Datasets:

librarian-bots

/

model_cards_with_metadata

like 7

cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual

cardiffnlp

transformers

text-classification

--- datasets: - cardiffnlp/tweet_sentiment_multilingual metrics: - f1 - accuracy model-index: - name: cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual results: - task: type: text-classification name: Text Classification dataset: name: cardiffnlp/tweet_sentiment_multilingual type: all split: test metrics: - name: Micro F1 (cardiffnlp/tweet_sentiment_multilingual/all) type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all value: 0.6931034482758621 - name: Macro F1 (cardiffnlp/tweet_sentiment_multilingual/all) type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all value: 0.692628774202147 - name: Accuracy (cardiffnlp/tweet_sentiment_multilingual/all) type: accuracy_cardiffnlp/tweet_sentiment_multilingual/all value: 0.6931034482758621 pipeline_tag: text-classification widget: - text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}} example_title: "topic_classification 1" - text: Yes, including Medicare and social security saving👍 example_title: "sentiment 1" - text: All two of them taste like ass. example_title: "offensive 1" - text: If you wanna look like a badass, have drama on social media example_title: "irony 1" - text: Whoever just unfollowed me you a bitch example_title: "hate 1" - text: I love swimming for the same reason I love meditating...the feeling of weightlessness. example_title: "emotion 1" - text: Beautiful sunset last night from the pontoon @TupperLakeNY example_title: "emoji 1" --- # cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base) on the [`cardiffnlp/tweet_sentiment_multilingual (all)`](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) via [`tweetnlp`](https://github.com/cardiffnlp/tweetnlp). Training split is `train` and parameters have been tuned on the validation split `validation`. Following metrics are achieved on the test split `test` ([link](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual/raw/main/metric.json)). - F1 (micro): 0.6931034482758621 - F1 (macro): 0.692628774202147 - Accuracy: 0.6931034482758621 ### Usage Install tweetnlp via pip. ```shell pip install tweetnlp ``` Load the model in python. ```python import tweetnlp model = tweetnlp.Classifier("cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual", max_length=128) model.predict('Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}') ``` ### Reference ``` @inproceedings{camacho-collados-etal-2022-tweetnlp, title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media", author = "Camacho-collados, Jose and Rezaee, Kiamehr and Riahi, Talayeh and Ushio, Asahi and Loureiro, Daniel and Antypas, Dimosthenis and Boisson, Joanne and Espinosa Anke, Luis and Liu, Fangyu and Mart{\'\i}nez C{\'a}mara, Eugenio" and others, booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = dec, year = "2022", address = "Abu Dhabi, UAE", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-demos.5", pages = "38--49" } ```

pkadambi/wav2textgrid

pkadambi

transformers

--- license: mit ---

Sahajtomar/German_Zeroshot

Sahajtomar

transformers

zero-shot-classification

--- language: multilingual tags: - text-classification - pytorch - nli - xnli - de datasets: - xnli pipeline_tag: zero-shot-classification widget: - text: "Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie" candidate_labels: "Verbrechen,Tragödie,Stehlen" hypothesis_template: "In deisem geht es um {}." --- # German Zeroshot ## Model Description This model has [GBERT Large](https://huggingface.co/deepset/gbert-large) as base model and fine-tuned it on xnli de dataset. The default hypothesis template is in English: `This text is {}`. While using this model , change it to "In deisem geht es um {}." or something different. While inferencing through huggingface api may give poor results as it uses by default english template. Since model is monolingual and not multilingual, hypothesis template needs to be changed accordingly. ## XNLI DEV (german) Accuracy: 85.5 ## XNLI TEST (german) Accuracy: 83.6 #### Zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="Sahajtomar/German_Zeroshot") sequence = "Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie" candidate_labels = ["Verbrechen","Tragödie","Stehlen"] hypothesis_template = "In deisem geht es um {}." ## Since monolingual model,its sensitive to hypothesis template. This can be experimented classifier(sequence, candidate_labels, hypothesis_template=hypothesis_template) """{'labels': ['Tragödie', 'Verbrechen', 'Stehlen'], 'scores': [0.8328856854438782, 0.10494536352157593, 0.06316883927583696], 'sequence': 'Letzte Woche gab es einen Selbstmord in einer nahe gelegenen Kolonie'}""" ```

flair/ner-english-ontonotes

flair

flair

token-classification

--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - ontonotes widget: - text: "On September 1st George Washington won 1 dollar." --- ## English NER in Flair (Ontonotes default model) This is the 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **89.27** (Ontonotes) Predicts 18 tags: | **tag** | **meaning** | |---------------------------------|-----------| | CARDINAL | cardinal value | | DATE | date value | | EVENT | event name | | FAC | building name | | GPE | geo-political entity | | LANGUAGE | language name | | LAW | law name | | LOC | location name | | MONEY | money name | | NORP | affiliation | | ORDINAL | ordinal value | | ORG | organization name | | PERCENT | percent value | | PERSON | person name | | PRODUCT | product name | | QUANTITY | quantity value | | TIME | time value | | WORK_OF_ART | name of work of art | Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. --- ### Demo: How to use in Flair Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/ner-english-ontonotes") # make example sentence sentence = Sentence("On September 1st George Washington won 1 dollar.") # 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 [2,3]: "September 1st" [− Labels: DATE (0.8824)] Span [4,5]: "George Washington" [− Labels: PERSON (0.9604)] Span [7,8]: "1 dollar" [− Labels: MONEY (0.9837)] ``` So, the entities "*September 1st*" (labeled as a **date**), "*George Washington*" (labeled as a **person**) and "*1 dollar*" (labeled as a **money**) are found in the sentence "*On September 1st George Washington won 1 dollar*". --- ### 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 ColumnCorpus from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself) corpus: Corpus = ColumnCorpus( "resources/tasks/onto-ner", column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"}, tag_to_bioes="ner", ) # 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('en-crawl'), # contextual string embeddings, forward FlairEmbeddings('news-forward'), # contextual string embeddings, backward FlairEmbeddings('news-backward'), ] # embedding stack consists of Flair and GloVe embeddings embeddings = StackedEmbeddings(embeddings=embedding_types) # 5. initialize sequence tagger from flair.models import SequenceTagger tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type) # 6. initialize trainer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus) # 7. run training trainer.train('resources/taggers/ner-english-ontonotes', 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/).

google/flan-t5-small

google

transformers

text2text-generation

--- language: - en - fr - ro - de - multilingual tags: - text2text-generation 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" 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 small <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg" alt="drawing" width="600"/> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 5. [Training Details](#training-details) 6. [Evaluation](#evaluation) 7. [Environmental Impact](#environmental-impact) 8. [Citation](#citation) 9. [Model Card Authors](#model-card-authors) # TL;DR If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : > Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large). # Model Details ## Model Description - **Model type:** Language model - **Language(s) (NLP):** English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian - **License:** Apache 2.0 - **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5) - **Original Checkpoints:** [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) - **Resources for more information:** - [Research paper](https://arxiv.org/pdf/2210.11416.pdf) - [GitHub Repo](https://github.com/google-research/t5x) - [Hugging Face FLAN-T5 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/t5) # Usage Find below some example scripts on how to use the model in `transformers`: ## Using the Pytorch model ### Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small") 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-small") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", 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-small") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", 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-small") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", 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-Small, 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} } ```

intfloat/multilingual-e5-large-instruct

intfloat

sentence-transformers

feature-extraction

--- tags: - mteb - sentence-transformers - transformers model-index: - name: multilingual-e5-large-instruct results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 76.23880597014924 - type: ap value: 39.07351965022687 - type: f1 value: 70.04836733862683 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 66.71306209850107 - type: ap value: 79.01499914759529 - type: f1 value: 64.81951817560703 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.85307346326837 - type: ap value: 22.447519885878737 - type: f1 value: 61.0162730745633 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 76.04925053533191 - type: ap value: 23.44983217128922 - type: f1 value: 62.5723230907759 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.28742500000001 - type: ap value: 94.8449918887462 - type: f1 value: 96.28680923610432 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 56.716 - type: f1 value: 55.76510398266401 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 52.99999999999999 - type: f1 value: 52.00829994765178 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.806000000000004 - type: f1 value: 48.082345914983634 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.507999999999996 - type: f1 value: 47.68752844642045 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.709999999999994 - type: f1 value: 47.05870376637181 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.662000000000006 - type: f1 value: 43.42371965372771 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 31.721 - type: map_at_10 value: 49.221 - type: map_at_100 value: 49.884 - type: map_at_1000 value: 49.888 - type: map_at_3 value: 44.31 - type: map_at_5 value: 47.276 - type: mrr_at_1 value: 32.432 - type: mrr_at_10 value: 49.5 - type: mrr_at_100 value: 50.163000000000004 - type: mrr_at_1000 value: 50.166 - type: mrr_at_3 value: 44.618 - type: mrr_at_5 value: 47.541 - type: ndcg_at_1 value: 31.721 - type: ndcg_at_10 value: 58.384 - type: ndcg_at_100 value: 61.111000000000004 - type: ndcg_at_1000 value: 61.187999999999995 - type: ndcg_at_3 value: 48.386 - type: ndcg_at_5 value: 53.708999999999996 - type: precision_at_1 value: 31.721 - type: precision_at_10 value: 8.741 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.057 - type: precision_at_5 value: 14.609 - type: recall_at_1 value: 31.721 - type: recall_at_10 value: 87.411 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 60.171 - type: recall_at_5 value: 73.044 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 46.40419580759799 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.48593255007969 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.889179122289995 - type: mrr value: 77.61146286769556 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.15075203727929 - type: cos_sim_spearman value: 86.9622224570873 - type: euclidean_pearson value: 86.70473853624121 - type: euclidean_spearman value: 86.9622224570873 - type: manhattan_pearson value: 86.21089380980065 - type: manhattan_spearman value: 86.75318154937008 - 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: 99.65553235908142 - type: f1 value: 99.60681976339595 - type: precision value: 99.58246346555325 - type: recall value: 99.65553235908142 - 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: 99.26260180497468 - type: f1 value: 99.14520507740848 - type: precision value: 99.08650671362535 - type: recall value: 99.26260180497468 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (ru-en) config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - 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task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.11244327231684 - type: cos_sim_spearman value: 88.37902438979035 - type: euclidean_pearson value: 87.86054279847336 - type: euclidean_spearman value: 88.37902438979035 - type: manhattan_pearson value: 87.77257757320378 - type: manhattan_spearman value: 88.25208966098123 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.87174608143563 - type: mrr value: 96.12836872640794 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 57.760999999999996 - type: map_at_10 value: 67.258 - type: map_at_100 value: 67.757 - type: map_at_1000 value: 67.78800000000001 - type: map_at_3 value: 64.602 - 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type: cos_sim_recall value: 78.25857519788919 - type: dot_accuracy value: 88.34714192048638 - type: dot_ap value: 80.26733698491206 - type: dot_f1 value: 73.53415148134374 - type: dot_precision value: 69.34767360299276 - type: dot_recall value: 78.25857519788919 - type: euclidean_accuracy value: 88.34714192048638 - type: euclidean_ap value: 80.26734337771738 - type: euclidean_f1 value: 73.53415148134374 - type: euclidean_precision value: 69.34767360299276 - type: euclidean_recall value: 78.25857519788919 - type: manhattan_accuracy value: 88.30541813196639 - type: manhattan_ap value: 80.19415808104145 - type: manhattan_f1 value: 73.55143870713441 - type: manhattan_precision value: 73.25307511122743 - type: manhattan_recall value: 73.85224274406332 - type: max_accuracy value: 88.34714192048638 - type: max_ap value: 80.26734337771738 - type: max_f1 value: 73.55143870713441 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.81061047075717 - type: cos_sim_ap value: 87.11747055081017 - type: cos_sim_f1 value: 80.04355498817256 - type: cos_sim_precision value: 78.1165262000733 - type: cos_sim_recall value: 82.06806282722513 - type: dot_accuracy value: 89.81061047075717 - type: dot_ap value: 87.11746902745236 - type: dot_f1 value: 80.04355498817256 - type: dot_precision value: 78.1165262000733 - type: dot_recall value: 82.06806282722513 - type: euclidean_accuracy value: 89.81061047075717 - type: euclidean_ap value: 87.11746919324248 - type: euclidean_f1 value: 80.04355498817256 - type: euclidean_precision value: 78.1165262000733 - type: euclidean_recall value: 82.06806282722513 - type: manhattan_accuracy value: 89.79508673885202 - type: manhattan_ap value: 87.11074390832218 - type: manhattan_f1 value: 80.13002540726349 - type: manhattan_precision value: 77.83826945412311 - type: manhattan_recall value: 82.56082537727133 - type: max_accuracy value: 89.81061047075717 - type: max_ap value: 87.11747055081017 - type: max_f1 value: 80.13002540726349 language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- ## Multilingual-E5-large-instruct [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 24 layers and the embedding size is 1024. ## Usage Below are examples to encode queries and passages from the MS-MARCO passage ranking dataset. ### Transformers ```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] def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery: {query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'how much protein should a female eat'), get_detailed_instruct(task, '南瓜的家常做法') ] # No need to add instruction for retrieval documents documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] input_texts = queries + documents tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large-instruct') model = AutoModel.from_pretrained('intfloat/multilingual-e5-large-instruct') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) # => [[91.92852783203125, 67.580322265625], [70.3814468383789, 92.1330795288086]] ``` ### Sentence Transformers ```python from sentence_transformers import SentenceTransformer def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery: {query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'how much protein should a female eat'), get_detailed_instruct(task, '南瓜的家常做法') ] # No need to add instruction for retrieval documents documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] input_texts = queries + documents model = SentenceTransformer('intfloat/multilingual-e5-large-instruct') embeddings = model.encode(input_texts, convert_to_tensor=True, normalize_embeddings=True) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) # [[91.92853546142578, 67.5802993774414], [70.38143157958984, 92.13307189941406]] ``` ## Supported Languages This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) **First stage**: contrastive pre-training with 1 billion weakly supervised text pairs. **Second stage**: fine-tuning on datasets from the [E5-mistral](https://arxiv.org/abs/2401.00368) paper. ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## FAQ **1. Do I need to add instructions to the query?** Yes, this is how the model is trained, otherwise you will see a performance degradation. The task definition should be a one-sentence instruction that describes the task. This is a way to customize text embeddings for different scenarios through natural language instructions. Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation. On the other hand, there is no need to add instructions to the document side. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.

openai/whisper-base

openai

transformers

automatic-speech-recognition

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

swl-models/hans-v4.4

swl-models

diffusers

text-to-image

--- license: cc-by-nc-4.0 language: - en library_name: diffusers pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers --- hansv4.4 模型作者：hans 发布时间：2023-01-31 模型类型：ckpt

naver/efficient-splade-VI-BT-large-doc

naver

transformers

fill-mask

--- license: cc-by-nc-sa-4.0 language: "en" tags: - splade - query-expansion - document-expansion - bag-of-words - passage-retrieval - knowledge-distillation - document encoder datasets: - ms_marco --- ## Efficient SPLADE Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **doc** one, please also download the **query** one (https://huggingface.co/naver/efficient-splade-VI-BT-large-query). For additional details, please visit: * paper: https://dl.acm.org/doi/10.1145/3477495.3531833 * code: https://github.com/naver/splade | | MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | Latency (PISA) ms | Latency (Inference) ms | --- | --- | --- | --- | --- | | `naver/efficient-splade-V-large` | 38.8 | 98.0 | 29.0 | 45.3 | `naver/efficient-splade-VI-BT-large` | 38.0 | 97.8 | 31.1 | 0.7 ## Citation If you use our checkpoint, please cite our work: ``` @inproceedings{10.1145/3477495.3531833, author = {Lassance, Carlos and Clinchant, St\'{e}phane}, title = {An Efficiency Study for SPLADE Models}, year = {2022}, isbn = {9781450387323}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3477495.3531833}, doi = {10.1145/3477495.3531833}, abstract = {Latency and efficiency issues are often overlooked when evaluating IR models based on Pretrained Language Models (PLMs) in reason of multiple hardware and software testing scenarios. Nevertheless, efficiency is an important part of such systems and should not be overlooked. In this paper, we focus on improving the efficiency of the SPLADE model since it has achieved state-of-the-art zero-shot performance and competitive results on TREC collections. SPLADE efficiency can be controlled via a regularization factor, but solely controlling this regularization has been shown to not be efficient enough. In order to reduce the latency gap between SPLADE and traditional retrieval systems, we propose several techniques including L1 regularization for queries, a separation of document/query encoders, a FLOPS-regularized middle-training, and the use of faster query encoders. Our benchmark demonstrates that we can drastically improve the efficiency of these models while increasing the performance metrics on in-domain data. To our knowledge, we propose the first neural models that, under the same computing constraints, achieve similar latency (less than 4ms difference) as traditional BM25, while having similar performance (less than 10% MRR@10 reduction) as the state-of-the-art single-stage neural rankers on in-domain data.}, booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval}, pages = {2220–2226}, numpages = {7}, keywords = {splade, latency, information retrieval, sparse representations}, location = {Madrid, Spain}, series = {SIGIR '22} } ```

sentence-transformers/distilbert-base-nli-mean-tokens

sentence-transformers

sentence-transformers

feature-extraction

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: feature-extraction --- **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)** # sentence-transformers/distilbert-base-nli-mean-tokens 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/distilbert-base-nli-mean-tokens') 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/distilbert-base-nli-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/distilbert-base-nli-mean-tokens') # 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/distilbert-base-nli-mean-tokens) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (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", } ```

digiplay/AbsoluteReality_v1.8.1

digiplay

diffusers

text-to-image

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/81458?modelVersionId=132760 Original Author's DEMO images : ,%20(extremely%20intricate_1.3),,%20(realistic),%20portrait%20of%20a%20girl,%20the%20most%20beautiful%20in%20the%20world,%20(medieval%20armor),%20m.jpeg) .jpeg) ),%20intricate,%20(steel%20metal%20[rust]),%20elegant,.jpeg)

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.

