67.62899999999999 - type: ndcg_at_1 value: 49.125 - type: ndcg_at_10 value: 34.827999999999996 - type: ndcg_at_100 value: 38.723 - type: ndcg_at_1000 value: 45.988 - type: ndcg_at_3 value: 40.302 - type: ndcg_at_5 value: 36.781000000000006 - type: precision_at_1 value: 60.5 - type: precision_at_10 value: 26.825 - type: precision_at_100 value: 8.445 - type: precision_at_1000 value: 1.7000000000000002 - type: precision_at_3 value: 43.25 - type: precision_at_5 value: 34.5 - type: recall_at_1 value: 7.961 - type: recall_at_10 value: 20.843 - type: recall_at_100 value: 43.839 - type: recall_at_1000 value: 67.33 - type: recall_at_3 value: 13.516 - type: recall_at_5 value: 15.956000000000001 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.06000000000001 - type: f1 value: 47.21494728335567 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 56.798 - type: map_at_10 value: 67.644 - type: map_at_100 value: 68.01700000000001 - type: map_at_1000 value: 68.038 - type: map_at_3 value: 65.539 - type: map_at_5 value: 66.912 - type: mrr_at_1 value: 61.221000000000004 - type: mrr_at_10 value: 71.97099999999999 - type: mrr_at_100 value: 72.262 - type: mrr_at_1000 value: 72.27 - type: mrr_at_3 value: 70.052 - type: mrr_at_5 value: 71.324 - type: ndcg_at_1 value: 61.221000000000004 - type: ndcg_at_10 value: 73.173 - type: ndcg_at_100 value: 74.779 - type: ndcg_at_1000 value: 75.229 - type: ndcg_at_3 value: 69.291 - type: ndcg_at_5 value: 71.552 - type: precision_at_1 value: 61.221000000000004 - type: precision_at_10 value: 9.449 - type: precision_at_100 value: 1.0370000000000001 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 27.467999999999996 - type: precision_at_5 value: 17.744 - type: recall_at_1 value: 56.798 - type: recall_at_10 value: 85.991 - type: recall_at_100 value: 92.973 - type: recall_at_1000 value: 96.089 - type: recall_at_3 value: 75.576 - type: recall_at_5 value: 81.12 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 18.323 - type: map_at_10 value: 30.279 - type: map_at_100 value: 32.153999999999996 - type: map_at_1000 value: 32.339 - type: map_at_3 value: 26.336 - type: map_at_5 value: 28.311999999999998 - type: mrr_at_1 value: 35.339999999999996 - type: mrr_at_10 value: 44.931 - type: mrr_at_100 value: 45.818999999999996 - type: mrr_at_1000 value: 45.864 - type: mrr_at_3 value: 42.618 - type: mrr_at_5 value: 43.736999999999995 - type: ndcg_at_1 value: 35.339999999999996 - type: ndcg_at_10 value: 37.852999999999994 - type: ndcg_at_100 value: 44.888 - type: ndcg_at_1000 value: 48.069 - type: ndcg_at_3 value: 34.127 - type: ndcg_at_5 value: 35.026 - type: precision_at_1 value: 35.339999999999996 - type: precision_at_10 value: 10.617 - type: precision_at_100 value: 1.7930000000000001 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 22.582 - type: precision_at_5 value: 16.605 - type: recall_at_1 value: 18.323 - type: recall_at_10 value: 44.948 - type: recall_at_100 value: 71.11800000000001 - type: recall_at_1000 value: 90.104 - type: recall_at_3 value: 31.661 - type: recall_at_5 value: 36.498000000000005 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 30.668 - type: map_at_10 value: 43.669999999999995 - type: map_at_100 value: 44.646 - type: map_at_1000 value: 44.731 - type: map_at_3 value: 40.897 - type: map_at_5 value: 42.559999999999995 - type: mrr_at_1 value: 61.336999999999996 - type: mrr_at_10 value: 68.496 - type: mrr_at_100 value: 68.916 - type: mrr_at_1000 value: 68.938 - type: mrr_at_3 value: 66.90700000000001 - type: mrr_at_5 value: 67.91199999999999 - type: ndcg_at_1 value: 61.336999999999996 - type: ndcg_at_10 value: 52.588 - type: ndcg_at_100 value: 56.389 - type: ndcg_at_1000 value: 58.187999999999995 - type: ndcg_at_3 value: 48.109 - type: ndcg_at_5 value: 50.498 - type: precision_at_1 value: 61.336999999999996 - type: precision_at_10 value: 11.033 - type: precision_at_100 value: 1.403 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 30.105999999999998 - type: precision_at_5 value: 19.954 - type: recall_at_1 value: 30.668 - type: recall_at_10 value: 55.165 - type: recall_at_100 value: 70.169 - type: recall_at_1000 value: 82.12 - type: recall_at_3 value: 45.159 - type: recall_at_5 value: 49.885000000000005 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 78.542 - type: ap value: 72.50692137216646 - type: f1 value: 78.40630687221642 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 18.613 - type: map_at_10 value: 29.98 - type: map_at_100 value: 31.136999999999997 - type: map_at_1000 value: 31.196 - type: map_at_3 value: 26.339000000000002 - type: map_at_5 value: 28.351 - type: mrr_at_1 value: 19.054 - type: mrr_at_10 value: 30.476 - type: mrr_at_100 value: 31.588 - type: mrr_at_1000 value: 31.641000000000002 - type: mrr_at_3 value: 26.834000000000003 - type: mrr_at_5 value: 28.849000000000004 - type: ndcg_at_1 value: 19.083 - type: ndcg_at_10 value: 36.541000000000004 - type: ndcg_at_100 value: 42.35 - type: ndcg_at_1000 value: 43.9 - type: ndcg_at_3 value: 29.015 - type: ndcg_at_5 value: 32.622 - type: precision_at_1 value: 19.083 - type: precision_at_10 value: 5.914 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 12.483 - type: precision_at_5 value: 9.315 - type: recall_at_1 value: 18.613 - type: recall_at_10 value: 56.88999999999999 - type: recall_at_100 value: 84.207 - type: recall_at_1000 value: 96.20100000000001 - type: recall_at_3 value: 36.262 - type: recall_at_5 value: 44.925 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.77656178750571 - type: f1 value: 94.37966073742972 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 77.72457820337438 - type: f1 value: 59.11327646329634 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.17753866846 - type: f1 value: 71.22604635414544 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.67787491593813 - type: f1 value: 76.87653151298177 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.3485843514749 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 29.792796913883617 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.310305659169963 - type: mrr value: 32.38286775798406 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 4.968 - type: map_at_10 value: 11.379 - type: map_at_100 value: 14.618999999999998 - type: map_at_1000 value: 16.055 - type: map_at_3 value: 8.34 - type: map_at_5 value: 9.690999999999999 - type: mrr_at_1 value: 43.034 - type: mrr_at_10 value: 51.019999999999996 - type: mrr_at_100 value: 51.63100000000001 - type: mrr_at_1000 value: 51.681 - type: mrr_at_3 value: 49.174 - type: mrr_at_5 value: 50.181 - type: ndcg_at_1 value: 41.176 - type: ndcg_at_10 value: 31.341 - type: ndcg_at_100 value: 29.451 - type: ndcg_at_1000 value: 38.007000000000005 - type: ndcg_at_3 value: 36.494 - type: ndcg_at_5 value: 34.499 - type: precision_at_1 value: 43.034 - type: precision_at_10 value: 23.375 - type: precision_at_100 value: 7.799 - type: precision_at_1000 value: 2.059 - type: precision_at_3 value: 34.675 - type: precision_at_5 value: 30.154999999999998 - type: recall_at_1 value: 4.968 - type: recall_at_10 value: 15.104999999999999 - type: recall_at_100 value: 30.741000000000003 - type: recall_at_1000 value: 61.182 - type: recall_at_3 value: 9.338000000000001 - type: recall_at_5 value: 11.484 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 23.716 - type: map_at_10 value: 38.32 - type: map_at_100 value: 39.565 - type: map_at_1000 value: 39.602 - type: map_at_3 value: 33.848 - type: map_at_5 value: 36.471 - type: mrr_at_1 value: 26.912000000000003 - type: mrr_at_10 value: 40.607 - type: mrr_at_100 value: 41.589 - type: mrr_at_1000 value: 41.614000000000004 - type: mrr_at_3 value: 36.684 - type: mrr_at_5 value: 39.036 - type: ndcg_at_1 value: 26.883000000000003 - type: ndcg_at_10 value: 46.096 - type: ndcg_at_100 value: 51.513 - type: ndcg_at_1000 value: 52.366 - type: ndcg_at_3 value: 37.549 - type: ndcg_at_5 value: 41.971000000000004 - type: precision_at_1 value: 26.883000000000003 - type: precision_at_10 value: 8.004 - type: precision_at_100 value: 1.107 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 17.516000000000002 - type: precision_at_5 value: 13.019 - type: recall_at_1 value: 23.716 - type: recall_at_10 value: 67.656 - type: recall_at_100 value: 91.413 - type: recall_at_1000 value: 97.714 - type: recall_at_3 value: 45.449 - type: recall_at_5 value: 55.598000000000006 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.486 - type: map_at_10 value: 84.292 - type: map_at_100 value: 84.954 - type: map_at_1000 value: 84.969 - type: map_at_3 value: 81.295 - type: map_at_5 value: 83.165 - type: mrr_at_1 value: 81.16 - type: mrr_at_10 value: 87.31 - type: mrr_at_100 value: 87.423 - type: mrr_at_1000 value: 87.423 - type: mrr_at_3 value: 86.348 - type: mrr_at_5 value: 86.991 - type: ndcg_at_1 value: 81.17 - type: ndcg_at_10 value: 88.067 - type: ndcg_at_100 value: 89.34 - type: ndcg_at_1000 value: 89.43900000000001 - type: ndcg_at_3 value: 85.162 - type: ndcg_at_5 value: 86.752 - type: precision_at_1 value: 81.17 - type: precision_at_10 value: 13.394 - type: precision_at_100 value: 1.5310000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.193 - type: precision_at_5 value: 24.482 - type: recall_at_1 value: 70.486 - type: recall_at_10 value: 95.184 - type: recall_at_100 value: 99.53999999999999 - type: recall_at_1000 value: 99.98700000000001 - type: recall_at_3 value: 86.89 - type: recall_at_5 value: 91.365 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 44.118229475102154 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 48.68049097629063 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.888 - type: map_at_10 value: 12.770999999999999 - type: map_at_100 value: 15.238 - type: map_at_1000 value: 15.616 - type: map_at_3 value: 8.952 - type: map_at_5 value: 10.639999999999999 - type: mrr_at_1 value: 24.099999999999998 - type: mrr_at_10 value: 35.375 - type: mrr_at_100 value: 36.442 - type: mrr_at_1000 value: 36.488 - type: mrr_at_3 value: 31.717000000000002 - type: mrr_at_5 value: 33.722 - type: ndcg_at_1 value: 24.099999999999998 - type: ndcg_at_10 value: 21.438 - type: ndcg_at_100 value: 30.601 - type: ndcg_at_1000 value: 36.678 - type: ndcg_at_3 value: 19.861 - type: ndcg_at_5 value: 17.263 - type: precision_at_1 value: 24.099999999999998 - type: precision_at_10 value: 11.4 - type: precision_at_100 value: 2.465 - type: precision_at_1000 value: 0.392 - type: precision_at_3 value: 18.733 - type: precision_at_5 value: 15.22 - type: recall_at_1 value: 4.888 - type: recall_at_10 value: 23.118 - type: recall_at_100 value: 49.995 - type: recall_at_1000 value: 79.577 - type: recall_at_3 value: 11.398 - type: recall_at_5 value: 15.428 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.33198632617024 - type: cos_sim_spearman value: 79.09232997136625 - type: euclidean_pearson value: 81.49986011523868 - type: euclidean_spearman value: 77.03530620283338 - type: manhattan_pearson value: 81.4741227286667 - type: manhattan_spearman value: 76.98641133116311 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.60103674582464 - type: cos_sim_spearman value: 75.03945035801914 - type: euclidean_pearson value: 80.82455267481467 - type: euclidean_spearman value: 70.3317366248871 - type: manhattan_pearson value: 80.8928091531445 - type: manhattan_spearman value: 70.43207370945672 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.52453177109315 - type: cos_sim_spearman value: 83.26431569305103 - type: euclidean_pearson value: 82.10494657997404 - type: euclidean_spearman value: 83.41028425949024 - type: manhattan_pearson value: 82.08669822983934 - type: manhattan_spearman value: 83.39959776442115 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.67472020277681 - type: cos_sim_spearman value: 78.61877889763109 - type: euclidean_pearson value: 80.07878012437722 - type: euclidean_spearman value: 77.44374494215397 - type: manhattan_pearson value: 79.95988483102258 - type: manhattan_spearman value: 77.36018101061366 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 85.55450610494437 - type: cos_sim_spearman value: 87.03494331841401 - type: euclidean_pearson value: 81.4319784394287 - type: euclidean_spearman value: 82.47893040599372 - type: manhattan_pearson value: 81.32627203699644 - type: manhattan_spearman value: 82.40660565070675 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 81.51576965454805 - type: cos_sim_spearman value: 83.0062959588245 - type: euclidean_pearson value: 79.98888882568556 - type: euclidean_spearman value: 81.08948911791873 - type: manhattan_pearson value: 79.77952719568583 - type: manhattan_spearman value: 80.79471040445408 - 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: 87.28313046682885 - type: cos_sim_spearman value: 87.35865211085007 - type: euclidean_pearson value: 84.11501613667811 - type: euclidean_spearman value: 82.82038954956121 - type: manhattan_pearson value: 83.891278147302 - type: manhattan_spearman value: 82.59947685165902 - 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: 67.80653738006102 - type: cos_sim_spearman value: 68.11259151179601 - type: euclidean_pearson value: 43.16707985094242 - type: euclidean_spearman value: 58.96200382968696 - type: manhattan_pearson value: 43.84146858566507 - type: manhattan_spearman value: 59.05193977207514 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 82.62068205073571 - type: cos_sim_spearman value: 84.40071593577095 - type: euclidean_pearson value: 80.90824726252514 - type: euclidean_spearman value: 80.54974812534094 - type: manhattan_pearson value: 80.6759008187939 - type: manhattan_spearman value: 80.31149103896973 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.13774787530915 - type: mrr value: 96.22233793802422 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 49.167 - type: map_at_10 value: 59.852000000000004 - type: map_at_100 value: 60.544 - type: map_at_1000 value: 60.577000000000005 - type: map_at_3 value: 57.242000000000004 - type: map_at_5 value: 58.704 - type: mrr_at_1 value: 51.0 - type: mrr_at_10 value: 60.575 - type: mrr_at_100 value: 61.144 - type: mrr_at_1000 value: 61.175000000000004 - type: mrr_at_3 value: 58.667 - type: mrr_at_5 value: 59.599999999999994 - type: ndcg_at_1 value: 51.0 - type: ndcg_at_10 value: 64.398 - type: ndcg_at_100 value: 67.581 - type: ndcg_at_1000 value: 68.551 - type: ndcg_at_3 value: 59.928000000000004 - type: ndcg_at_5 value: 61.986 - type: precision_at_1 value: 51.0 - type: precision_at_10 value: 8.7 - type: precision_at_100 value: 1.047 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 23.666999999999998 - type: precision_at_5 value: 15.6 - type: recall_at_1 value: 49.167 - type: recall_at_10 value: 77.333 - type: recall_at_100 value: 91.833 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 65.594 - type: recall_at_5 value: 70.52199999999999 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.77227722772277 - type: cos_sim_ap value: 94.14261011689366 - type: cos_sim_f1 value: 88.37209302325581 - type: cos_sim_precision value: 89.36605316973414 - type: cos_sim_recall value: 87.4 - type: dot_accuracy value: 99.07128712871287 - type: dot_ap value: 27.325649239129486 - type: dot_f1 value: 33.295838020247466 - type: dot_precision value: 38.04627249357326 - type: dot_recall value: 29.599999999999998 - type: euclidean_accuracy value: 99.74158415841585 - type: euclidean_ap value: 92.32695359979576 - type: euclidean_f1 value: 86.90534575772439 - type: euclidean_precision value: 85.27430221366699 - type: euclidean_recall value: 88.6 - type: manhattan_accuracy value: 99.74257425742574 - type: manhattan_ap value: 92.40335687760499 - type: manhattan_f1 value: 86.96507624200687 - type: manhattan_precision value: 85.57599225556632 - type: manhattan_recall value: 88.4 - type: max_accuracy value: 99.77227722772277 - type: max_ap value: 94.14261011689366 - type: max_f1 value: 88.37209302325581 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 53.113809982945035 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.90915908471812 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.36481271702464 - type: mrr value: 51.05628236142942 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.311305530381826 - type: cos_sim_spearman value: 31.22029657606254 - type: dot_pearson value: 12.157032445910177 - type: dot_spearman value: 13.275185888551805 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.167 - type: map_at_10 value: 1.113 - type: map_at_100 value: 5.926 - type: map_at_1000 value: 15.25 - type: map_at_3 value: 0.414 - type: map_at_5 value: 0.633 - type: mrr_at_1 value: 64.0 - type: mrr_at_10 value: 74.444 - type: mrr_at_100 value: 74.667 - type: mrr_at_1000 value: 74.679 - type: mrr_at_3 value: 72.0 - type: mrr_at_5 value: 74.0 - type: ndcg_at_1 value: 59.0 - type: ndcg_at_10 value: 51.468 - type: ndcg_at_100 value: 38.135000000000005 - type: ndcg_at_1000 value: 36.946 - type: ndcg_at_3 value: 55.827000000000005 - type: ndcg_at_5 value: 53.555 - type: precision_at_1 value: 64.0 - type: precision_at_10 value: 54.400000000000006 - type: precision_at_100 value: 39.08 - type: precision_at_1000 value: 16.618 - type: precision_at_3 value: 58.667 - type: precision_at_5 value: 56.8 - type: recall_at_1 value: 0.167 - type: recall_at_10 value: 1.38 - type: recall_at_100 value: 9.189 - type: recall_at_1000 value: 35.737 - type: recall_at_3 value: 0.455 - type: recall_at_5 value: 0.73 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.4299999999999997 - type: map_at_10 value: 8.539 - type: map_at_100 value: 14.155999999999999 - type: map_at_1000 value: 15.684999999999999 - type: map_at_3 value: 3.857 - type: map_at_5 value: 5.583 - type: mrr_at_1 value: 26.531 - type: mrr_at_10 value: 40.489999999999995 - type: mrr_at_100 value: 41.772999999999996 - type: mrr_at_1000 value: 41.772999999999996 - type: mrr_at_3 value: 35.034 - type: mrr_at_5 value: 38.81 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 20.787 - type: ndcg_at_100 value: 33.202 - type: ndcg_at_1000 value: 45.167 - type: ndcg_at_3 value: 18.233 - type: ndcg_at_5 value: 19.887 - type: precision_at_1 value: 26.531 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.4079999999999995 - type: precision_at_1000 value: 1.5310000000000001 - type: precision_at_3 value: 19.728 - type: precision_at_5 value: 21.633 - type: recall_at_1 value: 2.4299999999999997 - type: recall_at_10 value: 14.901 - type: recall_at_100 value: 46.422000000000004 - type: recall_at_1000 value: 82.83500000000001 - type: recall_at_3 value: 4.655 - type: recall_at_5 value: 8.092 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 72.90140000000001 - type: ap value: 15.138716624430662 - type: f1 value: 56.08803013269606 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.85285795132994 - type: f1 value: 60.17575819903709 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 41.125150148437065 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.96751505036657 - type: cos_sim_ap value: 70.45642872444971 - type: cos_sim_f1 value: 65.75274793133259 - type: cos_sim_precision value: 61.806361736707686 - type: cos_sim_recall value: 70.23746701846966 - type: dot_accuracy value: 77.84466829588126 - type: dot_ap value: 32.49904328313596 - type: dot_f1 value: 37.903122189387126 - type: dot_precision value: 25.050951086956523 - type: dot_recall value: 77.83641160949868 - type: euclidean_accuracy value: 84.5920009536866 - type: euclidean_ap value: 68.83700633574043 - type: euclidean_f1 value: 64.92803542871202 - type: euclidean_precision value: 60.820465545056464 - type: euclidean_recall value: 69.63060686015831 - type: manhattan_accuracy value: 84.52643500029802 - type: manhattan_ap value: 68.63286046599892 - type: manhattan_f1 value: 64.7476540705047 - type: manhattan_precision value: 62.3291015625 - type: manhattan_recall value: 67.36147757255937 - type: max_accuracy value: 84.96751505036657 - type: max_ap value: 70.45642872444971 - type: max_f1 value: 65.75274793133259 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.65603291031164 - type: cos_sim_ap value: 85.58148320880878 - type: cos_sim_f1 value: 77.63202920041064 - type: cos_sim_precision value: 76.68444377675957 - type: cos_sim_recall value: 78.60332614721281 - type: dot_accuracy value: 79.71048239996895 - type: dot_ap value: 59.31114839296281 - type: dot_f1 value: 57.13895527483783 - type: dot_precision value: 51.331125015335545 - type: dot_recall value: 64.4287034185402 - type: euclidean_accuracy value: 86.99305312997244 - type: euclidean_ap value: 81.87075965254876 - type: euclidean_f1 value: 73.53543008715421 - type: euclidean_precision value: 72.39964184450082 - type: euclidean_recall value: 74.70742223591007 - type: manhattan_accuracy value: 87.04156479217605 - type: manhattan_ap value: 81.7850497283247 - type: manhattan_f1 value: 73.52951955143475 - type: manhattan_precision value: 70.15875236030492 - type: manhattan_recall value: 77.2405297197413 - type: max_accuracy value: 88.65603291031164 - type: max_ap value: 85.58148320880878 - type: max_f1 value: 77.63202920041064 --- <h1 align="center">GIST Embedding v0 - all-MiniLM-L6-v2</h1> *GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions. Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829) # Data The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available: - Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets) - Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`). The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741). The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some. The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance. # Usage The model can be easily loaded using the Sentence Transformers library. ```Python import torch.nn.functional as F from sentence_transformers import SentenceTransformer revision = None # Replace with the specific revision to ensure reproducibility if the model is updated. model = SentenceTransformer("avsolatorio/GIST-all-MiniLM-L6-v2", revision=revision) texts = [ "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.", "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.", "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes" ] # Compute embeddings embeddings = model.encode(texts, convert_to_tensor=True) # Compute cosine-similarity for each pair of sentences scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) print(scores.cpu().numpy()) ``` # Training Parameters Below are the training parameters used to fine-tune the model: ``` Epochs = 40 Warmup ratio = 0.1 Learning rate = 5e-6 Batch size = 16 Checkpoint step = 102000 Contrastive loss temperature = 0.01 ``` # Evaluation The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. # Citation Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗 ``` @article{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, journal={arXiv preprint arXiv:2402.16829}, year={2024}, URL={https://arxiv.org/abs/2402.16829} eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # Acknowledgements This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444. The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Systran/faster-whisper-medium

Systran

ctranslate2

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 license: mit library_name: ctranslate2 --- # Whisper medium model for CTranslate2 This repository contains the conversion of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("medium") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-medium --output_dir faster-whisper-medium \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-medium).**

bigscience/bloomz-560m

bigscience

transformers

text-generation

--- datasets: - bigscience/xP3 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 - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation widget: - text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative?" example_title: "zh-en sentiment" - text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？" example_title: "zh-zh sentiment" - text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"." example_title: "vi-en query" - text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»." example_title: "fr-fr query" - text: "Explain in a sentence in Telugu what is backpropagation in neural networks." example_title: "te-en qa" - text: "Why is the sky blue?" example_title: "en-en qa" - text: "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):" example_title: "es-en fable" - text: "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):" example_title: "hi-en fable" model-index: - name: bloomz-560m results: - task: type: Coreference resolution dataset: type: winogrande name: Winogrande XL (xl) config: xl split: validation revision: a80f460359d1e9a67c006011c94de42a8759430c metrics: - type: Accuracy value: 52.41 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (en) config: en split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 51.01 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (fr) config: fr split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 51.81 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (jp) config: jp split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 52.03 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (pt) config: pt split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 53.99 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (ru) config: ru split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 53.97 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (zh) config: zh split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 54.76 - task: type: Natural language inference dataset: type: anli name: ANLI (r1) config: r1 split: validation revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 metrics: - type: Accuracy value: 33.4 - task: type: Natural language inference dataset: type: anli name: ANLI (r2) config: r2 split: validation revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 metrics: - type: Accuracy value: 33.4 - task: type: Natural language inference dataset: type: anli name: ANLI (r3) config: r3 split: validation revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 metrics: - type: Accuracy value: 33.5 - task: type: Natural language inference dataset: type: super_glue name: SuperGLUE (cb) config: cb split: validation revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 metrics: - type: Accuracy value: 53.57 - task: type: Natural language inference dataset: type: super_glue name: SuperGLUE (rte) config: rte split: validation revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 metrics: - type: Accuracy value: 67.15 - task: type: Natural language inference dataset: type: xnli name: XNLI (ar) config: ar split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.46 - task: type: Natural language inference dataset: type: xnli name: XNLI (bg) config: bg split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 39.76 - task: type: Natural language inference dataset: type: xnli name: XNLI (de) config: de split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 39.36 - task: type: Natural language inference dataset: type: xnli name: XNLI (el) config: el split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 40.96 - task: type: Natural language inference dataset: type: xnli name: XNLI (en) config: en split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 46.43 - task: type: Natural language inference dataset: type: xnli name: XNLI (es) config: es split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.98 - task: type: Natural language inference dataset: type: xnli name: XNLI (fr) config: fr split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 45.54 - task: type: Natural language inference dataset: type: xnli name: XNLI (hi) config: hi split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 41.81 - task: type: Natural language inference dataset: type: xnli name: XNLI (ru) config: ru split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 39.64 - task: type: Natural language inference dataset: type: xnli name: XNLI (sw) config: sw split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 38.35 - task: type: Natural language inference dataset: type: xnli name: XNLI (th) config: th split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 35.5 - task: type: Natural language inference dataset: type: xnli name: XNLI (tr) config: tr split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 37.31 - task: type: Natural language inference dataset: type: xnli name: XNLI (ur) config: ur split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 38.96 - task: type: Natural language inference dataset: type: xnli name: XNLI (vi) config: vi split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.74 - task: type: Natural language inference dataset: type: xnli name: XNLI (zh) config: zh split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.66 - task: type: Program synthesis dataset: type: openai_humaneval name: HumanEval config: None split: test revision: e8dc562f5de170c54b5481011dd9f4fa04845771 metrics: - type: Pass@1 value: 2.18 - type: Pass@10 value: 4.11 - type: Pass@100 value: 9.00 - task: type: Sentence completion dataset: type: story_cloze name: StoryCloze (2016) config: "2016" split: validation revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db metrics: - type: Accuracy value: 60.29 - task: type: Sentence completion dataset: type: super_glue name: SuperGLUE (copa) config: copa split: validation revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 metrics: - type: Accuracy value: 52.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (et) config: et split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 53.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (ht) config: ht split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 49.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (id) config: id split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 57.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (it) config: it split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 52.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (qu) config: qu split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 55.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (sw) config: sw split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 56.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (ta) config: ta split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 58.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (th) config: th split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 58.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (tr) config: tr split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 61.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (vi) config: vi split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 61.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (zh) config: zh split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 61.0 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (ar) config: ar split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 54.4 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (es) config: es split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 56.45 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (eu) config: eu split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 50.56 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (hi) config: hi split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 55.79 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (id) config: id split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 57.84 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (my) config: my split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 47.05 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (ru) config: ru split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 53.14 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (sw) config: sw split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 51.36 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (te) config: te split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 54.86 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (zh) config: zh split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 56.52 ---  # Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Limitations](#limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 7. [Citation](#citation) # Model Summary > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages. - **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf) - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co) - **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages. - **BLOOMZ & mT0 Model Family:** <div class="max-w-full overflow-auto"> <table> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English. </tr> <tr> <td>Parameters</td> <td>300M</td> <td>580M</td> <td>1.2B</td> <td>3.7B</td> <td>13B</td> <td>560M</td> <td>1.1B</td> <td>1.7B</td> <td>3B</td> <td>7.1B</td> <td>176B</td> </tr> <tr> <td>Finetuned Model</td> <td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td> <td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td> <td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> </tr> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td> </tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td> </tr> <th colspan="12">Original pretrained checkpoints. Not recommended.</th> <tr> <td>Pretrained Model</td> <td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td> <td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td> <td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td> <td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td> <td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td> </tr> </table> </div> # Use ## Intended use We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper: - 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评? - Suggest at least five related search terms to "Mạng neural nhân tạo". - Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish): - Explain in a sentence in Telugu what is backpropagation in neural networks. **Feel free to share your generations in the Community tab!** ## How to use ### CPU <details> <summary> Click to expand </summary> ```python # pip install -q transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigscience/bloomz-560m" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> ### GPU <details> <summary> Click to expand </summary> ```python # pip install -q transformers accelerate from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigscience/bloomz-560m" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto") inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> ### GPU in 8bit <details> <summary> Click to expand </summary> ```python # pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigscience/bloomz-560m" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> <!-- Necessary for whitespace --> ### # Limitations **Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*". # Training ## Model - **Architecture:** Same as [bloom-560m](https://huggingface.co/bigscience/bloom-560m), also refer to the `config.json` file - **Finetuning steps:** 1750 - **Finetuning tokens:** 3.67 billion - **Finetuning layout:** 1x pipeline parallel, 1x tensor parallel, 1x data parallel - **Precision:** float16 ## Hardware - **CPUs:** AMD CPUs with 512GB memory per node - **GPUs:** 64 A100 80GB GPUs with 8 GPUs per node (8 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links - **Communication:** NCCL-communications network with a fully dedicated subnet ## Software - **Orchestration:** [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) - **Optimizer & parallelism:** [DeepSpeed](https://github.com/microsoft/DeepSpeed) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) (pytorch-1.11 w/ CUDA-11.5) - **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex) # Evaluation We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config. # Citation ```bibtex @article{muennighoff2022crosslingual, title={Crosslingual generalization through multitask finetuning}, author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others}, journal={arXiv preprint arXiv:2211.01786}, year={2022} } ```