WhereIsAI/UAE-Large-V1

WhereIsAI

sentence-transformers

feature-extraction

--- tags: - mteb - sentence_embedding - feature_extraction - sentence-transformers - transformers - transformers.js model-index: - name: UAE-Large-V1 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.55223880597015 - type: ap value: 38.264070815317794 - type: f1 value: 69.40977934769845 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.84267499999999 - type: ap value: 89.57568507997713 - type: f1 value: 92.82590734337774 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.292 - type: f1 value: 47.90257816032778 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 42.105 - type: map_at_10 value: 58.181000000000004 - type: map_at_100 value: 58.653999999999996 - type: map_at_1000 value: 58.657000000000004 - type: map_at_3 value: 54.386 - type: map_at_5 value: 56.757999999999996 - type: mrr_at_1 value: 42.745 - type: mrr_at_10 value: 58.437 - type: mrr_at_100 value: 58.894999999999996 - type: mrr_at_1000 value: 58.897999999999996 - type: mrr_at_3 value: 54.635 - type: mrr_at_5 value: 56.99999999999999 - type: ndcg_at_1 value: 42.105 - type: ndcg_at_10 value: 66.14999999999999 - type: ndcg_at_100 value: 68.048 - type: ndcg_at_1000 value: 68.11399999999999 - type: ndcg_at_3 value: 58.477000000000004 - type: ndcg_at_5 value: 62.768 - type: precision_at_1 value: 42.105 - type: precision_at_10 value: 9.110999999999999 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 23.447000000000003 - type: precision_at_5 value: 16.159000000000002 - type: recall_at_1 value: 42.105 - type: recall_at_10 value: 91.11 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 70.341 - type: recall_at_5 value: 80.797 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 49.02580759154173 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.093601280163554 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.19590406875427 - type: mrr value: 77.09547992788991 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.86678362843676 - type: cos_sim_spearman value: 86.1423242570783 - type: euclidean_pearson value: 85.98994198511751 - type: euclidean_spearman value: 86.48209103503942 - type: manhattan_pearson value: 85.6446436316182 - type: manhattan_spearman value: 86.21039809734357 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.69155844155844 - type: f1 value: 87.68109381943547 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.37501687500394 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 37.23401405155885 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.232 - type: map_at_10 value: 41.404999999999994 - type: map_at_100 value: 42.896 - type: map_at_1000 value: 43.028 - type: map_at_3 value: 37.925 - type: map_at_5 value: 39.865 - type: mrr_at_1 value: 36.338 - type: mrr_at_10 value: 46.969 - type: mrr_at_100 value: 47.684 - type: mrr_at_1000 value: 47.731 - type: mrr_at_3 value: 44.063 - type: mrr_at_5 value: 45.908 - type: ndcg_at_1 value: 36.338 - type: ndcg_at_10 value: 47.887 - type: ndcg_at_100 value: 53.357 - type: ndcg_at_1000 value: 55.376999999999995 - type: ndcg_at_3 value: 42.588 - type: ndcg_at_5 value: 45.132 - type: precision_at_1 value: 36.338 - type: precision_at_10 value: 9.17 - type: precision_at_100 value: 1.4909999999999999 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 20.315 - type: precision_at_5 value: 14.793000000000001 - type: recall_at_1 value: 30.232 - type: recall_at_10 value: 60.67399999999999 - type: recall_at_100 value: 83.628 - type: recall_at_1000 value: 96.209 - type: recall_at_3 value: 45.48 - type: recall_at_5 value: 52.354 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.237 - type: map_at_10 value: 42.829 - type: map_at_100 value: 44.065 - type: map_at_1000 value: 44.199 - type: map_at_3 value: 39.885999999999996 - type: map_at_5 value: 41.55 - type: mrr_at_1 value: 40.064 - type: mrr_at_10 value: 48.611 - type: mrr_at_100 value: 49.245 - type: mrr_at_1000 value: 49.29 - type: mrr_at_3 value: 46.561 - type: mrr_at_5 value: 47.771 - type: ndcg_at_1 value: 40.064 - type: ndcg_at_10 value: 48.388 - type: ndcg_at_100 value: 52.666999999999994 - type: ndcg_at_1000 value: 54.67100000000001 - type: ndcg_at_3 value: 44.504 - type: ndcg_at_5 value: 46.303 - type: precision_at_1 value: 40.064 - type: precision_at_10 value: 9.051 - type: precision_at_100 value: 1.4500000000000002 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 21.444 - type: precision_at_5 value: 15.045 - type: recall_at_1 value: 32.237 - type: recall_at_10 value: 57.943999999999996 - type: recall_at_100 value: 75.98700000000001 - type: recall_at_1000 value: 88.453 - type: recall_at_3 value: 46.268 - type: recall_at_5 value: 51.459999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.797 - type: map_at_10 value: 51.263000000000005 - type: map_at_100 value: 52.333 - type: map_at_1000 value: 52.393 - type: map_at_3 value: 47.936 - type: map_at_5 value: 49.844 - type: mrr_at_1 value: 44.389 - type: mrr_at_10 value: 54.601 - type: mrr_at_100 value: 55.300000000000004 - type: mrr_at_1000 value: 55.333 - type: mrr_at_3 value: 52.068999999999996 - type: mrr_at_5 value: 53.627 - type: ndcg_at_1 value: 44.389 - type: ndcg_at_10 value: 57.193000000000005 - type: ndcg_at_100 value: 61.307 - type: ndcg_at_1000 value: 62.529 - type: ndcg_at_3 value: 51.607 - type: ndcg_at_5 value: 54.409 - type: precision_at_1 value: 44.389 - type: precision_at_10 value: 9.26 - type: precision_at_100 value: 1.222 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 23.03 - type: precision_at_5 value: 15.887 - type: recall_at_1 value: 38.797 - type: recall_at_10 value: 71.449 - type: recall_at_100 value: 88.881 - type: recall_at_1000 value: 97.52 - type: recall_at_3 value: 56.503 - type: recall_at_5 value: 63.392 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.291999999999998 - type: map_at_10 value: 35.65 - type: map_at_100 value: 36.689 - type: map_at_1000 value: 36.753 - type: map_at_3 value: 32.995000000000005 - type: map_at_5 value: 34.409 - type: mrr_at_1 value: 29.04 - type: mrr_at_10 value: 37.486000000000004 - type: mrr_at_100 value: 38.394 - type: mrr_at_1000 value: 38.445 - type: mrr_at_3 value: 35.028 - type: mrr_at_5 value: 36.305 - type: ndcg_at_1 value: 29.04 - type: ndcg_at_10 value: 40.613 - type: ndcg_at_100 value: 45.733000000000004 - type: ndcg_at_1000 value: 47.447 - type: ndcg_at_3 value: 35.339999999999996 - type: ndcg_at_5 value: 37.706 - type: precision_at_1 value: 29.04 - type: precision_at_10 value: 6.192 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 14.802000000000001 - type: precision_at_5 value: 10.305 - type: recall_at_1 value: 27.291999999999998 - type: recall_at_10 value: 54.25299999999999 - type: recall_at_100 value: 77.773 - type: recall_at_1000 value: 90.795 - type: recall_at_3 value: 39.731 - type: recall_at_5 value: 45.403999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.326 - type: map_at_10 value: 26.290999999999997 - type: map_at_100 value: 27.456999999999997 - type: map_at_1000 value: 27.583000000000002 - type: map_at_3 value: 23.578 - type: map_at_5 value: 25.113000000000003 - type: mrr_at_1 value: 22.637 - type: mrr_at_10 value: 31.139 - type: mrr_at_100 value: 32.074999999999996 - type: mrr_at_1000 value: 32.147 - type: mrr_at_3 value: 28.483000000000004 - type: mrr_at_5 value: 29.963 - type: ndcg_at_1 value: 22.637 - type: ndcg_at_10 value: 31.717000000000002 - type: ndcg_at_100 value: 37.201 - type: ndcg_at_1000 value: 40.088 - type: ndcg_at_3 value: 26.686 - type: ndcg_at_5 value: 29.076999999999998 - type: precision_at_1 value: 22.637 - type: precision_at_10 value: 5.7090000000000005 - type: precision_at_100 value: 0.979 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.894 - type: precision_at_5 value: 9.328 - type: recall_at_1 value: 18.326 - type: recall_at_10 value: 43.824999999999996 - type: recall_at_100 value: 67.316 - type: recall_at_1000 value: 87.481 - type: recall_at_3 value: 29.866999999999997 - type: recall_at_5 value: 35.961999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.875 - type: map_at_10 value: 40.458 - type: map_at_100 value: 41.772 - type: map_at_1000 value: 41.882999999999996 - type: map_at_3 value: 37.086999999999996 - type: map_at_5 value: 39.153 - type: mrr_at_1 value: 36.381 - type: mrr_at_10 value: 46.190999999999995 - type: mrr_at_100 value: 46.983999999999995 - type: mrr_at_1000 value: 47.032000000000004 - type: mrr_at_3 value: 43.486999999999995 - type: mrr_at_5 value: 45.249 - type: ndcg_at_1 value: 36.381 - type: ndcg_at_10 value: 46.602 - type: ndcg_at_100 value: 51.885999999999996 - type: ndcg_at_1000 value: 53.895 - type: ndcg_at_3 value: 41.155 - type: ndcg_at_5 value: 44.182 - type: precision_at_1 value: 36.381 - type: precision_at_10 value: 8.402 - type: precision_at_100 value: 1.278 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 19.346 - type: precision_at_5 value: 14.09 - type: recall_at_1 value: 29.875 - type: recall_at_10 value: 59.065999999999995 - type: recall_at_100 value: 80.923 - type: recall_at_1000 value: 93.927 - type: recall_at_3 value: 44.462 - type: recall_at_5 value: 51.89 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.94 - type: map_at_10 value: 35.125 - type: map_at_100 value: 36.476 - type: map_at_1000 value: 36.579 - type: map_at_3 value: 31.840000000000003 - type: map_at_5 value: 33.647 - type: mrr_at_1 value: 30.936000000000003 - type: mrr_at_10 value: 40.637 - type: mrr_at_100 value: 41.471000000000004 - type: mrr_at_1000 value: 41.525 - type: mrr_at_3 value: 38.013999999999996 - type: mrr_at_5 value: 39.469 - type: ndcg_at_1 value: 30.936000000000003 - type: ndcg_at_10 value: 41.295 - type: ndcg_at_100 value: 46.92 - type: ndcg_at_1000 value: 49.183 - type: ndcg_at_3 value: 35.811 - type: ndcg_at_5 value: 38.306000000000004 - type: precision_at_1 value: 30.936000000000003 - type: precision_at_10 value: 7.728 - type: precision_at_100 value: 1.226 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.237 - type: precision_at_5 value: 12.42 - type: recall_at_1 value: 24.94 - type: recall_at_10 value: 54.235 - type: recall_at_100 value: 78.314 - type: recall_at_1000 value: 93.973 - type: recall_at_3 value: 38.925 - type: recall_at_5 value: 45.505 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.250833333333333 - type: map_at_10 value: 35.46875 - type: map_at_100 value: 36.667 - type: map_at_1000 value: 36.78025 - type: map_at_3 value: 32.56733333333334 - type: map_at_5 value: 34.20333333333333 - type: mrr_at_1 value: 30.8945 - type: mrr_at_10 value: 39.636833333333335 - type: mrr_at_100 value: 40.46508333333333 - type: mrr_at_1000 value: 40.521249999999995 - type: mrr_at_3 value: 37.140166666666666 - type: mrr_at_5 value: 38.60999999999999 - type: ndcg_at_1 value: 30.8945 - type: ndcg_at_10 value: 40.93441666666667 - type: ndcg_at_100 value: 46.062416666666664 - type: ndcg_at_1000 value: 48.28341666666667 - type: ndcg_at_3 value: 35.97575 - type: ndcg_at_5 value: 38.3785 - type: precision_at_1 value: 30.8945 - type: precision_at_10 value: 7.180250000000001 - type: precision_at_100 value: 1.1468333333333334 - type: precision_at_1000 value: 0.15283333333333332 - type: precision_at_3 value: 16.525583333333334 - type: precision_at_5 value: 11.798333333333332 - type: recall_at_1 value: 26.250833333333333 - type: recall_at_10 value: 52.96108333333333 - type: recall_at_100 value: 75.45908333333334 - type: recall_at_1000 value: 90.73924999999998 - type: recall_at_3 value: 39.25483333333333 - type: recall_at_5 value: 45.37950000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.595 - type: map_at_10 value: 31.747999999999998 - type: map_at_100 value: 32.62 - type: map_at_1000 value: 32.713 - type: map_at_3 value: 29.48 - type: map_at_5 value: 30.635 - type: mrr_at_1 value: 27.607 - type: mrr_at_10 value: 34.449000000000005 - type: mrr_at_100 value: 35.182 - type: mrr_at_1000 value: 35.254000000000005 - type: mrr_at_3 value: 32.413 - type: mrr_at_5 value: 33.372 - type: ndcg_at_1 value: 27.607 - type: ndcg_at_10 value: 36.041000000000004 - type: ndcg_at_100 value: 40.514 - type: ndcg_at_1000 value: 42.851 - type: ndcg_at_3 value: 31.689 - type: ndcg_at_5 value: 33.479 - type: precision_at_1 value: 27.607 - type: precision_at_10 value: 5.66 - type: precision_at_100 value: 0.868 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 13.446 - type: precision_at_5 value: 9.264 - type: recall_at_1 value: 24.595 - type: recall_at_10 value: 46.79 - type: recall_at_100 value: 67.413 - type: recall_at_1000 value: 84.753 - type: recall_at_3 value: 34.644999999999996 - type: recall_at_5 value: 39.09 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.333000000000002 - type: map_at_10 value: 24.427 - type: map_at_100 value: 25.576 - type: map_at_1000 value: 25.692999999999998 - type: map_at_3 value: 22.002 - type: map_at_5 value: 23.249 - type: mrr_at_1 value: 20.716 - type: mrr_at_10 value: 28.072000000000003 - type: mrr_at_100 value: 29.067 - type: mrr_at_1000 value: 29.137 - type: mrr_at_3 value: 25.832 - type: mrr_at_5 value: 27.045 - type: ndcg_at_1 value: 20.716 - type: ndcg_at_10 value: 29.109 - type: ndcg_at_100 value: 34.797 - type: ndcg_at_1000 value: 37.503 - type: ndcg_at_3 value: 24.668 - type: ndcg_at_5 value: 26.552999999999997 - type: precision_at_1 value: 20.716 - type: precision_at_10 value: 5.351 - type: precision_at_100 value: 0.955 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 11.584999999999999 - type: precision_at_5 value: 8.362 - type: recall_at_1 value: 17.333000000000002 - type: recall_at_10 value: 39.604 - type: recall_at_100 value: 65.525 - type: recall_at_1000 value: 84.651 - type: recall_at_3 value: 27.199 - type: recall_at_5 value: 32.019 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.342 - type: map_at_10 value: 35.349000000000004 - type: map_at_100 value: 36.443 - type: map_at_1000 value: 36.548 - type: map_at_3 value: 32.307 - type: map_at_5 value: 34.164 - type: mrr_at_1 value: 31.063000000000002 - type: mrr_at_10 value: 39.703 - type: mrr_at_100 value: 40.555 - type: mrr_at_1000 value: 40.614 - type: mrr_at_3 value: 37.141999999999996 - type: mrr_at_5 value: 38.812000000000005 - type: ndcg_at_1 value: 31.063000000000002 - type: ndcg_at_10 value: 40.873 - type: ndcg_at_100 value: 45.896 - type: ndcg_at_1000 value: 48.205999999999996 - type: ndcg_at_3 value: 35.522 - type: ndcg_at_5 value: 38.419 - type: precision_at_1 value: 31.063000000000002 - type: precision_at_10 value: 6.866 - type: precision_at_100 value: 1.053 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 16.014 - type: precision_at_5 value: 11.604000000000001 - type: recall_at_1 value: 26.342 - type: recall_at_10 value: 53.40200000000001 - type: recall_at_100 value: 75.251 - type: recall_at_1000 value: 91.13799999999999 - type: recall_at_3 value: 39.103 - type: recall_at_5 value: 46.357 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.71 - type: map_at_10 value: 32.153999999999996 - type: map_at_100 value: 33.821 - type: map_at_1000 value: 34.034 - type: map_at_3 value: 29.376 - type: map_at_5 value: 30.878 - type: mrr_at_1 value: 28.458 - type: mrr_at_10 value: 36.775999999999996 - type: mrr_at_100 value: 37.804 - type: mrr_at_1000 value: 37.858999999999995 - type: mrr_at_3 value: 34.123999999999995 - type: mrr_at_5 value: 35.596 - type: ndcg_at_1 value: 28.458 - type: ndcg_at_10 value: 37.858999999999995 - type: ndcg_at_100 value: 44.194 - type: ndcg_at_1000 value: 46.744 - type: ndcg_at_3 value: 33.348 - type: ndcg_at_5 value: 35.448 - type: precision_at_1 value: 28.458 - type: precision_at_10 value: 7.4510000000000005 - type: precision_at_100 value: 1.5 - type: precision_at_1000 value: 0.23700000000000002 - type: precision_at_3 value: 15.809999999999999 - type: precision_at_5 value: 11.462 - type: recall_at_1 value: 23.71 - type: recall_at_10 value: 48.272999999999996 - type: recall_at_100 value: 77.134 - type: recall_at_1000 value: 93.001 - type: recall_at_3 value: 35.480000000000004 - type: recall_at_5 value: 41.19 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.331 - type: map_at_10 value: 28.926000000000002 - type: map_at_100 value: 29.855999999999998 - type: map_at_1000 value: 29.957 - type: map_at_3 value: 26.395999999999997 - type: map_at_5 value: 27.933000000000003 - type: mrr_at_1 value: 23.105 - type: mrr_at_10 value: 31.008000000000003 - type: mrr_at_100 value: 31.819999999999997 - type: mrr_at_1000 value: 31.887999999999998 - type: mrr_at_3 value: 28.466 - type: mrr_at_5 value: 30.203000000000003 - type: ndcg_at_1 value: 23.105 - type: ndcg_at_10 value: 33.635999999999996 - type: ndcg_at_100 value: 38.277 - type: ndcg_at_1000 value: 40.907 - type: ndcg_at_3 value: 28.791 - type: ndcg_at_5 value: 31.528 - type: precision_at_1 value: 23.105 - type: precision_at_10 value: 5.323 - type: precision_at_100 value: 0.815 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 12.384 - type: precision_at_5 value: 9.02 - type: recall_at_1 value: 21.331 - type: recall_at_10 value: 46.018 - type: recall_at_100 value: 67.364 - type: recall_at_1000 value: 86.97 - type: recall_at_3 value: 33.395 - type: recall_at_5 value: 39.931 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 17.011000000000003 - type: map_at_10 value: 28.816999999999997 - type: map_at_100 value: 30.761 - type: map_at_1000 value: 30.958000000000002 - type: map_at_3 value: 24.044999999999998 - type: map_at_5 value: 26.557 - type: mrr_at_1 value: 38.696999999999996 - type: mrr_at_10 value: 50.464 - type: mrr_at_100 value: 51.193999999999996 - type: mrr_at_1000 value: 51.219 - type: mrr_at_3 value: 47.339999999999996 - type: mrr_at_5 value: 49.346000000000004 - type: ndcg_at_1 value: 38.696999999999996 - type: ndcg_at_10 value: 38.53 - type: ndcg_at_100 value: 45.525 - type: ndcg_at_1000 value: 48.685 - type: ndcg_at_3 value: 32.282 - type: ndcg_at_5 value: 34.482 - type: precision_at_1 value: 38.696999999999996 - type: precision_at_10 value: 11.895999999999999 - type: precision_at_100 value: 1.95 - type: precision_at_1000 value: 0.254 - type: precision_at_3 value: 24.038999999999998 - type: precision_at_5 value: 18.332 - type: recall_at_1 value: 17.011000000000003 - type: recall_at_10 value: 44.452999999999996 - type: recall_at_100 value: 68.223 - type: recall_at_1000 value: 85.653 - type: recall_at_3 value: 28.784 - type: recall_at_5 value: 35.66 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.516 - type: map_at_10 value: 21.439 - type: map_at_100 value: 31.517 - type: map_at_1000 value: 33.267 - type: map_at_3 value: 15.004999999999999 - type: map_at_5 value: 17.793999999999997 - type: mrr_at_1 value: 71.25 - type: mrr_at_10 value: 79.071 - type: mrr_at_100 value: 79.325 - type: mrr_at_1000 value: 79.33 - type: mrr_at_3 value: 77.708 - type: mrr_at_5 value: 78.546 - type: ndcg_at_1 value: 58.62500000000001 - type: ndcg_at_10 value: 44.889 - type: ndcg_at_100 value: 50.536 - type: ndcg_at_1000 value: 57.724 - type: ndcg_at_3 value: 49.32 - type: ndcg_at_5 value: 46.775 - type: precision_at_1 value: 71.25 - type: precision_at_10 value: 36.175000000000004 - type: precision_at_100 value: 11.940000000000001 - type: precision_at_1000 value: 2.178 - type: precision_at_3 value: 53.583000000000006 - type: precision_at_5 value: 45.550000000000004 - type: recall_at_1 value: 9.516 - type: recall_at_10 value: 27.028000000000002 - type: recall_at_100 value: 57.581 - type: recall_at_1000 value: 80.623 - type: recall_at_3 value: 16.313 - type: recall_at_5 value: 20.674 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 51.74999999999999 - type: f1 value: 46.46706502669774 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 77.266 - type: map_at_10 value: 84.89999999999999 - type: map_at_100 value: 85.109 - type: map_at_1000 value: 85.123 - type: map_at_3 value: 83.898 - type: map_at_5 value: 84.541 - type: mrr_at_1 value: 83.138 - type: mrr_at_10 value: 89.37 - type: mrr_at_100 value: 89.432 - type: mrr_at_1000 value: 89.43299999999999 - type: mrr_at_3 value: 88.836 - type: mrr_at_5 value: 89.21 - type: ndcg_at_1 value: 83.138 - type: ndcg_at_10 value: 88.244 - type: ndcg_at_100 value: 88.98700000000001 - type: ndcg_at_1000 value: 89.21900000000001 - type: ndcg_at_3 value: 86.825 - type: ndcg_at_5 value: 87.636 - type: precision_at_1 value: 83.138 - type: precision_at_10 value: 10.47 - type: precision_at_100 value: 1.1079999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.933 - type: precision_at_5 value: 20.36 - type: recall_at_1 value: 77.266 - type: recall_at_10 value: 94.063 - type: recall_at_100 value: 96.993 - type: recall_at_1000 value: 98.414 - type: recall_at_3 value: 90.228 - type: recall_at_5 value: 92.328 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.319 - type: map_at_10 value: 36.943 - type: map_at_100 value: 38.951 - type: map_at_1000 value: 39.114 - type: map_at_3 value: 32.82 - type: map_at_5 value: 34.945 - type: mrr_at_1 value: 44.135999999999996 - type: mrr_at_10 value: 53.071999999999996 - type: mrr_at_100 value: 53.87 - type: mrr_at_1000 value: 53.90200000000001 - type: mrr_at_3 value: 50.77199999999999 - type: mrr_at_5 value: 52.129999999999995 - type: ndcg_at_1 value: 44.135999999999996 - type: ndcg_at_10 value: 44.836 - type: ndcg_at_100 value: 51.754 - type: ndcg_at_1000 value: 54.36 - type: ndcg_at_3 value: 41.658 - type: ndcg_at_5 value: 42.354 - type: precision_at_1 value: 44.135999999999996 - type: precision_at_10 value: 12.284 - type: precision_at_100 value: 1.952 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 27.828999999999997 - type: precision_at_5 value: 20.093 - type: recall_at_1 value: 22.319 - type: recall_at_10 value: 51.528 - type: recall_at_100 value: 76.70700000000001 - type: recall_at_1000 value: 92.143 - type: recall_at_3 value: 38.641 - type: recall_at_5 value: 43.653999999999996 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.182 - type: map_at_10 value: 65.146 - type: map_at_100 value: 66.023 - type: map_at_1000 value: 66.078 - type: map_at_3 value: 61.617999999999995 - type: map_at_5 value: 63.82299999999999 - type: mrr_at_1 value: 80.365 - type: mrr_at_10 value: 85.79 - type: mrr_at_100 value: 85.963 - type: mrr_at_1000 value: 85.968 - type: mrr_at_3 value: 84.952 - type: mrr_at_5 value: 85.503 - type: ndcg_at_1 value: 80.365 - type: ndcg_at_10 value: 73.13499999999999 - type: ndcg_at_100 value: 76.133 - type: ndcg_at_1000 value: 77.151 - type: ndcg_at_3 value: 68.255 - type: ndcg_at_5 value: 70.978 - type: precision_at_1 value: 80.365 - type: precision_at_10 value: 15.359 - type: precision_at_100 value: 1.7690000000000001 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 44.024 - type: precision_at_5 value: 28.555999999999997 - type: recall_at_1 value: 40.182 - type: recall_at_10 value: 76.793 - type: recall_at_100 value: 88.474 - type: recall_at_1000 value: 95.159 - type: recall_at_3 value: 66.036 - type: recall_at_5 value: 71.391 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.7796 - type: ap value: 89.24883716810874 - type: f1 value: 92.7706903433313 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.016 - type: map_at_10 value: 34.408 - type: map_at_100 value: 35.592 - type: map_at_1000 value: 35.64 - type: map_at_3 value: 30.459999999999997 - type: map_at_5 value: 32.721000000000004 - type: mrr_at_1 value: 22.593 - type: mrr_at_10 value: 34.993 - type: mrr_at_100 value: 36.113 - type: mrr_at_1000 value: 36.156 - type: mrr_at_3 value: 31.101 - type: mrr_at_5 value: 33.364 - type: ndcg_at_1 value: 22.579 - type: ndcg_at_10 value: 41.404999999999994 - type: ndcg_at_100 value: 47.018 - type: ndcg_at_1000 value: 48.211999999999996 - type: ndcg_at_3 value: 33.389 - type: ndcg_at_5 value: 37.425000000000004 - type: precision_at_1 value: 22.579 - type: precision_at_10 value: 6.59 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.241000000000001 - type: precision_at_5 value: 10.59 - type: recall_at_1 value: 22.016 - type: recall_at_10 value: 62.927 - type: recall_at_100 value: 88.72 - type: recall_at_1000 value: 97.80799999999999 - type: recall_at_3 value: 41.229 - type: recall_at_5 value: 50.88 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.01732786137711 - type: f1 value: 93.76353126402202 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.91746466028272 - type: f1 value: 57.715651682646765 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.5030262273033 - type: f1 value: 74.6693629986121 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.74781439139207 - type: f1 value: 79.96684171018774 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.2156206892017 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.180539484816137 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.51125957874274 - type: mrr value: 33.777037359249995 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.248 - type: map_at_10 value: 15.340000000000002 - type: map_at_100 value: 19.591 - type: map_at_1000 value: 21.187 - type: map_at_3 value: 11.329 - type: map_at_5 value: 13.209999999999999 - type: mrr_at_1 value: 47.678 - type: mrr_at_10 value: 57.493 - type: mrr_at_100 value: 58.038999999999994 - type: mrr_at_1000 value: 58.07 - type: mrr_at_3 value: 55.36600000000001 - type: mrr_at_5 value: 56.635999999999996 - type: ndcg_at_1 value: 46.129999999999995 - type: ndcg_at_10 value: 38.653999999999996 - type: ndcg_at_100 value: 36.288 - type: ndcg_at_1000 value: 44.765 - type: ndcg_at_3 value: 43.553 - type: ndcg_at_5 value: 41.317 - type: precision_at_1 value: 47.368 - type: precision_at_10 value: 28.669 - type: precision_at_100 value: 9.158 - type: precision_at_1000 value: 2.207 - type: precision_at_3 value: 40.97 - type: precision_at_5 value: 35.604 - type: recall_at_1 value: 7.248 - type: recall_at_10 value: 19.46 - type: recall_at_100 value: 37.214000000000006 - type: recall_at_1000 value: 67.64099999999999 - type: recall_at_3 value: 12.025 - type: recall_at_5 value: 15.443999999999999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.595000000000002 - type: map_at_10 value: 47.815999999999995 - type: map_at_100 value: 48.811 - type: map_at_1000 value: 48.835 - type: map_at_3 value: 43.225 - type: map_at_5 value: 46.017 - type: mrr_at_1 value: 35.689 - type: mrr_at_10 value: 50.341 - type: mrr_at_100 value: 51.044999999999995 - type: mrr_at_1000 value: 51.062 - type: mrr_at_3 value: 46.553 - type: mrr_at_5 value: 48.918 - type: ndcg_at_1 value: 35.66 - type: ndcg_at_10 value: 55.859 - type: ndcg_at_100 value: 59.864 - type: ndcg_at_1000 value: 60.419999999999995 - type: ndcg_at_3 value: 47.371 - type: ndcg_at_5 value: 51.995000000000005 - type: precision_at_1 value: 35.66 - type: precision_at_10 value: 9.27 - type: precision_at_100 value: 1.1520000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 21.63 - type: precision_at_5 value: 15.655 - type: recall_at_1 value: 31.595000000000002 - type: recall_at_10 value: 77.704 - type: recall_at_100 value: 94.774 - type: recall_at_1000 value: 98.919 - type: recall_at_3 value: 56.052 - type: recall_at_5 value: 66.623 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.489 - type: map_at_10 value: 85.411 - type: map_at_100 value: 86.048 - type: map_at_1000 value: 86.064 - type: map_at_3 value: 82.587 - type: map_at_5 value: 84.339 - type: mrr_at_1 value: 82.28 - type: mrr_at_10 value: 88.27199999999999 - type: mrr_at_100 value: 88.362 - type: mrr_at_1000 value: 88.362 - type: mrr_at_3 value: 87.372 - type: mrr_at_5 value: 87.995 - type: ndcg_at_1 value: 82.27 - type: ndcg_at_10 value: 89.023 - type: ndcg_at_100 value: 90.191 - type: ndcg_at_1000 value: 90.266 - type: ndcg_at_3 value: 86.37 - type: ndcg_at_5 value: 87.804 - type: precision_at_1 value: 82.27 - type: precision_at_10 value: 13.469000000000001 - type: precision_at_100 value: 1.533 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.797 - type: precision_at_5 value: 24.734 - type: recall_at_1 value: 71.489 - type: recall_at_10 value: 95.824 - type: recall_at_100 value: 99.70599999999999 - type: recall_at_1000 value: 99.979 - type: recall_at_3 value: 88.099 - type: recall_at_5 value: 92.285 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.52398807444541 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 65.34855891507871 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.188000000000001 - type: map_at_10 value: 13.987 - type: map_at_100 value: 16.438 - type: map_at_1000 value: 16.829 - type: map_at_3 value: 9.767000000000001 - type: map_at_5 value: 11.912 - type: mrr_at_1 value: 25.6 - type: mrr_at_10 value: 37.744 - type: mrr_at_100 value: 38.847 - type: mrr_at_1000 value: 38.894 - type: mrr_at_3 value: 34.166999999999994 - type: mrr_at_5 value: 36.207 - type: ndcg_at_1 value: 25.6 - type: ndcg_at_10 value: 22.980999999999998 - type: ndcg_at_100 value: 32.039 - type: ndcg_at_1000 value: 38.157000000000004 - type: ndcg_at_3 value: 21.567 - type: ndcg_at_5 value: 19.070999999999998 - type: precision_at_1 value: 25.6 - type: precision_at_10 value: 12.02 - type: precision_at_100 value: 2.5100000000000002 - type: precision_at_1000 value: 0.396 - type: precision_at_3 value: 20.333000000000002 - type: precision_at_5 value: 16.98 - type: recall_at_1 value: 5.188000000000001 - type: recall_at_10 value: 24.372 - type: recall_at_100 value: 50.934999999999995 - type: recall_at_1000 value: 80.477 - type: recall_at_3 value: 12.363 - type: recall_at_5 value: 17.203 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 87.24286275535398 - type: cos_sim_spearman value: 82.62333770991818 - type: euclidean_pearson value: 84.60353717637284 - type: euclidean_spearman value: 82.32990108810047 - type: manhattan_pearson value: 84.6089049738196 - type: manhattan_spearman value: 82.33361785438936 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 87.87428858503165 - type: cos_sim_spearman value: 79.09145886519929 - type: euclidean_pearson value: 86.42669231664036 - type: euclidean_spearman value: 80.03127375435449 - type: manhattan_pearson value: 86.41330338305022 - type: manhattan_spearman value: 80.02492538673368 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 88.67912277322645 - type: cos_sim_spearman value: 89.6171319711762 - type: euclidean_pearson value: 86.56571917398725 - type: euclidean_spearman value: 87.71216907898948 - type: manhattan_pearson value: 86.57459050182473 - type: manhattan_spearman value: 87.71916648349993 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 86.71957379085862 - type: cos_sim_spearman value: 85.01784075851465 - type: euclidean_pearson value: 84.7407848472801 - type: euclidean_spearman value: 84.61063091345538 - type: manhattan_pearson value: 84.71494352494403 - type: manhattan_spearman value: 84.58772077604254 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.40508326325175 - type: cos_sim_spearman value: 89.50912897763186 - type: euclidean_pearson value: 87.82349070086627 - type: euclidean_spearman value: 88.44179162727521 - type: manhattan_pearson value: 87.80181927025595 - type: manhattan_spearman value: 88.43205129636243 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 85.35846741715478 - type: cos_sim_spearman value: 86.61172476741842 - type: euclidean_pearson value: 84.60123125491637 - type: euclidean_spearman value: 85.3001948141827 - type: manhattan_pearson value: 84.56231142658329 - type: manhattan_spearman value: 85.23579900798813 - 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: 88.94539129818824 - type: cos_sim_spearman value: 88.99349064256742 - type: euclidean_pearson value: 88.7142444640351 - type: euclidean_spearman value: 88.34120813505011 - type: manhattan_pearson value: 88.70363008238084 - type: manhattan_spearman value: 88.31952816956954 - 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: 68.29910260369893 - type: cos_sim_spearman value: 68.79263346213466 - type: euclidean_pearson value: 68.41627521422252 - type: euclidean_spearman value: 66.61602587398579 - type: manhattan_pearson value: 68.49402183447361 - type: manhattan_spearman value: 66.80157792354453 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.43703906343708 - type: cos_sim_spearman value: 89.06081805093662 - type: euclidean_pearson value: 87.48311456299662 - type: euclidean_spearman value: 88.07417597580013 - type: manhattan_pearson value: 87.48202249768894 - type: manhattan_spearman value: 88.04758031111642 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.49080620485203 - type: mrr value: 96.19145378949301 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 59.317 - type: map_at_10 value: 69.296 - type: map_at_100 value: 69.738 - type: map_at_1000 value: 69.759 - type: map_at_3 value: 66.12599999999999 - type: map_at_5 value: 67.532 - type: mrr_at_1 value: 62 - type: mrr_at_10 value: 70.176 - type: mrr_at_100 value: 70.565 - type: mrr_at_1000 value: 70.583 - type: mrr_at_3 value: 67.833 - type: mrr_at_5 value: 68.93299999999999 - type: ndcg_at_1 value: 62 - type: ndcg_at_10 value: 74.069 - type: ndcg_at_100 value: 76.037 - type: ndcg_at_1000 value: 76.467 - type: ndcg_at_3 value: 68.628 - type: ndcg_at_5 value: 70.57600000000001 - type: precision_at_1 value: 62 - type: precision_at_10 value: 10 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.667 - type: precision_at_5 value: 17.4 - type: recall_at_1 value: 59.317 - type: recall_at_10 value: 87.822 - type: recall_at_100 value: 96.833 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 73.06099999999999 - type: recall_at_5 value: 77.928 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.88910891089108 - type: cos_sim_ap value: 97.236958456951 - type: cos_sim_f1 value: 94.39999999999999 - type: cos_sim_precision value: 94.39999999999999 - type: cos_sim_recall value: 94.39999999999999 - type: dot_accuracy value: 99.82574257425742 - type: dot_ap value: 94.94344759441888 - type: dot_f1 value: 91.17352056168507 - type: dot_precision value: 91.44869215291752 - type: dot_recall value: 90.9 - type: euclidean_accuracy value: 99.88415841584158 - type: euclidean_ap value: 97.2044250782305 - type: euclidean_f1 value: 94.210786739238 - type: euclidean_precision value: 93.24191968658178 - type: euclidean_recall value: 95.19999999999999 - type: manhattan_accuracy value: 99.88613861386139 - type: manhattan_ap value: 97.20683205497689 - type: manhattan_f1 value: 94.2643391521197 - type: manhattan_precision value: 94.02985074626866 - type: manhattan_recall value: 94.5 - type: max_accuracy value: 99.88910891089108 - type: max_ap value: 97.236958456951 - type: max_f1 value: 94.39999999999999 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.53940781726187 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.71865011295108 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.3218674533331 - type: mrr value: 56.28279910449028 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.723915667479673 - type: cos_sim_spearman value: 32.029070449745234 - type: dot_pearson value: 28.864944212481454 - type: dot_spearman value: 27.939266999596725 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.231 - type: map_at_10 value: 1.949 - type: map_at_100 value: 10.023 - type: map_at_1000 value: 23.485 - type: map_at_3 value: 0.652 - type: map_at_5 value: 1.054 - type: mrr_at_1 value: 86 - type: mrr_at_10 value: 92.067 - type: mrr_at_100 value: 92.067 - type: mrr_at_1000 value: 92.067 - type: mrr_at_3 value: 91.667 - type: mrr_at_5 value: 92.067 - type: ndcg_at_1 value: 83 - type: ndcg_at_10 value: 76.32900000000001 - type: ndcg_at_100 value: 54.662 - type: ndcg_at_1000 value: 48.062 - type: ndcg_at_3 value: 81.827 - type: ndcg_at_5 value: 80.664 - type: precision_at_1 value: 86 - type: precision_at_10 value: 80 - type: precision_at_100 value: 55.48 - type: precision_at_1000 value: 20.938000000000002 - type: precision_at_3 value: 85.333 - type: precision_at_5 value: 84.39999999999999 - type: recall_at_1 value: 0.231 - type: recall_at_10 value: 2.158 - type: recall_at_100 value: 13.344000000000001 - type: recall_at_1000 value: 44.31 - type: recall_at_3 value: 0.6779999999999999 - type: recall_at_5 value: 1.13 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.524 - type: map_at_10 value: 10.183 - type: map_at_100 value: 16.625 - type: map_at_1000 value: 18.017 - type: map_at_3 value: 5.169 - type: map_at_5 value: 6.772 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 47.128 - type: mrr_at_100 value: 48.458 - type: mrr_at_1000 value: 48.473 - type: mrr_at_3 value: 44.897999999999996 - type: mrr_at_5 value: 45.306000000000004 - type: ndcg_at_1 value: 30.612000000000002 - type: ndcg_at_10 value: 24.928 - type: ndcg_at_100 value: 37.613 - type: ndcg_at_1000 value: 48.528 - type: ndcg_at_3 value: 28.829 - type: ndcg_at_5 value: 25.237 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 22.448999999999998 - type: precision_at_100 value: 8.02 - type: precision_at_1000 value: 1.537 - type: precision_at_3 value: 30.612000000000002 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.524 - type: recall_at_10 value: 16.38 - type: recall_at_100 value: 49.529 - type: recall_at_1000 value: 83.598 - type: recall_at_3 value: 6.411 - type: recall_at_5 value: 8.932 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.09020000000001 - type: ap value: 14.451710060978993 - type: f1 value: 54.7874410609049 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.745331069609506 - type: f1 value: 60.08387848592697 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 51.71549485462037 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.39345532574357 - type: cos_sim_ap value: 78.16796549696478 - type: cos_sim_f1 value: 71.27713276123171 - type: cos_sim_precision value: 68.3115626511853 - type: cos_sim_recall value: 74.51187335092348 - type: dot_accuracy value: 85.12248912201228 - type: dot_ap value: 69.26039256107077 - type: dot_f1 value: 65.04294321240867 - type: dot_precision value: 63.251059586138126 - type: dot_recall value: 66.93931398416886 - type: euclidean_accuracy value: 87.07754664123503 - type: euclidean_ap value: 77.7872176038945 - type: euclidean_f1 value: 70.85587801278899 - type: euclidean_precision value: 66.3519115614924 - type: euclidean_recall value: 76.01583113456465 - type: manhattan_accuracy value: 87.07754664123503 - type: manhattan_ap value: 77.7341400185556 - type: manhattan_f1 value: 70.80310880829015 - type: manhattan_precision value: 69.54198473282443 - type: manhattan_recall value: 72.1108179419525 - type: max_accuracy value: 87.39345532574357 - type: max_ap value: 78.16796549696478 - type: max_f1 value: 71.27713276123171 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.09457833663213 - type: cos_sim_ap value: 86.33024314706873 - type: cos_sim_f1 value: 78.59623733719248 - type: cos_sim_precision value: 74.13322413322413 - type: cos_sim_recall value: 83.63104404065291 - type: dot_accuracy value: 88.3086894089339 - type: dot_ap value: 83.92225241805097 - type: dot_f1 value: 76.8721826377781 - type: dot_precision value: 72.8168044077135 - type: dot_recall value: 81.40591315060055 - type: euclidean_accuracy value: 88.77052043311213 - type: euclidean_ap value: 85.7410710218755 - type: euclidean_f1 value: 77.97705489398781 - type: euclidean_precision value: 73.77713657598241 - type: euclidean_recall value: 82.68401601478288 - type: manhattan_accuracy value: 88.73753250281368 - type: manhattan_ap value: 85.72867199072802 - type: manhattan_f1 value: 77.89774182922812 - type: manhattan_precision value: 74.23787931635857 - type: manhattan_recall value: 81.93717277486911 - type: max_accuracy value: 89.09457833663213 - type: max_ap value: 86.33024314706873 - type: max_f1 value: 78.59623733719248 license: mit language: - en --- # [Universal AnglE Embedding](https://github.com/SeanLee97/AnglE) Follow us on: - GitHub: https://github.com/SeanLee97/AnglE. - Arxiv: https://arxiv.org/abs/2309.12871 🔥 Our universal English sentence embedding `WhereIsAI/UAE-Large-V1` achieves **SOTA** on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) with an average score of 64.64!  # Usage ```bash python -m pip install -U angle-emb ``` 1) Non-Retrieval Tasks ```python from angle_emb import AnglE angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda() vec = angle.encode('hello world', to_numpy=True) print(vec) vecs = angle.encode(['hello world1', 'hello world2'], to_numpy=True) print(vecs) ``` 2) Retrieval Tasks For retrieval purposes, please use the prompt `Prompts.C`. ```python from angle_emb import AnglE, Prompts angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda() angle.set_prompt(prompt=Prompts.C) vec = angle.encode({'text': 'hello world'}, to_numpy=True) print(vec) vecs = angle.encode([{'text': 'hello world1'}, {'text': 'hello world2'}], to_numpy=True) print(vecs) ``` # Citation If you use our pre-trained models, welcome to support us by citing our work: ``` @article{li2023angle, title={AnglE-optimized Text Embeddings}, author={Li, Xianming and Li, Jing}, journal={arXiv preprint arXiv:2309.12871}, year={2023} } ```

madebyollin/sdxl-vae-fp16-fix

madebyollin

diffusers

--- license: mit tags: - stable-diffusion - stable-diffusion-diffusers inference: false --- # SDXL-VAE-FP16-Fix SDXL-VAE-FP16-Fix is the [SDXL VAE](https://huggingface.co/stabilityai/sdxl-vae)*, but modified to run in fp16 precision without generating NaNs. | VAE | Decoding in `float32` / `bfloat16` precision | Decoding in `float16` precision | | --------------------- | -------------------------------------------- | ------------------------------- | | SDXL-VAE | ✅  | ⚠️  | | SDXL-VAE-FP16-Fix | ✅  | ✅  | ## 🧨 Diffusers Usage Just load this checkpoint via `AutoencoderKL`: ```py import torch from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True) pipe.to("cuda") refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16") refiner.to("cuda") n_steps = 40 high_noise_frac = 0.7 prompt = "A majestic lion jumping from a big stone at night" image = pipe(prompt=prompt, num_inference_steps=n_steps, denoising_end=high_noise_frac, output_type="latent").images image = refiner(prompt=prompt, num_inference_steps=n_steps, denoising_start=high_noise_frac, image=image).images[0] image ```  ## Automatic1111 Usage 1. Download the fixed [sdxl.vae.safetensors](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/sdxl.vae.safetensors?download=true) file 2. Move this `sdxl.vae.safetensors` file into the webui folder under `stable-diffusion-webui/models/VAE` 3. In your webui settings, select the fixed VAE you just added 4. If you were using the `--no-half-vae` command line arg for SDXL (in `webui-user.bat` or wherever), you can now remove it (Disclaimer - I haven't tested this, just aggregating various instructions I've seen elsewhere :P PRs to improve these instructions are welcomed!) ## Details SDXL-VAE generates NaNs in fp16 because the internal activation values are too big:  SDXL-VAE-FP16-Fix was created by finetuning the SDXL-VAE to: 1. keep the final output the same, but 2. make the internal activation values smaller, by 3. scaling down weights and biases within the network There are slight discrepancies between the output of SDXL-VAE-FP16-Fix and SDXL-VAE, but the decoded images should be [close enough for most purposes](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/discussions/7#64c5c0f8e2e5c94bd04eaa80). --- \* `sdxl-vae-fp16-fix` is specifically based on [SDXL-VAE (0.9)](https://huggingface.co/stabilityai/sdxl-vae/discussions/6#64acea3f7ac35b7de0554490), but it works with SDXL 1.0 too

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

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}, } ```

naver/efficient-splade-VI-BT-large-query

naver

transformers

fill-mask

--- license: cc-by-nc-sa-4.0 language: "en" tags: - splade - query-expansion - document-expansion - bag-of-words - passage-retrieval - knowledge-distillation - document encoder datasets: - ms_marco --- ## Efficient SPLADE Efficient SPLADE model for passage retrieval. This architecture uses two distinct models for query and document inference. This is the **query** one, please also download the **doc** one (https://huggingface.co/naver/efficient-splade-VI-BT-large-doc). For additional details, please visit: * paper: https://dl.acm.org/doi/10.1145/3477495.3531833 * code: https://github.com/naver/splade | | MRR@10 (MS MARCO dev) | R@1000 (MS MARCO dev) | Latency (PISA) ms | Latency (Inference) ms | --- | --- | --- | --- | --- | | `naver/efficient-splade-V-large` | 38.8 | 98.0 | 29.0 | 45.3 | `naver/efficient-splade-VI-BT-large` | 38.0 | 97.8 | 31.1 | 0.7 ## Citation If you use our checkpoint, please cite our work: ``` @inproceedings{10.1145/3477495.3531833, author = {Lassance, Carlos and Clinchant, St\'{e}phane}, title = {An Efficiency Study for SPLADE Models}, year = {2022}, isbn = {9781450387323}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3477495.3531833}, doi = {10.1145/3477495.3531833}, abstract = {Latency and efficiency issues are often overlooked when evaluating IR models based on Pretrained Language Models (PLMs) in reason of multiple hardware and software testing scenarios. Nevertheless, efficiency is an important part of such systems and should not be overlooked. In this paper, we focus on improving the efficiency of the SPLADE model since it has achieved state-of-the-art zero-shot performance and competitive results on TREC collections. SPLADE efficiency can be controlled via a regularization factor, but solely controlling this regularization has been shown to not be efficient enough. In order to reduce the latency gap between SPLADE and traditional retrieval systems, we propose several techniques including L1 regularization for queries, a separation of document/query encoders, a FLOPS-regularized middle-training, and the use of faster query encoders. Our benchmark demonstrates that we can drastically improve the efficiency of these models while increasing the performance metrics on in-domain data. To our knowledge, we propose the first neural models that, under the same computing constraints, achieve similar latency (less than 4ms difference) as traditional BM25, while having similar performance (less than 10% MRR@10 reduction) as the state-of-the-art single-stage neural rankers on in-domain data.}, booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval}, pages = {2220–2226}, numpages = {7}, keywords = {splade, latency, information retrieval, sparse representations}, location = {Madrid, Spain}, series = {SIGIR '22} } ```