blackhole33/uzbek-speaker-verification-v9

blackhole33

nemo

--- license: cc-by-4.0 library_name: nemo tags: - pytorch - NeMo --- # Uzbek-speaker-verification-v9 <style> img { display: inline; } </style> [](#model-architecture) | [](#model-architecture) | [](#datasets) **Put a short model description here.** See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/index.html) for complete architecture details. ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model **NOTE**: Please update the model class below to match the class of the model being uploaded. ```python import nemo.core import ModelPT model = ModelPT.from_pretrained("ai-nightcoder/uzbek-speaker-verification-v9") ``` ### NOTE Add some information about how to use the model here. An example is provided for ASR inference below. ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="ai-nightcoder/uzbek-speaker-verification-v9" audio_dir="" ``` ### Input **Add some information about what are the inputs to this model** ### Output **Add some information about what are the outputs of this model** ## Model Architecture **Add information here discussing architectural details of the model or any comments to users about the model.** ## Training **Add information here about how the model was trained. It should be as detailed as possible, potentially including the the link to the script used to train as well as the base config used to train the model. If extraneous scripts are used to prepare the components of the model, please include them here.** ### NOTE An example is provided below for ASR The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml). The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). ### Datasets **Try to provide as detailed a list of datasets as possible. If possible, provide links to the datasets on HF by adding it to the manifest section at the top of the README (marked by ---).** ### NOTE An example for the manifest section is provided below for ASR datasets datasets: - librispeech_asr - fisher_corpus - Switchboard-1 - WSJ-0 - WSJ-1 - National-Singapore-Corpus-Part-1 - National-Singapore-Corpus-Part-6 - vctk - voxpopuli - europarl - multilingual_librispeech - mozilla-foundation/common_voice_8_0 - MLCommons/peoples_speech The corresponding text in this section for those datasets is stated below - The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams. The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets: - Librispeech 960 hours of English speech - Fisher Corpus - Switchboard-1 Dataset - WSJ-0 and WSJ-1 - National Speech Corpus (Part 1, Part 6) - VCTK - VoxPopuli (EN) - Europarl-ASR (EN) - Multilingual Librispeech (MLS EN) - 2,000 hour subset - Mozilla Common Voice (v7.0) - People's Speech - 12,000 hour subset ## Performance **Add information here about the performance of the model. Discuss what is the metric that is being used to evaluate the model and if there are external links explaning the custom metric, please link to it. ### NOTE An example is provided below for ASR metrics list that can be added to the top of the README model-index: - name: PUT_MODEL_NAME results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: AMI (Meetings test) type: edinburghcstr/ami config: ihm split: test args: language: en metrics: - name: Test WER type: wer value: 17.10 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Earnings-22 type: revdotcom/earnings22 split: test args: language: en metrics: - name: Test WER type: wer value: 14.11 Provide any caveats about the results presented in the top of the discussion so that nuance is not lost. It should ideally be in a tabular format (you can use the following website to make your tables in markdown format - https://www.tablesgenerator.com/markdown_tables)** ## Limitations **Discuss any practical limitations to the model when being used in real world cases. They can also be legal disclaimers, or discussion regarding the safety of the model (particularly in the case of LLMs).** ### Note An example is provided below Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## License License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license. ## References **Provide appropriate references in the markdown link format below. Please order them numerically.** [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

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

dmis-lab

transformers

Entry not found

openai/whisper-medium

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-medium 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: 2.9 - 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: 5.9 - 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: 53.87 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-medium") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium") >>> 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-medium") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium") >>> 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-medium") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium") >>> 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 Medium 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-medium") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium").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"])) 2.900409225488902 ``` ## 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-medium", >>> 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} } ```

lewtun/tiny-random-mt5

lewtun

transformers

feature-extraction

Entry not found

facebook/seamless-m4t-v2-large

facebook

transformers

automatic-speech-recognition

--- license: cc-by-nc-4.0 language: - af - am - ar - as - az - be - bn - bs - bg - ca - cs - zh - cy - da - de - el - en - et - fi - fr - or - om - ga - gl - gu - ha - he - hi - hr - hu - hy - ig - id - is - it - jv - ja - kn - ka - kk - mn - km - ky - ko - lo - ln - lt - lb - lg - lv - ml - mr - mk - mt - mi - my - nl - nb - ne - ny - oc - pa - ps - fa - pl - pt - ro - ru - sk - sl - sn - sd - so - es - sr - sv - sw - ta - te - tg - tl - th - tr - uk - ur - uz - vi - wo - xh - yo - ms - zu - ary - arz - yue - kea metrics: - bleu - wer - chrf inference: False pipeline_tag: automatic-speech-recognition tags: - audio-to-audio - text-to-speech - seamless_communication library_name: transformers widget: - src: https://cdn-media.huggingface.co/speech_samples/sample1.flac example_title: Librispeech sample 1 output: text: going along slushy country roads and speaking to damp audiences in draughty schoolrooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to us immediately afterwards - src: https://cdn-media.huggingface.co/speech_samples/sample2.flac example_title: Librispeech sample 2 output: text: before he had time to answer a much-encumbered vera burst into the room with the question i say can i leave these here these were a small black pig and a lusty specimen of black-red game-cock --- # SeamlessM4T v2 **SeamlessM4T** is our foundational all-in-one **M**assively **M**ultilingual and **M**ultimodal **M**achine **T**ranslation model delivering high-quality translation for speech and text in nearly 100 languages. SeamlessM4T models support the tasks of: - Speech-to-speech translation (S2ST) - Speech-to-text translation (S2TT) - Text-to-speech translation (T2ST) - Text-to-text translation (T2TT) - Automatic speech recognition (ASR). SeamlessM4T models support: - 🎤 101 languages for speech input. - 💬 96 Languages for text input/output. - 🔊 35 languages for speech output. 🌟 We are releasing SeamlessM4T v2, an updated version with our novel *UnitY2* architecture. This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks. The v2 version of SeamlessM4T is a multitask adaptation of our novel *UnitY2* architecture. *Unity2* with its hierarchical character-to-unit upsampling and non-autoregressive text-to-unit decoding considerably improves over SeamlessM4T v1 in quality and inference speed. **SeamlessM4T v2 is also supported by 🤗 Transformers, more on it [in the dedicated section below](#transformers-usage).**  ## SeamlessM4T models | Model Name | #params | checkpoint | metrics | | ------------------ | ------- | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ | | [SeamlessM4T-Large v2](https://huggingface.co/facebook/seamless-m4t-v2-large) | 2.3B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-v2-large/blob/main/seamlessM4T_v2_large.pt) | [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/seamlessM4T_large_v2.zip) | | [SeamlessM4T-Large (v1)](https://huggingface.co/facebook/seamless-m4t-large) | 2.3B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/blob/main/multitask_unity_large.pt) | [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/seamlessM4T_large.zip) | | [SeamlessM4T-Medium (v1)](https://huggingface.co/facebook/seamless-m4t-medium) | 1.2B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/blob/main/multitask_unity_medium.pt) | [metrics](https://dl.fbaipublicfiles.com/seamless/metrics/seamlessM4T_medium.zip) | We provide the extensive evaluation results of seamlessM4T-Large and SeamlessM4T-Medium reported in the paper (as averages) in the `metrics` files above. The evaluation data ids for FLEURS, CoVoST2 and CVSS-C can be found [here](https://dl.fbaipublicfiles.com/seamless/metrics/evaluation_data_ids.zip) ## Evaluating SeamlessM4T models To reproduce our results or to evaluate using the same metrics over your own test sets, please check out the [Evaluation README here](https://github.com/facebookresearch/seamless_communication/tree/main/src/seamless_communication/cli/m4t/evaluate). ## Finetuning SeamlessM4T models Please check out the [Finetuning README here](https://github.com/facebookresearch/seamless_communication/tree/main/src/seamless_communication/cli/m4t/finetune). ## Transformers usage SeamlessM4T is available in the 🤗 Transformers library, requiring minimal dependencies. Steps to get started: 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main and [sentencepiece](https://github.com/google/sentencepiece): ``` pip install git+https://github.com/huggingface/transformers.git sentencepiece ``` 2. Run the following Python code to generate speech samples. Here the target language is Russian: ```py from transformers import AutoProcessor, SeamlessM4Tv2Model import torchaudio processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large") model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large") # from text text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt") audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze() # from audio audio, orig_freq = torchaudio.load("https://www2.cs.uic.edu/~i101/SoundFiles/preamble10.wav") audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16_000) # must be a 16 kHz waveform array audio_inputs = processor(audios=audio, return_tensors="pt") audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze() ``` 3. Listen to the audio samples either in an ipynb notebook: ```py from IPython.display import Audio sample_rate = model.config.sampling_rate Audio(audio_array_from_text, rate=sample_rate) # Audio(audio_array_from_audio, rate=sample_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```py import scipy sample_rate = model.config.sampling_rate scipy.io.wavfile.write("out_from_text.wav", rate=sample_rate, data=audio_array_from_text) # scipy.io.wavfile.write("out_from_audio.wav", rate=sample_rate, data=audio_array_from_audio) ``` For more details on using the SeamlessM4T model for inference using the 🤗 Transformers library, refer to the **[SeamlessM4T v2 docs](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t_v2)** or to this **hands-on [Google Colab](https://colab.research.google.com/github/ylacombe/scripts_and_notebooks/blob/main/v2_seamless_m4t_hugging_face.ipynb).** ## Supported Languages: Listed below, are the languages supported by SeamlessM4T-large (v1/v2). The `source` column specifies whether a language is supported as source speech (`Sp`) and/or source text (`Tx`). The `target` column specifies whether a language is supported as target speech (`Sp`) and/or target text (`Tx`). | code | language | script | Source | Target | | ---- | ---------------------- | ---------- | ------ | ------ | | afr | Afrikaans | Latn | Sp, Tx | Tx | | amh | Amharic | Ethi | Sp, Tx | Tx | | arb | Modern Standard Arabic | Arab | Sp, Tx | Sp, Tx | | ary | Moroccan Arabic | Arab | Sp, Tx | Tx | | arz | Egyptian Arabic | Arab | Sp, Tx | Tx | | asm | Assamese | Beng | Sp, Tx | Tx | | ast | Asturian | Latn | Sp | \-- | | azj | North Azerbaijani | Latn | Sp, Tx | Tx | | bel | Belarusian | Cyrl | Sp, Tx | Tx | | ben | Bengali | Beng | Sp, Tx | Sp, Tx | | bos | Bosnian | Latn | Sp, Tx | Tx | | bul | Bulgarian | Cyrl | Sp, Tx | Tx | | cat | Catalan | Latn | Sp, Tx | Sp, Tx | | ceb | Cebuano | Latn | Sp, Tx | Tx | | ces | Czech | Latn | Sp, Tx | Sp, Tx | | ckb | Central Kurdish | Arab | Sp, Tx | Tx | | cmn | Mandarin Chinese | Hans | Sp, Tx | Sp, Tx | | cmn_Hant | Mandarin Chinese | Hant | Sp, Tx | Sp, Tx | | cym | Welsh | Latn | Sp, Tx | Sp, Tx | | dan | Danish | Latn | Sp, Tx | Sp, Tx | | deu | German | Latn | Sp, Tx | Sp, Tx | | ell | Greek | Grek | Sp, Tx | Tx | | eng | English | Latn | Sp, Tx | Sp, Tx | | est | Estonian | Latn | Sp, Tx | Sp, Tx | | eus | Basque | Latn | Sp, Tx | Tx | | fin | Finnish | Latn | Sp, Tx | Sp, Tx | | fra | French | Latn | Sp, Tx | Sp, Tx | | fuv | Nigerian Fulfulde | Latn | Sp, Tx | Tx | | gaz | West Central Oromo | Latn | Sp, Tx | Tx | | gle | Irish | Latn | Sp, Tx | Tx | | glg | Galician | Latn | Sp, Tx | Tx | | guj | Gujarati | Gujr | Sp, Tx | Tx | | heb | Hebrew | Hebr | Sp, Tx | Tx | | hin | Hindi | Deva | Sp, Tx | Sp, Tx | | hrv | Croatian | Latn | Sp, Tx | Tx | | hun | Hungarian | Latn | Sp, Tx | Tx | | hye | Armenian | Armn | Sp, Tx | Tx | | ibo | Igbo | Latn | Sp, Tx | Tx | | ind | Indonesian | Latn | Sp, Tx | Sp, Tx | | isl | Icelandic | Latn | Sp, Tx | Tx | | ita | Italian | Latn | Sp, Tx | Sp, Tx | | jav | Javanese | Latn | Sp, Tx | Tx | | jpn | Japanese | Jpan | Sp, Tx | Sp, Tx | | kam | Kamba | Latn | Sp | \-- | | kan | Kannada | Knda | Sp, Tx | Tx | | kat | Georgian | Geor | Sp, Tx | Tx | | kaz | Kazakh | Cyrl | Sp, Tx | Tx | | kea | Kabuverdianu | Latn | Sp | \-- | | khk | Halh Mongolian | Cyrl | Sp, Tx | Tx | | khm | Khmer | Khmr | Sp, Tx | Tx | | kir | Kyrgyz | Cyrl | Sp, Tx | Tx | | kor | Korean | Kore | Sp, Tx | Sp, Tx | | lao | Lao | Laoo | Sp, Tx | Tx | | lit | Lithuanian | Latn | Sp, Tx | Tx | | ltz | Luxembourgish | Latn | Sp | \-- | | lug | Ganda | Latn | Sp, Tx | Tx | | luo | Luo | Latn | Sp, Tx | Tx | | lvs | Standard Latvian | Latn | Sp, Tx | Tx | | mai | Maithili | Deva | Sp, Tx | Tx | | mal | Malayalam | Mlym | Sp, Tx | Tx | | mar | Marathi | Deva | Sp, Tx | Tx | | mkd | Macedonian | Cyrl | Sp, Tx | Tx | | mlt | Maltese | Latn | Sp, Tx | Sp, Tx | | mni | Meitei | Beng | Sp, Tx | Tx | | mya | Burmese | Mymr | Sp, Tx | Tx | | nld | Dutch | Latn | Sp, Tx | Sp, Tx | | nno | Norwegian Nynorsk | Latn | Sp, Tx | Tx | | nob | Norwegian Bokmål | Latn | Sp, Tx | Tx | | npi | Nepali | Deva | Sp, Tx | Tx | | nya | Nyanja | Latn | Sp, Tx | Tx | | oci | Occitan | Latn | Sp | \-- | | ory | Odia | Orya | Sp, Tx | Tx | | pan | Punjabi | Guru | Sp, Tx | Tx | | pbt | Southern Pashto | Arab | Sp, Tx | Tx | | pes | Western Persian | Arab | Sp, Tx | Sp, Tx | | pol | Polish | Latn | Sp, Tx | Sp, Tx | | por | Portuguese | Latn | Sp, Tx | Sp, Tx | | ron | Romanian | Latn | Sp, Tx | Sp, Tx | | rus | Russian | Cyrl | Sp, Tx | Sp, Tx | | slk | Slovak | Latn | Sp, Tx | Sp, Tx | | slv | Slovenian | Latn | Sp, Tx | Tx | | sna | Shona | Latn | Sp, Tx | Tx | | snd | Sindhi | Arab | Sp, Tx | Tx | | som | Somali | Latn | Sp, Tx | Tx | | spa | Spanish | Latn | Sp, Tx | Sp, Tx | | srp | Serbian | Cyrl | Sp, Tx | Tx | | swe | Swedish | Latn | Sp, Tx | Sp, Tx | | swh | Swahili | Latn | Sp, Tx | Sp, Tx | | tam | Tamil | Taml | Sp, Tx | Tx | | tel | Telugu | Telu | Sp, Tx | Sp, Tx | | tgk | Tajik | Cyrl | Sp, Tx | Tx | | tgl | Tagalog | Latn | Sp, Tx | Sp, Tx | | tha | Thai | Thai | Sp, Tx | Sp, Tx | | tur | Turkish | Latn | Sp, Tx | Sp, Tx | | ukr | Ukrainian | Cyrl | Sp, Tx | Sp, Tx | | urd | Urdu | Arab | Sp, Tx | Sp, Tx | | uzn | Northern Uzbek | Latn | Sp, Tx | Sp, Tx | | vie | Vietnamese | Latn | Sp, Tx | Sp, Tx | | xho | Xhosa | Latn | Sp | \-- | | yor | Yoruba | Latn | Sp, Tx | Tx | | yue | Cantonese | Hant | Sp, Tx | Tx | | zlm | Colloquial Malay | Latn | Sp | \-- | | zsm | Standard Malay | Latn | Tx | Tx | | zul | Zulu | Latn | Sp, Tx | Tx | Note that seamlessM4T-medium supports 200 languages in the text modality, and is based on NLLB-200 (see full list in [asset card](https://github.com/facebookresearch/seamless_communication/blob/main/src/seamless_communication/cards/unity_nllb-200.yaml)) ## Citation For SeamlessM4T v2, please cite : ```bibtex @inproceedings{seamless2023, title="Seamless: Multilingual Expressive and Streaming Speech Translation", author="{Seamless Communication}, Lo{\"i}c Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-juss{\`a}, Maha Elbayad, Hongyu Gong, Francisco Guzm{\'a}n, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alex Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, Mary Williamson", journal={ArXiv}, year={2023} } ``` [//]: # "https://arxiv.org/abs/2312.05187"

uer/roberta-base-finetuned-chinanews-chinese

uer

transformers

text-classification

--- language: zh widget: - text: "这本书真的很不错" --- # Chinese RoBERTa-Base Models for Text Classification ## Model description This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be fine-tuned by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework. You can download the 5 Chinese RoBERTa-Base classification models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below: | Dataset | Link | | :-----------: | :-------------------------------------------------------: | | **JD full** | [**roberta-base-finetuned-jd-full-chinese**][jd_full] | | **JD binary** | [**roberta-base-finetuned-jd-binary-chinese**][jd_binary] | | **Dianping** | [**roberta-base-finetuned-dianping-chinese**][dianping] | | **Ifeng** | [**roberta-base-finetuned-ifeng-chinese**][ifeng] | | **Chinanews** | [**roberta-base-finetuned-chinanews-chinese**][chinanews] | ## How to use You can use this model directly with a pipeline for text classification (take the case of roberta-base-finetuned-chinanews-chinese): ```python >>> from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline >>> model = AutoModelForSequenceClassification.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese') >>> tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese') >>> text_classification = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) >>> text_classification("北京上个月召开了两会") [{'label': 'mainland China politics', 'score': 0.7211663722991943}] ``` ## Training data 5 Chinese text classification datasets are used. JD full, JD binary, and Dianping datasets consist of user reviews of different sentiment polarities. Ifeng and Chinanews consist of first paragraphs of news articles of different topic classes. They are collected by [Glyph](https://github.com/zhangxiangxiao/glyph) project and more details are discussed in the corresponding [paper](https://arxiv.org/abs/1708.02657). ## Training procedure Models are fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune three epochs with a sequence length of 512 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved. We use the same hyper-parameters on different models. Taking the case of roberta-base-finetuned-chinanews-chinese ``` python3 finetune/run_classifier.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \ --vocab_path models/google_zh_vocab.txt \ --train_path datasets/glyph/chinanews/train.tsv \ --dev_path datasets/glyph/chinanews/dev.tsv \ --output_model_path models/chinanews_classifier_model.bin \ --learning_rate 3e-5 --epochs_num 3 --batch_size 32 --seq_length 512 ``` Finally, we convert the pre-trained model into Huggingface's format: ``` python3 scripts/convert_bert_text_classification_from_uer_to_huggingface.py --input_model_path models/chinanews_classifier_model.bin \ --output_model_path pytorch_model.bin \ --layers_num 12 ``` ### BibTeX entry and citation info ``` @article{liu2019roberta, title={Roberta: A robustly optimized bert pretraining approach}, author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1907.11692}, year={2019} } @article{zhang2017encoding, title={Which encoding is the best for text classification in chinese, english, japanese and korean?}, author={Zhang, Xiang and LeCun, Yann}, journal={arXiv preprint arXiv:1708.02657}, year={2017} } @article{zhao2019uer, title={UER: An Open-Source Toolkit for Pre-training Models}, author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong}, journal={EMNLP-IJCNLP 2019}, pages={241}, year={2019} } @article{zhao2023tencentpretrain, title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities}, author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others}, journal={ACL 2023}, pages={217}, year={2023} ``` [jd_full]:https://huggingface.co/uer/roberta-base-finetuned-jd-full-chinese [jd_binary]:https://huggingface.co/uer/roberta-base-finetuned-jd-binary-chinese [dianping]:https://huggingface.co/uer/roberta-base-finetuned-dianping-chinese [ifeng]:https://huggingface.co/uer/roberta-base-finetuned-ifeng-chinese [chinanews]:https://huggingface.co/uer/roberta-base-finetuned-chinanews-chinese

jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli

jbetker

transformers

automatic-speech-recognition

This checkpoint is a wav2vec2-large model that is useful for generating transcriptions with punctuation. It is intended for use in building transcriptions for TTS models, where punctuation is very important for prosody. This model was created by fine-tuning the `facebook/wav2vec2-large-robust-ft-libri-960h` checkpoint on the [libritts](https://research.google/tools/datasets/libri-tts/) and [voxpopuli](https://github.com/facebookresearch/voxpopuli) datasets with a new vocabulary that includes punctuation. The model gets a respectable WER of 4.45% on the librispeech validation set. The baseline, `facebook/wav2vec2-large-robust-ft-libri-960h`, got 4.3%. Since the model was fine-tuned on clean audio, it is not well-suited for noisy audio like CommonVoice (though I may upload a checkpoint for that soon too). It still does pretty good, though. The vocabulary is uploaded to the model hub as well `jbetker/tacotron_symbols`. Check out my speech transcription script repo, [ocotillo](https://github.com/neonbjb/ocotillo) for usage examples: https://github.com/neonbjb/ocotillo