NeuML/pubmedbert-base-embeddings

NeuML

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers language: en license: apache-2.0 --- # PubMedBERT Embeddings This is a [PubMedBERT-base](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) model fined-tuned using [sentence-transformers](https://www.SBERT.net). It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of [PubMed](https://pubmed.ncbi.nlm.nih.gov/) title-abstract pairs along with similar title pairs. PubMedBERT Embeddings produces higher quality embeddings than generalized models for medical literature. Further fine-tuning for a medical subdomain will result in even better performance. ## Usage (txtai) This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG). ```python import txtai embeddings = txtai.Embeddings(path="neuml/pubmedbert-base-embeddings", content=True) embeddings.index(documents()) # Run a query embeddings.search("query to run") ``` ## Usage (Sentence-Transformers) Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net). ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer("neuml/pubmedbert-base-embeddings") embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (Hugging Face Transformers) The model can also be used directly with Transformers. ```python from transformers import AutoTokenizer, AutoModel import torch # Mean Pooling - Take attention mask into account for correct averaging def meanpooling(output, mask): embeddings = output[0] # First element of model_output contains all token embeddings mask = mask.unsqueeze(-1).expand(embeddings.size()).float() return torch.sum(embeddings * mask, 1) / torch.clamp(mask.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("neuml/pubmedbert-base-embeddings") model = AutoModel.from_pretrained("neuml/pubmedbert-base-embeddings") # Tokenize sentences inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): output = model(**inputs) # Perform pooling. In this case, mean pooling. embeddings = meanpooling(output, inputs['attention_mask']) print("Sentence embeddings:") print(embeddings) ``` ## Evaluation Results Performance of this model compared to the top base models on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) is shown below. A popular smaller model was also evaluated along with the most downloaded PubMed similarity model on the Hugging Face Hub. The following datasets were used to evaluate model performance. - [PubMed QA](https://huggingface.co/datasets/pubmed_qa) - Subset: pqa_labeled, Split: train, Pair: (question, long_answer) - [PubMed Subset](https://huggingface.co/datasets/zxvix/pubmed_subset_new) - Split: test, Pair: (title, text) - [PubMed Summary](https://huggingface.co/datasets/scientific_papers) - Subset: pubmed, Split: validation, Pair: (article, abstract) Evaluation results are shown below. The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric. | Model | PubMed QA | PubMed Subset | PubMed Summary | Average | | ----------------------------------------------------------------------------- | --------- | ------------- | -------------- | --------- | | [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) | 90.40 | 95.86 | 94.07 | 93.44 | | [bge-base-en-v1.5](https://hf.co/BAAI/bge-large-en-v1.5) | 91.02 | 95.60 | 94.49 | 93.70 | | [gte-base](https://hf.co/thenlper/gte-base) | 92.97 | 96.83 | 96.24 | 95.35 | | [**pubmedbert-base-embeddings**](https://hf.co/neuml/pubmedbert-base-embeddings) | **93.27** | **97.07** | **96.58** | **95.64** | | [S-PubMedBert-MS-MARCO](https://hf.co/pritamdeka/S-PubMedBert-MS-MARCO) | 90.86 | 93.33 | 93.54 | 92.58 | ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 20191 with parameters: ``` {'batch_size': 24, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit() method: ``` { "epochs": 1, "evaluation_steps": 500, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-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: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## More Information Read more about this model and how it was built in [this article](https://medium.com/neuml/embeddings-for-medical-literature-74dae6abf5e0).

LiheYoung/depth-anything-small-hf

LiheYoung

transformers

depth-estimation

--- license: apache-2.0 tags: - vision pipeline_tag: depth-estimation widget: - inference: false --- # Depth Anything (small-sized model, Transformers version) Depth Anything model. It was introduced in the paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang et al. and first released in [this repository](https://github.com/LiheYoung/Depth-Anything). [Online demo](https://huggingface.co/spaces/LiheYoung/Depth-Anything) is also provided. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Depth Anything leverages the [DPT](https://huggingface.co/docs/transformers/model_doc/dpt) architecture with a [DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2) backbone. The model is trained on ~62 million images, obtaining state-of-the-art results for both relative and absolute depth estimation. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_anything_overview.jpg" alt="drawing" width="600"/> <small> Depth Anything overview. Taken from the <a href="https://arxiv.org/abs/2401.10891">original paper</a>.</small> ## Intended uses & limitations You can use the raw model for tasks like zero-shot depth estimation. See the [model hub](https://huggingface.co/models?search=depth-anything) to look for other versions on a task that interests you. ### How to use Here is how to use this model to perform zero-shot depth estimation: ```python from transformers import pipeline from PIL import Image import requests # load pipe pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-small-hf") # load image url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) # inference depth = pipe(image)["depth"] ``` Alternatively, one can use the classes themselves: ```python from transformers import AutoImageProcessor, AutoModelForDepthEstimation import torch import numpy as np from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-small-hf") model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-small-hf") # 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, ) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/depth_anything.html#). ### BibTeX entry and citation info ```bibtex @misc{yang2024depth, title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, author={Lihe Yang and Bingyi Kang and Zilong Huang and Xiaogang Xu and Jiashi Feng and Hengshuang Zhao}, year={2024}, eprint={2401.10891}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

immich-app/ViT-B-32__openai

immich-app

transformers

--- tags: - immich - clip --- # Model Description This repo contains ONNX exports for the CLIP model [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32). It separates the visual and textual encoders into separate models for the purpose of generating image and text embeddings. This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library.

sentence-transformers/stsb-roberta-base

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 --- **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)** # sentence-transformers/stsb-roberta-base 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-roberta-base') 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-roberta-base') model = AutoModel.from_pretrained('sentence-transformers/stsb-roberta-base') # 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-roberta-base) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) with Transformer model: RobertaModel (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", } ```

Rostlab/prot_t5_xl_uniref50

Rostlab

transformers

text2text-generation

--- tags: - protein language model datasets: - UniRef50 --- # ProtT5-XL-UniRef50 model Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in [this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in [this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids. ## Model description ProtT5-XL-UniRef50 is based on the `t5-3b` model and was pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences 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 protein sequences. One important difference between this T5 model and the original T5 version is the denosing objective. The original T5-3B model was pretrained using a span denosing objective, while this model was pre-trained with a Bart-like MLM denosing objective. The masking probability is consistent with the original T5 training by randomly masking 15% of the amino acids in the input. It has been shown that the features extracted from this self-supervised model (LM-embeddings) captured important biophysical properties governing protein shape. shape. This implied learning some of the grammar of the language of life realized in protein sequences. ## Intended uses & limitations The model could be used for protein feature extraction or to be fine-tuned on downstream tasks. We have noticed in some tasks on can gain more accuracy by fine-tuning the model rather than using it as a feature extractor. We have also noticed that for feature extraction, its better to use the feature extracted from the encoder not from the decoder. ### How to use Here is how to use this model to extract the features of a given protein sequence in PyTorch: ```python sequence_examples = ["PRTEINO", "SEQWENCE"] # this will replace all rare/ambiguous amino acids by X and introduce white-space between all amino acids sequence_examples = [" ".join(list(re.sub(r"[UZOB]", "X", sequence))) for sequence in sequence_examples] # tokenize sequences and pad up to the longest sequence in the batch ids = tokenizer.batch_encode_plus(sequence_examples, add_special_tokens=True, padding="longest") input_ids = torch.tensor(ids['input_ids']).to(device) attention_mask = torch.tensor(ids['attention_mask']).to(device) # generate embeddings with torch.no_grad(): embedding_repr = model(input_ids=input_ids,attention_mask=attention_mask) # extract embeddings for the first ([0,:]) sequence in the batch while removing padded & special tokens ([0,:7]) emb_0 = embedding_repr.last_hidden_state[0,:7] # shape (7 x 1024) print(f"Shape of per-residue embedding of first sequences: {emb_0.shape}") # do the same for the second ([1,:]) sequence in the batch while taking into account different sequence lengths ([1,:8]) emb_1 = embedding_repr.last_hidden_state[1,:8] # shape (8 x 1024) # if you want to derive a single representation (per-protein embedding) for the whole protein emb_0_per_protein = emb_0.mean(dim=0) # shape (1024) print(f"Shape of per-protein embedding of first sequences: {emb_0_per_protein.shape}") ``` ## Training data The ProtT5-XL-UniRef50 model was pretrained on [UniRef50](https://www.uniprot.org/help/uniref), a dataset consisting of 45 million protein sequences. ## Training procedure ### Preprocessing The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids "U,Z,O,B" were mapped to "X". The inputs of the model are then of the form: ``` Protein Sequence [EOS] ``` The preprocessing step was performed on the fly, by cutting and padding the protein sequences up to 512 tokens. The details of the masking procedure for each sequence are as follows: - 15% of the amino acids are masked. - In 90% of the cases, the masked amino acids are replaced by `[MASK]` token. - In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace. ### Pretraining The model was trained on a single TPU Pod V2-256 for 991.5 thousand steps in total, using sequence length 512 (batch size 2k). It was trained using ProtT5-XL-BFD model as an initial checkpoint, rather than training from scratch. It has a total of approximately 3B parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. ## Evaluation results When the model is used for feature extraction, this model achieves the following results: Test results : | Task/Dataset | secondary structure (3-states) | secondary structure (8-states) | Localization | Membrane | |:-----:|:-----:|:-----:|:-----:|:-----:| | CASP12 | 81 | 70 | | | | TS115 | 87 | 77 | | | | CB513 | 86 | 74 | | | | DeepLoc | | | 81 | 91 | ### BibTeX entry and citation info ```bibtex @article {Elnaggar2020.07.12.199554, author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard}, title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing}, elocation-id = {2020.07.12.199554}, year = {2020}, doi = {10.1101/2020.07.12.199554}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \&lt;a href="https://github.com/agemagician/ProtTrans"\&gt;https://github.com/agemagician/ProtTrans\&lt;/a\&gt;Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554}, eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf}, journal = {bioRxiv} } ``` > Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)

deepset/bert-large-uncased-whole-word-masking-squad2

deepset

transformers

question-answering

--- language: en license: cc-by-4.0 datasets: - squad_v2 model-index: - name: deepset/bert-large-uncased-whole-word-masking-squad2 results: - task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics: - type: exact_match value: 80.8846 name: Exact Match verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2E5ZGNkY2ExZWViZGEwNWE3OGRmMWM2ZmE4ZDU4ZDQ1OGM3ZWE0NTVmZjFmYmZjZmJmNjJmYTc3NTM3OTk3OSIsInZlcnNpb24iOjF9.aSblF4ywh1fnHHrN6UGL392R5KLaH3FCKQlpiXo_EdQ4XXEAENUCjYm9HWDiFsgfSENL35GkbSyz_GAhnefsAQ - type: f1 value: 83.8765 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFlNmEzMTk2NjRkNTI3ZTk3ZTU1NWNlYzIyN2E0ZDFlNDA2ZjYwZWJlNThkMmRmMmE0YzcwYjIyZDM5NmRiMCIsInZlcnNpb24iOjF9.-rc2_Bsp_B26-o12MFYuAU0Ad2Hg9PDx7Preuk27WlhYJDeKeEr32CW8LLANQABR3Mhw2x8uTYkEUrSDMxxLBw - task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics: - type: exact_match value: 85.904 name: Exact Match - type: f1 value: 92.586 name: F1 - task: type: question-answering name: Question Answering dataset: name: adversarial_qa type: adversarial_qa config: adversarialQA split: validation metrics: - type: exact_match value: 28.233 name: Exact Match - type: f1 value: 41.170 name: F1 - task: type: question-answering name: Question Answering dataset: name: squad_adversarial type: squad_adversarial config: AddOneSent split: validation metrics: - type: exact_match value: 78.064 name: Exact Match - type: f1 value: 83.591 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts amazon type: squadshifts config: amazon split: test metrics: - type: exact_match value: 65.615 name: Exact Match - type: f1 value: 80.733 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts new_wiki type: squadshifts config: new_wiki split: test metrics: - type: exact_match value: 81.570 name: Exact Match - type: f1 value: 91.199 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts nyt type: squadshifts config: nyt split: test metrics: - type: exact_match value: 83.279 name: Exact Match - type: f1 value: 91.090 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts reddit type: squadshifts config: reddit split: test metrics: - type: exact_match value: 69.305 name: Exact Match - type: f1 value: 82.405 name: F1 --- # bert-large-uncased-whole-word-masking-squad2 This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering. ## Overview **Language model:** bert-large **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system) ## Usage ### In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/bert-large-uncased-whole-word-masking-squad2") # or reader = TransformersReader(model_name_or_path="FILL",tokenizer="deepset/bert-large-uncased-whole-word-masking-squad2") ``` ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/bert-large-uncased-whole-word-masking-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## About us <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/> </div> <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/> </div> </div> [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc. Some of our other work: - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2) - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) ## Get in touch and join the Haystack community <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>. We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p> [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)

MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli

MoritzLaurer

transformers

zero-shot-classification

--- language: - en license: mit tags: - text-classification - zero-shot-classification datasets: - multi_nli - facebook/anli - fever - lingnli - alisawuffles/WANLI metrics: - accuracy pipeline_tag: zero-shot-classification model-index: - name: DeBERTa-v3-large-mnli-fever-anli-ling-wanli results: - task: type: text-classification name: Natural Language Inference dataset: name: MultiNLI-matched type: multi_nli split: validation_matched metrics: - type: accuracy value: 0,912 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: MultiNLI-mismatched type: multi_nli split: validation_mismatched metrics: - type: accuracy value: 0,908 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: ANLI-all type: anli split: test_r1+test_r2+test_r3 metrics: - type: accuracy value: 0,702 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: ANLI-r3 type: anli split: test_r3 metrics: - type: accuracy value: 0,64 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: WANLI type: alisawuffles/WANLI split: test metrics: - type: accuracy value: 0,77 verified: false - task: type: text-classification name: Natural Language Inference dataset: name: LingNLI type: lingnli split: test metrics: - type: accuracy value: 0,87 verified: false --- # DeBERTa-v3-large-mnli-fever-anli-ling-wanli ## Model description This model was fine-tuned on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best performing NLI model on the Hugging Face Hub as of 06.06.22 and can be used for zero-shot classification. It significantly outperforms all other large models on the [ANLI benchmark](https://github.com/facebookresearch/anli). The foundation model is [DeBERTa-v3-large from Microsoft](https://huggingface.co/microsoft/deberta-v3-large). DeBERTa-v3 combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the [paper](https://arxiv.org/abs/2111.09543) ### How to use the model #### Simple zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli") sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU" candidate_labels = ["politics", "economy", "entertainment", "environment"] output = classifier(sequence_to_classify, candidate_labels, multi_label=False) print(output) ``` #### NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." hypothesis = "The movie was not good." input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" prediction = torch.softmax(output["logits"][0], -1).tolist() label_names = ["entailment", "neutral", "contradiction"] prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} print(prediction) ``` ### Training data DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) datasets, which comprise 885 242 NLI hypothesis-premise pairs. Note that [SNLI](https://huggingface.co/datasets/snli) was explicitly excluded due to quality issues with the dataset. More data does not necessarily make for better NLI models. ### Training procedure DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained using the Hugging Face trainer with the following hyperparameters. Note that longer training with more epochs hurt performance in my tests (overfitting). ``` training_args = TrainingArguments( num_train_epochs=4, # total number of training epochs learning_rate=5e-06, per_device_train_batch_size=16, # batch size per device during training gradient_accumulation_steps=2, # doubles the effective batch_size to 32, while decreasing memory requirements per_device_eval_batch_size=64, # batch size for evaluation warmup_ratio=0.06, # number of warmup steps for learning rate scheduler weight_decay=0.01, # strength of weight decay fp16=True # mixed precision training ) ``` ### Eval results The model was evaluated using the test sets for MultiNLI, ANLI, LingNLI, WANLI and the dev set for Fever-NLI. The metric used is accuracy. The model achieves state-of-the-art performance on each dataset. Surprisingly, it outperforms the previous [state-of-the-art on ANLI](https://github.com/facebookresearch/anli) (ALBERT-XXL) by 8,3%. I assume that this is because ANLI was created to fool masked language models like RoBERTa (or ALBERT), while DeBERTa-v3 uses a better pre-training objective (RTD), disentangled attention and I fine-tuned it on higher quality NLI data. |Datasets|mnli_test_m|mnli_test_mm|anli_test|anli_test_r3|ling_test|wanli_test| | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |Accuracy|0.912|0.908|0.702|0.64|0.87|0.77| |Speed (text/sec, A100 GPU)|696.0|697.0|488.0|425.0|828.0|980.0| ## Limitations and bias Please consult the original DeBERTa-v3 paper and literature on different NLI datasets for more information on the training data and potential biases. The model will reproduce statistical patterns in the training data. ## Citation If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k. ### Ideas for cooperation or questions? If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) ### Debugging and issues Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.

amazon/MistralLite

amazon

transformers

text-generation

--- license: apache-2.0 inference: false --- # MistralLite Model MistralLite is a fine-tuned [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) language model, with enhanced capabilities of processing long context (up to 32K tokens). By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLite is able to **perform significantly better on several long context retrieve and answering tasks**, while keeping the simple model structure of the original model. MistralLite is useful for applications such as long context line and topic retrieval, summarization, question-answering, and etc. MistralLite can be deployed on a single AWS `g5.2x` instance with Sagemaker [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) endpoint, making it suitable for applications that require high performance in resource-constrained environments. You can also serve the MistralLite model directly using TGI docker containers. Also, MistralLite supports other ways of serving like [vLLM](https://github.com/vllm-project/vllm), and you can use MistralLite in Python by using the [HuggingFace transformers](https://huggingface.co/docs/transformers/index) and [FlashAttention-2](https://github.com/Dao-AILab/flash-attention) library. MistralLite is similar to [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), and their similarities and differences are summarized below: |Model|Fine-tuned on long contexts| Max context length| RotaryEmbedding adaptation| Sliding Window Size| |----------|-------------:|------------:|-----------:|-----------:| | Mistral-7B-Instruct-v0.1 | up to 8K tokens | 32K | rope_theta = 10000 | 4096 | | MistralLite | up to 16K tokens | 32K | **rope_theta = 1000000** | **16384** | **Important - Use the prompt template below for MistralLite:** ```<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>``` ## Motivation of Developing MistralLite Since the release of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), the model became increasingly popular because its strong performance on a wide range of benchmarks. But most of the benchmarks are evaluated on `short context`, and not much has been investigated on its performance on long context tasks. Then We evaluated `Mistral-7B-Instruct-v0.1` against benchmarks that are specifically designed to assess the capabilities of LLMs in handling longer context. Although the performance of the models on long context was fairly competitive on long context less than 4096 tokens, there were some limitations on its performance on longer context. Motivated by improving its performance on longer context, we finetuned the Mistral 7B model, and produced `Mistrallite`. The model managed to `significantly boost the performance of long context handling` over Mistral-7B-Instruct-v0.1. The detailed `long context evalutaion results` are as below: 1. [Topic Retrieval](https://lmsys.org/blog/2023-06-29-longchat/) |Model Name|Input length| Input length | Input length| Input length| Input length| |----------|-------------:|-------------:|------------:|-----------:|-----------:| | | 2851| 5568 |8313 | 11044 | 13780 | Mistral-7B-Instruct-v0.1 | 100% | 50% | 2% | 0% | 0% | | MistralLite | **100%** | **100%** | **100%** | **100%** | **98%** | 2. [Line Retrieval](https://lmsys.org/blog/2023-06-29-longchat/#longeval-results) |Model Name|Input length| Input length | Input length| Input length| Input length|Input length| |----------|-------------:|-------------:|------------:|-----------:|-----------:|-----------:| | | 3818| 5661 |7505 | 9354 | 11188 | 12657 | Mistral-7B-Instruct-v0.1 | **98%** | 62% | 42% | 42% | 32% | 30% | | MistralLite | **98%** | **92%** | **88%** | **76%** | **70%** | **60%** | 3. [Pass key Retrieval](https://github.com/epfml/landmark-attention/blob/main/llama/run_test.py#L101) |Model Name|Input length| Input length | Input length| Input length| |----------|-------------:|-------------:|------------:|-----------:| | | 3264| 5396 |8329 | 10197 | | Mistral-7B-Instruct-v0.1 | **100%** | 50% | 20% | 30% | | MistralLite | **100%** | **100%** | **100%** | **100%** | 4. [Question Answering with Long Input Texts](https://nyu-mll.github.io/quality/) |Model Name| Test set Accuracy | Hard subset Accuracy| |----------|-------------:|-------------:| | Mistral-7B-Instruct-v0.1 | 44.3% | 39.7% | | MistralLite | **64.4%** | **56.2%** | ### Example test on long context of 13400 tokens - Context is from [Amazon Aurora FAQs](https://aws.amazon.com/rds/aurora/faqs/) - Question: *please tell me how does pgvector help with Generative AI and give me some examples.* - Answer from MistralLite: ```code pgvector is an open-source extension for PostgreSQL supported by Amazon Aurora PostgreSQL-Compatible Edition. You can use pgvector to store, search, index, and query billions of embeddings that are generated from machine learning (ML) and artificial intelligence (AI) models in your database, such as those from Amazon Bedrock (limited preview) or Amazon SageMaker. A vector embedding is a numerical representation that represents the semantic meaning of content such as text, images, and video. With pgvector, you can query embeddings in your Aurora PostgreSQL database to perform efficient semantic similarity searches of these data types, represented as vectors, combined with other tabular data in Aurora. This enables the use of generative AI and other AI/ML systems for new types of applications such as personalized recommendations based on similar text descriptions or images, candidate match based on interview notes, customer service next best action recommendations based on successful transcripts or chat session dialogs, and more. ``` ## Model Details - **Developed by:** [AWS Contributors](https://github.com/orgs/aws-samples/teams/aws-prototype-ml-apac) - **Model type:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) - **Language:** English - **Finetuned from weights:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) - **Finetuned on data:** - [SLidingEncoder and Decoder (SLED)](https://huggingface.co/datasets/tau/sled) - [(Long) Natural Questions (NQ)](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections#multi-passage-qa-from-natural-questions) - [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1) - **Supported Serving Framework:** - [Text-Generation-Inference 1.1.0](https://github.com/huggingface/text-generation-inference/tree/v1.1.0) - [vLLM](https://github.com/vllm-project/vllm) - [HuggingFace transformers](https://huggingface.co/docs/transformers/index) - [HuggingFace Text Generation Inference (TGI) container on SageMaker](https://github.com/awslabs/llm-hosting-container) - **Model License:** Apache 2.0 - **Contact:** [GitHub issues](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/issues) - **Inference Code** [Github Repo](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/) ## MistralLite LM-Eval Results ### Methodology - Please see https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - revision=4ececff - Note: we used --model hf-causal-experimental instead of --model hf-causal ### Results |Average|hellaswag| arc_challenge|truthful_qa (mc2)| MMLU (acc)| |----------|-------------:|------------:|-----------:|-----------:| | 0.57221 | 0.81617 | 0.58874 | 0.38275 | 0.5012 | ## How to Use MistralLite from Python Code (HuggingFace transformers) ## **Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/huggingface-transformers/example_usage.ipynb). ### Install the necessary packages Requires: [transformers](https://pypi.org/project/transformers/) 4.34.0 or later, [flash-attn](https://pypi.org/project/flash-attn/) 2.3.1.post1 or later, and [accelerate](https://pypi.org/project/accelerate/) 0.23.0 or later. ```shell pip install transformers==4.34.0 pip install flash-attn==2.3.1.post1 --no-build-isolation pip install accelerate==0.23.0 ``` ### You can then try the following example code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import transformers import torch model_id = "amazon/MistralLite" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, use_flash_attention_2=True, device_map="auto",) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, ) prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>" sequences = pipeline( prompt, max_new_tokens=400, do_sample=False, return_full_text=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"{seq['generated_text']}") ``` **Important** - Use the prompt template below for MistralLite: ``` <|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|> ``` ## How to Serve MistralLite on TGI ## **Important:** - For an end-to-end example Jupyter notebook using the native TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi/example_usage.ipynb). - If the **input context length is greater than 12K tokens**, it is recommended using a custom TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi-custom/example_usage.ipynb). ### Start TGI server ### 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 docker run -d --gpus all --shm-size 1g -p 443:80 -v $(pwd)/models:/data ghcr.io/huggingface/text-generation-inference:1.1.0 \ --model-id amazon/MistralLite \ --max-input-length 16000 \ --max-total-tokens 16384 \ --max-batch-prefill-tokens 16384 \ --trust-remote-code ``` ### Perform Inference ### Example Python code for inference with TGI (requires `text_generation` 0.6.1 or later): ```shell pip install text_generation==0.6.1 ``` ```python from text_generation import Client SERVER_PORT = 443 SERVER_HOST = "localhost" SERVER_URL = f"{SERVER_HOST}:{SERVER_PORT}" tgi_client = Client(f"http://{SERVER_URL}", timeout=60) def invoke_tgi(prompt, random_seed=1, max_new_tokens=400, print_stream=True, assist_role=True): if (assist_role): prompt = f"<|prompter|>{prompt}</s><|assistant|>" output = "" for response in tgi_client.generate_stream( prompt, do_sample=False, max_new_tokens=max_new_tokens, return_full_text=False, #temperature=None, #truncate=None, #seed=random_seed, #typical_p=0.2, ): if hasattr(response, "token"): if not response.token.special: snippet = response.token.text output += snippet if (print_stream): print(snippet, end='', flush=True) return output prompt = "What are the main challenges to support a long context for LLM?" result = invoke_tgi(prompt) ``` **Important** - When using MistralLite for inference for the first time, it may require a brief 'warm-up' period that can take 10s of seconds. However, subsequent inferences should be faster and return results in a more timely manner. This warm-up period is normal and should not affect the overall performance of the system once the initialisation period has been completed. ## How to Deploy MistralLite on Amazon SageMaker ## **Important:** - For an end-to-end example Jupyter notebook using the SageMaker built-in container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi/example_usage.ipynb). - If the **input context length is greater than 12K tokens**, it is recommended using a custom docker container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi-custom/example_usage.ipynb). ### Install the necessary packages Requires: [sagemaker](https://pypi.org/project/sagemaker/) 2.192.1 or later. ```shell pip install sagemaker==2.192.1 ``` ### Deploy the Model as A SageMaker Endpoint ### To deploy MistralLite on a SageMaker endpoint, please follow the example code as below. ```python import sagemaker from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri import time sagemaker_session = sagemaker.Session() region = sagemaker_session.boto_region_name role = sagemaker.get_execution_role() image_uri = get_huggingface_llm_image_uri( backend="huggingface", # or lmi region=region, version="1.1.0" ) model_name = "MistralLite-" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime()) hub = { 'HF_MODEL_ID':'amazon/MistralLite', 'HF_TASK':'text-generation', 'SM_NUM_GPUS':'1', "MAX_INPUT_LENGTH": '16000', "MAX_TOTAL_TOKENS": '16384', "MAX_BATCH_PREFILL_TOKENS": '16384', "MAX_BATCH_TOTAL_TOKENS": '16384', } model = HuggingFaceModel( name=model_name, env=hub, role=role, image_uri=image_uri ) predictor = model.deploy( initial_instance_count=1, instance_type="ml.g5.2xlarge", endpoint_name=model_name, ) ``` ### Perform Inference ### To call the endpoint, please follow the example code as below: ```python input_data = { "inputs": "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>", "parameters": { "do_sample": False, "max_new_tokens": 400, "return_full_text": False, #"typical_p": 0.2, #"temperature":None, #"truncate":None, #"seed": 1, } } result = predictor.predict(input_data)[0]["generated_text"] print(result) ``` or via [boto3](https://pypi.org/project/boto3/), and the example code is shown as below: ```python import boto3 import json def call_endpoint(client, prompt, endpoint_name, paramters): client = boto3.client("sagemaker-runtime") payload = {"inputs": prompt, "parameters": parameters} response = client.invoke_endpoint(EndpointName=endpoint_name, Body=json.dumps(payload), ContentType="application/json") output = json.loads(response["Body"].read().decode()) result = output[0]["generated_text"] return result client = boto3.client("sagemaker-runtime") parameters = { "do_sample": False, "max_new_tokens": 400, "return_full_text": False, #"typical_p": 0.2, #"temperature":None, #"truncate":None, #"seed": 1, } endpoint_name = predictor.endpoint_name prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>" result = call_endpoint(client, prompt, endpoint_name, parameters) print(result) ``` ## How to Serve MistralLite on vLLM ## Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). **Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/vllm/example_usage.ipynb). ### Using vLLM as a server ### When using vLLM as a server, pass the --model amazon/MistralLite parameter, for example: ```shell python3 -m vllm.entrypoints.api_server --model amazon/MistralLite ``` ### Using vLLM in Python Code ### When using vLLM from Python code, Please see the example code as below: ```python from vllm import LLM, SamplingParams prompts = [ "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>", ] sampling_params = SamplingParams(temperature=0, max_tokens=100) llm = LLM(model="amazon/MistralLite",) 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}") ``` ## Limitations ## Before using the MistralLite model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.

Helsinki-NLP/opus-mt-pl-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-pl-en * source languages: pl * target languages: en * OPUS readme: [pl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pl-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/pl-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pl-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pl-en/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.pl.en | 54.9 | 0.701 |

google-bert/bert-large-uncased-whole-word-masking-finetuned-squad

google-bert

transformers

question-answering

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT large model (uncased) whole word masking finetuned on SQuAD Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same. The training is identical -- each masked WordPiece token is predicted independently. After pre-training, this model was fine-tuned on the SQuAD dataset with one of our fine-tuning scripts. See below for more information regarding this fine-tuning. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters. ## Intended uses & limitations This model should be used as a question-answering model. You may use it in a question answering pipeline, or use it to output raw results given a query and a context. You may see other use cases in the [task summary](https://huggingface.co/transformers/task_summary.html#extractive-question-answering) of the transformers documentation.## Training data The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### Fine-tuning After pre-training, this model was fine-tuned on the SQuAD dataset with one of our fine-tuning scripts. In order to reproduce the training, you may use the following command: ``` python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_qa.py \ --model_name_or_path bert-large-uncased-whole-word-masking \ --dataset_name squad \ --do_train \ --do_eval \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir ./examples/models/wwm_uncased_finetuned_squad/ \ --per_device_eval_batch_size=3 \ --per_device_train_batch_size=3 \ ``` ## Evaluation results The results obtained are the following: ``` f1 = 93.15 exact_match = 86.91 ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

Helsinki-NLP/opus-mt-en-es

Helsinki-NLP

transformers

translation

--- language: - en - es tags: - translation license: apache-2.0 --- ### eng-spa * source group: English * target group: Spanish * OPUS readme: [eng-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md) * model: transformer * source language(s): eng * target language(s): spa * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-08-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.zip) * test set translations: [opus-2020-08-18.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.test.txt) * test set scores: [opus-2020-08-18.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-engspa.eng.spa | 31.0 | 0.583 | | news-test2008-engspa.eng.spa | 29.7 | 0.564 | | newstest2009-engspa.eng.spa | 30.2 | 0.578 | | newstest2010-engspa.eng.spa | 36.9 | 0.620 | | newstest2011-engspa.eng.spa | 38.2 | 0.619 | | newstest2012-engspa.eng.spa | 39.0 | 0.625 | | newstest2013-engspa.eng.spa | 35.0 | 0.598 | | Tatoeba-test.eng.spa | 54.9 | 0.721 | ### System Info: - hf_name: eng-spa - source_languages: eng - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'es'] - src_constituents: {'eng'} - tgt_constituents: {'spa'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.test.txt - src_alpha3: eng - tgt_alpha3: spa - short_pair: en-es - chrF2_score: 0.721 - bleu: 54.9 - brevity_penalty: 0.978 - ref_len: 77311.0 - src_name: English - tgt_name: Spanish - train_date: 2020-08-18 00:00:00 - src_alpha2: en - tgt_alpha2: es - prefer_old: False - long_pair: eng-spa - helsinki_git_sha: d2f0910c89026c34a44e331e785dec1e0faa7b82 - transformers_git_sha: f7af09b4524b784d67ae8526f0e2fcc6f5ed0de9 - port_machine: brutasse - port_time: 2020-08-24-18:20

sentence-transformers/distiluse-base-multilingual-cased-v1

sentence-transformers

sentence-transformers

sentence-similarity

--- language: - multilingual - ar - zh - nl - en - fr - de - it - ko - pl - pt - ru - es - tr license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity pipeline_tag: sentence-similarity --- # sentence-transformers/distiluse-base-multilingual-cased-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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/distiluse-base-multilingual-cased-v1') embeddings = model.encode(sentences) print(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/distiluse-base-multilingual-cased-v1) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (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}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## 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", } ```

SpanBERT/spanbert-large-cased

SpanBERT

transformers

Entry not found

amazon/chronos-t5-large

amazon

transformers

other

--- license: apache-2.0 pipeline_tag: other tags: - time series - forecasting - pretrained models - foundation models - time series foundation models - time-series --- # Chronos-T5 (Large) Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). <p align="center"> <img src="figures/main-figure.png" width="100%"> <br /> <span> Fig. 1: High-level depiction of Chronos. (<b>Left</b>) The input time series is scaled and quantized to obtain a sequence of tokens. (<b>Center</b>) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (<b>Right</b>) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. </span> </p> --- ## Architecture The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | [**chronos-t5-tiny**](https://huggingface.co/amazon/chronos-t5-tiny) | 8M | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) | | [**chronos-t5-mini**](https://huggingface.co/amazon/chronos-t5-mini) | 20M | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini) | | [**chronos-t5-small**](https://huggingface.co/amazon/chronos-t5-small) | 46M | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) | | [**chronos-t5-base**](https://huggingface.co/amazon/chronos-t5-base) | 200M | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base) | | [**chronos-t5-large**](https://huggingface.co/amazon/chronos-t5-large) | 710M | [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) | ## Usage To perform inference with Chronos models, install the package in the GitHub [companion repo](https://github.com/amazon-science/chronos-forecasting) by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` A minimal example showing how to perform inference using Chronos models: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from chronos import ChronosPipeline pipeline = ChronosPipeline.from_pretrained( "amazon/chronos-t5-large", device_map="cuda", torch_dtype=torch.bfloat16, ) df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv") # context must be either a 1D tensor, a list of 1D tensors, # or a left-padded 2D tensor with batch as the first dimension context = torch.tensor(df["#Passengers"]) prediction_length = 12 forecast = pipeline.predict(context, prediction_length) # shape [num_series, num_samples, prediction_length] # visualize the forecast forecast_index = range(len(df), len(df) + prediction_length) low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0) plt.figure(figsize=(8, 4)) plt.plot(df["#Passengers"], color="royalblue", label="historical data") plt.plot(forecast_index, median, color="tomato", label="median forecast") plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval") plt.legend() plt.grid() plt.show() ``` ## Citation If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815): ``` @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ``` ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This project is licensed under the Apache-2.0 License.

segmind/SSD-1B

segmind

diffusers

text-to-image

--- license: apache-2.0 tags: - text-to-image - ultra-realistic - text-to-image - stable-diffusion - distilled-model - knowledge-distillation pinned: true datasets: - zzliang/GRIT - wanng/midjourney-v5-202304-clean library_name: diffusers --- # Segmind Stable Diffusion 1B (SSD-1B) Model Card  ## 📣 Read our [technical report](https://huggingface.co/papers/2401.02677) for more details on our disillation method ## AUTOMATIC1111 compatibility added. Supporting file [here](https://huggingface.co/segmind/SSD-1B/blob/main/SSD-1B-A1111.safetensors) ## Demo Try out the model at [Segmind SSD-1B](https://www.segmind.com/models/ssd-1b?utm_source=hf) for ⚡ fastest inference. You can also try it on [🤗 Spaces](https://huggingface.co/spaces/segmind/Segmind-Stable-Diffusion) ## Model Description The Segmind Stable Diffusion Model (SSD-1B) is a **distilled 50% smaller** version of the Stable Diffusion XL (SDXL), offering a **60% speedup** while maintaining high-quality text-to-image generation capabilities. It has been trained on diverse datasets, including Grit and Midjourney scrape data, to enhance its ability to create a wide range of visual content based on textual prompts. This model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive visual outputs. Special thanks to the HF team 🤗 especially [Sayak](https://huggingface.co/sayakpaul), [Patrick](https://github.com/patrickvonplaten) and [Poli](https://huggingface.co/multimodalart) for their collaboration and guidance on this work. ## Image Comparision (SDXL-1.0 vs SSD-1B)  ## Usage: This model can be used via the 🧨 Diffusers library. Make sure to install diffusers from source by running ``` pip install git+https://github.com/huggingface/diffusers ``` In addition, please install `transformers`, `safetensors` and `accelerate`: ``` pip install transformers accelerate safetensors ``` To use the model, you can run the following: ```py from diffusers import StableDiffusionXLPipeline import torch pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" # Your prompt here neg_prompt = "ugly, blurry, poor quality" # Negative prompt here image = pipe(prompt=prompt, negative_prompt=neg_prompt).images[0] ``` ### Update: Our model should now be usable in ComfyUI. ### Please do use negative prompting, and a CFG around 9.0 for the best quality! ### Model Description - **Developed by:** [Segmind](https://www.segmind.com/) - **Developers:** [Yatharth Gupta](https://huggingface.co/Warlord-K) and [Vishnu Jaddipal](https://huggingface.co/Icar). - **Model type:** Diffusion-based text-to-image generative model - **License:** Apache 2.0 - **Distilled From** [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) ### Key Features - **Text-to-Image Generation:** The model excels at generating images from text prompts, enabling a wide range of creative applications. - **Distilled for Speed:** Designed for efficiency, this model offers a 60% speedup, making it a practical choice for real-time applications and scenarios where rapid image generation is essential. - **Diverse Training Data:** Trained on diverse datasets, the model can handle a variety of textual prompts and generate corresponding images effectively. - **Knowledge Distillation:** By distilling knowledge from multiple expert models, the Segmind Stable Diffusion Model combines their strengths and minimizes their limitations, resulting in improved performance. ### Model Architecture The SSD-1B Model is a 1.3B Parameter Model which has several layers removed from the Base SDXL Model  ### Training info These are the key hyperparameters used during training: * Steps: 251000 * Learning rate: 1e-5 * Batch size: 32 * Gradient accumulation steps: 4 * Image resolution: 1024 * Mixed-precision: fp16 ### Multi-Resolution Support  SSD-1B can support the following output resolutions. * 1024 x 1024 (1:1 Square) * 1152 x 896 (9:7) * 896 x 1152 (7:9) * 1216 x 832 (19:13) * 832 x 1216 (13:19) * 1344 x 768 (7:4 Horizontal) * 768 x 1344 (4:7 Vertical) * 1536 x 640 (12:5 Horizontal) * 640 x 1536 (5:12 Vertical) ### Speed Comparision We have observed that SSD-1B is upto 60% faster than the Base SDXL Model. Below is a comparision on an A100 80GB.  Below are the speed up metrics on a RTX 4090 GPU.  ### Model Sources For research and development purposes, the SSD-1B Model can be accessed via the Segmind AI platform. For more information and access details, please visit [Segmind](https://www.segmind.com/models/ssd-1b). ## Uses ### Direct Use The Segmind Stable Diffusion Model is suitable for research and practical applications in various domains, including: - **Art and Design:** It can be used to generate artworks, designs, and other creative content, providing inspiration and enhancing the creative process. - **Education:** The model can be applied in educational tools to create visual content for teaching and learning purposes. - **Research:** Researchers can use the model to explore generative models, evaluate its performance, and push the boundaries of text-to-image generation. - **Safe Content Generation:** It offers a safe and controlled way to generate content, reducing the risk of harmful or inappropriate outputs. - **Bias and Limitation Analysis:** Researchers and developers can use the model to probe its limitations and biases, contributing to a better understanding of generative models' behavior. ### Downstream Use The Segmind Stable Diffusion Model can also be used directly with the 🧨 Diffusers library training scripts for further training, including: - **[LoRA](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora_sdxl.py):** ```bash export MODEL_NAME="segmind/SSD-1B" export VAE_NAME="madebyollin/sdxl-vae-fp16-fix" export DATASET_NAME="lambdalabs/pokemon-blip-captions" accelerate launch train_text_to_image_lora_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --pretrained_vae_model_name_or_path=$VAE_NAME \ --dataset_name=$DATASET_NAME --caption_column="text" \ --resolution=1024 --random_flip \ --train_batch_size=1 \ --num_train_epochs=2 --checkpointing_steps=500 \ --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \ --mixed_precision="fp16" \ --seed=42 \ --output_dir="sd-pokemon-model-lora-ssd" \ --validation_prompt="cute dragon creature" --report_to="wandb" \ --push_to_hub ``` - **[Fine-Tune](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py):** ```bash export MODEL_NAME="segmind/SSD-1B" export VAE_NAME="madebyollin/sdxl-vae-fp16-fix" export DATASET_NAME="lambdalabs/pokemon-blip-captions" accelerate launch train_text_to_image_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --pretrained_vae_model_name_or_path=$VAE_NAME \ --dataset_name=$DATASET_NAME \ --enable_xformers_memory_efficient_attention \ --resolution=512 --center_crop --random_flip \ --proportion_empty_prompts=0.2 \ --train_batch_size=1 \ --gradient_accumulation_steps=4 --gradient_checkpointing \ --max_train_steps=10000 \ --use_8bit_adam \ --learning_rate=1e-06 --lr_scheduler="constant" --lr_warmup_steps=0 \ --mixed_precision="fp16" \ --report_to="wandb" \ --validation_prompt="a cute Sundar Pichai creature" --validation_epochs 5 \ --checkpointing_steps=5000 \ --output_dir="ssd-pokemon-model" \ --push_to_hub ``` - **[Dreambooth LoRA](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py):** ```bash export MODEL_NAME="segmind/SSD-1B" export INSTANCE_DIR="dog" export OUTPUT_DIR="lora-trained-xl" export VAE_PATH="madebyollin/sdxl-vae-fp16-fix" accelerate launch train_dreambooth_lora_sdxl.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --instance_data_dir=$INSTANCE_DIR \ --pretrained_vae_model_name_or_path=$VAE_PATH \ --output_dir=$OUTPUT_DIR \ --mixed_precision="fp16" \ --instance_prompt="a photo of sks dog" \ --resolution=1024 \ --train_batch_size=1 \ --gradient_accumulation_steps=4 \ --learning_rate=1e-5 \ --report_to="wandb" \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --max_train_steps=500 \ --validation_prompt="A photo of sks dog in a bucket" \ --validation_epochs=25 \ --seed="0" \ --push_to_hub ``` ### Out-of-Scope Use The SSD-1B Model is not suitable for creating factual or accurate representations of people, events, or real-world information. It is not intended for tasks requiring high precision and accuracy. ## Limitations and Bias Limitations & Bias The SSD-1B Model has some challenges in embodying absolute photorealism, especially in human depictions. While it grapples with incorporating clear text and maintaining the fidelity of complex compositions due to its autoencoding approach, these hurdles pave the way for future enhancements. Importantly, the model's exposure to a diverse dataset, though not a panacea for ingrained societal and digital biases, represents a foundational step towards more equitable technology. Users are encouraged to interact with this pioneering tool with an understanding of its current limitations, fostering an environment of conscious engagement and anticipation for its continued evolution. ## Citation ``` @misc{gupta2024progressive, title={Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss}, author={Yatharth Gupta and Vishnu V. Jaddipal and Harish Prabhala and Sayak Paul and Patrick Von Platen}, year={2024}, eprint={2401.02677}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