avsolatorio/GIST-Embedding-v0

avsolatorio

sentence-transformers

sentence-similarity

--- language: - en library_name: sentence-transformers license: mit pipeline_tag: sentence-similarity tags: - feature-extraction - mteb - sentence-similarity - sentence-transformers model-index: - name: GIST-Embedding-v0 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.95522388059702 - type: ap value: 38.940434354439276 - type: f1 value: 69.88686275888114 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.51357499999999 - type: ap value: 90.30414241486682 - type: f1 value: 93.50552829047328 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 50.446000000000005 - type: f1 value: 49.76432659699279 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 38.265 - type: map_at_10 value: 54.236 - type: map_at_100 value: 54.81399999999999 - type: map_at_1000 value: 54.81700000000001 - type: map_at_3 value: 49.881 - type: map_at_5 value: 52.431000000000004 - type: mrr_at_1 value: 38.265 - type: mrr_at_10 value: 54.152 - type: mrr_at_100 value: 54.730000000000004 - type: mrr_at_1000 value: 54.733 - type: mrr_at_3 value: 49.644 - type: mrr_at_5 value: 52.32599999999999 - type: ndcg_at_1 value: 38.265 - type: ndcg_at_10 value: 62.62 - type: ndcg_at_100 value: 64.96600000000001 - type: ndcg_at_1000 value: 65.035 - type: ndcg_at_3 value: 53.691 - type: ndcg_at_5 value: 58.303000000000004 - type: precision_at_1 value: 38.265 - type: precision_at_10 value: 8.919 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 21.573999999999998 - type: precision_at_5 value: 15.192 - type: recall_at_1 value: 38.265 - type: recall_at_10 value: 89.189 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 64.723 - type: recall_at_5 value: 75.96000000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.287087887491744 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.74244928943812 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.68814324295771 - type: mrr value: 75.46266983247591 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 90.45240209600391 - type: cos_sim_spearman value: 87.95079919934645 - type: euclidean_pearson value: 88.93438602492702 - type: euclidean_spearman value: 88.28152962682988 - type: manhattan_pearson value: 88.92193964325268 - type: manhattan_spearman value: 88.21466063329498 - 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: 15.605427974947808 - type: f1 value: 14.989877233698866 - type: precision value: 14.77906814441261 - type: recall value: 15.605427974947808 - 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: 33.38102575390711 - type: f1 value: 32.41704114719127 - type: precision value: 32.057363829835964 - type: recall value: 33.38102575390711 - 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: 0.1939729823346034 - type: f1 value: 0.17832215223820772 - type: precision value: 0.17639155671715423 - type: recall value: 0.1939729823346034 - 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: 3.0542390731964195 - type: f1 value: 2.762857644374232 - type: precision value: 2.6505178163945935 - type: recall value: 3.0542390731964195 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.29545454545453 - type: f1 value: 87.26415991342238 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.035319537839484 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.667313307057285 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.979 - type: map_at_10 value: 46.275 - type: map_at_100 value: 47.975 - type: map_at_1000 value: 48.089 - type: map_at_3 value: 42.507 - type: map_at_5 value: 44.504 - type: mrr_at_1 value: 42.346000000000004 - type: mrr_at_10 value: 53.013 - type: mrr_at_100 value: 53.717000000000006 - type: mrr_at_1000 value: 53.749 - type: mrr_at_3 value: 50.405 - type: mrr_at_5 value: 51.915 - type: ndcg_at_1 value: 42.346000000000004 - type: ndcg_at_10 value: 53.179 - type: ndcg_at_100 value: 58.458 - type: ndcg_at_1000 value: 60.057 - type: ndcg_at_3 value: 48.076 - type: ndcg_at_5 value: 50.283 - type: precision_at_1 value: 42.346000000000004 - type: precision_at_10 value: 10.386 - type: precision_at_100 value: 1.635 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 23.413999999999998 - type: precision_at_5 value: 16.624 - type: recall_at_1 value: 33.979 - type: recall_at_10 value: 65.553 - type: recall_at_100 value: 87.18599999999999 - type: recall_at_1000 value: 97.25200000000001 - type: recall_at_3 value: 50.068999999999996 - type: recall_at_5 value: 56.882 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.529 - type: map_at_10 value: 42.219 - type: map_at_100 value: 43.408 - type: map_at_1000 value: 43.544 - type: map_at_3 value: 39.178000000000004 - type: map_at_5 value: 40.87 - type: mrr_at_1 value: 39.873 - type: mrr_at_10 value: 48.25 - type: mrr_at_100 value: 48.867 - type: mrr_at_1000 value: 48.908 - type: mrr_at_3 value: 46.03 - type: mrr_at_5 value: 47.355000000000004 - type: ndcg_at_1 value: 39.873 - type: ndcg_at_10 value: 47.933 - type: ndcg_at_100 value: 52.156000000000006 - type: ndcg_at_1000 value: 54.238 - type: ndcg_at_3 value: 43.791999999999994 - type: ndcg_at_5 value: 45.678999999999995 - type: precision_at_1 value: 39.873 - type: precision_at_10 value: 9.032 - type: precision_at_100 value: 1.419 - type: precision_at_1000 value: 0.192 - type: precision_at_3 value: 21.231 - type: precision_at_5 value: 14.981 - type: recall_at_1 value: 31.529 - type: recall_at_10 value: 57.925000000000004 - type: recall_at_100 value: 75.89 - type: recall_at_1000 value: 89.007 - type: recall_at_3 value: 45.363 - type: recall_at_5 value: 50.973 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 41.289 - type: map_at_10 value: 54.494 - type: map_at_100 value: 55.494 - type: map_at_1000 value: 55.545 - type: map_at_3 value: 51.20099999999999 - type: map_at_5 value: 53.147 - type: mrr_at_1 value: 47.335 - type: mrr_at_10 value: 57.772 - type: mrr_at_100 value: 58.428000000000004 - type: mrr_at_1000 value: 58.453 - type: mrr_at_3 value: 55.434000000000005 - type: mrr_at_5 value: 56.8 - type: ndcg_at_1 value: 47.335 - type: ndcg_at_10 value: 60.382999999999996 - type: ndcg_at_100 value: 64.294 - type: ndcg_at_1000 value: 65.211 - type: ndcg_at_3 value: 55.098 - type: ndcg_at_5 value: 57.776 - type: precision_at_1 value: 47.335 - type: precision_at_10 value: 9.724 - type: precision_at_100 value: 1.26 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 24.786 - type: precision_at_5 value: 16.977999999999998 - type: recall_at_1 value: 41.289 - type: recall_at_10 value: 74.36399999999999 - type: recall_at_100 value: 91.19800000000001 - type: recall_at_1000 value: 97.508 - type: recall_at_3 value: 60.285 - type: recall_at_5 value: 66.814 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.816999999999997 - type: map_at_10 value: 37.856 - type: map_at_100 value: 38.824 - type: map_at_1000 value: 38.902 - type: map_at_3 value: 34.982 - type: map_at_5 value: 36.831 - type: mrr_at_1 value: 31.073 - type: mrr_at_10 value: 39.985 - type: mrr_at_100 value: 40.802 - type: mrr_at_1000 value: 40.861999999999995 - type: mrr_at_3 value: 37.419999999999995 - type: mrr_at_5 value: 39.104 - type: ndcg_at_1 value: 31.073 - type: ndcg_at_10 value: 42.958 - type: ndcg_at_100 value: 47.671 - type: ndcg_at_1000 value: 49.633 - type: ndcg_at_3 value: 37.602000000000004 - type: ndcg_at_5 value: 40.688 - type: precision_at_1 value: 31.073 - type: precision_at_10 value: 6.531000000000001 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 15.857 - type: precision_at_5 value: 11.209 - type: recall_at_1 value: 28.816999999999997 - type: recall_at_10 value: 56.538999999999994 - type: recall_at_100 value: 78.17699999999999 - type: recall_at_1000 value: 92.92200000000001 - type: recall_at_3 value: 42.294 - type: recall_at_5 value: 49.842999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.397 - type: map_at_10 value: 27.256999999999998 - type: map_at_100 value: 28.541 - type: map_at_1000 value: 28.658 - type: map_at_3 value: 24.565 - type: map_at_5 value: 26.211000000000002 - type: mrr_at_1 value: 22.761 - type: mrr_at_10 value: 32.248 - type: mrr_at_100 value: 33.171 - type: mrr_at_1000 value: 33.227000000000004 - type: mrr_at_3 value: 29.498 - type: mrr_at_5 value: 31.246000000000002 - type: ndcg_at_1 value: 22.761 - type: ndcg_at_10 value: 32.879999999999995 - type: ndcg_at_100 value: 38.913 - type: ndcg_at_1000 value: 41.504999999999995 - type: ndcg_at_3 value: 27.988000000000003 - type: ndcg_at_5 value: 30.548 - type: precision_at_1 value: 22.761 - type: precision_at_10 value: 6.045 - type: precision_at_100 value: 1.044 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 13.433 - type: precision_at_5 value: 9.925 - type: recall_at_1 value: 18.397 - type: recall_at_10 value: 45.14 - type: recall_at_100 value: 71.758 - type: recall_at_1000 value: 89.854 - type: recall_at_3 value: 31.942999999999998 - type: recall_at_5 value: 38.249 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.604 - type: map_at_10 value: 42.132 - type: map_at_100 value: 43.419000000000004 - type: map_at_1000 value: 43.527 - type: map_at_3 value: 38.614 - type: map_at_5 value: 40.705000000000005 - type: mrr_at_1 value: 37.824999999999996 - type: mrr_at_10 value: 47.696 - type: mrr_at_100 value: 48.483 - type: mrr_at_1000 value: 48.53 - type: mrr_at_3 value: 45.123999999999995 - type: mrr_at_5 value: 46.635 - type: ndcg_at_1 value: 37.824999999999996 - type: ndcg_at_10 value: 48.421 - type: ndcg_at_100 value: 53.568000000000005 - type: ndcg_at_1000 value: 55.574999999999996 - type: ndcg_at_3 value: 42.89 - type: ndcg_at_5 value: 45.683 - type: precision_at_1 value: 37.824999999999996 - type: precision_at_10 value: 8.758000000000001 - type: precision_at_100 value: 1.319 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 20.244 - type: precision_at_5 value: 14.533 - type: recall_at_1 value: 30.604 - type: recall_at_10 value: 61.605 - type: recall_at_100 value: 82.787 - type: recall_at_1000 value: 95.78 - type: recall_at_3 value: 46.303 - type: recall_at_5 value: 53.351000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.262999999999998 - type: map_at_10 value: 36.858999999999995 - type: map_at_100 value: 38.241 - type: map_at_1000 value: 38.346999999999994 - type: map_at_3 value: 33.171 - type: map_at_5 value: 35.371 - type: mrr_at_1 value: 32.42 - type: mrr_at_10 value: 42.361 - type: mrr_at_100 value: 43.219 - type: mrr_at_1000 value: 43.271 - type: mrr_at_3 value: 39.593 - type: mrr_at_5 value: 41.248000000000005 - type: ndcg_at_1 value: 32.42 - type: ndcg_at_10 value: 43.081 - type: ndcg_at_100 value: 48.837 - type: ndcg_at_1000 value: 50.954 - type: ndcg_at_3 value: 37.413000000000004 - type: ndcg_at_5 value: 40.239000000000004 - type: precision_at_1 value: 32.42 - type: precision_at_10 value: 8.071 - type: precision_at_100 value: 1.272 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 17.922 - type: precision_at_5 value: 13.311 - type: recall_at_1 value: 26.262999999999998 - type: recall_at_10 value: 56.062999999999995 - type: recall_at_100 value: 80.636 - type: recall_at_1000 value: 94.707 - type: recall_at_3 value: 40.425 - type: recall_at_5 value: 47.663 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.86616666666667 - type: map_at_10 value: 37.584999999999994 - type: map_at_100 value: 38.80291666666667 - type: map_at_1000 value: 38.91358333333333 - type: map_at_3 value: 34.498 - type: map_at_5 value: 36.269999999999996 - type: mrr_at_1 value: 33.07566666666667 - type: mrr_at_10 value: 41.92366666666666 - type: mrr_at_100 value: 42.73516666666667 - type: mrr_at_1000 value: 42.785666666666664 - type: mrr_at_3 value: 39.39075 - type: mrr_at_5 value: 40.89133333333334 - type: ndcg_at_1 value: 33.07566666666667 - type: ndcg_at_10 value: 43.19875 - type: ndcg_at_100 value: 48.32083333333334 - type: ndcg_at_1000 value: 50.418000000000006 - type: ndcg_at_3 value: 38.10308333333333 - type: ndcg_at_5 value: 40.5985 - type: precision_at_1 value: 33.07566666666667 - type: precision_at_10 value: 7.581916666666666 - type: precision_at_100 value: 1.1975 - type: precision_at_1000 value: 0.15699999999999997 - type: precision_at_3 value: 17.49075 - type: precision_at_5 value: 12.5135 - type: recall_at_1 value: 27.86616666666667 - type: recall_at_10 value: 55.449749999999995 - type: recall_at_100 value: 77.92516666666666 - type: recall_at_1000 value: 92.31358333333333 - type: recall_at_3 value: 41.324416666666664 - type: recall_at_5 value: 47.72533333333333 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.648 - type: map_at_10 value: 33.155 - type: map_at_100 value: 34.149 - type: map_at_1000 value: 34.239000000000004 - type: map_at_3 value: 30.959999999999997 - type: map_at_5 value: 32.172 - type: mrr_at_1 value: 30.061 - type: mrr_at_10 value: 36.229 - type: mrr_at_100 value: 37.088 - type: mrr_at_1000 value: 37.15 - type: mrr_at_3 value: 34.254 - type: mrr_at_5 value: 35.297 - type: ndcg_at_1 value: 30.061 - type: ndcg_at_10 value: 37.247 - type: ndcg_at_100 value: 42.093 - type: ndcg_at_1000 value: 44.45 - type: ndcg_at_3 value: 33.211 - type: ndcg_at_5 value: 35.083999999999996 - type: precision_at_1 value: 30.061 - type: precision_at_10 value: 5.7059999999999995 - type: precision_at_100 value: 0.8880000000000001 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 13.957 - type: precision_at_5 value: 9.663 - type: recall_at_1 value: 26.648 - type: recall_at_10 value: 46.85 - type: recall_at_100 value: 68.87 - type: recall_at_1000 value: 86.508 - type: recall_at_3 value: 35.756 - type: recall_at_5 value: 40.376 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.058 - type: map_at_10 value: 26.722 - type: map_at_100 value: 27.863 - type: map_at_1000 value: 27.988000000000003 - type: map_at_3 value: 24.258 - type: map_at_5 value: 25.531 - type: mrr_at_1 value: 23.09 - type: mrr_at_10 value: 30.711 - type: mrr_at_100 value: 31.628 - type: mrr_at_1000 value: 31.702 - type: mrr_at_3 value: 28.418 - type: mrr_at_5 value: 29.685 - type: ndcg_at_1 value: 23.09 - type: ndcg_at_10 value: 31.643 - type: ndcg_at_100 value: 37.047999999999995 - type: ndcg_at_1000 value: 39.896 - type: ndcg_at_3 value: 27.189999999999998 - type: ndcg_at_5 value: 29.112 - type: precision_at_1 value: 23.09 - type: precision_at_10 value: 5.743 - type: precision_at_100 value: 1 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 12.790000000000001 - type: precision_at_5 value: 9.195 - type: recall_at_1 value: 19.058 - type: recall_at_10 value: 42.527 - type: recall_at_100 value: 66.833 - type: recall_at_1000 value: 87.008 - type: recall_at_3 value: 29.876 - type: recall_at_5 value: 34.922 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.066999999999997 - type: map_at_10 value: 37.543 - type: map_at_100 value: 38.725 - type: map_at_1000 value: 38.815 - type: map_at_3 value: 34.488 - type: map_at_5 value: 36.222 - type: mrr_at_1 value: 33.116 - type: mrr_at_10 value: 41.743 - type: mrr_at_100 value: 42.628 - type: mrr_at_1000 value: 42.675999999999995 - type: mrr_at_3 value: 39.241 - type: mrr_at_5 value: 40.622 - type: ndcg_at_1 value: 33.116 - type: ndcg_at_10 value: 43.089 - type: ndcg_at_100 value: 48.61 - type: ndcg_at_1000 value: 50.585 - type: ndcg_at_3 value: 37.816 - type: ndcg_at_5 value: 40.256 - type: precision_at_1 value: 33.116 - type: precision_at_10 value: 7.313 - type: precision_at_100 value: 1.1320000000000001 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.102 - type: precision_at_5 value: 12.09 - type: recall_at_1 value: 28.066999999999997 - type: recall_at_10 value: 55.684 - type: recall_at_100 value: 80.092 - type: recall_at_1000 value: 93.605 - type: recall_at_3 value: 41.277 - type: recall_at_5 value: 47.46 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.094 - type: map_at_10 value: 35.939 - type: map_at_100 value: 37.552 - type: map_at_1000 value: 37.771 - type: map_at_3 value: 32.414 - type: map_at_5 value: 34.505 - type: mrr_at_1 value: 32.609 - type: mrr_at_10 value: 40.521 - type: mrr_at_100 value: 41.479 - type: mrr_at_1000 value: 41.524 - type: mrr_at_3 value: 37.451 - type: mrr_at_5 value: 39.387 - type: ndcg_at_1 value: 32.609 - type: ndcg_at_10 value: 41.83 - type: ndcg_at_100 value: 47.763 - type: ndcg_at_1000 value: 50.102999999999994 - type: ndcg_at_3 value: 36.14 - type: ndcg_at_5 value: 39.153999999999996 - type: precision_at_1 value: 32.609 - type: precision_at_10 value: 7.925 - type: precision_at_100 value: 1.591 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 16.337 - type: precision_at_5 value: 12.411 - type: recall_at_1 value: 27.094 - type: recall_at_10 value: 53.32900000000001 - type: recall_at_100 value: 79.52 - type: recall_at_1000 value: 93.958 - type: recall_at_3 value: 37.773 - type: recall_at_5 value: 45.321 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.649 - type: map_at_10 value: 30.569000000000003 - type: map_at_100 value: 31.444 - type: map_at_1000 value: 31.538 - type: map_at_3 value: 27.638 - type: map_at_5 value: 29.171000000000003 - type: mrr_at_1 value: 24.399 - type: mrr_at_10 value: 32.555 - type: mrr_at_100 value: 33.312000000000005 - type: mrr_at_1000 value: 33.376 - type: mrr_at_3 value: 29.820999999999998 - type: mrr_at_5 value: 31.402 - type: ndcg_at_1 value: 24.399 - type: ndcg_at_10 value: 35.741 - type: ndcg_at_100 value: 40.439 - type: ndcg_at_1000 value: 42.809000000000005 - type: ndcg_at_3 value: 30.020999999999997 - type: ndcg_at_5 value: 32.68 - type: precision_at_1 value: 24.399 - type: precision_at_10 value: 5.749 - type: precision_at_100 value: 0.878 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 12.815999999999999 - type: precision_at_5 value: 9.242 - type: recall_at_1 value: 22.649 - type: recall_at_10 value: 49.818 - type: recall_at_100 value: 72.155 - type: recall_at_1000 value: 89.654 - type: recall_at_3 value: 34.528999999999996 - type: recall_at_5 value: 40.849999999999994 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 13.587 - type: map_at_10 value: 23.021 - type: map_at_100 value: 25.095 - type: map_at_1000 value: 25.295 - type: map_at_3 value: 19.463 - type: map_at_5 value: 21.389 - type: mrr_at_1 value: 29.576999999999998 - type: mrr_at_10 value: 41.44 - type: mrr_at_100 value: 42.497 - type: mrr_at_1000 value: 42.529 - type: mrr_at_3 value: 38.284 - type: mrr_at_5 value: 40.249 - type: ndcg_at_1 value: 29.576999999999998 - type: ndcg_at_10 value: 31.491000000000003 - type: ndcg_at_100 value: 39.352 - type: ndcg_at_1000 value: 42.703 - type: ndcg_at_3 value: 26.284999999999997 - type: ndcg_at_5 value: 28.218 - type: precision_at_1 value: 29.576999999999998 - type: precision_at_10 value: 9.713 - type: precision_at_100 value: 1.8079999999999998 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 19.608999999999998 - type: precision_at_5 value: 14.957999999999998 - type: recall_at_1 value: 13.587 - type: recall_at_10 value: 37.001 - type: recall_at_100 value: 63.617999999999995 - type: recall_at_1000 value: 82.207 - type: recall_at_3 value: 24.273 - type: recall_at_5 value: 29.813000000000002 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.98 - type: map_at_10 value: 20.447000000000003 - type: map_at_100 value: 29.032999999999998 - type: map_at_1000 value: 30.8 - type: map_at_3 value: 15.126999999999999 - type: map_at_5 value: 17.327 - type: mrr_at_1 value: 71.25 - type: mrr_at_10 value: 78.014 - type: mrr_at_100 value: 78.303 - type: mrr_at_1000 value: 78.309 - type: mrr_at_3 value: 76.375 - type: mrr_at_5 value: 77.58699999999999 - type: ndcg_at_1 value: 57.99999999999999 - type: ndcg_at_10 value: 41.705 - type: ndcg_at_100 value: 47.466 - type: ndcg_at_1000 value: 55.186 - type: ndcg_at_3 value: 47.089999999999996 - type: ndcg_at_5 value: 43.974000000000004 - type: precision_at_1 value: 71.25 - type: precision_at_10 value: 32.65 - type: precision_at_100 value: 10.89 - type: precision_at_1000 value: 2.197 - type: precision_at_3 value: 50.5 - type: precision_at_5 value: 42.199999999999996 - type: recall_at_1 value: 9.98 - type: recall_at_10 value: 25.144 - type: recall_at_100 value: 53.754999999999995 - type: recall_at_1000 value: 78.56400000000001 - type: recall_at_3 value: 15.964 - type: recall_at_5 value: 19.186 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 54.67999999999999 - type: f1 value: 49.48247525503583 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 74.798 - type: map_at_10 value: 82.933 - type: map_at_100 value: 83.157 - type: map_at_1000 value: 83.173 - type: map_at_3 value: 81.80199999999999 - type: map_at_5 value: 82.55 - type: mrr_at_1 value: 80.573 - type: mrr_at_10 value: 87.615 - type: mrr_at_100 value: 87.69 - type: mrr_at_1000 value: 87.69200000000001 - type: mrr_at_3 value: 86.86399999999999 - type: mrr_at_5 value: 87.386 - type: ndcg_at_1 value: 80.573 - type: ndcg_at_10 value: 86.64500000000001 - type: ndcg_at_100 value: 87.407 - type: ndcg_at_1000 value: 87.68299999999999 - type: ndcg_at_3 value: 84.879 - type: ndcg_at_5 value: 85.921 - type: precision_at_1 value: 80.573 - type: precision_at_10 value: 10.348 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 32.268 - type: precision_at_5 value: 20.084 - type: recall_at_1 value: 74.798 - type: recall_at_10 value: 93.45400000000001 - type: recall_at_100 value: 96.42500000000001 - type: recall_at_1000 value: 98.158 - type: recall_at_3 value: 88.634 - type: recall_at_5 value: 91.295 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.567 - type: map_at_10 value: 32.967999999999996 - type: map_at_100 value: 35.108 - type: map_at_1000 value: 35.272999999999996 - type: map_at_3 value: 28.701999999999998 - type: map_at_5 value: 31.114000000000004 - type: mrr_at_1 value: 40.432 - type: mrr_at_10 value: 48.956 - type: mrr_at_100 value: 49.832 - type: mrr_at_1000 value: 49.87 - type: mrr_at_3 value: 46.759 - type: mrr_at_5 value: 47.886 - type: ndcg_at_1 value: 40.432 - type: ndcg_at_10 value: 40.644000000000005 - type: ndcg_at_100 value: 48.252 - type: ndcg_at_1000 value: 51.099000000000004 - type: ndcg_at_3 value: 36.992000000000004 - type: ndcg_at_5 value: 38.077 - type: precision_at_1 value: 40.432 - type: precision_at_10 value: 11.296000000000001 - type: precision_at_100 value: 1.9009999999999998 - type: precision_at_1000 value: 0.241 - type: precision_at_3 value: 24.537 - type: precision_at_5 value: 17.963 - type: recall_at_1 value: 20.567 - type: recall_at_10 value: 47.052 - type: recall_at_100 value: 75.21600000000001 - type: recall_at_1000 value: 92.285 - type: recall_at_3 value: 33.488 - type: recall_at_5 value: 39.334 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.196999999999996 - type: map_at_10 value: 60.697 - type: map_at_100 value: 61.624 - type: map_at_1000 value: 61.692 - type: map_at_3 value: 57.421 - type: map_at_5 value: 59.455000000000005 - type: mrr_at_1 value: 76.39399999999999 - type: mrr_at_10 value: 82.504 - type: mrr_at_100 value: 82.71300000000001 - type: mrr_at_1000 value: 82.721 - type: mrr_at_3 value: 81.494 - type: mrr_at_5 value: 82.137 - type: ndcg_at_1 value: 76.39399999999999 - type: ndcg_at_10 value: 68.92200000000001 - type: ndcg_at_100 value: 72.13199999999999 - type: ndcg_at_1000 value: 73.392 - type: ndcg_at_3 value: 64.226 - type: ndcg_at_5 value: 66.815 - type: precision_at_1 value: 76.39399999999999 - type: precision_at_10 value: 14.442 - type: precision_at_100 value: 1.694 - type: precision_at_1000 value: 0.186 - type: precision_at_3 value: 41.211 - type: precision_at_5 value: 26.766000000000002 - type: recall_at_1 value: 38.196999999999996 - type: recall_at_10 value: 72.208 - type: recall_at_100 value: 84.71300000000001 - type: recall_at_1000 value: 92.971 - type: recall_at_3 value: 61.816 - type: recall_at_5 value: 66.914 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 89.6556 - type: ap value: 85.27600392682054 - type: f1 value: 89.63353655386406 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.482 - type: map_at_10 value: 33.701 - type: map_at_100 value: 34.861 - type: map_at_1000 value: 34.914 - type: map_at_3 value: 29.793999999999997 - type: map_at_5 value: 32.072 - type: mrr_at_1 value: 22.163 - type: mrr_at_10 value: 34.371 - type: mrr_at_100 value: 35.471000000000004 - type: mrr_at_1000 value: 35.518 - type: mrr_at_3 value: 30.554 - type: mrr_at_5 value: 32.799 - type: ndcg_at_1 value: 22.163 - type: ndcg_at_10 value: 40.643 - type: ndcg_at_100 value: 46.239999999999995 - type: ndcg_at_1000 value: 47.526 - type: ndcg_at_3 value: 32.714999999999996 - type: ndcg_at_5 value: 36.791000000000004 - type: precision_at_1 value: 22.163 - type: precision_at_10 value: 6.4799999999999995 - type: precision_at_100 value: 0.928 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.002 - type: precision_at_5 value: 10.453 - type: recall_at_1 value: 21.482 - type: recall_at_10 value: 61.953 - type: recall_at_100 value: 87.86500000000001 - type: recall_at_1000 value: 97.636 - type: recall_at_3 value: 40.441 - type: recall_at_5 value: 50.27 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 95.3032375740994 - type: f1 value: 95.01515022686607 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 78.10077519379846 - type: f1 value: 58.240739725625644 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.0053799596503 - type: f1 value: 74.11733965804146 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.64021519838602 - type: f1 value: 79.8513960091438 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.92425767945184 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.249612382060754 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.35584955492918 - type: mrr value: 33.545865224584674 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.978 - type: map_at_10 value: 14.749 - type: map_at_100 value: 19.192 - type: map_at_1000 value: 20.815 - type: map_at_3 value: 10.927000000000001 - type: map_at_5 value: 12.726 - type: mrr_at_1 value: 49.536 - type: mrr_at_10 value: 57.806999999999995 - type: mrr_at_100 value: 58.373 - type: mrr_at_1000 value: 58.407 - type: mrr_at_3 value: 55.779 - type: mrr_at_5 value: 57.095 - type: ndcg_at_1 value: 46.749 - type: ndcg_at_10 value: 37.644 - type: ndcg_at_100 value: 35.559000000000005 - type: ndcg_at_1000 value: 44.375 - type: ndcg_at_3 value: 43.354 - type: ndcg_at_5 value: 41.022999999999996 - type: precision_at_1 value: 48.607 - type: precision_at_10 value: 28.08 - type: precision_at_100 value: 9.155000000000001 - type: precision_at_1000 value: 2.2270000000000003 - type: precision_at_3 value: 40.764 - type: precision_at_5 value: 35.728 - type: recall_at_1 value: 6.978 - type: recall_at_10 value: 17.828 - type: recall_at_100 value: 36.010999999999996 - type: recall_at_1000 value: 68.34700000000001 - type: recall_at_3 value: 11.645999999999999 - type: recall_at_5 value: 14.427000000000001 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 30.219 - type: map_at_10 value: 45.633 - type: map_at_100 value: 46.752 - type: map_at_1000 value: 46.778999999999996 - type: map_at_3 value: 41.392 - type: map_at_5 value: 43.778 - type: mrr_at_1 value: 34.327999999999996 - type: mrr_at_10 value: 48.256 - type: mrr_at_100 value: 49.076 - type: mrr_at_1000 value: 49.092999999999996 - type: mrr_at_3 value: 44.786 - type: mrr_at_5 value: 46.766000000000005 - type: ndcg_at_1 value: 34.299 - type: ndcg_at_10 value: 53.434000000000005 - type: ndcg_at_100 value: 58.03 - type: ndcg_at_1000 value: 58.633 - type: ndcg_at_3 value: 45.433 - type: ndcg_at_5 value: 49.379 - type: precision_at_1 value: 34.299 - type: precision_at_10 value: 8.911 - type: precision_at_100 value: 1.145 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 20.896 - type: precision_at_5 value: 14.832 - type: recall_at_1 value: 30.219 - type: recall_at_10 value: 74.59400000000001 - type: recall_at_100 value: 94.392 - type: recall_at_1000 value: 98.832 - type: recall_at_3 value: 53.754000000000005 - type: recall_at_5 value: 62.833000000000006 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.139 - type: map_at_10 value: 85.141 - type: map_at_100 value: 85.78099999999999 - type: map_at_1000 value: 85.795 - type: map_at_3 value: 82.139 - type: map_at_5 value: 84.075 - type: mrr_at_1 value: 81.98 - type: mrr_at_10 value: 88.056 - type: mrr_at_100 value: 88.152 - type: mrr_at_1000 value: 88.152 - type: mrr_at_3 value: 87.117 - type: mrr_at_5 value: 87.78099999999999 - type: ndcg_at_1 value: 82.02000000000001 - type: ndcg_at_10 value: 88.807 - type: ndcg_at_100 value: 89.99000000000001 - type: ndcg_at_1000 value: 90.068 - type: ndcg_at_3 value: 85.989 - type: ndcg_at_5 value: 87.627 - type: precision_at_1 value: 82.02000000000001 - type: precision_at_10 value: 13.472999999999999 - type: precision_at_100 value: 1.534 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.788 - type: recall_at_1 value: 71.139 - type: recall_at_10 value: 95.707 - type: recall_at_100 value: 99.666 - type: recall_at_1000 value: 99.983 - type: recall_at_3 value: 87.64699999999999 - type: recall_at_5 value: 92.221 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 59.11035509193503 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.44241881422526 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.122999999999999 - type: map_at_10 value: 14.45 - type: map_at_100 value: 17.108999999999998 - type: map_at_1000 value: 17.517 - type: map_at_3 value: 10.213999999999999 - type: map_at_5 value: 12.278 - type: mrr_at_1 value: 25.3 - type: mrr_at_10 value: 37.791999999999994 - type: mrr_at_100 value: 39.086 - type: mrr_at_1000 value: 39.121 - type: mrr_at_3 value: 34.666999999999994 - type: mrr_at_5 value: 36.472 - type: ndcg_at_1 value: 25.3 - type: ndcg_at_10 value: 23.469 - type: ndcg_at_100 value: 33.324 - type: ndcg_at_1000 value: 39.357 - type: ndcg_at_3 value: 22.478 - type: ndcg_at_5 value: 19.539 - type: precision_at_1 value: 25.3 - type: precision_at_10 value: 12.3 - type: precision_at_100 value: 2.654 - type: precision_at_1000 value: 0.40800000000000003 - type: precision_at_3 value: 21.667 - type: precision_at_5 value: 17.5 - type: recall_at_1 value: 5.122999999999999 - type: recall_at_10 value: 24.937 - type: recall_at_100 value: 53.833 - type: recall_at_1000 value: 82.85 - type: recall_at_3 value: 13.178 - type: recall_at_5 value: 17.747 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 86.76549431206278 - type: cos_sim_spearman value: 81.28563534883214 - type: euclidean_pearson value: 84.17180713818567 - type: euclidean_spearman value: 81.1684082302606 - type: manhattan_pearson value: 84.12189753972959 - type: manhattan_spearman value: 81.1134998997958 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.75137587182017 - type: cos_sim_spearman value: 76.155337187325 - type: euclidean_pearson value: 83.54551546726665 - type: euclidean_spearman value: 76.30324990565346 - type: manhattan_pearson value: 83.52192617483797 - type: manhattan_spearman value: 76.30017227216015 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 87.13890050398628 - type: cos_sim_spearman value: 87.84898360302155 - type: euclidean_pearson value: 86.89491809082031 - type: euclidean_spearman value: 87.99935689905651 - type: manhattan_pearson value: 86.86526424376366 - type: manhattan_spearman value: 87.96850732980495 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 86.01978753231558 - type: cos_sim_spearman value: 83.38989083933329 - type: euclidean_pearson value: 85.28405032045376 - type: euclidean_spearman value: 83.51703914276501 - type: manhattan_pearson value: 85.25775133078966 - type: manhattan_spearman value: 83.52815667821727 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.28482294437876 - type: cos_sim_spearman value: 89.42976214499576 - type: euclidean_pearson value: 88.72677957272468 - type: euclidean_spearman value: 89.30001736116229 - type: manhattan_pearson value: 88.64119331622562 - type: manhattan_spearman value: 89.21771022634893 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.79810159351987 - type: cos_sim_spearman value: 85.34918402034273 - type: euclidean_pearson value: 84.76058606229002 - type: euclidean_spearman value: 85.45159829941214 - type: manhattan_pearson value: 84.73926491888156 - type: manhattan_spearman value: 85.42568221985898 - 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: 88.92796712570272 - type: cos_sim_spearman value: 88.58925922945812 - type: euclidean_pearson value: 88.97231215531797 - type: euclidean_spearman value: 88.27036385068719 - type: manhattan_pearson value: 88.95761469412228 - type: manhattan_spearman value: 88.23980432487681 - 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: 66.85679810182282 - type: cos_sim_spearman value: 67.80696709003128 - type: euclidean_pearson value: 68.77524185947989 - type: euclidean_spearman value: 68.032438075422 - type: manhattan_pearson value: 68.60489100404182 - type: manhattan_spearman value: 67.75418889226138 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.33287880999367 - type: cos_sim_spearman value: 87.32401087204754 - type: euclidean_pearson value: 87.27961069148029 - type: euclidean_spearman value: 87.3547683085868 - type: manhattan_pearson value: 87.24405442789622 - type: manhattan_spearman value: 87.32896271166672 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.71553665286558 - type: mrr value: 96.42436176749902 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 61.094 - type: map_at_10 value: 71.066 - type: map_at_100 value: 71.608 - type: map_at_1000 value: 71.629 - type: map_at_3 value: 68.356 - type: map_at_5 value: 70.15 - type: mrr_at_1 value: 64 - type: mrr_at_10 value: 71.82300000000001 - type: mrr_at_100 value: 72.251 - type: mrr_at_1000 value: 72.269 - type: mrr_at_3 value: 69.833 - type: mrr_at_5 value: 71.11699999999999 - type: ndcg_at_1 value: 64 - type: ndcg_at_10 value: 75.286 - type: ndcg_at_100 value: 77.40700000000001 - type: ndcg_at_1000 value: 77.806 - type: ndcg_at_3 value: 70.903 - type: ndcg_at_5 value: 73.36399999999999 - type: precision_at_1 value: 64 - type: precision_at_10 value: 9.9 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 27.667 - type: precision_at_5 value: 18.333 - type: recall_at_1 value: 61.094 - type: recall_at_10 value: 87.256 - type: recall_at_100 value: 96.5 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 75.6 - type: recall_at_5 value: 81.789 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82871287128712 - type: cos_sim_ap value: 95.9325677692287 - type: cos_sim_f1 value: 91.13924050632912 - type: cos_sim_precision value: 92.3076923076923 - type: cos_sim_recall value: 90 - type: dot_accuracy value: 99.7980198019802 - type: dot_ap value: 94.56107207796 - type: dot_f1 value: 89.41908713692946 - type: dot_precision value: 92.88793103448276 - type: dot_recall value: 86.2 - type: euclidean_accuracy value: 99.82871287128712 - type: euclidean_ap value: 95.94390332507025 - type: euclidean_f1 value: 91.17797042325346 - type: euclidean_precision value: 93.02809573361083 - type: euclidean_recall value: 89.4 - type: manhattan_accuracy value: 99.82871287128712 - type: manhattan_ap value: 95.97587114452257 - type: manhattan_f1 value: 91.25821121778675 - type: manhattan_precision value: 92.23697650663942 - type: manhattan_recall value: 90.3 - type: max_accuracy value: 99.82871287128712 - type: max_ap value: 95.97587114452257 - type: max_f1 value: 91.25821121778675 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.13974351708839 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.594544722932234 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 54.718738983377726 - type: mrr value: 55.61655154486037 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.37028359646597 - type: cos_sim_spearman value: 30.866534307244443 - 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type: ndcg_at_10 value: 20.578 - type: ndcg_at_100 value: 34.285 - type: ndcg_at_1000 value: 45.853 - type: ndcg_at_3 value: 19.869999999999997 - type: ndcg_at_5 value: 22.081999999999997 - type: precision_at_1 value: 18.367 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.714 - type: precision_at_1000 value: 1.547 - type: precision_at_3 value: 23.128999999999998 - type: precision_at_5 value: 24.898 - type: recall_at_1 value: 1.5779999999999998 - type: recall_at_10 value: 14.801 - type: recall_at_100 value: 48.516999999999996 - type: recall_at_1000 value: 83.30300000000001 - type: recall_at_3 value: 5.267 - type: recall_at_5 value: 9.415999999999999 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 72.4186 - type: ap value: 14.536282543597242 - type: f1 value: 55.47661372005608 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.318053197509904 - type: f1 value: 59.68272481532353 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.155753554312 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.99409906419503 - type: cos_sim_ap value: 76.91824322304332 - type: cos_sim_f1 value: 70.97865694950546 - type: cos_sim_precision value: 70.03081664098613 - type: cos_sim_recall value: 71.95250659630607 - type: dot_accuracy value: 85.37879239434942 - type: dot_ap value: 71.86454698478344 - type: dot_f1 value: 66.48115355426259 - type: dot_precision value: 63.84839650145773 - type: dot_recall value: 69.34036939313984 - type: euclidean_accuracy value: 87.00005960541218 - type: euclidean_ap value: 76.9165913835565 - type: euclidean_f1 value: 71.23741557283039 - type: euclidean_precision value: 68.89327088982007 - type: euclidean_recall value: 73.7467018469657 - type: manhattan_accuracy value: 87.06562555880075 - type: manhattan_ap value: 76.85445703747546 - type: manhattan_f1 value: 70.95560571858539 - type: manhattan_precision value: 67.61472275334609 - type: manhattan_recall value: 74.64379947229551 - type: max_accuracy value: 87.06562555880075 - type: max_ap value: 76.91824322304332 - type: max_f1 value: 71.23741557283039 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.93934101758063 - type: cos_sim_ap value: 86.1071528049007 - type: cos_sim_f1 value: 78.21588263552714 - type: cos_sim_precision value: 75.20073900376609 - type: cos_sim_recall value: 81.48290729904527 - type: dot_accuracy value: 88.2504754142896 - type: dot_ap value: 84.19709379723844 - type: dot_f1 value: 76.92307692307693 - type: dot_precision value: 71.81969949916528 - type: dot_recall value: 82.80720665229443 - type: euclidean_accuracy value: 88.97232894787906 - type: euclidean_ap value: 86.02763993294909 - type: euclidean_f1 value: 78.18372741427383 - type: euclidean_precision value: 73.79861918107868 - type: euclidean_recall value: 83.12288266091777 - type: manhattan_accuracy value: 88.86948422400745 - type: manhattan_ap value: 86.0009157821563 - type: manhattan_f1 value: 78.10668017659404 - type: manhattan_precision value: 73.68564795848695 - type: manhattan_recall value: 83.09208500153989 - type: max_accuracy value: 88.97232894787906 - type: max_ap value: 86.1071528049007 - type: max_f1 value: 78.21588263552714 --- <h1 align="center">GIST Embedding v0</h1> *GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions. Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829) # Data The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available: - Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets) - Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`). The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741). The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some. The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance. # Usage The model can be easily loaded using the Sentence Transformers library. ```Python import torch.nn.functional as F from sentence_transformers import SentenceTransformer revision = None # Replace with the specific revision to ensure reproducibility if the model is updated. model = SentenceTransformer("avsolatorio/GIST-Embedding-v0", revision=revision) texts = [ "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.", "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.", "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes" ] # Compute embeddings embeddings = model.encode(texts, convert_to_tensor=True) # Compute cosine-similarity for each pair of sentences scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) print(scores.cpu().numpy()) ``` # Training Parameters Below are the training parameters used to fine-tune the model: ``` Epochs = 80 Warmup ratio = 0.1 Learning rate = 5e-6 Batch size = 32 Checkpoint step = 103500 Contrastive loss temperature = 0.01 ``` # Evaluation The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. # Citation Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗 ``` @article{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, journal={arXiv preprint arXiv:2402.16829}, year={2024}, URL={https://arxiv.org/abs/2402.16829} eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # Acknowledgements This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444. The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Alibaba-NLP/gte-base-en-v1.5

Alibaba-NLP

transformers

sentence-similarity

--- library_name: transformers tags: - sentence-transformers - gte - mteb - transformers.js - sentence-similarity license: apache-2.0 language: - en model-index: - name: gte-base-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.7910447761194 - type: ap value: 37.053785713650626 - type: f1 value: 68.51101510998551 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.016875 - type: ap value: 89.17750268426342 - type: f1 value: 92.9970977240524 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 53.312000000000005 - type: f1 value: 52.98175784163017 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 38.193 - type: map_at_10 value: 54.848 - type: map_at_100 value: 55.388000000000005 - type: map_at_1000 value: 55.388999999999996 - type: map_at_3 value: 50.427 - type: map_at_5 value: 53.105000000000004 - type: mrr_at_1 value: 39.047 - type: mrr_at_10 value: 55.153 - type: mrr_at_100 value: 55.686 - type: mrr_at_1000 value: 55.688 - type: mrr_at_3 value: 50.676 - type: mrr_at_5 value: 53.417 - type: ndcg_at_1 value: 38.193 - type: ndcg_at_10 value: 63.486 - type: ndcg_at_100 value: 65.58 - type: ndcg_at_1000 value: 65.61 - type: ndcg_at_3 value: 54.494 - type: ndcg_at_5 value: 59.339 - type: precision_at_1 value: 38.193 - type: precision_at_10 value: 9.075 - type: precision_at_100 value: 0.9939999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.096 - type: precision_at_5 value: 15.619 - type: recall_at_1 value: 38.193 - type: recall_at_10 value: 90.754 - type: recall_at_100 value: 99.431 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 66.28699999999999 - type: recall_at_5 value: 78.094 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.508221208908964 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.04668382560096 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.828759903716815 - type: mrr value: 74.37343358395991 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.03673698773017 - type: cos_sim_spearman value: 83.6470866785058 - type: euclidean_pearson value: 82.64048673096565 - type: euclidean_spearman value: 83.63142367101115 - type: manhattan_pearson value: 82.71493099760228 - type: manhattan_spearman value: 83.60491704294326 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.73376623376623 - type: f1 value: 86.70294049278262 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.31923804167062 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 37.552547125348454 - task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 30.567 - type: map_at_10 value: 41.269 - type: map_at_100 value: 42.689 - type: map_at_1000 value: 42.84 - type: map_at_3 value: 37.567 - type: map_at_5 value: 39.706 - type: mrr_at_1 value: 37.053000000000004 - type: mrr_at_10 value: 46.900999999999996 - type: mrr_at_100 value: 47.662 - type: mrr_at_1000 value: 47.713 - type: mrr_at_3 value: 43.801 - type: mrr_at_5 value: 45.689 - type: ndcg_at_1 value: 37.053000000000004 - type: ndcg_at_10 value: 47.73 - type: ndcg_at_100 value: 53.128 - type: ndcg_at_1000 value: 55.300000000000004 - type: ndcg_at_3 value: 42.046 - type: ndcg_at_5 value: 44.782 - type: precision_at_1 value: 37.053000000000004 - type: precision_at_10 value: 9.142 - type: precision_at_100 value: 1.485 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 20.076 - type: precision_at_5 value: 14.535 - type: recall_at_1 value: 30.567 - type: recall_at_10 value: 60.602999999999994 - type: recall_at_100 value: 83.22800000000001 - type: recall_at_1000 value: 96.696 - type: recall_at_3 value: 44.336999999999996 - type: recall_at_5 value: 51.949 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 28.538000000000004 - type: map_at_10 value: 38.757999999999996 - type: map_at_100 value: 40.129 - type: map_at_1000 value: 40.262 - type: map_at_3 value: 35.866 - type: map_at_5 value: 37.417 - type: mrr_at_1 value: 36.051 - type: mrr_at_10 value: 44.868 - type: mrr_at_100 value: 45.568999999999996 - type: mrr_at_1000 value: 45.615 - type: mrr_at_3 value: 42.558 - type: mrr_at_5 value: 43.883 - type: ndcg_at_1 value: 36.051 - type: ndcg_at_10 value: 44.584 - type: ndcg_at_100 value: 49.356 - type: ndcg_at_1000 value: 51.39 - type: ndcg_at_3 value: 40.389 - type: ndcg_at_5 value: 42.14 - type: precision_at_1 value: 36.051 - type: precision_at_10 value: 8.446 - type: precision_at_100 value: 1.411 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 19.639 - type: precision_at_5 value: 13.796 - type: recall_at_1 value: 28.538000000000004 - type: recall_at_10 value: 54.99000000000001 - type: recall_at_100 value: 75.098 - type: recall_at_1000 value: 87.848 - type: recall_at_3 value: 42.236000000000004 - type: recall_at_5 value: 47.377 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 37.188 - type: map_at_10 value: 50.861000000000004 - type: map_at_100 value: 51.917 - type: map_at_1000 value: 51.964999999999996 - type: map_at_3 value: 47.144000000000005 - type: map_at_5 value: 49.417 - type: mrr_at_1 value: 42.571 - type: mrr_at_10 value: 54.086999999999996 - type: mrr_at_100 value: 54.739000000000004 - type: mrr_at_1000 value: 54.762 - type: mrr_at_3 value: 51.285000000000004 - type: mrr_at_5 value: 53.0 - type: ndcg_at_1 value: 42.571 - type: ndcg_at_10 value: 57.282 - type: ndcg_at_100 value: 61.477000000000004 - type: ndcg_at_1000 value: 62.426 - type: ndcg_at_3 value: 51.0 - type: ndcg_at_5 value: 54.346000000000004 - type: precision_at_1 value: 42.571 - type: precision_at_10 value: 9.467 - type: precision_at_100 value: 1.2550000000000001 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 23.114 - type: precision_at_5 value: 16.250999999999998 - type: recall_at_1 value: 37.188 - type: recall_at_10 value: 73.068 - type: recall_at_100 value: 91.203 - type: recall_at_1000 value: 97.916 - type: recall_at_3 value: 56.552 - type: recall_at_5 value: 64.567 - task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 25.041000000000004 - type: map_at_10 value: 33.86 - type: map_at_100 value: 34.988 - type: map_at_1000 value: 35.064 - type: map_at_3 value: 31.049 - type: map_at_5 value: 32.845 - type: mrr_at_1 value: 26.893 - type: mrr_at_10 value: 35.594 - type: mrr_at_100 value: 36.617 - type: mrr_at_1000 value: 36.671 - type: mrr_at_3 value: 33.051 - type: mrr_at_5 value: 34.61 - type: ndcg_at_1 value: 26.893 - type: ndcg_at_10 value: 38.674 - type: ndcg_at_100 value: 44.178 - type: ndcg_at_1000 value: 46.089999999999996 - type: ndcg_at_3 value: 33.485 - type: ndcg_at_5 value: 36.402 - type: precision_at_1 value: 26.893 - type: precision_at_10 value: 5.989 - type: precision_at_100 value: 0.918 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 14.2 - type: precision_at_5 value: 10.26 - type: recall_at_1 value: 25.041000000000004 - type: recall_at_10 value: 51.666000000000004 - type: recall_at_100 value: 76.896 - type: recall_at_1000 value: 91.243 - type: recall_at_3 value: 38.035999999999994 - type: recall_at_5 value: 44.999 - task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 15.909999999999998 - type: map_at_10 value: 23.901 - type: map_at_100 value: 25.165 - type: map_at_1000 value: 25.291000000000004 - type: map_at_3 value: 21.356 - type: map_at_5 value: 22.816 - type: mrr_at_1 value: 20.025000000000002 - type: mrr_at_10 value: 28.382 - type: mrr_at_100 value: 29.465000000000003 - type: mrr_at_1000 value: 29.535 - type: mrr_at_3 value: 25.933 - type: mrr_at_5 value: 27.332 - type: ndcg_at_1 value: 20.025000000000002 - type: ndcg_at_10 value: 29.099000000000004 - type: ndcg_at_100 value: 35.127 - type: ndcg_at_1000 value: 38.096000000000004 - type: ndcg_at_3 value: 24.464 - type: ndcg_at_5 value: 26.709 - type: precision_at_1 value: 20.025000000000002 - type: precision_at_10 value: 5.398 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.774 - type: precision_at_5 value: 8.632 - type: recall_at_1 value: 15.909999999999998 - type: recall_at_10 value: 40.672000000000004 - type: recall_at_100 value: 66.855 - type: recall_at_1000 value: 87.922 - type: recall_at_3 value: 28.069 - type: recall_at_5 value: 33.812 - task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 30.175 - type: map_at_10 value: 41.36 - type: map_at_100 value: 42.701 - type: map_at_1000 value: 42.817 - type: map_at_3 value: 37.931 - type: map_at_5 value: 39.943 - type: mrr_at_1 value: 35.611 - type: mrr_at_10 value: 46.346 - type: mrr_at_100 value: 47.160000000000004 - type: mrr_at_1000 value: 47.203 - type: mrr_at_3 value: 43.712 - type: mrr_at_5 value: 45.367000000000004 - type: ndcg_at_1 value: 35.611 - type: ndcg_at_10 value: 47.532000000000004 - type: ndcg_at_100 value: 53.003 - type: ndcg_at_1000 value: 55.007 - type: ndcg_at_3 value: 42.043 - type: ndcg_at_5 value: 44.86 - type: precision_at_1 value: 35.611 - type: precision_at_10 value: 8.624 - type: precision_at_100 value: 1.332 - type: precision_at_1000 value: 0.169 - type: precision_at_3 value: 20.083000000000002 - type: precision_at_5 value: 14.437 - type: recall_at_1 value: 30.175 - type: recall_at_10 value: 60.5 - type: recall_at_100 value: 83.399 - type: recall_at_1000 value: 96.255 - type: recall_at_3 value: 45.448 - type: recall_at_5 value: 52.432 - task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 22.467000000000002 - type: map_at_10 value: 33.812999999999995 - type: map_at_100 value: 35.248000000000005 - type: map_at_1000 value: 35.359 - type: map_at_3 value: 30.316 - type: map_at_5 value: 32.233000000000004 - type: mrr_at_1 value: 28.310999999999996 - type: mrr_at_10 value: 38.979 - type: mrr_at_100 value: 39.937 - type: mrr_at_1000 value: 39.989999999999995 - type: mrr_at_3 value: 36.244 - type: mrr_at_5 value: 37.871 - type: ndcg_at_1 value: 28.310999999999996 - type: ndcg_at_10 value: 40.282000000000004 - type: ndcg_at_100 value: 46.22 - type: ndcg_at_1000 value: 48.507 - type: ndcg_at_3 value: 34.596 - type: ndcg_at_5 value: 37.267 - type: precision_at_1 value: 28.310999999999996 - type: precision_at_10 value: 7.831 - type: precision_at_100 value: 1.257 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 17.275 - type: precision_at_5 value: 12.556999999999999 - type: recall_at_1 value: 22.467000000000002 - type: recall_at_10 value: 54.14099999999999 - type: recall_at_100 value: 79.593 - type: recall_at_1000 value: 95.063 - type: recall_at_3 value: 38.539 - type: recall_at_5 value: 45.403 - task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 24.18591666666667 - type: map_at_10 value: 33.84258333333333 - type: map_at_100 value: 35.11391666666666 - type: map_at_1000 value: 35.23258333333333 - type: map_at_3 value: 30.764249999999997 - type: map_at_5 value: 32.52333333333334 - type: mrr_at_1 value: 28.54733333333333 - type: mrr_at_10 value: 37.81725 - type: mrr_at_100 value: 38.716499999999996 - type: mrr_at_1000 value: 38.77458333333333 - type: mrr_at_3 value: 35.157833333333336 - type: mrr_at_5 value: 36.69816666666667 - type: ndcg_at_1 value: 28.54733333333333 - type: ndcg_at_10 value: 39.51508333333334 - type: ndcg_at_100 value: 44.95316666666666 - type: ndcg_at_1000 value: 47.257083333333334 - type: ndcg_at_3 value: 34.205833333333324 - type: ndcg_at_5 value: 36.78266666666667 - type: precision_at_1 value: 28.54733333333333 - type: precision_at_10 value: 7.082583333333334 - type: precision_at_100 value: 1.1590833333333332 - type: precision_at_1000 value: 0.15516666666666662 - type: precision_at_3 value: 15.908750000000001 - type: precision_at_5 value: 11.505416666666669 - type: recall_at_1 value: 24.18591666666667 - type: recall_at_10 value: 52.38758333333333 - type: recall_at_100 value: 76.13666666666667 - type: recall_at_1000 value: 91.99066666666667 - type: recall_at_3 value: 37.78333333333334 - type: recall_at_5 value: 44.30141666666666 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 21.975 - type: map_at_10 value: 29.781000000000002 - type: map_at_100 value: 30.847 - type: map_at_1000 value: 30.94 - type: map_at_3 value: 27.167 - type: map_at_5 value: 28.633999999999997 - type: mrr_at_1 value: 24.387 - type: mrr_at_10 value: 32.476 - type: mrr_at_100 value: 33.337 - type: mrr_at_1000 value: 33.403 - type: mrr_at_3 value: 29.881999999999998 - type: mrr_at_5 value: 31.339 - type: ndcg_at_1 value: 24.387 - type: ndcg_at_10 value: 34.596 - type: ndcg_at_100 value: 39.635 - type: ndcg_at_1000 value: 42.079 - type: ndcg_at_3 value: 29.516 - type: ndcg_at_5 value: 31.959 - type: precision_at_1 value: 24.387 - type: precision_at_10 value: 5.6129999999999995 - type: precision_at_100 value: 0.8909999999999999 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 12.73 - type: precision_at_5 value: 9.171999999999999 - type: recall_at_1 value: 21.975 - type: recall_at_10 value: 46.826 - type: recall_at_100 value: 69.554 - type: recall_at_1000 value: 87.749 - type: recall_at_3 value: 33.016 - type: recall_at_5 value: 38.97 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 15.614 - type: map_at_10 value: 22.927 - type: map_at_100 value: 24.185000000000002 - type: map_at_1000 value: 24.319 - type: map_at_3 value: 20.596 - type: map_at_5 value: 21.854000000000003 - type: mrr_at_1 value: 18.858 - type: mrr_at_10 value: 26.535999999999998 - type: mrr_at_100 value: 27.582 - type: mrr_at_1000 value: 27.665 - type: mrr_at_3 value: 24.295 - type: mrr_at_5 value: 25.532 - type: ndcg_at_1 value: 18.858 - type: ndcg_at_10 value: 27.583000000000002 - type: ndcg_at_100 value: 33.635 - type: ndcg_at_1000 value: 36.647 - type: ndcg_at_3 value: 23.348 - type: ndcg_at_5 value: 25.257 - type: precision_at_1 value: 18.858 - type: precision_at_10 value: 5.158 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 11.092 - type: precision_at_5 value: 8.1 - type: recall_at_1 value: 15.614 - type: recall_at_10 value: 37.916 - type: recall_at_100 value: 65.205 - type: recall_at_1000 value: 86.453 - type: recall_at_3 value: 26.137 - type: recall_at_5 value: 31.087999999999997 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 23.078000000000003 - type: map_at_10 value: 31.941999999999997 - type: map_at_100 value: 33.196999999999996 - type: map_at_1000 value: 33.303 - type: map_at_3 value: 28.927000000000003 - type: map_at_5 value: 30.707 - type: mrr_at_1 value: 26.866 - type: mrr_at_10 value: 35.557 - type: mrr_at_100 value: 36.569 - type: mrr_at_1000 value: 36.632 - type: mrr_at_3 value: 32.897999999999996 - type: mrr_at_5 value: 34.437 - type: ndcg_at_1 value: 26.866 - type: ndcg_at_10 value: 37.372 - type: ndcg_at_100 value: 43.248 - type: ndcg_at_1000 value: 45.632 - type: ndcg_at_3 value: 31.852999999999998 - type: ndcg_at_5 value: 34.582 - type: precision_at_1 value: 26.866 - type: precision_at_10 value: 6.511 - type: precision_at_100 value: 1.078 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 14.582999999999998 - type: precision_at_5 value: 10.634 - type: recall_at_1 value: 23.078000000000003 - type: recall_at_10 value: 50.334 - type: recall_at_100 value: 75.787 - type: recall_at_1000 value: 92.485 - type: recall_at_3 value: 35.386 - type: recall_at_5 value: 42.225 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 22.203999999999997 - type: map_at_10 value: 31.276 - type: map_at_100 value: 32.844 - type: map_at_1000 value: 33.062999999999995 - type: map_at_3 value: 27.733999999999998 - type: map_at_5 value: 29.64 - type: mrr_at_1 value: 27.272999999999996 - type: mrr_at_10 value: 36.083 - type: mrr_at_100 value: 37.008 - type: mrr_at_1000 value: 37.076 - type: mrr_at_3 value: 33.004 - type: mrr_at_5 value: 34.664 - type: ndcg_at_1 value: 27.272999999999996 - type: ndcg_at_10 value: 37.763000000000005 - type: ndcg_at_100 value: 43.566 - type: ndcg_at_1000 value: 46.356 - type: ndcg_at_3 value: 31.673000000000002 - type: ndcg_at_5 value: 34.501 - type: precision_at_1 value: 27.272999999999996 - type: precision_at_10 value: 7.470000000000001 - type: precision_at_100 value: 1.502 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 14.756 - type: precision_at_5 value: 11.225 - type: recall_at_1 value: 22.203999999999997 - type: recall_at_10 value: 51.437999999999995 - type: recall_at_100 value: 76.845 - type: recall_at_1000 value: 94.38600000000001 - type: recall_at_3 value: 34.258 - type: recall_at_5 value: 41.512 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 17.474 - type: map_at_10 value: 26.362999999999996 - type: map_at_100 value: 27.456999999999997 - type: map_at_1000 value: 27.567999999999998 - type: map_at_3 value: 23.518 - type: map_at_5 value: 25.068 - type: mrr_at_1 value: 18.669 - type: mrr_at_10 value: 27.998 - type: mrr_at_100 value: 28.953 - type: mrr_at_1000 value: 29.03 - type: mrr_at_3 value: 25.230999999999998 - type: mrr_at_5 value: 26.654 - type: ndcg_at_1 value: 18.669 - type: ndcg_at_10 value: 31.684 - type: ndcg_at_100 value: 36.864999999999995 - type: ndcg_at_1000 value: 39.555 - type: ndcg_at_3 value: 26.057000000000002 - type: ndcg_at_5 value: 28.587 - type: precision_at_1 value: 18.669 - type: precision_at_10 value: 5.3420000000000005 - type: precision_at_100 value: 0.847 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 11.583 - type: precision_at_5 value: 8.466 - type: recall_at_1 value: 17.474 - type: recall_at_10 value: 46.497 - type: recall_at_100 value: 69.977 - type: recall_at_1000 value: 89.872 - type: recall_at_3 value: 31.385999999999996 - type: recall_at_5 value: 37.283 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 17.173 - type: map_at_10 value: 30.407 - type: map_at_100 value: 32.528 - type: map_at_1000 value: 32.698 - type: map_at_3 value: 25.523 - type: map_at_5 value: 28.038 - type: mrr_at_1 value: 38.958 - type: mrr_at_10 value: 51.515 - type: mrr_at_100 value: 52.214000000000006 - type: mrr_at_1000 value: 52.237 - type: mrr_at_3 value: 48.502 - type: mrr_at_5 value: 50.251000000000005 - type: ndcg_at_1 value: 38.958 - type: ndcg_at_10 value: 40.355000000000004 - type: ndcg_at_100 value: 47.68 - type: ndcg_at_1000 value: 50.370000000000005 - type: ndcg_at_3 value: 33.946 - type: ndcg_at_5 value: 36.057 - type: precision_at_1 value: 38.958 - type: precision_at_10 value: 12.508 - type: precision_at_100 value: 2.054 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 25.581 - type: precision_at_5 value: 19.256999999999998 - type: recall_at_1 value: 17.173 - type: recall_at_10 value: 46.967 - type: recall_at_100 value: 71.47200000000001 - type: recall_at_1000 value: 86.238 - type: recall_at_3 value: 30.961 - type: recall_at_5 value: 37.539 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 8.999 - type: map_at_10 value: 18.989 - type: map_at_100 value: 26.133 - type: map_at_1000 value: 27.666 - type: map_at_3 value: 13.918 - type: map_at_5 value: 16.473 - type: mrr_at_1 value: 66.25 - type: mrr_at_10 value: 74.161 - type: mrr_at_100 value: 74.516 - type: mrr_at_1000 value: 74.524 - type: mrr_at_3 value: 72.875 - type: mrr_at_5 value: 73.613 - type: ndcg_at_1 value: 54.37499999999999 - type: ndcg_at_10 value: 39.902 - type: ndcg_at_100 value: 44.212 - type: ndcg_at_1000 value: 51.62 - type: ndcg_at_3 value: 45.193 - type: ndcg_at_5 value: 42.541000000000004 - type: precision_at_1 value: 66.25 - type: precision_at_10 value: 30.425 - type: precision_at_100 value: 9.754999999999999 - type: precision_at_1000 value: 2.043 - type: precision_at_3 value: 48.25 - type: precision_at_5 value: 40.65 - type: recall_at_1 value: 8.999 - type: recall_at_10 value: 24.133 - type: recall_at_100 value: 49.138999999999996 - type: recall_at_1000 value: 72.639 - type: recall_at_3 value: 15.287999999999998 - type: recall_at_5 value: 19.415 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.38999999999999 - type: f1 value: 41.444205512055234 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 87.35000000000001 - type: map_at_10 value: 92.837 - type: map_at_100 value: 92.996 - type: map_at_1000 value: 93.006 - type: map_at_3 value: 92.187 - type: map_at_5 value: 92.595 - type: mrr_at_1 value: 93.864 - type: mrr_at_10 value: 96.723 - type: mrr_at_100 value: 96.72500000000001 - type: mrr_at_1000 value: 96.72500000000001 - type: mrr_at_3 value: 96.64 - type: mrr_at_5 value: 96.71499999999999 - type: ndcg_at_1 value: 93.864 - type: ndcg_at_10 value: 94.813 - type: ndcg_at_100 value: 95.243 - type: ndcg_at_1000 value: 95.38600000000001 - type: ndcg_at_3 value: 94.196 - type: ndcg_at_5 value: 94.521 - type: precision_at_1 value: 93.864 - type: precision_at_10 value: 10.951 - type: precision_at_100 value: 1.1400000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 35.114000000000004 - type: precision_at_5 value: 21.476 - type: recall_at_1 value: 87.35000000000001 - type: recall_at_10 value: 96.941 - type: recall_at_100 value: 98.397 - type: recall_at_1000 value: 99.21600000000001 - type: recall_at_3 value: 95.149 - type: recall_at_5 value: 96.131 - task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 24.476 - type: map_at_10 value: 40.11 - type: map_at_100 value: 42.229 - type: map_at_1000 value: 42.378 - type: map_at_3 value: 34.512 - type: map_at_5 value: 38.037 - type: mrr_at_1 value: 47.839999999999996 - type: mrr_at_10 value: 57.053 - type: mrr_at_100 value: 57.772 - type: mrr_at_1000 value: 57.799 - type: mrr_at_3 value: 54.552 - type: mrr_at_5 value: 56.011 - type: ndcg_at_1 value: 47.839999999999996 - type: ndcg_at_10 value: 48.650999999999996 - type: ndcg_at_100 value: 55.681000000000004 - type: ndcg_at_1000 value: 57.979 - type: ndcg_at_3 value: 43.923 - type: ndcg_at_5 value: 46.037 - type: precision_at_1 value: 47.839999999999996 - type: precision_at_10 value: 13.395000000000001 - type: precision_at_100 value: 2.0660000000000003 - type: precision_at_1000 value: 0.248 - type: precision_at_3 value: 29.064 - type: precision_at_5 value: 22.006 - type: recall_at_1 value: 24.476 - type: recall_at_10 value: 56.216 - type: recall_at_100 value: 81.798 - type: recall_at_1000 value: 95.48299999999999 - type: recall_at_3 value: 39.357 - type: recall_at_5 value: 47.802 - task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 42.728 - type: map_at_10 value: 57.737 - type: map_at_100 value: 58.531 - type: map_at_1000 value: 58.594 - type: map_at_3 value: 54.869 - type: map_at_5 value: 56.55 - type: mrr_at_1 value: 85.456 - type: mrr_at_10 value: 