hustvl/yolos-tiny

hustvl

transformers

object-detection

--- license: apache-2.0 tags: - object-detection - vision datasets: - coco widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg example_title: Savanna - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg example_title: Airport --- # YOLOS (tiny-sized) model YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models. ### How to use Here is how to use this model: ```python from transformers import YolosImageProcessor, YolosForObjectDetection from PIL import Image import torch import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny') image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny") inputs = image_processor(images=image, return_tensors="pt") outputs = model(**inputs) # model predicts bounding boxes and corresponding COCO classes logits = outputs.logits bboxes = outputs.pred_boxes # print results target_sizes = torch.tensor([image.size[::-1]]) results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ### Training The model was pre-trained for 300 epochs on ImageNet-1k and fine-tuned for 300 epochs on COCO. ## Evaluation results This model achieves an AP (average precision) of **28.7** on COCO 2017 validation. For more details regarding evaluation results, we refer to the original paper. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-00666, author = {Yuxin Fang and Bencheng Liao and Xinggang Wang and Jiemin Fang and Jiyang Qi and Rui Wu and Jianwei Niu and Wenyu Liu}, title = {You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection}, journal = {CoRR}, volume = {abs/2106.00666}, year = {2021}, url = {https://arxiv.org/abs/2106.00666}, eprinttype = {arXiv}, eprint = {2106.00666}, timestamp = {Fri, 29 Apr 2022 19:49:16 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

flair/chunk-english

flair

flair

token-classification

--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - conll2000 widget: - text: "The happy man has been eating at the diner" --- ## English Chunking in Flair (default model) This is the standard phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: **96,48** (CoNLL-2000) Predicts 4 tags: | **tag** | **meaning** | |---------------------------------|-----------| | ADJP | adjectival | | ADVP | adverbial | | CONJP | conjunction | | INTJ | interjection | | LST | list marker | | NP | noun phrase | | PP | prepositional | | PRT | particle | | SBAR | subordinate clause | | VP | verb phrase | 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/chunk-english") # make example sentence sentence = Sentence("The happy man has been eating at the diner") # 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('np'): print(entity) ``` This yields the following output: ``` Span [1,2,3]: "The happy man" [− Labels: NP (0.9958)] Span [4,5,6]: "has been eating" [− Labels: VP (0.8759)] Span [7]: "at" [− Labels: PP (1.0)] Span [8,9]: "the diner" [− Labels: NP (0.9991)] ``` So, the spans "*The happy man*" and "*the diner*" are labeled as **noun phrases** (NP) and "*has been eating*" is labeled as a **verb phrase** (VP) in the sentence "*The happy man has been eating at the diner*". --- ### Training: Script to train this model The following Flair script was used to train this model: ```python from flair.data import Corpus from flair.datasets import CONLL_2000 from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. get the corpus corpus: Corpus = CONLL_2000() # 2. what tag do we want to predict? tag_type = 'np' # 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 = [ # contextual string embeddings, forward FlairEmbeddings('news-forward'), # contextual string embeddings, backward FlairEmbeddings('news-backward'), ] # embedding stack consists of Flair and GloVe embeddings embeddings = StackedEmbeddings(embeddings=embedding_types) # 5. initialize sequence tagger from flair.models import SequenceTagger tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type) # 6. initialize trainer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus) # 7. run training trainer.train('resources/taggers/chunk-english', train_with_dev=True, max_epochs=150) ``` --- ### Cite Please cite the following paper when using this model. ``` @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` --- ### Issues? The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).

TheBloke/LLaMA2-13B-Tiefighter-GPTQ

TheBloke

transformers

text-generation

--- base_model: KoboldAI/LLaMA2-13B-Tiefighter inference: false license: llama2 model_creator: KoboldAI model_name: Llama2 13B Tiefighter model_type: llama prompt_template: "### Instruction: \n{prompt}\n### Response:\n" quantized_by: TheBloke --- <!-- 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 --> # Llama2 13B Tiefighter - GPTQ - Model creator: [KoboldAI](https://huggingface.co/KoboldAI) - Original model: [Llama2 13B Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) <!-- description start --> ## Description This repo contains GPTQ model files for [KoboldAI's Llama2 13B Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-GGUF) * [KoboldAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Alpaca-Tiefighter ``` ### Instruction: {prompt} ### Response: ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers These GPTQs are known to work in the following inference servers/webuis: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KobaldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/LLaMA2-13B-Tiefighter-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/LLaMA2-13B-Tiefighter-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `LLaMA2-13B-Tiefighter-GPTQ`: ```shell mkdir LLaMA2-13B-Tiefighter-GPTQ huggingface-cli download TheBloke/LLaMA2-13B-Tiefighter-GPTQ --local-dir LLaMA2-13B-Tiefighter-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir LLaMA2-13B-Tiefighter-GPTQ huggingface-cli download TheBloke/LLaMA2-13B-Tiefighter-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir LLaMA2-13B-Tiefighter-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir LLaMA2-13B-Tiefighter-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/LLaMA2-13B-Tiefighter-GPTQ --local-dir LLaMA2-13B-Tiefighter-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/LLaMA2-13B-Tiefighter-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) 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/LLaMA2-13B-Tiefighter-GPTQ`. - To download from a specific branch, enter for example `TheBloke/LLaMA2-13B-Tiefighter-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `LLaMA2-13B-Tiefighter-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to 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/LLaMA2-13B-Tiefighter-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires 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'''### Instruction: {prompt} ### Response: ''' 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_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/LLaMA2-13B-Tiefighter-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''### Instruction: {prompt} ### Response: ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: KoboldAI's Llama2 13B Tiefighter # LLaMA2-13B-Tiefighter Tiefighter is a merged model achieved trough merging two different lora's on top of a well established existing merge. To achieve this the following recipe was used: * We begin with the base model Undi95/Xwin-MLewd-13B-V0.2 which is a well established merged, contrary to the name this model does not have a strong NSFW bias. * Then we applied the PocketDoc/Dans-RetroRodeo-13b lora which is a finetune on the Choose your own Adventure datasets from our Skein model. * After applying this lora we merged the new model with PocketDoc/Dans-RetroRodeo-13b at 5% to weaken the newly introduced adventure bias. * The resulting merge was used as a new basemodel to which we applied Blackroot/Llama-2-13B-Storywriter-LORA and repeated the same trick, this time at 10%. This means this model contains the following ingredients from their upstream models for as far as we can track them: - Undi95/Xwin-MLewd-13B-V0.2 - - Undi95/ReMM-S-Light - Undi95/CreativeEngine - Brouz/Slerpeno - - elinas/chronos-13b-v2 - jondurbin/airoboros-l2-13b-2.1 - NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2 - CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2 - - KoboldAI/LLAMA2-13B-Holodeck-1 - NousResearch/Nous-Hermes-13b - OpenAssistant/llama2-13b-orca-8k-3319 - ehartford/WizardLM-1.0-Uncensored-Llama2-13b - Henk717/spring-dragon - The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible) - - SuperCOT (Undisclosed version) - elinas/chronos-13b-v2 (Version assumed) - NousResearch/Nous-Hermes-Llama2-13b - stabilityai/StableBeluga-13B (Version assumed) - zattio770/120-Days-of-LORA-v2-13B - PygmalionAI/pygmalion-2-13b - Undi95/Storytelling-v1-13B-lora - TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter - nRuaif/Kimiko-v2-13B - The-Face-Of-Goonery/Huginn-13b-FP16 - - "a lot of different models, like hermes, beluga, airoboros, chronos.. limarp" - lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT - Xwin-LM/Xwin-LM-13B-V0.2 - PocketDoc/Dans-RetroRodeo-13b - Blackroot/Llama-2-13B-Storywriter-LORA While we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible! Thanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible. # Usage This model is meant to be creative, If you let it improvise you get better results than if you drown it in details. ## Story Writing Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story. ### Generate a story on demand To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong. ## Chatbots and persona's This model has been tested with various forms of chatting, testers have found that typically less is more and the model is good at improvising. Don't drown the model in paragraphs of detailed information, instead keep it simple first and see how far you can lean on the models own ability to figure out your character. Copy pasting paragraphs of background information is not suitable for a 13B model such as this one, code formatted characters or an instruction prompt describing who you wish to talk to goes much further. For example, you can put this in memory in regular chat mode: ``` ### Instruction: Generate a conversation between Alice and Henk where they discuss language models. In this conversation Henk is excited to teach Alice about Tiefigther. ### Response: ``` Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format. ## Instruct Prompting This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work. During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias. Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up. ## Adventuring and Adventure Games This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode). It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns. ## Discovered something cool and want to engage with us? Join our community at https://koboldai.org/discord !