90.062 - type: mrr_at_100 value: 90.159 - type: mrr_at_1000 value: 90.16 - type: mrr_at_3 value: 89.37899999999999 - type: mrr_at_5 value: 89.81 - type: ndcg_at_1 value: 85.456 - type: ndcg_at_10 value: 67.755 - type: ndcg_at_100 value: 70.341 - type: ndcg_at_1000 value: 71.538 - type: ndcg_at_3 value: 63.735 - type: ndcg_at_5 value: 65.823 - type: precision_at_1 value: 85.456 - type: precision_at_10 value: 13.450000000000001 - type: precision_at_100 value: 1.545 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 38.861000000000004 - type: precision_at_5 value: 24.964 - type: recall_at_1 value: 42.728 - type: recall_at_10 value: 67.252 - type: recall_at_100 value: 77.265 - type: recall_at_1000 value: 85.246 - type: recall_at_3 value: 58.292 - type: recall_at_5 value: 62.41100000000001 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 87.4836 - type: ap value: 82.29552224030336 - type: f1 value: 87.42791432227448 - task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 23.015 - type: map_at_10 value: 35.621 - type: map_at_100 value: 36.809 - type: map_at_1000 value: 36.853 - type: map_at_3 value: 31.832 - type: map_at_5 value: 34.006 - type: mrr_at_1 value: 23.738999999999997 - type: mrr_at_10 value: 36.309999999999995 - type: mrr_at_100 value: 37.422 - type: mrr_at_1000 value: 37.461 - type: mrr_at_3 value: 32.592999999999996 - type: mrr_at_5 value: 34.736 - type: ndcg_at_1 value: 23.724999999999998 - type: ndcg_at_10 value: 42.617 - type: ndcg_at_100 value: 48.217999999999996 - type: ndcg_at_1000 value: 49.309 - type: ndcg_at_3 value: 34.905 - type: ndcg_at_5 value: 38.769 - type: precision_at_1 value: 23.724999999999998 - type: precision_at_10 value: 6.689 - type: precision_at_100 value: 0.9480000000000001 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.89 - type: precision_at_5 value: 10.897 - type: recall_at_1 value: 23.015 - type: recall_at_10 value: 64.041 - type: recall_at_100 value: 89.724 - type: recall_at_1000 value: 98.00999999999999 - type: recall_at_3 value: 43.064 - type: recall_at_5 value: 52.31099999999999 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.49794801641588 - type: f1 value: 96.28931114498003 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 82.81121751025992 - type: f1 value: 63.18740125901853 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 77.66644250168123 - type: f1 value: 74.93211186867839 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 81.77202420981843 - type: f1 value: 81.63681969283554 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.596687684870645 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.26965660101405 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.33619694846802 - type: mrr value: 32.53719657720334 - task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 6.0729999999999995 - type: map_at_10 value: 13.245999999999999 - type: map_at_100 value: 16.747999999999998 - type: map_at_1000 value: 18.163 - type: map_at_3 value: 10.064 - type: map_at_5 value: 11.513 - type: mrr_at_1 value: 49.536 - type: mrr_at_10 value: 58.092 - type: mrr_at_100 value: 58.752 - type: mrr_at_1000 value: 58.78 - type: mrr_at_3 value: 56.398 - type: mrr_at_5 value: 57.389 - type: ndcg_at_1 value: 47.059 - type: ndcg_at_10 value: 35.881 - type: ndcg_at_100 value: 32.751999999999995 - type: ndcg_at_1000 value: 41.498000000000005 - type: ndcg_at_3 value: 42.518 - type: ndcg_at_5 value: 39.550999999999995 - type: precision_at_1 value: 49.536 - type: precision_at_10 value: 26.316 - type: precision_at_100 value: 8.084 - type: precision_at_1000 value: 2.081 - type: precision_at_3 value: 39.938 - type: precision_at_5 value: 34.056 - type: recall_at_1 value: 6.0729999999999995 - type: recall_at_10 value: 16.593 - type: recall_at_100 value: 32.883 - type: recall_at_1000 value: 64.654 - type: recall_at_3 value: 11.174000000000001 - type: recall_at_5 value: 13.528 - task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 30.043 - type: map_at_10 value: 45.318999999999996 - type: map_at_100 value: 46.381 - type: map_at_1000 value: 46.412 - type: map_at_3 value: 40.941 - type: map_at_5 value: 43.662 - type: mrr_at_1 value: 33.98 - type: mrr_at_10 value: 47.870000000000005 - type: mrr_at_100 value: 48.681999999999995 - type: mrr_at_1000 value: 48.703 - type: mrr_at_3 value: 44.341 - type: mrr_at_5 value: 46.547 - type: ndcg_at_1 value: 33.98 - type: ndcg_at_10 value: 52.957 - type: ndcg_at_100 value: 57.434 - type: ndcg_at_1000 value: 58.103 - type: ndcg_at_3 value: 44.896 - type: ndcg_at_5 value: 49.353 - type: precision_at_1 value: 33.98 - type: precision_at_10 value: 8.786 - type: precision_at_100 value: 1.1280000000000001 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 20.577 - type: precision_at_5 value: 14.942 - type: recall_at_1 value: 30.043 - type: recall_at_10 value: 73.593 - type: recall_at_100 value: 93.026 - type: recall_at_1000 value: 97.943 - type: recall_at_3 value: 52.955 - type: recall_at_5 value: 63.132 - task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.808 - type: map_at_10 value: 84.675 - type: map_at_100 value: 85.322 - type: map_at_1000 value: 85.33800000000001 - type: map_at_3 value: 81.68900000000001 - type: map_at_5 value: 83.543 - type: mrr_at_1 value: 81.5 - type: mrr_at_10 value: 87.59700000000001 - type: mrr_at_100 value: 87.705 - type: mrr_at_1000 value: 87.70599999999999 - type: mrr_at_3 value: 86.607 - type: mrr_at_5 value: 87.289 - type: ndcg_at_1 value: 81.51 - type: ndcg_at_10 value: 88.41799999999999 - type: ndcg_at_100 value: 89.644 - type: ndcg_at_1000 value: 89.725 - type: ndcg_at_3 value: 85.49900000000001 - type: ndcg_at_5 value: 87.078 - type: precision_at_1 value: 81.51 - type: precision_at_10 value: 13.438 - type: precision_at_100 value: 1.532 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.363 - type: precision_at_5 value: 24.57 - type: recall_at_1 value: 70.808 - type: recall_at_10 value: 95.575 - type: recall_at_100 value: 99.667 - type: recall_at_1000 value: 99.98899999999999 - type: recall_at_3 value: 87.223 - type: recall_at_5 value: 91.682 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 58.614831329137715 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 66.86580408560826 - task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.093 - type: map_at_10 value: 13.014000000000001 - type: map_at_100 value: 15.412999999999998 - type: map_at_1000 value: 15.756999999999998 - type: map_at_3 value: 9.216000000000001 - type: map_at_5 value: 11.036999999999999 - type: mrr_at_1 value: 25.1 - type: mrr_at_10 value: 37.133 - type: mrr_at_100 value: 38.165 - type: mrr_at_1000 value: 38.198 - type: mrr_at_3 value: 33.217 - type: mrr_at_5 value: 35.732 - type: ndcg_at_1 value: 25.1 - type: ndcg_at_10 value: 21.918000000000003 - type: ndcg_at_100 value: 30.983 - type: ndcg_at_1000 value: 36.629 - type: ndcg_at_3 value: 20.544999999999998 - type: ndcg_at_5 value: 18.192 - type: precision_at_1 value: 25.1 - type: precision_at_10 value: 11.44 - type: precision_at_100 value: 2.459 - type: precision_at_1000 value: 0.381 - type: precision_at_3 value: 19.267 - type: precision_at_5 value: 16.16 - type: recall_at_1 value: 5.093 - type: recall_at_10 value: 23.215 - type: recall_at_100 value: 49.902 - type: recall_at_1000 value: 77.403 - type: recall_at_3 value: 11.733 - type: recall_at_5 value: 16.372999999999998 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.9365442977452 - type: cos_sim_spearman value: 79.36960687383745 - type: euclidean_pearson value: 79.6045204840714 - type: euclidean_spearman value: 79.26382712751337 - type: manhattan_pearson value: 79.4805084789529 - type: manhattan_spearman value: 79.21847863209523 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 83.27906192961453 - type: cos_sim_spearman value: 74.38364712099211 - type: euclidean_pearson value: 78.54358927241223 - type: euclidean_spearman value: 74.22185560806376 - type: manhattan_pearson value: 78.50904327377751 - type: manhattan_spearman value: 74.2627500781748 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.66863742649639 - type: cos_sim_spearman value: 84.70630905216271 - type: euclidean_pearson value: 84.64498334705334 - type: euclidean_spearman value: 84.87204770690148 - type: manhattan_pearson value: 84.65774227976077 - type: manhattan_spearman value: 84.91251851797985 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.1577763924467 - type: cos_sim_spearman value: 80.10314039230198 - type: euclidean_pearson value: 81.51346991046043 - type: euclidean_spearman value: 80.08678485109435 - type: manhattan_pearson value: 81.57058914661894 - type: manhattan_spearman value: 80.1516230725106 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.40310839662533 - type: cos_sim_spearman value: 87.16293477217867 - type: euclidean_pearson value: 86.50688711184775 - type: euclidean_spearman value: 87.08651444923031 - type: manhattan_pearson value: 86.54674677557857 - type: manhattan_spearman value: 87.15079017870971 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.32886275207817 - type: cos_sim_spearman value: 85.0190460590732 - type: euclidean_pearson value: 84.42553652784679 - type: euclidean_spearman value: 85.20027364279328 - type: manhattan_pearson value: 84.42926246281078 - type: manhattan_spearman value: 85.20187419804306 - 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: 90.76732216967812 - type: cos_sim_spearman value: 90.63701653633909 - type: euclidean_pearson value: 90.26678186114682 - type: euclidean_spearman value: 90.67288073455427 - type: manhattan_pearson value: 90.20772020584582 - type: manhattan_spearman value: 90.60764863983702 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 69.09280387698125 - type: cos_sim_spearman value: 68.62743151172162 - type: euclidean_pearson value: 69.89386398104689 - type: euclidean_spearman value: 68.71191066733556 - type: manhattan_pearson value: 69.92516500604872 - type: manhattan_spearman value: 68.80452846992576 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.13178592019887 - type: cos_sim_spearman value: 86.03947178806887 - type: euclidean_pearson value: 85.87029414285313 - type: euclidean_spearman value: 86.04960843306998 - type: manhattan_pearson value: 85.92946858580146 - type: manhattan_spearman value: 86.12575341860442 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.16657063002837 - type: mrr value: 95.73671063867141 - task: type: Retrieval dataset: type: mteb/scifact name: MTEB SciFact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 63.510999999999996 - type: map_at_10 value: 72.76899999999999 - type: map_at_100 value: 73.303 - type: map_at_1000 value: 73.32499999999999 - type: map_at_3 value: 70.514 - type: map_at_5 value: 71.929 - type: mrr_at_1 value: 66.333 - type: mrr_at_10 value: 73.75 - type: mrr_at_100 value: 74.119 - type: mrr_at_1000 value: 74.138 - type: mrr_at_3 value: 72.222 - type: mrr_at_5 value: 73.122 - type: ndcg_at_1 value: 66.333 - type: ndcg_at_10 value: 76.774 - type: ndcg_at_100 value: 78.78500000000001 - type: ndcg_at_1000 value: 79.254 - type: ndcg_at_3 value: 73.088 - type: ndcg_at_5 value: 75.002 - type: precision_at_1 value: 66.333 - type: precision_at_10 value: 9.833 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 28.222 - type: precision_at_5 value: 18.333 - type: recall_at_1 value: 63.510999999999996 - type: recall_at_10 value: 87.98899999999999 - type: recall_at_100 value: 96.5 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 77.86699999999999 - type: recall_at_5 value: 82.73899999999999 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.78514851485149 - type: cos_sim_ap value: 94.94214383862038 - type: cos_sim_f1 value: 89.02255639097744 - type: cos_sim_precision value: 89.2462311557789 - type: cos_sim_recall value: 88.8 - type: dot_accuracy value: 99.78217821782178 - type: dot_ap value: 94.69965247836805 - type: dot_f1 value: 88.78695208970439 - type: dot_precision value: 90.54054054054053 - type: dot_recall value: 87.1 - type: euclidean_accuracy value: 99.78118811881188 - type: euclidean_ap value: 94.9865187695411 - type: euclidean_f1 value: 88.99950223992036 - type: euclidean_precision value: 88.60257680872151 - type: euclidean_recall value: 89.4 - type: manhattan_accuracy value: 99.78811881188119 - type: manhattan_ap value: 95.0021236766459 - type: manhattan_f1 value: 89.12071535022356 - type: manhattan_precision value: 88.54886475814413 - type: manhattan_recall value: 89.7 - type: max_accuracy value: 99.78811881188119 - type: max_ap value: 95.0021236766459 - type: max_f1 value: 89.12071535022356 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 68.93190546593995 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 37.602808534760655 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.29214480978073 - type: mrr value: 53.123169722434426 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.967800769650022 - type: cos_sim_spearman value: 31.168490040206926 - type: dot_pearson value: 30.888603021128553 - type: dot_spearman value: 31.028241262520385 - task: type: Retrieval dataset: type: mteb/trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22300000000000003 - type: map_at_10 value: 1.781 - type: map_at_100 value: 9.905999999999999 - type: map_at_1000 value: 23.455000000000002 - type: map_at_3 value: 0.569 - type: map_at_5 value: 0.918 - type: mrr_at_1 value: 84.0 - 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: 78.0 - type: ndcg_at_10 value: 73.13499999999999 - type: ndcg_at_100 value: 55.32 - type: ndcg_at_1000 value: 49.532 - type: ndcg_at_3 value: 73.715 - type: ndcg_at_5 value: 72.74199999999999 - type: precision_at_1 value: 84.0 - type: precision_at_10 value: 78.8 - type: precision_at_100 value: 56.32 - type: precision_at_1000 value: 21.504 - type: precision_at_3 value: 77.333 - type: precision_at_5 value: 78.0 - type: recall_at_1 value: 0.22300000000000003 - type: recall_at_10 value: 2.049 - type: recall_at_100 value: 13.553 - type: recall_at_1000 value: 46.367999999999995 - type: recall_at_3 value: 0.604 - type: recall_at_5 value: 1.015 - task: type: Retrieval dataset: type: mteb/touche2020 name: MTEB Touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 3.0380000000000003 - type: map_at_10 value: 10.188 - type: map_at_100 value: 16.395 - type: map_at_1000 value: 18.024 - type: map_at_3 value: 6.236 - type: map_at_5 value: 7.276000000000001 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 46.292 - type: mrr_at_100 value: 47.446 - type: mrr_at_1000 value: 47.446 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 44.32 - type: ndcg_at_1 value: 32.653 - type: ndcg_at_10 value: 25.219 - type: ndcg_at_100 value: 37.802 - type: ndcg_at_1000 value: 49.274 - type: ndcg_at_3 value: 28.605999999999998 - type: ndcg_at_5 value: 26.21 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 21.837 - type: precision_at_100 value: 7.776 - type: precision_at_1000 value: 1.522 - type: precision_at_3 value: 28.571 - type: precision_at_5 value: 25.306 - type: recall_at_1 value: 3.0380000000000003 - type: recall_at_10 value: 16.298000000000002 - type: recall_at_100 value: 48.712 - type: recall_at_1000 value: 83.16799999999999 - type: recall_at_3 value: 7.265000000000001 - type: recall_at_5 value: 9.551 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 83.978 - type: ap value: 24.751887949330015 - type: f1 value: 66.8685134049279 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.573288058856825 - type: f1 value: 61.973261751726604 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 48.75483298792469 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.36824223639506 - type: cos_sim_ap value: 75.53126388573047 - type: cos_sim_f1 value: 67.9912831688245 - type: cos_sim_precision value: 66.11817501869858 - type: cos_sim_recall value: 69.9736147757256 - type: dot_accuracy value: 86.39804494248078 - type: dot_ap value: 75.27598891718046 - type: dot_f1 value: 67.91146284159763 - type: dot_precision value: 63.90505003490807 - type: dot_recall value: 72.45382585751979 - type: euclidean_accuracy value: 86.36228169517793 - type: euclidean_ap value: 75.51438087434647 - type: euclidean_f1 value: 68.02370523061066 - type: euclidean_precision value: 66.46525679758308 - type: euclidean_recall value: 69.65699208443272 - type: manhattan_accuracy value: 86.46361089586935 - type: manhattan_ap value: 75.50800785730111 - type: manhattan_f1 value: 67.9220437187253 - type: manhattan_precision value: 67.79705573080967 - type: manhattan_recall value: 68.04749340369392 - type: max_accuracy value: 86.46361089586935 - type: max_ap value: 75.53126388573047 - type: max_f1 value: 68.02370523061066 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.80350836341057 - type: cos_sim_ap value: 85.51101933260743 - type: cos_sim_f1 value: 77.9152271629704 - type: cos_sim_precision value: 75.27815662910056 - type: cos_sim_recall value: 80.74376347397599 - type: dot_accuracy value: 88.84425815966158 - type: dot_ap value: 85.49726945962519 - type: dot_f1 value: 77.94445269567801 - type: dot_precision value: 75.27251864601261 - type: dot_recall value: 80.81305820757623 - type: euclidean_accuracy value: 88.80350836341057 - type: euclidean_ap value: 85.4882880790211 - type: euclidean_f1 value: 77.87063284615103 - type: euclidean_precision value: 74.61022927689595 - type: euclidean_recall value: 81.42901139513397 - type: manhattan_accuracy value: 88.7161873714441 - type: manhattan_ap value: 85.45753871906821 - type: manhattan_f1 value: 77.8686401480111 - type: manhattan_precision value: 74.95903683123174 - type: manhattan_recall value: 81.01324299353249 - type: max_accuracy value: 88.84425815966158 - type: max_ap value: 85.51101933260743 - type: max_f1 value: 77.94445269567801 --- <!-- **English** | [中文](./README_zh.md) --> # gte-base-en-v1.5 We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance. The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU). The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)). We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct), a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB. <!-- Provide a longer summary of what this model is. --> - **Developed by:** Institute for Intelligent Computing, Alibaba Group - **Model type:** Text Embeddings - **Paper:** Coming soon. <!-- - **Demo [optional]:** [More Information Needed] --> ### Model list | Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo | |:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: | |[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 | |[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 | |[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 | ## How to Get Started with the Model Use the code below to get started with the model. ```python # Requires transformers>=4.36.0 import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] model_path = 'Alibaba-NLP/gte-base-en-v1.5' tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModel.from_pretrained(model_path, trust_remote_code=True) # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = outputs.last_hidden_state[:, 0] # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` **It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).** Use with `sentence-transformers`: ```python # Requires sentence_transformers>=2.7.0 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('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True) embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` Use with `transformers.js`: ```js // npm i @xenova/transformers import { pipeline, dot } from '@xenova/transformers'; // Create feature extraction pipeline const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-base-en-v1.5', { quantized: false, // Comment out this line to use the quantized version }); // Generate sentence embeddings const sentences = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] const output = await extractor(sentences, { normalize: true, pooling: 'cls' }); // Compute similarity scores const [source_embeddings, ...document_embeddings ] = output.tolist(); const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x)); console.log(similarities); // [34.504930869007296, 64.03973265120138, 19.520042686034362] ``` ## Training Details ### Training Data - Masked language modeling (MLM): `c4-en` - Weak-supervised contrastive (WSC) pre-training: [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data - Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data ### Training Procedure To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. The model first undergoes preliminary MLM pre-training on shorter lengths. And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training. The entire training process is as follows: - MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000 - MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000 - WSC: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000 - Fine-tuning: TODO ## Evaluation ### MTEB The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2). | Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 434 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 | | [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 | | [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 | | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | | [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 | | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 | ### LoCo | Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 | | [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 | | [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 | ## 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} } ```

google-t5/t5-3b

google-t5

transformers

translation

--- language: - en - fr - ro - de - multilingual license: apache-2.0 tags: - summarization - translation datasets: - c4 --- # Model Card for T5-3B  # 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. T5-3B is the checkpoint with 3 billion 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 T5-3B, 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 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 context on how to get started with this checkpoint.

timm/resnet101.a1h_in1k

timm

timm

image-classification

--- license: apache-2.0 library_name: timm tags: - image-classification - timm --- # Model card for resnet101.a1h_in1k A ResNet-B image classification model. This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * Based on [ResNet Strikes Back](https://arxiv.org/abs/2110.00476) `A1` recipe * LAMB optimizer * Stronger dropout, stochastic depth, and RandAugment than paper `A1` recipe * Cosine LR schedule with warmup ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 44.5 - GMACs: 7.8 - Activations (M): 16.2 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **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('resnet101.a1h_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( 'resnet101.a1h_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, 64, 112, 112]) # torch.Size([1, 256, 56, 56]) # torch.Size([1, 512, 28, 28]) # torch.Size([1, 1024, 14, 14]) # torch.Size([1, 2048, 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( 'resnet101.a1h_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, 2048, 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). |model |img_size|top1 |top5 |param_count|gmacs|macts|img/sec| |------------------------------------------|--------|-----|-----|-----------|-----|-----|-------| |[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|320 |86.72|98.17|93.6 |35.2 |69.7 |451 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|288 |86.51|98.08|93.6 |28.5 |56.4 |560 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|288 |86.49|98.03|93.6 |28.5 |56.4 |557 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|224 |85.96|97.82|93.6 |17.2 |34.2 |923 | |[resnext101_32x32d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x32d.fb_wsl_ig1b_ft_in1k)|224 |85.11|97.44|468.5 |87.3 |91.1 |254 | |[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|416 |85.0 |97.12|191.9 |108.4|213.8|134 | |[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|352 |84.96|97.22|102.1 |50.2 |101.2|291 | |[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|320 |84.73|97.18|102.1 |41.5 |83.7 |353 | |[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|384 |84.71|96.99|164.0 |77.6 |154.7|183 | |[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|288 |84.57|97.08|93.6 |28.5 |56.4 |557 | |[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|320 |84.45|97.08|93.2 |31.5 |67.8 |446 | |[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|352 |84.43|96.97|129.9 |51.1 |105.5|280 | |[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|288 |84.36|96.92|93.6 |27.6 |53.0 |595 | |[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|320 |84.35|97.04|66.8 |24.1 |47.7 |610 | |[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|288 |84.3 |96.94|164.0 |43.7 |87.1 |333 | |[resnext101_32x8d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_swsl_ig1b_ft_in1k)|224 |84.28|97.17|88.8 |16.5 |31.2 |1100 | |[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|320 |84.24|96.86|191.9 |64.2 |126.6|228 | |[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|288 |84.19|96.87|93.6 |27.2 |51.6 |613 | |[resnext101_32x16d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_wsl_ig1b_ft_in1k)|224 |84.18|97.19|194.0 |36.3 |51.2 |581 | |[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|288 |84.11|97.11|44.6 |15.1 |29.0 |1144 | |[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|320 |83.97|96.82|64.7 |31.2 |67.3 |518 | |[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|256 |83.87|96.75|93.2 |20.2 |43.4 |692 | |[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|224 |83.86|96.65|93.6 |17.2 |34.2 |923 | |[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|320 |83.72|96.61|86.6 |24.3 |48.1 |617 | |[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|256 |83.69|96.78|66.8 |15.4 |30.6 |943 | |[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|224 |83.68|96.61|93.6 |16.7 |32.0 |986 | |[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|320 |83.67|96.74|60.2 |24.1 |47.7 |706 | |[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|256 |83.59|96.61|129.9 |27.1 |55.8 |526 | |[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|224 |83.58|96.4 |93.6 |16.5 |31.2 |1013 | |[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|224 |83.54|96.83|44.6 |9.1 |17.6 |1864 | |[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|288 |83.46|96.54|60.2 |19.1 |37.3 |904 | |[resnext101_32x16d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_swsl_ig1b_ft_in1k)|224 |83.35|96.85|194.0 |36.3 |51.2 |582 | |[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|256 |83.23|96.53|64.7 |20.0 |43.1 |809 | |[resnext101_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_swsl_ig1b_ft_in1k)|224 |83.22|96.75|44.2 |8.0 |21.2 |1814 | |[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|288 |83.16|96.38|83.5 |25.7 |51.6 |590 | |[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|256 |83.14|96.38|60.2 |15.4 |30.5 |1096 | |[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|320 |83.02|96.45|44.6 |16.5 |34.8 |992 | |[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|288 |82.98|96.54|44.6 |13.4 |28.2 |1077 | |[resnext101_64x4d.tv_in1k](https://huggingface.co/timm/resnext101_64x4d.tv_in1k)|224 |82.98|96.25|83.5 |15.5 |31.2 |989 | |[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|256 |82.86|96.28|86.6 |15.6 |30.8 |951 | |[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|224 |82.83|96.22|88.8 |16.5 |31.2 |1099 | |[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|224 |82.8 |96.13|60.2 |11.6 |22.6 |1486 | |[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|288 |82.8 |96.32|44.6 |13.0 |26.8 |1291 | |[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|288 |82.74|95.71|60.2 |19.1 |37.3 |905 | |[resnext101_32x8d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_wsl_ig1b_ft_in1k)|224 |82.69|96.63|88.8 |16.5 |31.2 |1100 | |[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|288 |82.62|95.75|60.2 |19.1 |37.3 |904 | |[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|288 |82.61|96.49|25.6 |8.9 |20.6 |1729 | |[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|288 |82.53|96.13|36.8 |9.9 |21.5 |1773 | |[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|224 |82.5 |96.02|126.9 |22.8 |21.2 |1078 | |[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|224 |82.46|95.92|83.5 |15.5 |31.2 |987 | |[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|288 |82.36|96.18|35.7 |8.1 |20.9 |1964 | |[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|320 |82.35|96.14|25.6 |8.8 |24.1 |1386 | |[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|288 |82.31|95.63|44.6 |13.0 |26.8 |1291 | |[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|288 |82.29|96.01|63.6 |13.6 |28.5 |1078 | |[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|224 |82.29|96.0 |60.2 |11.6 |22.6 |1484 | |[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|288 |82.27|96.06|68.9 |18.9 |23.8 |1176 | |[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|256 |82.26|96.07|44.6 |10.6 |22.2 |1542 | |[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|288 |82.24|95.73|44.6 |13.0 |26.8 |1290 | |[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|288 |82.2 |96.14|27.6 |7.0 |23.8 |1547 | |[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|224 |82.18|96.05|44.6 |8.1 |17.1 |1771 | |[resnext50_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k)|224 |82.17|96.22|25.0 |4.3 |14.4 |2943 | |[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|288 |82.12|95.65|25.6 |7.1 |19.6 |1704 | |[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|288 |82.03|95.94|25.0 |7.0 |23.8 |1745 | |[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|288 |82.0 |96.15|24.9 |5.8 |12.7 |1787 | |[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|256 |81.99|95.85|36.8 |7.8 |17.0 |2230 | |[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|176 |81.98|95.72|88.8 |10.3 |19.4 |1768 | |[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|224 |81.97|95.24|60.2 |11.6 |22.6 |1486 | |[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|224 |81.93|95.75|44.6 |7.8 |16.2 |2122 | |[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|224 |81.9 |95.77|44.6 |7.8 |16.2 |2118 | |[resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k)|224 |81.84|96.1 |194.0 |36.3 |51.2 |583 | |[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|256 |81.78|95.94|35.7 |6.4 |16.6 |2471 | |[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|224 |81.77|95.22|60.2 |11.6 |22.6 |1485 | |[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|224 |81.74|96.06|25.6 |5.4 |12.4 |2813 | |[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|288 |81.65|95.54|25.6 |7.1 |19.6 |1703 | |[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|288 |81.64|95.88|25.6 |7.2 |19.7 |1694 | |[resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k)|224 |81.62|96.04|88.8 |16.5 |31.2 |1101 | |[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|224 |81.61|95.76|68.9 |11.4 |14.4 |1930 | |[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|288 |81.61|95.83|25.6 |8.5 |19.2 |1868 | |[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|224 |81.5 |95.16|44.6 |7.8 |16.2 |2125 | |[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|288 |81.48|95.16|25.0 |7.0 |23.8 |1745 | |[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|288 |81.47|95.71|25.9 |6.9 |18.6 |2071 | |[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|224 |81.45|95.53|68.9 |11.4 |14.4 |1929 | |[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|288 |81.44|95.22|25.6 |7.2 |19.7 |1908 | |[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|256 |81.44|95.67|25.6 |5.6 |15.4 |2168 | |[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|288 |81.4 |95.82|30.2 |6.8 |13.9 |2132 | |[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|288 |81.37|95.74|25.6 |7.2 |19.7 |1910 | |[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|224 |81.32|95.19|44.6 |7.8 |16.2 |2125 | |[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|288 |81.3 |95.65|28.1 |6.8 |18.4 |1803 | |[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|288 |81.3 |95.11|25.0 |7.0 |23.8 |1746 | |[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|224 |81.27|95.62|27.6 |4.3 |14.4 |2591 | |[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|224 |81.26|95.16|25.6 |4.3 |11.8 |2823 | |[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|288 |81.23|95.54|15.7 |4.8 |19.6 |2117 | |[senet154.gluon_in1k](https://huggingface.co/timm/senet154.gluon_in1k)|224 |81.23|95.35|115.1 |20.8 |38.7 |545 | |[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|288 |81.22|95.11|25.6 |6.8 |18.4 |2089 | |[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|288 |81.22|95.63|25.6 |6.8 |18.4 |676 | |[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|288 |81.18|95.09|25.6 |7.2 |19.7 |1908 | |[resnet50.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet50.fb_swsl_ig1b_ft_in1k)|224 |81.18|95.98|25.6 |4.1 |11.1 |3455 | |[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|224 |81.17|95.34|25.0 |4.3 |14.4 |2933 | |[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|224 |81.1 |95.33|25.0 |4.3 |14.4 |2934 | |[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|288 |81.1 |95.23|28.1 |6.8 |18.4 |1801 | |[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|288 |81.1 |95.12|28.1 |6.8 |18.4 |1799 | |[resnet152s.gluon_in1k](https://huggingface.co/timm/resnet152s.gluon_in1k)|224 |81.02|95.41|60.3 |12.9 |25.0 |1347 | |[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|288 |80.97|95.44|25.6 |6.8 |18.4 |2085 | |[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|256 |80.94|95.45|25.9 |5.4 |14.7 |2571 | |[resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.93|95.73|44.2 |8.0 |21.2 |1814 | |[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|288 |80.91|95.55|25.6 |6.8 |18.4 |2084 | |[seresnext101_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_32x4d.gluon_in1k)|224 |80.9 |95.31|49.0 |8.0 |21.3 |1585 | |[seresnext101_64x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_64x4d.gluon_in1k)|224 |80.9 |95.3 |88.2 |15.5 |31.2 |918 | |[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|288 |80.86|95.52|25.6 |6.8 |18.4 |2085 | |[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|224 |80.85|95.43|25.6 |4.1 |11.1 |3450 | |[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|224 |80.84|95.02|25.6 |4.3 |11.8 |2821 | |[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|224 |80.79|95.62|24.9 |3.5 |7.7 |2961 | |[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|288 |80.79|95.36|19.8 |6.0 |14.8 |2506 | |[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|288 |80.79|95.58|19.9 |4.2 |10.6 |2349 | |[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|288 |80.78|94.99|25.6 |6.8 |18.4 |2088 | |[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|288 |80.71|95.43|25.6 |6.8 |18.4 |2087 | |[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|288 |80.7 |95.39|25.0 |7.0 |23.8 |1749 | |[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|192 |80.69|95.24|63.6 |6.0 |12.7 |2270 | |[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|224 |80.68|94.71|25.6 |4.4 |11.9 |3162 | |[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|288 |80.68|95.36|19.7 |6.0 |14.8 |2637 | |[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|224 |80.67|95.3 |25.6 |4.1 |11.1 |3452 | |[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|288 |80.67|95.42|25.0 |7.4 |25.1 |1626 | |[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|224 |80.63|95.21|25.6 |5.2 |11.6 |3034 | |[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|224 |80.61|95.32|25.6 |4.4 |11.9 |2813 | |[resnext101_64x4d.gluon_in1k](https://huggingface.co/timm/resnext101_64x4d.gluon_in1k)|224 |80.61|94.99|83.5 |15.5 |31.2 |989 | |[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|288 |80.6 |95.31|19.9 |6.0 |14.8 |2578 | |[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|256 |80.57|95.17|15.7 |3.8 |15.5 |2710 | |[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|224 |80.56|95.0 |60.2 |11.6 |22.6 |1483 | |[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|224 |80.53|95.16|25.6 |4.4 |11.9 |3164 | |[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|224 |80.53|94.46|25.0 |4.3 |14.4 |2930 | |[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|176 |80.48|94.98|126.9 |14.3 |13.2 |1719 | |[resnet152d.gluon_in1k](https://huggingface.co/timm/resnet152d.gluon_in1k)|224 |80.47|95.2 |60.2 |11.8 |23.4 |1428 | |[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|288 |80.45|95.32|25.6 |6.8 |18.4 |2086 | |[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|224 |80.45|95.24|30.2 |4.1 |8.4 |3530 | |[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|224 |80.45|94.63|25.0 |4.3 |14.4 |2936 | |[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|176 |80.43|95.09|68.9 |7.3 |9.0 |3015 | |[resnet101d.gluon_in1k](https://huggingface.co/timm/resnet101d.gluon_in1k)|224 |80.42|95.01|44.6 |8.1 |17.0 |2007 | |[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|224 |80.38|94.6 |25.6 |4.1 |11.1 |3461 | |[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|256 |80.36|95.1 |19.8 |4.8 |11.7 |3267 | |[resnext101_32x4d.gluon_in1k](https://huggingface.co/timm/resnext101_32x4d.gluon_in1k)|224 |80.34|94.93|44.2 |8.0 |21.2 |1814 | |[resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.32|95.4 |25.0 |4.3 |14.4 |2941 | |[resnet101s.gluon_in1k](https://huggingface.co/timm/resnet101s.gluon_in1k)|224 |80.28|95.16|44.7 |9.2 |18.6 |1851 | |[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|224 |80.26|95.08|28.1 |4.1 |11.1 |2972 | |[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|288 |80.24|95.24|25.6 |8.5 |19.9 |1523 | |[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|224 |80.22|94.63|25.6 |4.4 |11.9 |3162 | |[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|176 |80.2 |94.64|60.2 |7.2 |14.0 |2346 | |[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|224 |80.08|94.74|28.1 |4.1 |11.1 |2969 | |[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|256 |80.08|94.97|19.7 |4.8 |11.7 |3284 | |[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|256 |80.06|94.99|19.9 |4.8 |11.7 |3216 | |[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|224 |80.06|94.95|25.6 |4.1 |11.1 |1109 | |[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|224 |80.02|94.71|28.1 |4.1 |11.1 |2962 | |[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|288 |79.97|95.05|25.6 |6.8 |18.4 |2086 | |[resnet152c.gluon_in1k](https://huggingface.co/timm/resnet152c.gluon_in1k)|224 |79.92|94.84|60.2 |11.8 |23.4 |1455 | |[seresnext50_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext50_32x4d.gluon_in1k)|224 |79.91|94.82|27.6 |4.3 |14.4 |2591 | |[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|224 |79.91|94.67|25.6 |4.1 |11.1 |3456 | |[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|176 |79.9 |94.6 |44.6 |4.9 |10.1 |3341 | |[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|224 |79.89|94.97|35.7 |4.5 |12.1 |2774 | |[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|224 |79.88|94.87|25.6 |4.1 |11.1 |3455 | |[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|320 |79.86|95.07|16.0 |5.2 |16.4 |2168 | |[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|224 |79.85|94.56|25.6 |4.1 |11.1 |3460 | |[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|288 |79.83|94.97|25.6 |6.8 |18.4 |2087 | |[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|224 |79.82|94.62|44.6 |7.8 |16.2 |2114 | |[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|224 |79.76|94.6 |25.0 |4.3 |14.4 |2943 | |[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|224 |79.74|94.95|25.6 |4.1 |11.1 |3455 | |[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|224 |79.74|94.87|19.9 |2.5 |6.4 |3929 | |[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|288 |79.71|94.83|19.7 |6.0 |14.8 |2710 | |[resnet152.gluon_in1k](https://huggingface.co/timm/resnet152.gluon_in1k)|224 |79.68|94.74|60.2 |11.6 |22.6 |1486 | |[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|224 |79.67|94.87|25.0 |4.5 |15.2 |2729 | |[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|288 |79.63|94.91|25.6 |6.8 |18.4 |2086 | |[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|224 |79.56|94.72|25.6 |4.3 |11.8 |2805 | |[resnet101c.gluon_in1k](https://huggingface.co/timm/resnet101c.gluon_in1k)|224 |79.53|94.58|44.6 |8.1 |17.0 |2062 | |[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|224 |79.52|94.61|25.6 |4.1 |11.1 |3459 | |[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|176 |79.42|94.64|25.6 |2.6 |6.9 |5397 | |[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|288 |79.4 |94.66|18.0 |5.9 |14.6 |2752 | |[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|224 |79.38|94.57|25.6 |4.1 |11.1 |3459 | |[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|176 |79.37|94.3 |25.0 |2.7 |9.0 |4577 | |[resnext50_32x4d.gluon_in1k](https://huggingface.co/timm/resnext50_32x4d.gluon_in1k)|224 |79.36|94.43|25.0 |4.3 |14.4 |2942 | |[resnext101_32x8d.tv_in1k](https://huggingface.co/timm/resnext101_32x8d.tv_in1k)|224 |79.31|94.52|88.8 |16.5 |31.2 |1100 | |[resnet101.gluon_in1k](https://huggingface.co/timm/resnet101.gluon_in1k)|224 |79.31|94.53|44.6 |7.8 |16.2 |2125 | |[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|224 |79.31|94.63|25.6 |5.2 |12.0 |2524 | |[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|176 |79.27|94.49|25.6 |2.6 |6.9 |5404 | |[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|224 |79.25|94.31|25.0 |4.3 |14.4 |2931 | |[resnet50.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet50.fb_ssl_yfcc100m_ft_in1k)|224 |79.22|94.84|25.6 |4.1 |11.1 |3451 | |[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|256 |79.21|94.56|19.7 |4.8 |11.7 |3392 | |[resnet50d.gluon_in1k](https://huggingface.co/timm/resnet50d.gluon_in1k)|224 |79.07|94.48|25.6 |4.4 |11.9 |3162 | |[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|224 |79.03|94.38|25.6 |4.1 |11.1 |3453 | |[resnet50.am_in1k](https://huggingface.co/timm/resnet50.am_in1k)|224 |79.01|94.39|25.6 |4.1 |11.1 |3461 | |[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|256 |79.01|94.37|18.0 |4.6 |11.6 |3440 | |[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|256 |78.9 |94.54|16.0 |3.4 |10.5 |3421 | |[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|160 |78.89|94.11|60.2 |5.9 |11.5 |2745 | |[wide_resnet101_2.tv_in1k](https://huggingface.co/timm/wide_resnet101_2.tv_in1k)|224 |78.84|94.28|126.9 |22.8 |21.2 |1079 | |[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|288 |78.83|94.24|16.8 |4.5 |16.8 |2251 | |[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|224 |78.81|94.32|25.6 |4.1 |11.1 |3454 | |[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|288 |78.74|94.33|16.8 |4.5 |16.7 |2264 | |[resnet50s.gluon_in1k](https://huggingface.co/timm/resnet50s.gluon_in1k)|224 |78.72|94.23|25.7 |5.5 |13.5 |2796 | |[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|224 |78.71|94.24|25.6 |4.4 |11.9 |3154 | |[wide_resnet50_2.tv_in1k](https://huggingface.co/timm/wide_resnet50_2.tv_in1k)|224 |78.47|94.09|68.9 |11.4 |14.4 |1934 | |[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|224 |78.46|94.27|25.6 |4.1 |11.1 |3454 | |[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|288 |78.43|94.35|21.8 |6.5 |7.5 |3291 | |[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|288 |78.42|94.04|10.5 |3.1 |13.3 |3226 | |[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|320 |78.33|94.13|16.0 |5.2 |16.4 |2391 | |[resnet152.tv_in1k](https://huggingface.co/timm/resnet152.tv_in1k)|224 |78.32|94.04|60.2 |11.6 |22.6 |1487 | |[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|288 |78.28|94.1 |10.4 |3.1 |13.3 |3062 | |[bat_resnext26ts.ch_in1k](https://huggingface.co/timm/bat_resnext26ts.ch_in1k)|256 |78.25|94.1 |10.7 |2.5 |12.5 |3393 | |[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|224 |78.06|93.78|25.6 |4.1 |11.1 |3450 | |[resnet50c.gluon_in1k](https://huggingface.co/timm/resnet50c.gluon_in1k)|224 |78.0 |93.99|25.6 |4.4 |11.9 |3286 | |[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|288 |78.0 |93.91|10.3 |3.1 |13.3 |3297 | |[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|224 |77.98|93.75|16.8 |2.7 |10.1 |3841 | |[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|288 |77.92|93.77|21.8 |6.1 |6.2 |3609 | |[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|160 |77.88|93.71|44.6 |4.0 |8.3 |3926 | |[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|256 |77.87|93.84|16.0 |3.4 |10.5 |3772 | |[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|256 |77.86|93.79|10.4 |2.4 |10.5 |4263 | |[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|160 |77.82|93.81|35.7 |2.3 |6.2 |5238 | |[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|256 |77.81|93.82|10.5 |2.4 |10.5 |4183 | |[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|160 |77.79|93.6 |25.6 |2.2 |6.0 |5329 | |[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|160 |77.73|93.32|25.0 |2.2 |7.4 |5576 | |[resnext50_32x4d.tv_in1k](https://huggingface.co/timm/resnext50_32x4d.tv_in1k)|224 |77.61|93.7 |25.0 |4.3 |14.4 |2944 | |[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|224 |77.59|93.61|16.8 |2.7 |10.2 |3807 | |[resnet50.gluon_in1k](https://huggingface.co/timm/resnet50.gluon_in1k)|224 |77.58|93.72|25.6 |4.1 |11.1 |3455 | |[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|256 |77.44|93.56|10.3 |2.4 |10.5 |4284 | |[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|288 |77.41|93.63|16.0 |4.3 |13.5 |2907 | |[resnet101.tv_in1k](https://huggingface.co/timm/resnet101.tv_in1k)|224 |77.38|93.54|44.6 |7.8 |16.2 |2125 | |[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|160 |77.22|93.27|25.6 |2.2 |6.1 |5982 | |[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|288 |77.17|93.47|10.3 |3.1 |13.3 |3392 | |[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|288 |77.15|93.27|21.8 |6.1 |6.2 |3615 | |[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|224 |77.1 |93.37|21.8 |3.9 |4.5 |5436 | |[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|224 |77.02|93.07|28.1 |4.1 |11.1 |2952 | |[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|256 |76.78|93.13|10.3 |2.4 |10.5 |4410 | |[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|224 |76.7 |93.17|16.0 |2.6 |8.2 |4859 | |[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|288 |76.5 |93.35|21.8 |6.1 |6.2 |3617 | |[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|224 |76.42|92.87|21.8 |3.7 |3.7 |5984 | |[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|288 |76.35|93.18|16.0 |3.9 |12.2 |3331 | |[resnet50.tv_in1k](https://huggingface.co/timm/resnet50.tv_in1k)|224 |76.13|92.86|25.6 |4.1 |11.1 |3457 | |[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|160 |75.96|92.5 |25.6 |2.1 |5.7 |6490 | |[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|224 |75.52|92.44|21.8 |3.7 |3.7 |5991 | |[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|224 |75.3 |92.58|16.0 |2.4 |7.4 |5583 | |[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|224 |75.16|92.18|21.8 |3.7 |3.7 |5994 | |[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|160 |75.1 |92.08|28.1 |2.1 |5.7 |5513 | |[resnet34.gluon_in1k](https://huggingface.co/timm/resnet34.gluon_in1k)|224 |74.57|91.98|21.8 |3.7 |3.7 |5984 | |[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|288 |73.81|91.83|11.7 |3.4 |5.4 |5196 | |[resnet34.tv_in1k](https://huggingface.co/timm/resnet34.tv_in1k)|224 |73.32|91.42|21.8 |3.7 |3.7 |5979 | |[resnet18.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet18.fb_swsl_ig1b_ft_in1k)|224 |73.28|91.73|11.7 |1.8 |2.5 |10213 | |[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|288 |73.16|91.03|11.7 |3.0 |4.1 |6050 | |[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|224 |72.98|91.11|21.8 |3.7 |3.7 |5967 | |[resnet18.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet18.fb_ssl_yfcc100m_ft_in1k)|224 |72.6 |91.42|11.7 |1.8 |2.5 |10213 | |[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|288 |72.37|90.59|11.7 |3.0 |4.1 |6051 | |[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|224 |72.26|90.31|10.1 |1.7 |5.8 |7026 | |[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|224 |72.26|90.68|11.7 |2.1 |3.3 |8707 | |[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|224 |71.49|90.07|11.7 |1.8 |2.5 |10187 | |[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|176 |71.31|89.69|10.1 |1.1 |3.6 |10970 | |[resnet18.gluon_in1k](https://huggingface.co/timm/resnet18.gluon_in1k)|224 |70.84|89.76|11.7 |1.8 |2.5 |10210 | |[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|224 |70.64|89.47|11.7 |1.8 |2.5 |10194 | |[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|160 |70.56|89.52|21.8 |1.9 |1.9 |10737 | |[resnet18.tv_in1k](https://huggingface.co/timm/resnet18.tv_in1k)|224 |69.76|89.07|11.7 |1.8 |2.5 |10205 | |[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|224 |68.34|88.03|5.4 |1.1 |2.4 |13079 | |[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|224 |68.25|88.17|11.7 |1.8 |2.5 |10167 | |[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|176 |66.71|86.96|5.4 |0.7 |1.5 |20327 | |[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|160 |65.66|86.26|11.7 |0.9 |1.3 |18229 | ## Citation ```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} } ``` ```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 @article{He2015, author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, title = {Deep Residual Learning for Image Recognition}, journal = {arXiv preprint arXiv:1512.03385}, year = {2015} } ```