hkunlp/instructor-large

hkunlp

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - text-embedding - embeddings - information-retrieval - beir - text-classification - language-model - text-clustering - text-semantic-similarity - text-evaluation - prompt-retrieval - text-reranking - sentence-transformers - feature-extraction - sentence-similarity - transformers - t5 - English - Sentence Similarity - natural_questions - ms_marco - fever - hotpot_qa - mteb language: en inference: false license: apache-2.0 model-index: - name: INSTRUCTOR results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 88.13432835820896 - type: ap value: 59.298209334395665 - type: f1 value: 83.31769058643586 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.526375 - type: ap value: 88.16327709705504 - type: f1 value: 91.51095801287843 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.856 - type: f1 value: 45.41490917650942 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 31.223 - type: map_at_10 value: 47.947 - type: map_at_100 value: 48.742000000000004 - type: map_at_1000 value: 48.745 - type: map_at_3 value: 43.137 - type: map_at_5 value: 45.992 - type: mrr_at_1 value: 32.432 - type: mrr_at_10 value: 48.4 - type: mrr_at_100 value: 49.202 - type: mrr_at_1000 value: 49.205 - type: mrr_at_3 value: 43.551 - type: mrr_at_5 value: 46.467999999999996 - type: ndcg_at_1 value: 31.223 - type: ndcg_at_10 value: 57.045 - type: ndcg_at_100 value: 60.175 - type: ndcg_at_1000 value: 60.233000000000004 - type: ndcg_at_3 value: 47.171 - type: ndcg_at_5 value: 52.322 - type: precision_at_1 value: 31.223 - type: precision_at_10 value: 8.599 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.63 - type: precision_at_5 value: 14.282 - type: recall_at_1 value: 31.223 - type: recall_at_10 value: 85.989 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 58.89 - type: recall_at_5 value: 71.408 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 43.1621946393635 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.56417132407894 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.29539304390207 - type: mrr value: 76.44484017060196 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_spearman value: 84.38746499431112 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.51298701298701 - type: f1 value: 77.49041754069235 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.61848554098577 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 31.32623280148178 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 35.803000000000004 - type: map_at_10 value: 48.848 - type: map_at_100 value: 50.5 - type: map_at_1000 value: 50.602999999999994 - type: map_at_3 value: 45.111000000000004 - type: map_at_5 value: 47.202 - type: mrr_at_1 value: 44.635000000000005 - type: mrr_at_10 value: 55.593 - type: mrr_at_100 value: 56.169999999999995 - type: mrr_at_1000 value: 56.19499999999999 - type: mrr_at_3 value: 53.361999999999995 - type: mrr_at_5 value: 54.806999999999995 - type: ndcg_at_1 value: 44.635000000000005 - type: ndcg_at_10 value: 55.899 - type: ndcg_at_100 value: 60.958 - type: ndcg_at_1000 value: 62.302 - type: ndcg_at_3 value: 51.051 - type: ndcg_at_5 value: 53.351000000000006 - type: precision_at_1 value: 44.635000000000005 - type: precision_at_10 value: 10.786999999999999 - type: precision_at_100 value: 1.6580000000000001 - type: precision_at_1000 value: 0.213 - type: precision_at_3 value: 24.893 - type: precision_at_5 value: 17.740000000000002 - type: recall_at_1 value: 35.803000000000004 - type: recall_at_10 value: 68.657 - type: recall_at_100 value: 89.77199999999999 - type: recall_at_1000 value: 97.67 - type: recall_at_3 value: 54.066 - type: recall_at_5 value: 60.788 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.706 - type: map_at_10 value: 44.896 - type: map_at_100 value: 46.299 - type: map_at_1000 value: 46.44 - type: map_at_3 value: 41.721000000000004 - type: map_at_5 value: 43.486000000000004 - type: mrr_at_1 value: 41.592 - type: mrr_at_10 value: 50.529 - type: mrr_at_100 value: 51.22 - type: mrr_at_1000 value: 51.258 - type: mrr_at_3 value: 48.205999999999996 - type: mrr_at_5 value: 49.528 - type: ndcg_at_1 value: 41.592 - type: ndcg_at_10 value: 50.77199999999999 - type: ndcg_at_100 value: 55.383 - type: ndcg_at_1000 value: 57.288 - type: ndcg_at_3 value: 46.324 - type: ndcg_at_5 value: 48.346000000000004 - type: precision_at_1 value: 41.592 - type: precision_at_10 value: 9.516 - type: precision_at_100 value: 1.541 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 22.399 - type: precision_at_5 value: 15.770999999999999 - type: recall_at_1 value: 33.706 - type: recall_at_10 value: 61.353 - type: recall_at_100 value: 80.182 - type: recall_at_1000 value: 91.896 - type: recall_at_3 value: 48.204 - type: recall_at_5 value: 53.89699999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 44.424 - type: map_at_10 value: 57.169000000000004 - type: map_at_100 value: 58.202 - type: map_at_1000 value: 58.242000000000004 - type: map_at_3 value: 53.825 - type: map_at_5 value: 55.714 - type: mrr_at_1 value: 50.470000000000006 - type: mrr_at_10 value: 60.489000000000004 - type: mrr_at_100 value: 61.096 - type: mrr_at_1000 value: 61.112 - type: mrr_at_3 value: 58.192 - type: mrr_at_5 value: 59.611999999999995 - type: ndcg_at_1 value: 50.470000000000006 - type: ndcg_at_10 value: 63.071999999999996 - type: ndcg_at_100 value: 66.964 - type: ndcg_at_1000 value: 67.659 - type: ndcg_at_3 value: 57.74399999999999 - type: ndcg_at_5 value: 60.367000000000004 - type: precision_at_1 value: 50.470000000000006 - type: precision_at_10 value: 10.019 - type: precision_at_100 value: 1.29 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 25.558999999999997 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 44.424 - type: recall_at_10 value: 77.02 - type: recall_at_100 value: 93.738 - type: recall_at_1000 value: 98.451 - type: recall_at_3 value: 62.888 - type: recall_at_5 value: 69.138 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.294 - type: map_at_10 value: 34.503 - type: map_at_100 value: 35.641 - type: map_at_1000 value: 35.724000000000004 - type: map_at_3 value: 31.753999999999998 - type: map_at_5 value: 33.190999999999995 - type: mrr_at_1 value: 28.362 - type: mrr_at_10 value: 36.53 - type: mrr_at_100 value: 37.541000000000004 - type: mrr_at_1000 value: 37.602000000000004 - type: mrr_at_3 value: 33.917 - type: mrr_at_5 value: 35.358000000000004 - type: ndcg_at_1 value: 28.362 - type: ndcg_at_10 value: 39.513999999999996 - type: ndcg_at_100 value: 44.815 - type: ndcg_at_1000 value: 46.839 - type: ndcg_at_3 value: 34.02 - type: ndcg_at_5 value: 36.522 - type: precision_at_1 value: 28.362 - type: precision_at_10 value: 6.101999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.161999999999999 - type: precision_at_5 value: 9.966 - type: recall_at_1 value: 26.294 - type: recall_at_10 value: 53.098 - type: recall_at_100 value: 76.877 - type: recall_at_1000 value: 91.834 - type: recall_at_3 value: 38.266 - type: recall_at_5 value: 44.287 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.407 - type: map_at_10 value: 25.185999999999996 - type: map_at_100 value: 26.533 - type: map_at_1000 value: 26.657999999999998 - type: map_at_3 value: 22.201999999999998 - type: map_at_5 value: 23.923 - type: mrr_at_1 value: 20.522000000000002 - type: mrr_at_10 value: 29.522 - type: mrr_at_100 value: 30.644 - type: mrr_at_1000 value: 30.713 - type: mrr_at_3 value: 26.679000000000002 - type: mrr_at_5 value: 28.483000000000004 - type: ndcg_at_1 value: 20.522000000000002 - type: ndcg_at_10 value: 30.656 - type: ndcg_at_100 value: 36.864999999999995 - type: ndcg_at_1000 value: 39.675 - type: ndcg_at_3 value: 25.319000000000003 - type: ndcg_at_5 value: 27.992 - type: precision_at_1 value: 20.522000000000002 - type: precision_at_10 value: 5.795999999999999 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 12.396 - type: precision_at_5 value: 9.328 - type: recall_at_1 value: 16.407 - type: recall_at_10 value: 43.164 - type: recall_at_100 value: 69.695 - type: recall_at_1000 value: 89.41900000000001 - type: recall_at_3 value: 28.634999999999998 - type: recall_at_5 value: 35.308 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.473 - type: map_at_10 value: 41.676 - type: map_at_100 value: 43.120999999999995 - type: map_at_1000 value: 43.230000000000004 - type: map_at_3 value: 38.306000000000004 - type: map_at_5 value: 40.355999999999995 - type: mrr_at_1 value: 37.536 - type: mrr_at_10 value: 47.643 - type: mrr_at_100 value: 48.508 - type: mrr_at_1000 value: 48.551 - type: mrr_at_3 value: 45.348 - type: mrr_at_5 value: 46.744 - type: ndcg_at_1 value: 37.536 - type: ndcg_at_10 value: 47.823 - type: ndcg_at_100 value: 53.395 - type: ndcg_at_1000 value: 55.271 - type: ndcg_at_3 value: 42.768 - type: ndcg_at_5 value: 45.373000000000005 - type: precision_at_1 value: 37.536 - type: precision_at_10 value: 8.681 - type: precision_at_100 value: 1.34 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 20.468 - type: precision_at_5 value: 14.495 - type: recall_at_1 value: 30.473 - type: recall_at_10 value: 60.092999999999996 - type: recall_at_100 value: 82.733 - type: recall_at_1000 value: 94.875 - type: recall_at_3 value: 45.734 - type: recall_at_5 value: 52.691 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.976000000000003 - type: map_at_10 value: 41.097 - type: map_at_100 value: 42.547000000000004 - type: map_at_1000 value: 42.659000000000006 - type: map_at_3 value: 37.251 - type: map_at_5 value: 39.493 - type: mrr_at_1 value: 37.557 - type: mrr_at_10 value: 46.605000000000004 - type: mrr_at_100 value: 47.487 - type: mrr_at_1000 value: 47.54 - type: mrr_at_3 value: 43.721 - type: mrr_at_5 value: 45.411 - type: ndcg_at_1 value: 37.557 - type: ndcg_at_10 value: 47.449000000000005 - type: ndcg_at_100 value: 53.052 - type: ndcg_at_1000 value: 55.010999999999996 - type: ndcg_at_3 value: 41.439 - type: ndcg_at_5 value: 44.292 - type: precision_at_1 value: 37.557 - type: precision_at_10 value: 8.847 - type: precision_at_100 value: 1.357 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 20.091 - type: precision_at_5 value: 14.384 - type: recall_at_1 value: 29.976000000000003 - type: recall_at_10 value: 60.99099999999999 - type: recall_at_100 value: 84.245 - type: recall_at_1000 value: 96.97200000000001 - type: recall_at_3 value: 43.794 - type: recall_at_5 value: 51.778999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.099166666666665 - type: map_at_10 value: 38.1365 - type: map_at_100 value: 39.44491666666667 - type: map_at_1000 value: 39.55858333333334 - type: map_at_3 value: 35.03641666666666 - type: map_at_5 value: 36.79833333333334 - type: mrr_at_1 value: 33.39966666666667 - type: mrr_at_10 value: 42.42583333333333 - type: mrr_at_100 value: 43.28575 - type: mrr_at_1000 value: 43.33741666666667 - type: mrr_at_3 value: 39.94975 - type: mrr_at_5 value: 41.41633333333334 - type: ndcg_at_1 value: 33.39966666666667 - type: ndcg_at_10 value: 43.81741666666667 - type: ndcg_at_100 value: 49.08166666666667 - type: ndcg_at_1000 value: 51.121166666666674 - type: ndcg_at_3 value: 38.73575 - type: ndcg_at_5 value: 41.18158333333333 - type: precision_at_1 value: 33.39966666666667 - type: precision_at_10 value: 7.738916666666667 - type: precision_at_100 value: 1.2265833333333331 - type: precision_at_1000 value: 0.15983333333333336 - type: precision_at_3 value: 17.967416666666665 - type: precision_at_5 value: 12.78675 - type: recall_at_1 value: 28.099166666666665 - type: recall_at_10 value: 56.27049999999999 - type: recall_at_100 value: 78.93291666666667 - type: recall_at_1000 value: 92.81608333333334 - type: recall_at_3 value: 42.09775 - type: recall_at_5 value: 48.42533333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.663 - type: map_at_10 value: 30.377 - type: map_at_100 value: 31.426 - type: map_at_1000 value: 31.519000000000002 - type: map_at_3 value: 28.069 - type: map_at_5 value: 29.256999999999998 - type: mrr_at_1 value: 26.687 - type: mrr_at_10 value: 33.107 - type: mrr_at_100 value: 34.055 - type: mrr_at_1000 value: 34.117999999999995 - type: mrr_at_3 value: 31.058000000000003 - type: mrr_at_5 value: 32.14 - type: ndcg_at_1 value: 26.687 - type: ndcg_at_10 value: 34.615 - type: ndcg_at_100 value: 39.776 - type: ndcg_at_1000 value: 42.05 - type: ndcg_at_3 value: 30.322 - type: ndcg_at_5 value: 32.157000000000004 - type: precision_at_1 value: 26.687 - type: precision_at_10 value: 5.491 - type: precision_at_100 value: 0.877 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 13.139000000000001 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 23.663 - type: recall_at_10 value: 45.035 - type: recall_at_100 value: 68.554 - type: recall_at_1000 value: 85.077 - type: recall_at_3 value: 32.982 - type: recall_at_5 value: 37.688 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.403 - type: map_at_10 value: 25.197000000000003 - type: map_at_100 value: 26.355 - type: map_at_1000 value: 26.487 - type: map_at_3 value: 22.733 - type: map_at_5 value: 24.114 - type: mrr_at_1 value: 21.37 - type: mrr_at_10 value: 29.091 - type: mrr_at_100 value: 30.018 - type: mrr_at_1000 value: 30.096 - type: mrr_at_3 value: 26.887 - type: mrr_at_5 value: 28.157 - type: ndcg_at_1 value: 21.37 - type: ndcg_at_10 value: 30.026000000000003 - type: ndcg_at_100 value: 35.416 - type: ndcg_at_1000 value: 38.45 - type: ndcg_at_3 value: 25.764 - type: ndcg_at_5 value: 27.742 - type: precision_at_1 value: 21.37 - type: precision_at_10 value: 5.609 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 12.423 - type: precision_at_5 value: 9.009 - type: recall_at_1 value: 17.403 - type: recall_at_10 value: 40.573 - type: recall_at_100 value: 64.818 - type: recall_at_1000 value: 86.53699999999999 - type: recall_at_3 value: 28.493000000000002 - type: recall_at_5 value: 33.660000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.639 - type: map_at_10 value: 38.951 - type: map_at_100 value: 40.238 - type: map_at_1000 value: 40.327 - type: map_at_3 value: 35.842 - type: map_at_5 value: 37.617 - type: mrr_at_1 value: 33.769 - type: mrr_at_10 value: 43.088 - type: mrr_at_100 value: 44.03 - type: mrr_at_1000 value: 44.072 - type: mrr_at_3 value: 40.656 - type: mrr_at_5 value: 42.138999999999996 - type: ndcg_at_1 value: 33.769 - type: ndcg_at_10 value: 44.676 - type: ndcg_at_100 value: 50.416000000000004 - type: ndcg_at_1000 value: 52.227999999999994 - type: ndcg_at_3 value: 39.494 - type: ndcg_at_5 value: 42.013 - type: precision_at_1 value: 33.769 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.18 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 18.221 - type: precision_at_5 value: 12.966 - type: recall_at_1 value: 28.639 - type: recall_at_10 value: 57.687999999999995 - type: recall_at_100 value: 82.541 - type: recall_at_1000 value: 94.896 - type: recall_at_3 value: 43.651 - type: recall_at_5 value: 49.925999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.57 - type: map_at_10 value: 40.004 - type: map_at_100 value: 41.75 - type: map_at_1000 value: 41.97 - type: map_at_3 value: 36.788 - type: map_at_5 value: 38.671 - type: mrr_at_1 value: 35.375 - type: mrr_at_10 value: 45.121 - type: mrr_at_100 value: 45.994 - type: mrr_at_1000 value: 46.04 - type: mrr_at_3 value: 42.227 - type: mrr_at_5 value: 43.995 - type: ndcg_at_1 value: 35.375 - type: ndcg_at_10 value: 46.392 - type: ndcg_at_100 value: 52.196 - type: ndcg_at_1000 value: 54.274 - type: ndcg_at_3 value: 41.163 - type: ndcg_at_5 value: 43.813 - type: precision_at_1 value: 35.375 - type: precision_at_10 value: 8.676 - type: precision_at_100 value: 1.678 - type: precision_at_1000 value: 0.253 - type: precision_at_3 value: 19.104 - type: precision_at_5 value: 13.913 - type: recall_at_1 value: 29.57 - type: recall_at_10 value: 58.779 - type: recall_at_100 value: 83.337 - type: recall_at_1000 value: 95.979 - type: recall_at_3 value: 44.005 - type: recall_at_5 value: 50.975 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.832 - type: map_at_10 value: 29.733999999999998 - type: map_at_100 value: 30.727 - type: map_at_1000 value: 30.843999999999998 - type: map_at_3 value: 26.834999999999997 - type: map_at_5 value: 28.555999999999997 - type: mrr_at_1 value: 22.921 - type: mrr_at_10 value: 31.791999999999998 - type: mrr_at_100 value: 32.666000000000004 - type: mrr_at_1000 value: 32.751999999999995 - type: mrr_at_3 value: 29.144 - type: mrr_at_5 value: 30.622 - type: ndcg_at_1 value: 22.921 - type: ndcg_at_10 value: 34.915 - type: ndcg_at_100 value: 39.744 - type: ndcg_at_1000 value: 42.407000000000004 - type: ndcg_at_3 value: 29.421000000000003 - type: ndcg_at_5 value: 32.211 - type: precision_at_1 value: 22.921 - type: precision_at_10 value: 5.675 - type: precision_at_100 value: 0.872 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 12.753999999999998 - type: precision_at_5 value: 9.353 - type: recall_at_1 value: 20.832 - type: recall_at_10 value: 48.795 - type: recall_at_100 value: 70.703 - type: recall_at_1000 value: 90.187 - type: recall_at_3 value: 34.455000000000005 - type: recall_at_5 value: 40.967 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.334 - type: map_at_10 value: 19.009999999999998 - type: map_at_100 value: 21.129 - type: map_at_1000 value: 21.328 - type: map_at_3 value: 15.152 - type: map_at_5 value: 17.084 - type: mrr_at_1 value: 23.453 - type: mrr_at_10 value: 36.099 - type: mrr_at_100 value: 37.069 - type: mrr_at_1000 value: 37.104 - type: mrr_at_3 value: 32.096000000000004 - type: mrr_at_5 value: 34.451 - type: ndcg_at_1 value: 23.453 - type: ndcg_at_10 value: 27.739000000000004 - type: ndcg_at_100 value: 35.836 - type: ndcg_at_1000 value: 39.242 - type: ndcg_at_3 value: 21.263 - type: ndcg_at_5 value: 23.677 - type: precision_at_1 value: 23.453 - type: precision_at_10 value: 9.199 - type: precision_at_100 value: 1.791 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 16.2 - type: precision_at_5 value: 13.147 - type: recall_at_1 value: 10.334 - type: recall_at_10 value: 35.177 - type: recall_at_100 value: 63.009 - type: recall_at_1000 value: 81.938 - type: recall_at_3 value: 19.914 - type: recall_at_5 value: 26.077 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.212 - type: map_at_10 value: 17.386 - type: map_at_100 value: 24.234 - type: map_at_1000 value: 25.724999999999998 - type: map_at_3 value: 12.727 - type: map_at_5 value: 14.785 - type: mrr_at_1 value: 59.25 - type: mrr_at_10 value: 68.687 - type: mrr_at_100 value: 69.133 - type: mrr_at_1000 value: 69.14099999999999 - type: mrr_at_3 value: 66.917 - type: mrr_at_5 value: 67.742 - type: ndcg_at_1 value: 48.625 - type: ndcg_at_10 value: 36.675999999999995 - type: ndcg_at_100 value: 41.543 - type: ndcg_at_1000 value: 49.241 - type: ndcg_at_3 value: 41.373 - type: ndcg_at_5 value: 38.707 - type: precision_at_1 value: 59.25 - type: precision_at_10 value: 28.525 - type: precision_at_100 value: 9.027000000000001 - type: precision_at_1000 value: 1.8339999999999999 - type: precision_at_3 value: 44.833 - type: precision_at_5 value: 37.35 - type: recall_at_1 value: 8.212 - type: recall_at_10 value: 23.188 - type: recall_at_100 value: 48.613 - type: recall_at_1000 value: 73.093 - type: recall_at_3 value: 14.419 - type: recall_at_5 value: 17.798 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.725 - type: f1 value: 46.50743309855908 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 55.086 - type: map_at_10 value: 66.914 - type: map_at_100 value: 67.321 - type: map_at_1000 value: 67.341 - type: map_at_3 value: 64.75800000000001 - type: map_at_5 value: 66.189 - type: mrr_at_1 value: 59.28600000000001 - type: mrr_at_10 value: 71.005 - type: mrr_at_100 value: 71.304 - type: mrr_at_1000 value: 71.313 - type: mrr_at_3 value: 69.037 - type: mrr_at_5 value: 70.35 - type: ndcg_at_1 value: 59.28600000000001 - type: ndcg_at_10 value: 72.695 - type: ndcg_at_100 value: 74.432 - type: ndcg_at_1000 value: 74.868 - type: ndcg_at_3 value: 68.72200000000001 - type: ndcg_at_5 value: 71.081 - type: precision_at_1 value: 59.28600000000001 - type: precision_at_10 value: 9.499 - type: precision_at_100 value: 1.052 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 27.503 - type: precision_at_5 value: 17.854999999999997 - type: recall_at_1 value: 55.086 - type: recall_at_10 value: 86.453 - type: recall_at_100 value: 94.028 - type: recall_at_1000 value: 97.052 - type: recall_at_3 value: 75.821 - type: recall_at_5 value: 81.6 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.262999999999998 - type: map_at_10 value: 37.488 - type: map_at_100 value: 39.498 - type: map_at_1000 value: 39.687 - type: map_at_3 value: 32.529 - type: map_at_5 value: 35.455 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.239000000000004 - type: mrr_at_100 value: 54.086 - type: mrr_at_1000 value: 54.122 - type: mrr_at_3 value: 51.235 - type: mrr_at_5 value: 52.415 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 45.446 - type: ndcg_at_100 value: 52.429 - type: ndcg_at_1000 value: 55.169000000000004 - type: ndcg_at_3 value: 41.882000000000005 - type: ndcg_at_5 value: 43.178 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.931999999999999 - type: precision_at_100 value: 2.025 - type: precision_at_1000 value: 0.248 - type: precision_at_3 value: 28.652 - type: precision_at_5 value: 21.204 - type: recall_at_1 value: 22.262999999999998 - type: recall_at_10 value: 52.447 - type: recall_at_100 value: 78.045 - type: recall_at_1000 value: 94.419 - type: recall_at_3 value: 38.064 - type: recall_at_5 value: 44.769 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 32.519 - type: map_at_10 value: 45.831 - type: map_at_100 value: 46.815 - type: map_at_1000 value: 46.899 - type: map_at_3 value: 42.836 - type: map_at_5 value: 44.65 - type: mrr_at_1 value: 65.037 - type: mrr_at_10 value: 72.16 - type: mrr_at_100 value: 72.51100000000001 - type: mrr_at_1000 value: 72.53 - type: mrr_at_3 value: 70.682 - type: mrr_at_5 value: 71.54599999999999 - type: ndcg_at_1 value: 65.037 - type: ndcg_at_10 value: 55.17999999999999 - type: ndcg_at_100 value: 58.888 - type: ndcg_at_1000 value: 60.648 - type: ndcg_at_3 value: 50.501 - type: ndcg_at_5 value: 52.977 - type: precision_at_1 value: 65.037 - type: precision_at_10 value: 11.530999999999999 - type: precision_at_100 value: 1.4460000000000002 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 31.483 - type: precision_at_5 value: 20.845 - type: recall_at_1 value: 32.519 - type: recall_at_10 value: 57.657000000000004 - type: recall_at_100 value: 72.30199999999999 - type: recall_at_1000 value: 84.024 - type: recall_at_3 value: 47.225 - type: recall_at_5 value: 52.113 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.3168 - type: ap value: 83.80165516037135 - type: f1 value: 88.29942471066407 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 20.724999999999998 - type: map_at_10 value: 32.736 - type: map_at_100 value: 33.938 - type: map_at_1000 value: 33.991 - type: map_at_3 value: 28.788000000000004 - type: map_at_5 value: 31.016 - type: mrr_at_1 value: 21.361 - type: mrr_at_10 value: 33.323 - type: mrr_at_100 value: 34.471000000000004 - type: mrr_at_1000 value: 34.518 - type: mrr_at_3 value: 29.453000000000003 - type: mrr_at_5 value: 31.629 - type: ndcg_at_1 value: 21.361 - type: ndcg_at_10 value: 39.649 - type: ndcg_at_100 value: 45.481 - type: ndcg_at_1000 value: 46.775 - type: ndcg_at_3 value: 31.594 - type: ndcg_at_5 value: 35.543 - type: precision_at_1 value: 21.361 - type: precision_at_10 value: 6.3740000000000006 - type: precision_at_100 value: 0.931 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.514999999999999 - type: precision_at_5 value: 10.100000000000001 - type: recall_at_1 value: 20.724999999999998 - type: recall_at_10 value: 61.034 - type: recall_at_100 value: 88.062 - type: recall_at_1000 value: 97.86399999999999 - type: recall_at_3 value: 39.072 - type: recall_at_5 value: 48.53 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.8919288645691 - type: f1 value: 93.57059586398059 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 67.97993616051072 - type: f1 value: 48.244319183606535 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 68.90047074646941 - type: f1 value: 66.48999056063725 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.34566240753195 - type: f1 value: 73.54164154290658 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.21866934757011 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.000936217235534 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.68189362520352 - type: mrr value: 32.69603637784303 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.078 - type: map_at_10 value: 12.671 - type: map_at_100 value: 16.291 - type: map_at_1000 value: 17.855999999999998 - type: map_at_3 value: 9.610000000000001 - type: map_at_5 value: 11.152 - type: mrr_at_1 value: 43.963 - type: mrr_at_10 value: 53.173 - type: mrr_at_100 value: 53.718999999999994 - type: mrr_at_1000 value: 53.756 - type: mrr_at_3 value: 50.980000000000004 - type: mrr_at_5 value: 52.42 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.086 - type: ndcg_at_100 value: 32.545 - type: ndcg_at_1000 value: 41.144999999999996 - type: ndcg_at_3 value: 39.434999999999995 - type: ndcg_at_5 value: 37.888 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.014999999999997 - type: precision_at_100 value: 8.594 - type: precision_at_1000 value: 2.169 - type: precision_at_3 value: 37.049 - type: precision_at_5 value: 33.065 - type: recall_at_1 value: 6.078 - type: recall_at_10 value: 16.17 - type: recall_at_100 value: 34.512 - type: recall_at_1000 value: 65.447 - type: recall_at_3 value: 10.706 - type: recall_at_5 value: 13.158 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 27.378000000000004 - type: map_at_10 value: 42.178 - type: map_at_100 value: 43.32 - type: map_at_1000 value: 43.358000000000004 - type: map_at_3 value: 37.474000000000004 - type: map_at_5 value: 40.333000000000006 - type: mrr_at_1 value: 30.823 - type: mrr_at_10 value: 44.626 - type: mrr_at_100 value: 45.494 - type: mrr_at_1000 value: 45.519 - type: mrr_at_3 value: 40.585 - type: mrr_at_5 value: 43.146 - type: ndcg_at_1 value: 30.794 - type: ndcg_at_10 value: 50.099000000000004 - type: ndcg_at_100 value: 54.900999999999996 - type: ndcg_at_1000 value: 55.69499999999999 - type: ndcg_at_3 value: 41.238 - type: ndcg_at_5 value: 46.081 - type: precision_at_1 value: 30.794 - type: precision_at_10 value: 8.549 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 18.926000000000002 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 27.378000000000004 - type: recall_at_10 value: 71.842 - type: recall_at_100 value: 92.565 - type: recall_at_1000 value: 98.402 - type: recall_at_3 value: 49.053999999999995 - type: recall_at_5 value: 60.207 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.557 - type: map_at_10 value: 84.729 - type: map_at_100 value: 85.369 - type: map_at_1000 value: 85.382 - type: map_at_3 value: 81.72 - type: map_at_5 value: 83.613 - type: mrr_at_1 value: 81.3 - type: mrr_at_10 value: 87.488 - type: mrr_at_100 value: 87.588 - type: mrr_at_1000 value: 87.589 - type: mrr_at_3 value: 86.53 - type: mrr_at_5 value: 87.18599999999999 - type: ndcg_at_1 value: 81.28999999999999 - type: ndcg_at_10 value: 88.442 - type: ndcg_at_100 value: 89.637 - type: ndcg_at_1000 value: 89.70700000000001 - type: ndcg_at_3 value: 85.55199999999999 - type: ndcg_at_5 value: 87.154 - type: precision_at_1 value: 81.28999999999999 - type: precision_at_10 value: 13.489999999999998 - type: precision_at_100 value: 1.54 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.708 - type: recall_at_1 value: 70.557 - type: recall_at_10 value: 95.645 - type: recall_at_100 value: 99.693 - type: recall_at_1000 value: 99.995 - type: recall_at_3 value: 87.359 - type: recall_at_5 value: 91.89699999999999 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 63.65060114776209 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.63271250680617 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.263 - type: map_at_10 value: 10.801 - type: map_at_100 value: 12.888 - type: map_at_1000 value: 13.224 - type: map_at_3 value: 7.362 - type: map_at_5 value: 9.149000000000001 - type: mrr_at_1 value: 21 - type: mrr_at_10 value: 31.416 - type: mrr_at_100 value: 32.513 - type: mrr_at_1000 value: 32.58 - type: mrr_at_3 value: 28.116999999999997 - type: mrr_at_5 value: 29.976999999999997 - type: ndcg_at_1 value: 21 - type: ndcg_at_10 value: 18.551000000000002 - type: ndcg_at_100 value: 26.657999999999998 - type: ndcg_at_1000 value: 32.485 - type: ndcg_at_3 value: 16.834 - type: ndcg_at_5 value: 15.204999999999998 - type: precision_at_1 value: 21 - type: precision_at_10 value: 9.84 - type: precision_at_100 value: 2.16 - type: precision_at_1000 value: 0.35500000000000004 - type: precision_at_3 value: 15.667 - type: precision_at_5 value: 13.62 - type: recall_at_1 value: 4.263 - type: recall_at_10 value: 19.922 - type: recall_at_100 value: 43.808 - type: recall_at_1000 value: 72.14500000000001 - type: recall_at_3 value: 9.493 - type: recall_at_5 value: 13.767999999999999 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_spearman value: 81.27446313317233 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_spearman value: 76.27963301217527 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_spearman value: 88.18495048450949 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_spearman value: 81.91982338692046 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_spearman value: 89.00896818385291 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_spearman value: 85.48814644586132 - 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_spearman value: 90.30116926966582 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_spearman value: 67.74132963032342 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_spearman value: 86.87741355780479 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 82.0019012295875 - type: mrr value: 94.70267024188593 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 50.05 - type: map_at_10 value: 59.36 - type: map_at_100 value: 59.967999999999996 - type: map_at_1000 value: 60.023 - type: map_at_3 value: 56.515 - type: map_at_5 value: 58.272999999999996 - type: mrr_at_1 value: 53 - type: mrr_at_10 value: 61.102000000000004 - type: mrr_at_100 value: 61.476 - type: mrr_at_1000 value: 61.523 - type: mrr_at_3 value: 58.778 - type: mrr_at_5 value: 60.128 - type: ndcg_at_1 value: 53 - type: ndcg_at_10 value: 64.43100000000001 - type: ndcg_at_100 value: 66.73599999999999 - type: ndcg_at_1000 value: 68.027 - type: ndcg_at_3 value: 59.279 - type: ndcg_at_5 value: 61.888 - type: precision_at_1 value: 53 - type: precision_at_10 value: 8.767 - type: precision_at_100 value: 1.01 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 23.444000000000003 - type: precision_at_5 value: 15.667 - type: recall_at_1 value: 50.05 - type: recall_at_10 value: 78.511 - type: recall_at_100 value: 88.5 - type: recall_at_1000 value: 98.333 - type: recall_at_3 value: 64.117 - type: recall_at_5 value: 70.867 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - 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task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 68.77583615550819 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.151636938606956 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.16607939471187 - type: mrr value: 52.95172046091163 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.314646669495666 - type: cos_sim_spearman value: 31.83562491439455 - type: dot_pearson value: 31.314590842874157 - type: dot_spearman value: 31.83363065810437 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.198 - type: map_at_10 value: 1.3010000000000002 - type: map_at_100 value: 7.2139999999999995 - type: map_at_1000 value: 20.179 - type: map_at_3 value: 0.528 - type: map_at_5 value: 0.8019999999999999 - type: mrr_at_1 value: 72 - type: mrr_at_10 value: 83.39999999999999 - type: mrr_at_100 value: 83.39999999999999 - type: mrr_at_1000 value: 83.39999999999999 - type: mrr_at_3 value: 81.667 - type: mrr_at_5 value: 83.06700000000001 - type: ndcg_at_1 value: 66 - type: ndcg_at_10 value: 58.059000000000005 - type: ndcg_at_100 value: 44.316 - type: ndcg_at_1000 value: 43.147000000000006 - type: ndcg_at_3 value: 63.815999999999995 - type: ndcg_at_5 value: 63.005 - type: precision_at_1 value: 72 - type: precision_at_10 value: 61.4 - type: precision_at_100 value: 45.62 - 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type: ndcg_at_5 value: 21.770999999999997 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.142999999999999 - type: precision_at_1000 value: 1.541 - type: precision_at_3 value: 24.490000000000002 - type: precision_at_5 value: 22.448999999999998 - type: recall_at_1 value: 1.894 - type: recall_at_10 value: 14.931 - type: recall_at_100 value: 45.524 - type: recall_at_1000 value: 83.243 - type: recall_at_3 value: 5.712 - type: recall_at_5 value: 8.386000000000001 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.049 - type: ap value: 13.85116971310922 - type: f1 value: 54.37504302487686 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.1312959818902 - type: f1 value: 64.11413877009383 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 54.13103431861502 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.327889372355 - type: cos_sim_ap value: 77.42059895975699 - type: cos_sim_f1 value: 71.02706903250873 - type: cos_sim_precision value: 69.75324344950394 - type: cos_sim_recall value: 72.34828496042216 - type: dot_accuracy value: 87.327889372355 - type: dot_ap value: 77.4209479346677 - type: dot_f1 value: 71.02706903250873 - type: dot_precision value: 69.75324344950394 - type: dot_recall value: 72.34828496042216 - type: euclidean_accuracy value: 87.327889372355 - type: euclidean_ap value: 77.42096495861037 - type: euclidean_f1 value: 71.02706903250873 - type: euclidean_precision value: 69.75324344950394 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 87.31000774870358 - type: manhattan_ap value: 77.38930750711619 - type: manhattan_f1 value: 71.07935314027831 - type: manhattan_precision value: 67.70957726295677 - type: manhattan_recall value: 74.80211081794195 - type: max_accuracy value: 87.327889372355 - type: max_ap value: 77.42096495861037 - type: max_f1 value: 71.07935314027831 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.58939729110878 - type: cos_sim_ap value: 87.17594155025475 - type: cos_sim_f1 value: 79.21146953405018 - type: cos_sim_precision value: 76.8918527109307 - type: cos_sim_recall value: 81.67539267015707 - type: dot_accuracy value: 89.58939729110878 - type: dot_ap value: 87.17593963273593 - type: dot_f1 value: 79.21146953405018 - type: dot_precision value: 76.8918527109307 - type: dot_recall value: 81.67539267015707 - type: euclidean_accuracy value: 89.58939729110878 - type: euclidean_ap value: 87.17592466925834 - type: euclidean_f1 value: 79.21146953405018 - type: euclidean_precision value: 76.8918527109307 - type: euclidean_recall value: 81.67539267015707 - type: manhattan_accuracy value: 89.62626615438352 - type: manhattan_ap value: 87.16589873161546 - type: manhattan_f1 value: 79.25143598295348 - type: manhattan_precision value: 76.39494177323712 - type: manhattan_recall value: 82.32984293193716 - type: max_accuracy value: 89.62626615438352 - type: max_ap value: 87.17594155025475 - type: max_f1 value: 79.25143598295348 --- # hkunlp/instructor-large We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! **************************** **Updates** **************************** * 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance. * 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out! ## Quick start <hr /> ## Installation ```bash pip install InstructorEmbedding ``` ## Compute your customized embeddings Then you can use the model like this to calculate domain-specific and task-aware embeddings: ```python from InstructorEmbedding import INSTRUCTOR model = INSTRUCTOR('hkunlp/instructor-large') sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" instruction = "Represent the Science title:" embeddings = model.encode([[instruction,sentence]]) print(embeddings) ``` ## Use cases <hr /> ## Calculate embeddings for your customized texts If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc. ## Calculate Sentence similarities You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**. ```python from sklearn.metrics.pairwise import cosine_similarity sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']] sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'], ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']] embeddings_a = model.encode(sentences_a) embeddings_b = model.encode(sentences_b) similarities = cosine_similarity(embeddings_a,embeddings_b) print(similarities) ``` ## Information Retrieval You can also use **customized embeddings** for information retrieval. ```python import numpy as np from sklearn.metrics.pairwise import cosine_similarity query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']] corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'], ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"], ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']] query_embeddings = model.encode(query) corpus_embeddings = model.encode(corpus) similarities = cosine_similarity(query_embeddings,corpus_embeddings) retrieved_doc_id = np.argmax(similarities) print(retrieved_doc_id) ``` ## Clustering Use **customized embeddings** for clustering texts in groups. ```python import sklearn.cluster sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'], ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'], ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'], ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"], ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']] embeddings = model.encode(sentences) clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2) clustering_model.fit(embeddings) cluster_assignment = clustering_model.labels_ print(cluster_assignment) ```