LanguageBind/Video-LLaVA-7B

LanguageBind

transformers

text-generation

--- license: apache-2.0 --- <p align="center"> <img src="https://z1.ax1x.com/2023/11/07/pil4sqH.png" width="150" style="margin-bottom: 0.2;"/> <p> <h2 align="center"> <a href="https://arxiv.org/abs/2311.10122">Video-LLaVA: Learning United Visual Representation by Alignment Before Projection</a></h2> <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> ## 📰 News * **[2024.01.27]** 👀👀👀 Our [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA) is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters. * **[2024.01.17]** 🔥🔥🔥 Our [LanguageBind](https://github.com/PKU-YuanGroup/LanguageBind) has been accepted at ICLR 2024! * **[2024.01.16]** 🔥🔥🔥 We reorganize the code and support LoRA fine-tuning, checking [finetune_lora.sh](scripts/v1_5/finetune_lora.sh). * **[2023.11.30]** 🤝 Thanks to the generous contributions of the community, the [OpenXLab's demo](https://openxlab.org.cn/apps/detail/houshaowei/Video-LLaVA) is now accessible. * **[2023.11.23]** We are training a new and powerful model. * **[2023.11.21]** 🤝 Check out the [replicate demo](https://replicate.com/nateraw/video-llava), created by [@nateraw](https://github.com/nateraw), who has generously supported our research! * **[2023.11.20]** 🤗 [Hugging Face demo](https://huggingface.co/spaces/LanguageBind/Video-LLaVA) and **all codes & datasets** are available now! Welcome to **watch** 👀 this repository for the latest updates. ## 😮 Highlights Video-LLaVA exhibits remarkable interactive capabilities between images and videos, despite the absence of image-video pairs in the dataset. ### 💡 Simple baseline, learning united visual representation by alignment before projection - With **the binding of unified visual representations to the language feature space**, we enable an LLM to perform visual reasoning capabilities on both images and videos simultaneously. ### 🔥 High performance, complementary learning with video and image - Extensive experiments demonstrate **the complementarity of modalities**, showcasing significant superiority when compared to models specifically designed for either images or videos. ## 🤗 Demo ### Gradio Web UI Highly recommend trying out our web demo by the following command, which incorporates all features currently supported by Video-LLaVA. We also provide [online demo](https://huggingface.co/spaces/LanguageBind/Video-LLaVA) in Huggingface Spaces. ```bash python -m videollava.serve.gradio_web_server ``` ### CLI Inference ```bash python -m videollava.serve.cli --model-path "LanguageBind/Video-LLaVA-7B" --file "path/to/your/video.mp4" --load-4bit ``` ```bash python -m videollava.serve.cli --model-path "LanguageBind/Video-LLaVA-7B" --file "path/to/your/image.jpg" --load-4bit ``` ## 🛠️ Requirements and Installation * Python >= 3.10 * Pytorch == 2.0.1 * CUDA Version >= 11.7 * Install required packages: ```bash git clone https://github.com/PKU-YuanGroup/Video-LLaVA cd Video-LLaVA conda create -n videollava python=3.10 -y conda activate videollava pip install --upgrade pip # enable PEP 660 support pip install -e . pip install -e ".[train]" pip install flash-attn --no-build-isolation pip install decord opencv-python git+https://github.com/facebookresearch/pytorchvideo.git@28fe037d212663c6a24f373b94cc5d478c8c1a1d ``` ## 🤖 API **We open source all codes.** If you want to load the model (e.g. ```LanguageBind/Video-LLaVA-7B```) on local, you can use the following code snippets. ### Inference for image ```python import torch from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN from videollava.conversation import conv_templates, SeparatorStyle from videollava.model.builder import load_pretrained_model from videollava.utils import disable_torch_init from videollava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria def main(): disable_torch_init() image = 'videollava/serve/examples/extreme_ironing.jpg' inp = 'What is unusual about this image?' model_path = 'LanguageBind/Video-LLaVA-7B' cache_dir = 'cache_dir' device = 'cuda' load_4bit, load_8bit = True, False model_name = get_model_name_from_path(model_path) tokenizer, model, processor, _ = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device, cache_dir=cache_dir) image_processor = processor['image'] conv_mode = "llava_v1" conv = conv_templates[conv_mode].copy() roles = conv.roles image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'] if type(image_tensor) is list: tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor] else: tensor = image_tensor.to(model.device, dtype=torch.float16) print(f"{roles[1]}: {inp}") inp = DEFAULT_IMAGE_TOKEN + '\n' + inp conv.append_message(conv.roles[0], inp) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images=tensor, do_sample=True, temperature=0.2, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria]) outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip() print(outputs) if __name__ == '__main__': main() ``` ### Inference for video ```python import torch from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN from videollava.conversation import conv_templates, SeparatorStyle from videollava.model.builder import load_pretrained_model from videollava.utils import disable_torch_init from videollava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria def main(): disable_torch_init() video = 'videollava/serve/examples/sample_demo_1.mp4' inp = 'Why is this video funny?' model_path = 'LanguageBind/Video-LLaVA-7B' cache_dir = 'cache_dir' device = 'cuda' load_4bit, load_8bit = True, False model_name = get_model_name_from_path(model_path) tokenizer, model, processor, _ = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device, cache_dir=cache_dir) video_processor = processor['video'] conv_mode = "llava_v1" conv = conv_templates[conv_mode].copy() roles = conv.roles video_tensor = video_processor(video, return_tensors='pt')['pixel_values'] if type(video_tensor) is list: tensor = [video.to(model.device, dtype=torch.float16) for video in video_tensor] else: tensor = video_tensor.to(model.device, dtype=torch.float16) print(f"{roles[1]}: {inp}") inp = ' '.join([DEFAULT_IMAGE_TOKEN] * model.get_video_tower().config.num_frames) + '\n' + inp conv.append_message(conv.roles[0], inp) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images=tensor, do_sample=True, temperature=0.1, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria]) outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip() print(outputs) if __name__ == '__main__': main() ``` ## 🗝️ Training & Validating The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md). ## 👍 Acknowledgement * [LLaVA](https://github.com/haotian-liu/LLaVA) The codebase we built upon and it is an efficient large language and vision assistant. * [Video-ChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT) Great job contributing the evaluation code and dataset. ## 🙌 Related Projects * [LanguageBind](https://github.com/PKU-YuanGroup/LanguageBind) An open source five modalities language-based retrieval framework. * [Chat-UniVi](https://github.com/PKU-YuanGroup/Chat-UniVi) This framework empowers the model to efficiently utilize a limited number of visual tokens. ## 🔒 License * The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](https://github.com/PKU-YuanGroup/Video-LLaVA/blob/main/LICENSE) file. * The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. ## ✏️ Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. ```BibTeX @article{lin2023video, title={Video-LLaVA: Learning United Visual Representation by Alignment Before Projection}, author={Lin, Bin and Zhu, Bin and Ye, Yang and Ning, Munan and Jin, Peng and Yuan, Li}, journal={arXiv preprint arXiv:2311.10122}, year={2023} } ``` ```BibTeX @article{zhu2023languagebind, title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment}, author={Zhu, Bin and Lin, Bin and Ning, Munan and Yan, Yang and Cui, Jiaxi and Wang, HongFa and Pang, Yatian and Jiang, Wenhao and Zhang, Junwu and Li, Zongwei and others}, journal={arXiv preprint arXiv:2310.01852}, year={2023} } ``` <!----> ## ✨ Star History [](https://star-history.com/#PKU-YuanGroup/Video-LLaVA&Date) ## 🤝 Contributors <a href="https://github.com/PKU-YuanGroup/Video-LLaVA/graphs/contributors"> <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/Video-LLaVA" /> </a>

timbrooks/instruct-pix2pix

timbrooks

diffusers

image-to-image

--- license: mit tags: - image-to-image --- # InstructPix2Pix: Learning to Follow Image Editing Instructions GitHub: https://github.com/timothybrooks/instruct-pix2pix <img src='https://instruct-pix2pix.timothybrooks.com/teaser.jpg'/> ## Example To use `InstructPix2Pix`, install `diffusers` using `main` for now. The pipeline will be available in the next release ```bash pip install diffusers accelerate safetensors transformers ``` ```python import PIL import requests import torch from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler model_id = "timbrooks/instruct-pix2pix" pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) url = "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/main/imgs/example.jpg" def download_image(url): image = PIL.Image.open(requests.get(url, stream=True).raw) image = PIL.ImageOps.exif_transpose(image) image = image.convert("RGB") return image image = download_image(url) prompt = "turn him into cyborg" images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images images[0] ```

peft-internal-testing/tiny-clip-text-2

peft-internal-testing

transformers

Entry not found

openmmlab/upernet-swin-small

openmmlab

transformers

image-segmentation

--- language: en license: mit tags: - vision - image-segmentation model_name: openmmlab/upernet-swin-small --- # UperNet, Swin Transformer small-sized backbone UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al. Combining UperNet with a Swin Transformer backbone was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030). Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM). Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.  ## Intended uses & limitations You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for fine-tuned versions (with various backbones) on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).

stabilityai/sd-vae-ft-mse

stabilityai

diffusers

--- license: mit tags: - stable-diffusion - stable-diffusion-diffusers inference: false --- # Improved Autoencoders ## Utilizing These weights are intended to be used with the [🧨 diffusers library](https://github.com/huggingface/diffusers). If you are looking for the model to use with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion), [come here](https://huggingface.co/stabilityai/sd-vae-ft-mse-original). #### How to use with 🧨 diffusers You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline` ```py from diffusers.models import AutoencoderKL from diffusers import StableDiffusionPipeline model = "CompVis/stable-diffusion-v1-4" vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse") pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae) ``` ## Decoder Finetuning We publish two kl-f8 autoencoder versions, finetuned from the original [kl-f8 autoencoder](https://github.com/CompVis/latent-diffusion#pretrained-autoencoding-models) on a 1:1 ratio of [LAION-Aesthetics](https://laion.ai/blog/laion-aesthetics/) and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces. The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS). The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU). To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder. _Original kl-f8 VAE vs f8-ft-EMA vs f8-ft-MSE_ ## Evaluation ### COCO 2017 (256x256, val, 5000 images) | Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments |----------|---------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| | | | | | | | | | | original | 246803 | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD | | ft-EMA | 560001 | 4.42 | 23.8 +/- 3.9 | 0.69 +/- 0.13 | 0.96 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA | | ft-MSE | 840001 | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs | ### LAION-Aesthetics 5+ (256x256, subset, 10000 images) | Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments |----------|-----------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| | | | | | | | | | | original | 246803 | 2.61 | 26.0 +/- 4.4 | 0.81 +/- 0.12 | 0.75 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD | | ft-EMA | 560001 | 1.77 | 26.7 +/- 4.8 | 0.82 +/- 0.12 | 0.67 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA | | ft-MSE | 840001 | 1.88 | 27.3 +/- 4.7 | 0.83 +/- 0.11 | 0.65 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs | ### Visual _Visualization of reconstructions on 256x256 images from the COCO2017 validation dataset._ <p align="center"> <br> <b> 256x256: ft-EMA (left), ft-MSE (middle), original (right)</b> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00025_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00011_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00037_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00043_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00053_merged.png /> </p> <p align="center"> <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00029_merged.png /> </p>

benjamin/wtp-bert-mini

benjamin

transformers

token-classification

--- license: mit language: - multilingual - am - ar - az - be - bg - bn - ca - ceb - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lt - lv - mg - mk - ml - mn - mr - ms - mt - my - ne - nl - no - pa - pl - ps - pt - ro - ru - si - sk - sl - sq - sr - sv - ta - te - tg - th - tr - uk - ur - uz - vi - xh - yi - yo - zh - zu --- # wtp-bert-mini Model for [`wtpsplit`](https://github.com/bminixhofer/wtpsplit).

flair/ner-german-large

flair

flair

token-classification

--- tags: - flair - token-classification - sequence-tagger-model language: de datasets: - conll2003 widget: - text: "George Washington ging nach Washington" --- ## German NER in Flair (large model) This is the large 4-class NER model for German that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **92,31** (CoNLL-03 German revised) Predicts 4 tags: | **tag** | **meaning** | |---------------------------------|-----------| | PER | person name | | LOC | location name | | ORG | organization name | | MISC | other name | Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf). --- ### 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-german-large") # make example sentence sentence = Sentence("George Washington ging nach 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 (1.0)] Span [5]: "Washington" [− Labels: LOC (1.0)] ``` So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*". --- ### Training: Script to train this model The following Flair script was used to train this model: ```python import torch # 1. get the corpus from flair.datasets import CONLL_03_GERMAN corpus = CONLL_03_GERMAN() # 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 fine-tuneable transformer embeddings WITH document context from flair.embeddings import TransformerWordEmbeddings embeddings = TransformerWordEmbeddings( model='xlm-roberta-large', layers="-1", subtoken_pooling="first", fine_tune=True, use_context=True, ) # 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection) from flair.models import SequenceTagger tagger = SequenceTagger( hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type='ner', use_crf=False, use_rnn=False, reproject_embeddings=False, ) # 6. initialize trainer with AdamW optimizer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW) # 7. run training with XLM parameters (20 epochs, small LR) from torch.optim.lr_scheduler import OneCycleLR trainer.train('resources/taggers/ner-german-large', learning_rate=5.0e-6, mini_batch_size=4, mini_batch_chunk_size=1, max_epochs=20, scheduler=OneCycleLR, embeddings_storage_mode='none', weight_decay=0., ) ) ``` --- ### Cite Please cite the following paper when using this model. ``` @misc{schweter2020flert, title={FLERT: Document-Level Features for Named Entity Recognition}, author={Stefan Schweter and Alan Akbik}, year={2020}, eprint={2011.06993}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` --- ### Issues? The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).

TheBloke/zephyr-7B-beta-AWQ

TheBloke

transformers

text-generation

--- base_model: HuggingFaceH4/zephyr-7b-beta datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized inference: false language: - en license: mit model-index: - name: zephyr-7b-beta results: [] model_creator: Hugging Face H4 model_name: Zephyr 7B Beta model_type: mistral prompt_template: '<|system|> </s> <|user|> {prompt}</s> <|assistant|> ' quantized_by: TheBloke tags: - generated_from_trainer --- <!-- markdownlint-disable MD041 --> <!-- 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 --> # Zephyr 7B Beta - AWQ - Model creator: [Hugging Face H4](https://huggingface.co/HuggingFaceH4) - Original model: [Zephyr 7B Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) <!-- description start --> ## Description This repo contains AWQ model files for [Hugging Face H4's Zephyr 7B Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/zephyr-7B-beta-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF) * [Hugging Face H4's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Zephyr ``` <|system|> </s> <|user|> {prompt}</s> <|assistant|> ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/zephyr-7B-beta-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.15 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) 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/zephyr-7B-beta-AWQ`. 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: `zephyr-7B-beta-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. 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. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 python -m vllm.entrypoints.api_server --model TheBloke/zephyr-7B-beta-AWQ --quantization awq ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''<|system|> </s> <|user|> {prompt}</s> <|assistant|> ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/zephyr-7B-beta-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/zephyr-7B-beta-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<|system|> </s> <|user|> {prompt}</s> <|assistant|> ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using AutoAWQ ### Install the AutoAWQ package Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.1 or later. ```shell pip3 install autoawq ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### AutoAWQ example code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer model_name_or_path = "TheBloke/zephyr-7B-beta-AWQ" # Load tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False) # Load model model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True, trust_remote_code=False, safetensors=True) prompt = "Tell me about AI" prompt_template=f'''<|system|> </s> <|user|> {prompt}</s> <|assistant|> ''' print("*** Running model.generate:") token_input = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() # Generate output generation_output = model.generate( token_input, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, max_new_tokens=512 ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("LLM output: ", text_output) """ # Inference should be possible with transformers pipeline as well in future # But currently this is not yet supported by AutoAWQ (correct as of September 25th 2023) from transformers import 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_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.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: Hugging Face H4's Zephyr 7B Beta <!-- 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. --> <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so and should only be used for educational and research purposes. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944). ## Model description - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** MIT - **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org ## Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks: | Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) | |-------------|-----|----|---------------|--------------| | StableLM-Tuned-α | 7B| dSFT |2.75| -| | MPT-Chat | 7B |dSFT |5.42| -| | Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83| | Mistral-Instructv0.1 | 7B| - | 6.84 |-| | Zephyr-7b-α |7B| dDPO| 6.88| -| | **Zephyr-7b-β** 🪁 | **7B** | **dDPO** | **7.34** | **90.60** | | Falcon-Instruct | 40B |dSFT |5.17 |45.71| | Guanaco | 65B | SFT |6.41| 71.80| | Llama2-Chat | 70B |RLHF |6.86| 92.66| | Vicuna v1.3 | 33B |dSFT |7.12 |88.99| | WizardLM v1.0 | 70B |dSFT |7.71 |-| | Xwin-LM v0.1 | 70B |dPPO |- |95.57| | GPT-3.5-turbo | - |RLHF |7.94 |89.37| | Claude 2 | - |RLHF |8.06| 91.36| | GPT-4 | -| RLHF |8.99| 95.28| In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:  However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. ## Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed 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 contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66) Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```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="HuggingFaceH4/zephyr-7b-beta", 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|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Zephyr-7B-β has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data During DPO training, this model achieves the following results on the evaluation set: - Loss: 0.7496 - Rewards/chosen: -4.5221 - Rewards/rejected: -8.3184 - Rewards/accuracies: 0.7812 - Rewards/margins: 3.7963 - Logps/rejected: -340.1541 - Logps/chosen: -299.4561 - Logits/rejected: -2.3081 - Logits/chosen: -2.3531 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results The table below shows the full set of DPO training metrics: | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6284 | 0.05 | 100 | 0.6098 | 0.0425 | -0.1872 | 0.7344 | 0.2297 | -258.8416 | -253.8099 | -2.7976 | -2.8234 | | 0.4908 | 0.1 | 200 | 0.5426 | -0.0279 | -0.6842 | 0.75 | 0.6563 | -263.8124 | -254.5145 | -2.7719 | -2.7960 | | 0.5264 | 0.15 | 300 | 0.5324 | 0.0414 | -0.9793 | 0.7656 | 1.0207 | -266.7627 | -253.8209 | -2.7892 | -2.8122 | | 0.5536 | 0.21 | 400 | 0.4957 | -0.0185 | -1.5276 | 0.7969 | 1.5091 | -272.2460 | -254.4203 | -2.8542 | -2.8764 | | 0.5362 | 0.26 | 500 | 0.5031 | -0.2630 | -1.5917 | 0.7812 | 1.3287 | -272.8869 | -256.8653 | -2.8702 | -2.8958 | | 0.5966 | 0.31 | 600 | 0.5963 | -0.2993 | -1.6491 | 0.7812 | 1.3499 | -273.4614 | -257.2279 | -2.8778 | -2.8986 | | 0.5014 | 0.36 | 700 | 0.5382 | -0.2859 | -1.4750 | 0.75 | 1.1891 | -271.7204 | -257.0942 | -2.7659 | -2.7869 | | 0.5334 | 0.41 | 800 | 0.5677 | -0.4289 | -1.8968 | 0.7969 | 1.4679 | -275.9378 | -258.5242 | -2.7053 | -2.7265 | | 0.5251 | 0.46 | 900 | 0.5772 | -0.2116 | -1.3107 | 0.7344 | 1.0991 | -270.0768 | -256.3507 | -2.8463 | -2.8662 | | 0.5205 | 0.52 | 1000 | 0.5262 | -0.3792 | -1.8585 | 0.7188 | 1.4793 | -275.5552 | -258.0276 | -2.7893 | -2.7979 | | 0.5094 | 0.57 | 1100 | 0.5433 | -0.6279 | -1.9368 | 0.7969 | 1.3089 | -276.3377 | -260.5136 | -2.7453 | -2.7536 | | 0.5837 | 0.62 | 1200 | 0.5349 | -0.3780 | -1.9584 | 0.7656 | 1.5804 | -276.5542 | -258.0154 | -2.7643 | -2.7756 | | 0.5214 | 0.67 | 1300 | 0.5732 | -1.0055 | -2.2306 | 0.7656 | 1.2251 | -279.2761 | -264.2903 | -2.6986 | -2.7113 | | 0.6914 | 0.72 | 1400 | 0.5137 | -0.6912 | -2.1775 | 0.7969 | 1.4863 | -278.7448 | -261.1467 | -2.7166 | -2.7275 | | 0.4655 | 0.77 | 1500 | 0.5090 | -0.7987 | -2.2930 | 0.7031 | 1.4943 | -279.8999 | -262.2220 | -2.6651 | -2.6838 | | 0.5731 | 0.83 | 1600 | 0.5312 | -0.8253 | -2.3520 | 0.7812 | 1.5268 | -280.4902 | -262.4876 | -2.6543 | -2.6728 | | 0.5233 | 0.88 | 1700 | 0.5206 | -0.4573 | -2.0951 | 0.7812 | 1.6377 | -277.9205 | -258.8084 | -2.6870 | -2.7097 | | 0.5593 | 0.93 | 1800 | 0.5231 | -0.5508 | -2.2000 | 0.7969 | 1.6492 | -278.9703 | -259.7433 | -2.6221 | -2.6519 | | 0.4967 | 0.98 | 1900 | 0.5290 | -0.5340 | -1.9570 | 0.8281 | 1.4230 | -276.5395 | -259.5749 | -2.6564 | -2.6878 | | 0.0921 | 1.03 | 2000 | 0.5368 | -1.1376 | -3.1615 | 0.7812 | 2.0239 | -288.5854 | -265.6111 | -2.6040 | -2.6345 | | 0.0733 | 1.08 | 2100 | 0.5453 | -1.1045 | -3.4451 | 0.7656 | 2.3406 | -291.4208 | -265.2799 | -2.6289 | -2.6595 | | 0.0972 | 1.14 | 2200 | 0.5571 | -1.6915 | -3.9823 | 0.8125 | 2.2908 | -296.7934 | -271.1505 | -2.6471 | -2.6709 | | 0.1058 | 1.19 | 2300 | 0.5789 | -1.0621 | -3.8941 | 0.7969 | 2.8319 | -295.9106 | -264.8563 | -2.5527 | -2.5798 | | 0.2423 | 1.24 | 2400 | 0.5455 | -1.1963 | -3.5590 | 0.7812 | 2.3627 | -292.5599 | -266.1981 | -2.5414 | -2.5784 | | 0.1177 | 1.29 | 2500 | 0.5889 | -1.8141 | -4.3942 | 0.7969 | 2.5801 | -300.9120 | -272.3761 | -2.4802 | -2.5189 | | 0.1213 | 1.34 | 2600 | 0.5683 | -1.4608 | -3.8420 | 0.8125 | 2.3812 | -295.3901 | -268.8436 | -2.4774 | -2.5207 | | 0.0889 | 1.39 | 2700 | 0.5890 | -1.6007 | -3.7337 | 0.7812 | 2.1330 | -294.3068 | -270.2423 | -2.4123 | -2.4522 | | 0.0995 | 1.45 | 2800 | 0.6073 | -1.5519 | -3.8362 | 0.8281 | 2.2843 | -295.3315 | -269.7538 | -2.4685 | -2.5050 | | 0.1145 | 1.5 | 2900 | 0.5790 | -1.7939 | -4.2876 | 0.8438 | 2.4937 | -299.8461 | -272.1744 | -2.4272 | -2.4674 | | 0.0644 | 1.55 | 3000 | 0.5735 | -1.7285 | -4.2051 | 0.8125 | 2.4766 | -299.0209 | -271.5201 | -2.4193 | -2.4574 | | 0.0798 | 1.6 | 3100 | 0.5537 | -1.7226 | -4.2850 | 0.8438 | 2.5624 | -299.8200 | -271.4610 | -2.5367 | -2.5696 | | 0.1013 | 1.65 | 3200 | 0.5575 | -1.5715 | -3.9813 | 0.875 | 2.4098 | -296.7825 | -269.9498 | -2.4926 | -2.5267 | | 0.1254 | 1.7 | 3300 | 0.5905 | -1.6412 | -4.4703 | 0.8594 | 2.8291 | -301.6730 | -270.6473 | -2.5017 | -2.5340 | | 0.085 | 1.76 | 3400 | 0.6133 | -1.9159 | -4.6760 | 0.8438 | 2.7601 | -303.7296 | -273.3941 | -2.4614 | -2.4960 | | 0.065 | 1.81 | 3500 | 0.6074 | -1.8237 | -4.3525 | 0.8594 | 2.5288 | -300.4951 | -272.4724 | -2.4597 | -2.5004 | | 0.0755 | 1.86 | 3600 | 0.5836 | -1.9252 | -4.4005 | 0.8125 | 2.4753 | -300.9748 | -273.4872 | -2.4327 | -2.4716 | | 0.0746 | 1.91 | 3700 | 0.5789 | -1.9280 | -4.4906 | 0.8125 | 2.5626 | -301.8762 | -273.5149 | -2.4686 | -2.5115 | | 0.1348 | 1.96 | 3800 | 0.6015 | -1.8658 | -4.2428 | 0.8281 | 2.3769 | -299.3976 | -272.8936 | -2.4943 | -2.5393 | | 0.0217 | 2.01 | 3900 | 0.6122 | -2.3335 | -4.9229 | 0.8281 | 2.5894 | -306.1988 | -277.5699 | -2.4841 | -2.5272 | | 0.0219 | 2.07 | 4000 | 0.6522 | -2.9890 | -6.0164 | 0.8281 | 3.0274 | -317.1334 | -284.1248 | -2.4105 | -2.4545 | | 0.0119 | 2.12 | 4100 | 0.6922 | -3.4777 | -6.6749 | 0.7969 | 3.1972 | -323.7187 | -289.0121 | -2.4272 | -2.4699 | | 0.0153 | 2.17 | 4200 | 0.6993 | -3.2406 | -6.6775 | 0.7969 | 3.4369 | -323.7453 | -286.6413 | -2.4047 | -2.4465 | | 0.011 | 2.22 | 4300 | 0.7178 | -3.7991 | -7.4397 | 0.7656 | 3.6406 | -331.3667 | -292.2260 | -2.3843 | -2.4290 | | 0.0072 | 2.27 | 4400 | 0.6840 | -3.3269 | -6.8021 | 0.8125 | 3.4752 | -324.9908 | -287.5042 | -2.4095 | -2.4536 | | 0.0197 | 2.32 | 4500 | 0.7013 | -3.6890 | -7.3014 | 0.8125 | 3.6124 | -329.9841 | -291.1250 | -2.4118 | -2.4543 | | 0.0182 | 2.37 | 4600 | 0.7476 | -3.8994 | -7.5366 | 0.8281 | 3.6372 | -332.3356 | -293.2291 | -2.4163 | -2.4565 | | 0.0125 | 2.43 | 4700 | 0.7199 | -4.0560 | -7.5765 | 0.8438 | 3.5204 | -332.7345 | -294.7952 | -2.3699 | -2.4100 | | 0.0082 | 2.48 | 4800 | 0.7048 | -3.6613 | -7.1356 | 0.875 | 3.4743 | -328.3255 | -290.8477 | -2.3925 | -2.4303 | | 0.0118 | 2.53 | 4900 | 0.6976 | -3.7908 | -7.3152 | 0.8125 | 3.5244 | -330.1224 | -292.1431 | -2.3633 | -2.4047 | | 0.0118 | 2.58 | 5000 | 0.7198 | -3.9049 | -7.5557 | 0.8281 | 3.6508 | -332.5271 | -293.2844 | -2.3764 | -2.4194 | | 0.006 | 2.63 | 5100 | 0.7506 | -4.2118 | -7.9149 | 0.8125 | 3.7032 | -336.1194 | -296.3530 | -2.3407 | -2.3860 | | 0.0143 | 2.68 | 5200 | 0.7408 | -4.2433 | -7.9802 | 0.8125 | 3.7369 | -336.7721 | -296.6682 | -2.3509 | -2.3946 | | 0.0057 | 2.74 | 5300 | 0.7552 | -4.3392 | -8.0831 | 0.7969 | 3.7439 | -337.8013 | -297.6275 | -2.3388 | -2.3842 | | 0.0138 | 2.79 | 5400 | 0.7404 | -4.2395 | -7.9762 | 0.8125 | 3.7367 | -336.7322 | -296.6304 | -2.3286 | -2.3737 | | 0.0079 | 2.84 | 5500 | 0.7525 | -4.4466 | -8.2196 | 0.7812 | 3.7731 | -339.1662 | -298.7007 | -2.3200 | -2.3641 | | 0.0077 | 2.89 | 5600 | 0.7520 | -4.5586 | -8.3485 | 0.7969 | 3.7899 | -340.4545 | -299.8206 | -2.3078 | -2.3517 | | 0.0094 | 2.94 | 5700 | 0.7527 | -4.5542 | -8.3509 | 0.7812 | 3.7967 | -340.4790 | -299.7773 | -2.3062 | -2.3510 | | 0.0054 | 2.99 | 5800 | 0.7520 | -4.5169 | -8.3079 | 0.7812 | 3.7911 | -340.0493 | -299.4038 | -2.3081 | -2.3530 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0 ## Citation If you find Zephyr-7B-β is useful in your work, please cite it with: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

google/flan-t5-xl

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 XL <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) # 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-xl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl") 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-xl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", 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-xl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", 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-xl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", 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-XL, 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} } ```

facebook/mask2former-swin-large-cityscapes-semantic

facebook

transformers

image-segmentation

--- license: other tags: - vision - image-segmentation datasets: - coco widget: - src: http://images.cocodataset.org/val2017/000000039769.jpg example_title: Cats - src: http://images.cocodataset.org/val2017/000000039770.jpg example_title: Castle --- # Mask2Former Mask2Former model trained on Cityscapes semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, [MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.  ## Intended uses & limitations You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python import requests import torch from PIL import Image from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation # load Mask2Former fine-tuned on Cityscapes semantic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-cityscapes-semantic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-cityscapes-semantic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to processor for postprocessing predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).

cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR

cambridgeltl

transformers

feature-extraction

--- language: multilingual tags: - biomedical - lexical-semantics - cross-lingual datasets: - UMLS **[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br> **[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**! ### SapBERT-XLMR SapBERT [(Liu et al. 2020)](https://arxiv.org/pdf/2010.11784.pdf) trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AB, using [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the base model. Please use [CLS] as the representation of the input. #### Extracting embeddings from SapBERT The following script converts a list of strings (entity names) into embeddings. ```python import numpy as np import torch from tqdm.auto import tqdm from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext") model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext").cuda() # replace with your own list of entity names all_names = ["covid-19", "Coronavirus infection", "high fever", "Tumor of posterior wall of oropharynx"] bs = 128 # batch size during inference all_embs = [] for i in tqdm(np.arange(0, len(all_names), bs)): toks = tokenizer.batch_encode_plus(all_names[i:i+bs], padding="max_length", max_length=25, truncation=True, return_tensors="pt") toks_cuda = {} for k,v in toks.items(): toks_cuda[k] = v.cuda() cls_rep = model(**toks_cuda)[0][:,0,:] # use CLS representation as the embedding all_embs.append(cls_rep.cpu().detach().numpy()) all_embs = np.concatenate(all_embs, axis=0) ``` For more details about training and eval, see SapBERT [github repo](https://github.com/cambridgeltl/sapbert). ### Citation ```bibtex @inproceedings{liu2021learning, title={Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking}, author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel}, booktitle={Proceedings of ACL-IJCNLP 2021}, month = aug, year={2021} } ```

benjamin/gpt2-wechsel-german

benjamin

transformers

text-generation

--- language: de license: mit --- # gpt2-wechsel-german Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. See the code here: https://github.com/CPJKU/wechsel And the paper here: https://aclanthology.org/2022.naacl-main.293/ ## Performance ### RoBERTa | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-french` | **82.43** | **90.88** | **86.65** | | `camembert-base` | 80.88 | 90.26 | 85.57 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-german` | **81.79** | **89.72** | **85.76** | | `deepset/gbert-base` | 78.64 | 89.46 | 84.05 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-chinese` | **78.32** | 80.55 | **79.44** | | `bert-base-chinese` | 76.55 | **82.05** | 79.30 | | Model | NLI Score | NER Score | Avg Score | |---|---|---|---| | `roberta-base-wechsel-swahili` | **75.05** | **87.39** | **81.22** | | `xlm-roberta-base` | 69.18 | 87.37 | 78.28 | ### GPT2 | Model | PPL | |---|---| | `gpt2-wechsel-french` | **19.71** | | `gpt2` (retrained from scratch) | 20.47 | | Model | PPL | |---|---| | `gpt2-wechsel-german` | **26.8** | | `gpt2` (retrained from scratch) | 27.63 | | Model | PPL | |---|---| | `gpt2-wechsel-chinese` | **51.97** | | `gpt2` (retrained from scratch) | 52.98 | | Model | PPL | |---|---| | `gpt2-wechsel-swahili` | **10.14** | | `gpt2` (retrained from scratch) | 10.58 | See our paper for details. ## Citation Please cite WECHSEL as ``` @inproceedings{minixhofer-etal-2022-wechsel, title = "{WECHSEL}: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models", author = "Minixhofer, Benjamin and Paischer, Fabian and Rekabsaz, Navid", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.293", pages = "3992--4006", abstract = "Large pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceedingly expensive to train these models in other languages. To alleviate this problem, we introduce a novel method {--} called WECHSEL {--} to efficiently and effectively transfer pretrained LMs to new languages. WECHSEL can be applied to any model which uses subword-based tokenization and learns an embedding for each subword. The tokenizer of the source model (in English) is replaced with a tokenizer in the target language and token embeddings are initialized such that they are semantically similar to the English tokens by utilizing multilingual static word embeddings covering English and the target language. We use WECHSEL to transfer the English RoBERTa and GPT-2 models to four languages (French, German, Chinese and Swahili). We also study the benefits of our method on very low-resource languages. WECHSEL improves over proposed methods for cross-lingual parameter transfer and outperforms models of comparable size trained from scratch with up to 64x less training effort. Our method makes training large language models for new languages more accessible and less damaging to the environment. We make our code and models publicly available.", } ```

sentence-transformers/stsb-xlm-r-multilingual

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/stsb-xlm-r-multilingual 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/stsb-xlm-r-multilingual') 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/stsb-xlm-r-multilingual') model = AutoModel.from_pretrained('sentence-transformers/stsb-xlm-r-multilingual') # 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/stsb-xlm-r-multilingual) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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", } ```

microsoft/layoutxlm-base

microsoft

transformers

--- license: cc-by-nc-sa-4.0 --- # LayoutXLM **Multimodal (text + layout/format + image) pre-training for document AI** LayoutXLM is a multilingual variant of LayoutLMv2. The documentation of this model in the Transformers library can be found [here](https://huggingface.co/docs/transformers/model_doc/layoutxlm). [Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://github.com/microsoft/unilm/tree/master/layoutxlm) ## Introduction LayoutXLM is a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding. Experiment results show that it has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUND dataset. [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei, arXiv Preprint 2021

timm/vit_large_patch16_384.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_large_patch16_384.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): 304.7 - GMACs: 174.8 - Activations (M): 128.2 - Image size: 384 x 384 - **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_large_patch16_384.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_large_patch16_384.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, 577, 1024) 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}} } ```

fxmarty/really-tiny-falcon-testing

fxmarty

transformers

text-generation

--- license: mit --- tiny = <10 MB

sentence-transformers/paraphrase-albert-small-v2

sentence-transformers

sentence-transformers

sentence-similarity

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - flax-sentence-embeddings/stackexchange_xml - s2orc - ms_marco - wiki_atomic_edits - snli - multi_nli - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/flickr30k-captions - embedding-data/coco_captions - embedding-data/sentence-compression - embedding-data/QQP - yahoo_answers_topics pipeline_tag: sentence-similarity --- # sentence-transformers/paraphrase-albert-small-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-albert-small-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-albert-small-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-albert-small-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-albert-small-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 100, 'do_lower_case': False}) with Transformer model: AlbertModel (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", } ```

facebook/dinov2-large

facebook

transformers

image-feature-extraction

--- license: apache-2.0 tags: - dino - vision --- # Vision Transformer (large-sized model) trained using DINOv2 Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Oquab et al. and first released in [this repository](https://github.com/facebookresearch/dinov2). Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion. Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not include any fine-tuned heads. 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 images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Intended uses & limitations You can use the raw model for feature extraction. See the [model hub](https://huggingface.co/models?search=facebook/dinov2) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python from transformers import AutoImageProcessor, AutoModel from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained('facebook/dinov2-large') model = AutoModel.from_pretrained('facebook/dinov2-large') inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex misc{oquab2023dinov2, title={DINOv2: Learning Robust Visual Features without Supervision}, author={Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski}, year={2023}, eprint={2304.07193}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

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.

cledoux42/Ethnicity_Test_v003

cledoux42

transformers

image-classification

--- tags: - autotrain - vision - image-classification datasets: - cledoux42/autotrain-data-ethnicity-test_v003 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 co2_eq_emissions: emissions: 6.022813032092885 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 47959117029 - CO2 Emissions (in grams): 6.0228 ## Validation Metrics - Loss: 0.530 - Accuracy: 0.796 - Macro F1: 0.797 - Micro F1: 0.796 - Weighted F1: 0.796 - Macro Precision: 0.797 - Micro Precision: 0.796 - Weighted Precision: 0.796 - Macro Recall: 0.798 - Micro Recall: 0.796 - Weighted Recall: 0.796

microsoft/speecht5_tts

microsoft

transformers

text-to-speech

--- license: mit tags: - audio - text-to-speech datasets: - libritts --- # SpeechT5 (TTS task) SpeechT5 model fine-tuned for speech synthesis (text-to-speech) on LibriTTS. This model was introduced in [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei. SpeechT5 was first released in [this repository](https://github.com/microsoft/SpeechT5/), [original weights](https://huggingface.co/mechanicalsea/speecht5-tts). The license used is [MIT](https://github.com/microsoft/SpeechT5/blob/main/LICENSE). ## Model Description Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder. Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder. Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification. - **Developed by:** Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei. - **Shared by [optional]:** [Matthijs Hollemans](https://huggingface.co/Matthijs) - **Model type:** text-to-speech - **Language(s) (NLP):** [More Information Needed] - **License:** [MIT](https://github.com/microsoft/SpeechT5/blob/main/LICENSE) - **Finetuned from model [optional]:** [More Information Needed] ## Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [https://github.com/microsoft/SpeechT5/] - **Paper:** [https://arxiv.org/pdf/2110.07205.pdf] - **Blog Post:** [https://huggingface.co/blog/speecht5] - **Demo:** [https://huggingface.co/spaces/Matthijs/speecht5-tts-demo] # Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ## 🤗 Transformers Usage You can run SpeechT5 TTS locally with the 🤗 Transformers library. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers), sentencepiece, soundfile and datasets(optional): ``` pip install --upgrade pip pip install --upgrade transformers sentencepiece datasets[audio] ``` 2. Run inference via the `Text-to-Speech` (TTS) pipeline. You can access the SpeechT5 model via the TTS pipeline in just a few lines of code! ```python from transformers import pipeline from datasets import load_dataset import soundfile as sf synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts") embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) # You can replace this embedding with your own as well. speech = synthesiser("Hello, my dog is cooler than you!", forward_params={"speaker_embeddings": speaker_embedding}) sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"]) ``` 3. Run inference via the Transformers modelling code - You can use the processor + generate code to convert text into a mono 16 kHz speech waveform for more fine-grained control. ```python from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan from datasets import load_dataset import torch import soundfile as sf from datasets import load_dataset processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts") vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") inputs = processor(text="Hello, my dog is cute.", return_tensors="pt") # load xvector containing speaker's voice characteristics from a dataset embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) sf.write("speech.wav", speech.numpy(), samplerate=16000) ``` ### Fine-tuning the Model Refer to [this Colab notebook](https://colab.research.google.com/drive/1i7I5pzBcU3WDFarDnzweIj4-sVVoIUFJ) for an example of how to fine-tune SpeechT5 for TTS on a different dataset or a new language. ## Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> You can use this model for speech synthesis. See the [model hub](https://huggingface.co/models?search=speecht5) to look for fine-tuned versions on a task that interests you. ## Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ## Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] # Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ## Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> LibriTTS ## Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [optional] Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. ### Training hyperparameters - **Precision:** [More Information Needed] <!--fp16, bf16, fp8, fp32 --> - **Regime:** [More Information Needed] <!--mixed precision or not --> ### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] # Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ## Testing Data, Factors & Metrics ### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] ### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] ### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ## Results [More Information Needed] ### Summary # Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification. # Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> 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:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder. ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** ```bibtex @inproceedings{ao-etal-2022-speecht5, title = {{S}peech{T}5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing}, author = {Ao, Junyi and Wang, Rui and Zhou, Long and Wang, Chengyi and Ren, Shuo and Wu, Yu and Liu, Shujie and Ko, Tom and Li, Qing and Zhang, Yu and Wei, Zhihua and Qian, Yao and Li, Jinyu and Wei, Furu}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = {May}, year = {2022}, pages={5723--5738}, } ``` # Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> - **text-to-speech** to synthesize audio # More Information [optional] [More Information Needed] # Model Card Authors [optional] Disclaimer: The team releasing SpeechT5 did not write a model card for this model so this model card has been written by the Hugging Face team. # Model Card Contact [More Information Needed]

bigscience/bloom-560m

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-560m <!-- 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 | **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.) ## 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 * 559,214,592 parameters: * 256,901,120 embedding parameters * 24 layers, 16 attention heads * Hidden layers are 1024-dimensional * Sequence length of 2048 tokens (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: 384 A100 80GB GPUs (48 nodes): * Additional 32 A100 80GB GPUs (4 nodes) in reserve * 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links * CPU: AMD * CPU memory: 512GB per node * GPU memory: 640GB 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** Training logs: [Tensorboard link](https://huggingface.co/bigscience/tr11e-350M-logs) - Training throughput: About 150 TFLOPs per GPU - Number of epochs: 1 (*current target*) - Dates: - Started 11th March, 2022 11:42am PST - Ended 5th July, 2022 - Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes) - 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

DeepPavlov/rubert-base-cased

DeepPavlov

transformers

feature-extraction

--- language: - ru --- # rubert-base-cased RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT\[1\]. 08.11.2021: upload model with MLM and NSP heads \[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language. arXiv preprint [arXiv:1905.07213](https://arxiv.org/abs/1905.07213).

charsiu/g2p_multilingual_byT5_small_100

charsiu

transformers

text2text-generation

Entry not found

nvidia/parakeet-tdt-1.1b

nvidia

nemo

automatic-speech-recognition

--- language: - en library_name: nemo datasets: - librispeech_asr - fisher_corpus - Switchboard-1 - WSJ-0 - WSJ-1 - National-Singapore-Corpus-Part-1 - National-Singapore-Corpus-Part-6 - vctk - voxpopuli - europarl - multilingual_librispeech - mozilla-foundation/common_voice_8_0 - MLCommons/peoples_speech thumbnail: null tags: - automatic-speech-recognition - speech - audio - Transducer - TDT - FastConformer - Conformer - pytorch - NeMo - hf-asr-leaderboard license: cc-by-4.0 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: parakeet_tdt_1.1b results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: AMI (Meetings test) type: edinburghcstr/ami config: ihm split: test args: language: en metrics: - name: Test WER type: wer value: 15.90 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Earnings-22 type: revdotcom/earnings22 split: test args: language: en metrics: - name: Test WER type: wer value: 14.65 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: GigaSpeech type: speechcolab/gigaspeech split: test args: language: en metrics: - name: Test WER type: wer value: 9.55 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 1.39 - 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: 2.62 - task: type: Automatic Speech Recognition name: automatic-speech-recognition dataset: name: SPGI Speech type: kensho/spgispeech config: test split: test args: language: en metrics: - name: Test WER type: wer value: 3.42 - task: type: Automatic Speech Recognition name: automatic-speech-recognition dataset: name: tedlium-v3 type: LIUM/tedlium config: release1 split: test args: language: en metrics: - name: Test WER type: wer value: 3.56 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Vox Populi type: facebook/voxpopuli config: en split: test args: language: en metrics: - name: Test WER type: wer value: 5.48 - task: type: Automatic Speech Recognition name: automatic-speech-recognition dataset: name: Mozilla Common Voice 9.0 type: mozilla-foundation/common_voice_9_0 config: en split: test args: language: en metrics: - name: Test WER type: wer value: 5.97 metrics: - wer pipeline_tag: automatic-speech-recognition --- # Parakeet TDT 1.1B (en) <style> img { display: inline; } </style> [](#model-architecture) | [](#model-architecture) | [](#datasets) `parakeet-tdt-1.1b` is an ASR model that transcribes speech in lower case English alphabet. This model is jointly developed by [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) and [Suno.ai](https://www.suno.ai/) teams. It is an XXL version of FastConformer [1] TDT [2] (around 1.1B parameters) model. See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) for complete architecture details. ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/parakeet-tdt-1.1b") ``` ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/parakeet-tdt-1.1b" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16000 Hz mono-channel audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture This model uses a FastConformer-TDT architecture. FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer). TDT (Token-and-Duration Transducer) [2] is a generalization of conventional Transducers by decoupling token and duration predictions. Unlike conventional Transducers which produces a lot of blanks during inference, a TDT model can skip majority of blank predictions by using the duration output (up to 4 frames for this parakeet-tdt-1.1b model), thus brings significant inference speed-up. The detail of TDT can be found here: [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795). ## Training The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml). The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). ### Datasets The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams. The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets: - Librispeech 960 hours of English speech - Fisher Corpus - Switchboard-1 Dataset - WSJ-0 and WSJ-1 - National Speech Corpus (Part 1, Part 6) - VCTK - VoxPopuli (EN) - Europarl-ASR (EN) - Multilingual Librispeech (MLS EN) - 2,000 hour subset - Mozilla Common Voice (v7.0) - People's Speech - 12,000 hour subset ## Performance The performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general. The following tables summarizes the performance of the available models in this collection with the Transducer decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. |**Version**|**Tokenizer**|**Vocabulary Size**|**AMI**|**Earnings-22**|**Giga Speech**|**LS test-clean**|**SPGI Speech**|**TEDLIUM-v3**|**Vox Populi**|**Common Voice**| |---------|-----------------------|-----------------|---------------|---------------|------------|-----------|-----|-------|------|------| | 1.22.0 | SentencePiece Unigram | 1024 | 15.90 | 14.65 | 9.55 | 1.39 | 2.62 | 3.42 | 3.56 | 5.48 | 5.97 | These are greedy WER numbers without external LM. More details on evaluation can be found at [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard) ## Model Fairness Evaluation As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversations Dataset", we assessed the parakeet-tdt-1.1b model for fairness. The model was evaluated on the CausalConversations-v1 dataset, and the results are reported as follows: ### Gender Bias: | Gender | Male | Female | N/A | Other | | :--- | :--- | :--- | :--- | :--- | | Num utterances | 19325 | 24532 | 926 | 33 | | % WER | 17.18 | 14.61 | 19.06 | 37.57 | ### Age Bias: | Age Group | $(18-30)$ | $(31-45)$ | $(46-85)$ | $(1-100)$ | | :--- | :--- | :--- | :--- | :--- | | Num utterances | 15956 | 14585 | 13349 | 43890 | | % WER | 15.83 | 15.89 | 15.46 | 15.74 | (Error rates for fairness evaluation are determined by normalizing both the reference and predicted text, similar to the methods used in the evaluations found at https://github.com/huggingface/open_asr_leaderboard.) ## NVIDIA Riva: Deployment [NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides: * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization * Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support Although this model isn’t supported yet by Riva, the [list of supported models is here](https://huggingface.co/models?other=Riva). Check out [Riva live demo](https://developer.nvidia.com/riva#demos). ## References [1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084) [2] [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795) [3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) [4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) [5] [Suno.ai](https://suno.ai/) [6] [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard) [7] [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://arxiv.org/abs/2104.02821) ## Licence License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.

fxmarty/tiny-random-GemmaForCausalLM

fxmarty

transformers

text-generation

--- license: mit --- This one with a custom `config.head_dim` as allowed by the architecture (see 7b model).

google/gemma-7b

google

transformers

text-generation

--- library_name: transformers 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 base version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). **Resources and Technical Documentation**: * [Gemma Technical Report](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf) * [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-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. ### Context Length Models are trained on a context length of 8192 tokens. ### 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 examples You can find fine-tuning notebooks under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples). We provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using [QLoRA](https://huggingface.co/papers/2305.14314) * 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. You can also find the copy of the notebook [here](https://github.com/huggingface/notebooks/blob/main/peft/gemma_7b_english_quotes.ipynb). #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b") 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 tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", 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])) ``` #### Running the model on a GPU using different precisions * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", 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") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", 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])) ``` #### 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") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", 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") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", 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) ``` ### 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.

Salesforce/instructblip-vicuna-7b

Salesforce

transformers

image-to-text

--- language: en license: other tags: - vision - image-captioning pipeline_tag: image-to-text --- # InstructBLIP model InstructBLIP model using [Vicuna-7b](https://github.com/lm-sys/FastChat#model-weights) as language model. InstructBLIP was introduced in the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Dai et al. Disclaimer: The team releasing InstructBLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description InstructBLIP is a visual instruction tuned version of [BLIP-2](https://huggingface.co/docs/transformers/main/model_doc/blip-2). Refer to the paper for details.  ## Intended uses & limitations Usage is as follows: ``` from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration import torch from PIL import Image import requests model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b") processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b") device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") prompt = "What is unusual about this image?" inputs = processor(images=image, text=prompt, return_tensors="pt").to(device) outputs = model.generate( **inputs, do_sample=False, num_beams=5, max_length=256, min_length=1, top_p=0.9, repetition_penalty=1.5, length_penalty=1.0, temperature=1, ) generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip() print(generated_text) ``` ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/instructblip).

MaziyarPanahi/Qwen2-1.5B-Instruct-GGUF

MaziyarPanahi

transformers

text-generation

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - llama-3 - llama - text-generation model_name: Qwen2-1.5B-Instruct-GGUF base_model: Qwen/Qwen2-1.5B-Instruct inference: false model_creator: Qwen pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Qwen2-1.5B-Instruct-GGUF](https://huggingface.co/MaziyarPanahi/Qwen2-1.5B-Instruct-GGUF) - Model creator: [Qwen](https://huggingface.co/Qwen) - Original model: [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) ## Description [MaziyarPanahi/Qwen2-1.5B-Instruct-GGUF](https://huggingface.co/MaziyarPanahi/Qwen2-1.5B-Instruct-GGUF) contains GGUF format model files for [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

google/mt5-small

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.

michellejieli/emotion_text_classifier

michellejieli

transformers

text-classification

--- language: "en" tags: - distilroberta - sentiment - emotion - twitter - reddit widget: - text: "Oh my God, he's lost it. He's totally lost it." - text: "What?" - text: "Wow, congratulations! So excited for you!" --- # Fine-tuned DistilRoBERTa-base for Emotion Classification 🤬🤢😀😐😭😲 # Model Description DistilRoBERTa-base is a transformer model that performs sentiment analysis. I fine-tuned the model on transcripts from the Friends show with the goal of classifying emotions from text data, specifically dialogue from Netflix shows or movies. The model predicts 6 Ekman emotions and a neutral class. These emotions include anger, disgust, fear, joy, neutrality, sadness, and surprise. The model is a fine-tuned version of [Emotion English DistilRoBERTa-base](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/) and [DistilRoBERTa-base](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base). This model was initially trained on the following table from [Emotion English DistilRoBERTa-base](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/): |Name|anger|disgust|fear|joy|neutral|sadness|surprise| |---|---|---|---|---|---|---|---| |Crowdflower (2016)|Yes|-|-|Yes|Yes|Yes|Yes| |Emotion Dataset, Elvis et al. (2018)|Yes|-|Yes|Yes|-|Yes|Yes| |GoEmotions, Demszky et al. (2020)|Yes|Yes|Yes|Yes|Yes|Yes|Yes| |ISEAR, Vikash (2018)|Yes|Yes|Yes|Yes|-|Yes|-| |MELD, Poria et al. (2019)|Yes|Yes|Yes|Yes|Yes|Yes|Yes| |SemEval-2018, EI-reg, Mohammad et al. (2018) |Yes|-|Yes|Yes|-|Yes|-| It was fine-tuned on: |Name|anger|disgust|fear|joy|neutral|sadness|surprise| |---|---|---|---|---|---|---|---| |Emotion Lines (Friends)|Yes|Yes|Yes|Yes|Yes|Yes|Yes| # How to Use ```python from transformers import pipeline classifier = pipeline("sentiment-analysis", model="michellejieli/emotion_text_classifier") classifier("I love this!") ``` ```python Output: [{'label': 'joy', 'score': 0.9887555241584778}] ``` # Contact Please reach out to [michelleli1999@gmail.com](mailto:michelleli1999@gmail.com) if you have any questions or feedback. # Reference ``` Jochen Hartmann, "Emotion English DistilRoBERTa-base". https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/, 2022. Ashritha R Murthy and K M Anil Kumar 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1110 012009 ```

dbmdz/bert-base-italian-xxl-cased

dbmdz

transformers

fill-mask

--- language: it license: mit datasets: - wikipedia --- # 🤗 + 📚 dbmdz BERT and ELECTRA models In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources Italian BERT and ELECTRA models 🎉 # Italian BERT The source data for the Italian BERT model consists of a recent Wikipedia dump and various texts from the [OPUS corpora](http://opus.nlpl.eu/) collection. The final training corpus has a size of 13GB and 2,050,057,573 tokens. For sentence splitting, we use NLTK (faster compared to spacy). Our cased and uncased models are training with an initial sequence length of 512 subwords for ~2-3M steps. For the XXL Italian models, we use the same training data from OPUS and extend it with data from the Italian part of the [OSCAR corpus](https://traces1.inria.fr/oscar/). Thus, the final training corpus has a size of 81GB and 13,138,379,147 tokens. Note: Unfortunately, a wrong vocab size was used when training the XXL models. This explains the mismatch of the "real" vocab size of 31102, compared to the vocab size specified in `config.json`. However, the model is working and all evaluations were done under those circumstances. See [this issue](https://github.com/dbmdz/berts/issues/7) for more information. The Italian ELECTRA model was trained on the "XXL" corpus for 1M steps in total using a batch size of 128. We pretty much following the ELECTRA training procedure as used for [BERTurk](https://github.com/stefan-it/turkish-bert/tree/master/electra). ## Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | ---------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | `dbmdz/bert-base-italian-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/vocab.txt) | `dbmdz/bert-base-italian-uncased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/vocab.txt) | `dbmdz/bert-base-italian-xxl-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/vocab.txt) | `dbmdz/bert-base-italian-xxl-uncased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/vocab.txt) | `dbmdz/electra-base-italian-xxl-cased-discriminator` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-discriminator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/vocab.txt) | `dbmdz/electra-base-italian-xxl-cased-generator` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-generator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/vocab.txt) ## Results For results on downstream tasks like NER or PoS tagging, please refer to [this repository](https://github.com/stefan-it/italian-bertelectra). ## Usage With Transformers >= 2.3 our Italian BERT models can be loaded like: ```python from transformers import AutoModel, AutoTokenizer model_name = "dbmdz/bert-base-italian-cased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` To load the (recommended) Italian XXL BERT models, just use: ```python from transformers import AutoModel, AutoTokenizer model_name = "dbmdz/bert-base-italian-xxl-cased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` To load the Italian XXL ELECTRA model (discriminator), just use: ```python from transformers import AutoModel, AutoTokenizer model_name = "dbmdz/electra-base-italian-xxl-cased-discriminator" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelWithLMHead.from_pretrained(model_name) ``` # Huggingface model hub All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). # Contact (Bugs, Feedback, Contribution and more) For questions about our BERT/ELECTRA models just open an issue [here](https://github.com/dbmdz/berts/issues/new) 🤗 # Acknowledgments Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage 🤗