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

LanguageBind/LanguageBind_Video_merge

LanguageBind

transformers

zero-shot-image-classification

--- license: mit --- <p align="center"> <img src="https://s11.ax1x.com/2024/02/01/pFMDAm9.png" width="250" style="margin-bottom: 0.2;"/> <p> <h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</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. * **[2024.01.16]** 🔥🔥🔥 Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) [here](https://openreview.net/forum?id=QmZKc7UZCy&noteId=OgsxQxAleA). * **[2023.12.15]** 💪💪💪 We expand the 💥💥💥 VIDAL dataset and now have **10M video-text data**. We launch **LanguageBind_Video 1.5**, checking our [model zoo](#-model-zoo). * **[2023.12.10]** We expand the 💥💥💥 VIDAL dataset and now have **10M depth and 10M thermal data**. We are in the process of uploading thermal and depth data on [Hugging Face](https://huggingface.co/datasets/LanguageBind/VIDAL-Depth-Thermal) and expect the whole process to last 1-2 months. * **[2023.11.27]** 🔥🔥🔥 We have updated our [paper](https://arxiv.org/abs/2310.01852) with emergency zero-shot results., checking our ✨ [results](#emergency-results). * **[2023.11.26]** 💥💥💥 We have open-sourced all textual sources and corresponding YouTube IDs [here](DATASETS.md). * **[2023.11.26]** 📣📣📣 We have open-sourced fully fine-tuned **Video & Audio**, achieving improved performance once again, checking our [model zoo](#-model-zoo). * **[2023.11.22]** We are about to release a fully fine-tuned version, and the **HUGE** version is currently undergoing training. * **[2023.11.21]** 💥 We are releasing sample data in [DATASETS.md](DATASETS.md) so that individuals who are interested can further modify the code to train it on their own data. * **[2023.11.20]** 🚀🚀🚀 [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) builds a large visual-language model to achieve 🎉SOTA performances based on LanguageBind encoders. * **[2023.10.23]** 🎶 LanguageBind-Audio achieves 🎉🎉🎉**state-of-the-art (SOTA) performance on 5 datasets**, checking our ✨ [results](#multiple-modalities)! * **[2023.10.14]** 😱 Released a stronger LanguageBind-Video, checking our ✨ [results](#video-language)! The video checkpoint **have updated** on Huggingface Model Hub! * **[2023.10.10]** We provide sample data, which can be found in [assets](assets), and [emergency zero-shot usage](#emergency-zero-shot) is described. * **[2023.10.07]** The checkpoints are available on 🤗 [Huggingface Model](https://huggingface.co/LanguageBind). * **[2023.10.04]** Code and [demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) are available now! Welcome to **watch** 👀 this repository for the latest updates. ## 😮 Highlights ### 💡 High performance, but NO intermediate modality required LanguageBind is a **language-centric** multimodal pretraining approach, **taking the language as the bind across different modalities** because the language modality is well-explored and contains rich semantics. * The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities. ### ⚡️ A multimodal, fully aligned and voluminous dataset We propose **VIDAL-10M**, **10 Million data** with **V**ideo, **I**nfrared, **D**epth, **A**udio and their corresponding **L**anguage, which greatly expands the data beyond visual modalities. * The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language. ### 🔥 Multi-view enhanced description for training We make multi-view enhancements to language. We produce multi-view description that combines **meta-data**, **spatial**, and **temporal** to greatly enhance the semantic information of the language. In addition we further **enhance the language with ChatGPT** to create a good semantic space for each modality aligned language. ## 🤗 Demo * **Local demo.** Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind. ```bash python gradio_app.py ``` * **Online demo.** We provide the [online demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image. ## 🛠️ Requirements and Installation * Python >= 3.8 * Pytorch >= 1.13.1 * CUDA Version >= 11.6 * Install required packages: ```bash git clone https://github.com/PKU-YuanGroup/LanguageBind cd LanguageBind pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116 pip install -r requirements.txt ``` ## 🐳 Model Zoo The names in the table represent different encoder models. For example, `LanguageBind/LanguageBind_Video_FT` represents the fully fine-tuned version, while `LanguageBind/LanguageBind_Video` represents the LoRA-tuned version. You can freely replace them in the recommended [API usage](#-api). We recommend using the fully fine-tuned version, as it offers stronger performance. <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th> </tr> <tr align="center"> <td>Video</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">LanguageBind_Video</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">LanguageBind_Video_FT</a></td> </tr> <tr align="center"> <td>Audio</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio">LanguageBind_Audio</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio_FT">LanguageBind_Audio_FT</a></td> </tr> <tr align="center"> <td>Depth</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Depth">LanguageBind_Depth</a></td><td>-</td> </tr> <tr align="center"> <td>Thermal</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Thermal">LanguageBind_Thermal</a></td><td>-</td> </tr> </table> </div> <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th> </tr> <tr align="center"> <td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">Link</a></td><td>42.6</td><td>37.8</td><td>35.1</td><td>52.2</td> </tr> <tr align="center"> <td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">Link</a></td><td>42.7</td><td>38.1</td><td>36.9</td><td>53.5</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_V1.5_FT">Link</a></td><td>42.8</td><td>39.7</td><td>38.4</td><td>54.1</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_Huge_V1.5_FT">Link</a></td><td>44.8</td><td>39.9</td><td>41.0</td><td>53.7</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td> </tr> </table> </div> ## 🤖 API **We open source all modalities preprocessing code.** If you want to load the model (e.g. ```LanguageBind/LanguageBind_Thermal```) from the model hub on Huggingface or on local, you can use the following code snippets! ### Inference for Multi-modal Binding We have provided some sample datasets in [assets](assets) to quickly see how languagebind works. ```python import torch from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer if __name__ == '__main__': device = 'cuda:0' device = torch.device(device) clip_type = { 'video': 'LanguageBind_Video_FT', # also LanguageBind_Video 'audio': 'LanguageBind_Audio_FT', # also LanguageBind_Audio 'thermal': 'LanguageBind_Thermal', 'image': 'LanguageBind_Image', 'depth': 'LanguageBind_Depth', } model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir') model = model.to(device) model.eval() pretrained_ckpt = f'lb203/LanguageBind_Image' tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir') modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type.keys()} image = ['assets/image/0.jpg', 'assets/image/1.jpg'] audio = ['assets/audio/0.wav', 'assets/audio/1.wav'] video = ['assets/video/0.mp4', 'assets/video/1.mp4'] depth = ['assets/depth/0.png', 'assets/depth/1.png'] thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg'] language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.'] inputs = { 'image': to_device(modality_transform['image'](image), device), 'video': to_device(modality_transform['video'](video), device), 'audio': to_device(modality_transform['audio'](audio), device), 'depth': to_device(modality_transform['depth'](depth), device), 'thermal': to_device(modality_transform['thermal'](thermal), device), } inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length', truncation=True, return_tensors='pt'), device) with torch.no_grad(): embeddings = model(inputs) print("Video x Text: \n", torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Image x Text: \n", torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Depth x Text: \n", torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Audio x Text: \n", torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Thermal x Text: \n", torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) ``` Then returns the following result. ```bash Video x Text: [[9.9989331e-01 1.0667283e-04] [1.3255903e-03 9.9867439e-01]] Image x Text: [[9.9990666e-01 9.3292067e-05] [4.6132666e-08 1.0000000e+00]] Depth x Text: [[0.9954276 0.00457235] [0.12042473 0.8795753 ]] Audio x Text: [[0.97634876 0.02365119] [0.02917843 0.97082156]] Thermal x Text: [[0.9482511 0.0517489 ] [0.48746133 0.5125386 ]] ``` ### Emergency zero-shot Since languagebind binds each modality together, we also found the **emergency zero-shot**. It's very simple to use. ```python print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy()) print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy()) print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy()) ``` Then, you will get: ``` Video x Audio: [[1.0000000e+00 0.0000000e+00] [3.1150486e-32 1.0000000e+00]] Image x Depth: [[1. 0.] [0. 1.]] Image x Thermal: [[1. 0.] [0. 1.]] ``` ### Different branches for X-Language task Additionally, LanguageBind can be **disassembled into different branches** to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP. #### Thermal ```python import torch from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Thermal' model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') thermal_process = LanguageBindThermalProcessor(model.config, tokenizer) model.eval() data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Depth ```python import torch from languagebind import LanguageBindDepth, LanguageBindDepthTokenizer, LanguageBindDepthProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Depth' model = LanguageBindDepth.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindDepthTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') depth_process = LanguageBindDepthProcessor(model.config, tokenizer) model.eval() data = depth_process([r"your/depth.png"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Video ```python import torch from languagebind import LanguageBindVideo, LanguageBindVideoTokenizer, LanguageBindVideoProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Video_FT' # also 'LanguageBind/LanguageBind_Video' model = LanguageBindVideo.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindVideoTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') video_process = LanguageBindVideoProcessor(model.config, tokenizer) model.eval() data = video_process(["your/video.mp4"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Audio ```python import torch from languagebind import LanguageBindAudio, LanguageBindAudioTokenizer, LanguageBindAudioProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Audio_FT' # also 'LanguageBind/LanguageBind_Audio' model = LanguageBindAudio.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindAudioTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') audio_process = LanguageBindAudioProcessor(model.config, tokenizer) model.eval() data = audio_process([r"your/audio.wav"], ['your audio.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Image Note that our image encoder is the same as OpenCLIP. **Not** as fine-tuned as other modalities. ```python import torch from languagebind import LanguageBindImage, LanguageBindImageTokenizer, LanguageBindImageProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Image' model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') image_process = LanguageBindImageProcessor(model.config, tokenizer) model.eval() data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` ## 💥 VIDAL-10M The datasets is in [DATASETS.md](DATASETS.md). ## 🗝️ Training & Validating The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md). ## 👍 Acknowledgement * [OpenCLIP](https://github.com/mlfoundations/open_clip) An open source pretraining framework. * [CLIP4Clip](https://github.com/ArrowLuo/CLIP4Clip) An open source Video-Text retrieval framework. * [sRGB-TIR](https://github.com/rpmsnu/sRGB-TIR) An open source framework to generate infrared (thermal) images. * [GLPN](https://github.com/vinvino02/GLPDepth) An open source framework to generate depth images. ## 🔒 License * The majority of this project is released under the MIT license as found in the [LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/LICENSE) file. * The dataset of this project is released under the CC-BY-NC 4.0 license as found in the [DATASET_LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/DATASET_LICENSE) file. ## ✏️ Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. ```BibTeX @misc{zhu2023languagebind, title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment}, author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and Wang HongFa and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Cai Wan Zhang and Zhifeng Li and Wei Liu and Li Yuan}, year={2023}, eprint={2310.01852}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## ✨ Star History [](https://star-history.com/#PKU-YuanGroup/LanguageBind&Date) ## 🤝 Contributors <a href="https://github.com/PKU-YuanGroup/LanguageBind/graphs/contributors"> <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/LanguageBind" /> </a>

bhadresh-savani/bert-base-uncased-emotion

bhadresh-savani

transformers

text-classification

--- language: - en license: apache-2.0 tags: - text-classification - emotion - pytorch datasets: - emotion metrics: - Accuracy, F1 Score thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4 model-index: - name: bhadresh-savani/bert-base-uncased-emotion results: - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion config: default split: test metrics: - type: accuracy value: 0.9265 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWQzNzA2MTFkY2RkNDMxYTFhOGUzMTdiZTgwODA3ODdmZTVhNTVjOTAwMGM5NjU1OGY0MjMzZWU0OTU2MzY1YiIsInZlcnNpb24iOjF9.f6iWK0iyU8_g32W2oMfh1ChevMsl0StI402cB6DNzJCYj9xywTnFltBY36jAJFDRK41HXdMnPMl64Bynr-Q9CA - type: precision value: 0.8859601677706858 name: Precision Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTc2ZjRmMzYzNTE0ZDQ1ZDdkYWViYWNhZDhkOTE2ZDhmMDFjZmZiZjRkZWVlMzQ3MWE4NDNlYzlmM2I4ZGM2OCIsInZlcnNpb24iOjF9.jR-gFrrBIAfiYV352RDhK3nzgqIgNCPd55OhIcCfVdVAWHQSZSJXhFyg8yChC7DwoVmUQy1Ya-d8Hflp7Wi-AQ - type: precision value: 0.9265 name: Precision Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDAyMWZjZTM5NWNjNTcyMWQzMWQyNDcyN2RlZTQyZTM4ZDQ4Y2FlNzM2OTZkMzM3YzI4YTAwNzg4MGNjZmZjZCIsInZlcnNpb24iOjF9.cmkuDmhhETKIKAL81K28oiO889sZ0hvEpZ6Ep7dW_KB9VOTFs15BzFY9vwcpdXQDugWBbB2g7r3FUgRLwIEpAg - type: precision value: 0.9265082039990273 name: Precision Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTA2NzY2NTJmZTExZWM3OGIzYzg3ZDM3Y2I5MTU3Mjg3Y2NmZGEyMjFmNjExZWM3ZDFjNzdhOTZkNTYwYWQxYyIsInZlcnNpb24iOjF9.DJgeA6ZovHoxgCqhzilIzafet8uN3-Xbx1ZYcEEc4jXzFbRtErE__QHGaaSaUQEzPp4BAztp1ageOaBoEmXSDg - type: recall value: 0.879224648382427 name: Recall Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGU3MmQ1Yjg5OGJlYTE1NWJmNGVjY2ExMDZiZjVjYmVkOGYxYWFkOTVlMDVjOWVhZGFjOGFkYzcwMGIyMTAyZCIsInZlcnNpb24iOjF9.jwgaNEBSQENlx3vojBi1WKJOQ7pSuP4Iyw4kKPsq9IUaW-Ah8KdgPV9Nm2DY1cwEtMayvVeIVmQ3Wo8PORDRAg - type: recall value: 0.9265 name: Recall Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDE3OWQ0ZGZjNzAxY2I0NGMxNDU0OWE1OGM2N2Q3OTUwYWI0NmZjMDQ3MDc0NDA4YTc2NDViM2Y0ZTMyMjYyZCIsInZlcnNpb24iOjF9.Ihc61PSO3K63t5hUSAve4Gt1tC8R_ZruZo492dTD9CsKOF10LkvrCskJJaOATjFJgqb3FFiJ8-nDL9Pa3HF-Dg - type: recall value: 0.9265 name: Recall Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzJkYTg5YjA0YTBlNDY3ZjFjZWIzOWVhYjI4Y2YxM2FhMmUwMDZlZTE0NTIzNjMxMjE3NzgwNGFjYTkzOWM1YyIsInZlcnNpb24iOjF9.LlBX4xTjKuTX0NPK0jYzYDXRVnUEoUKVwIHfw5xUzaFgtF4wuqaYV7F0VKoOd3JZxzxNgf7JzeLof0qTquE9Cw - type: f1 value: 0.8821398657055098 name: F1 Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTE4OThiMmE0NDEzZjBkY2RmZWNjMGI3YWNmNTFjNTY5NjIwNjFkZjk1ZjIxMjI4M2ZiZGJhYzJmNzVhZTU1NSIsInZlcnNpb24iOjF9.gzYyUbO4ycvP1RXnrKKZH3E8ym0DjwwUFf4Vk9j0wrg2sWIchjmuloZz0SLryGqwHiAV8iKcSBWWy61Q480XAw - type: f1 value: 0.9265 name: F1 Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGM2Y2E0NjMyNmJhMTE4NjYyMjI2MTJlZjUzNmRmY2U3Yjk3ZGUyYzU2OWYzMWM2ZjY4ZTg0OTliOTY3YmI2MSIsInZlcnNpb24iOjF9.hEz_yExs6LV0RBpFBoUbnAQZHitxN57HodCJpDx0yyW6dQwWaza0JxdO-kBf8JVBK8JyISkNgOYskBY5LD4ZDQ - type: f1 value: 0.9262425173620311 name: F1 Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmMyY2NhNTRhOGMwM2M5OTQxNDQ0NjRkZDdiMDExMWFkMmI4MmYwZGQ1OGRiYmRjMmE2YTc0MGZmMWMwN2Q4MSIsInZlcnNpb24iOjF9.ljbb2L4R08NCGjcfuX1878HRilJ_p9qcDJpWhsu-5EqWCco80e9krb7VvIJV0zBfmi7Z3C2qGGRsfsAIhtQ5Dw - type: loss value: 0.17315374314785004 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmQwN2I2Nzg4OWU1ODE5NTBhMTZiMjljMjJhN2JiYmY0MTkzMTA1NmVhMGU0Y2Y0NjgyOTU3ZjgyYTc3ODE5NCIsInZlcnNpb24iOjF9.EEp3Gxm58ab-9335UGQEk-3dFQcMRgJgViI7fpz7mfY2r5Pg-AOel5w4SMzmBM-hiUFwStgxe5he_kG2yPGFCw --- # bert-base-uncased-emotion ## Model description: [Bert](https://arxiv.org/abs/1810.04805) is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective [bert-base-uncased](https://huggingface.co/bert-base-uncased) finetuned on the emotion dataset using HuggingFace Trainer with below training parameters ``` learning rate 2e-5, batch size 64, num_train_epochs=8, ``` ## Model Performance Comparision on Emotion Dataset from Twitter: | Model | Accuracy | F1 Score | Test Sample per Second | | --- | --- | --- | --- | | [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 | | [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 | | [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 | | [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 | ## How to Use the model: ```python from transformers import pipeline classifier = pipeline("text-classification",model='bhadresh-savani/bert-base-uncased-emotion', return_all_scores=True) prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", ) print(prediction) """ output: [[ {'label': 'sadness', 'score': 0.0005138228880241513}, {'label': 'joy', 'score': 0.9972520470619202}, {'label': 'love', 'score': 0.0007443308713845909}, {'label': 'anger', 'score': 0.0007404946954920888}, {'label': 'fear', 'score': 0.00032938539516180754}, {'label': 'surprise', 'score': 0.0004197491507511586} ]] """ ``` ## Dataset: [Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion). ## Training procedure [Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb) follow the above notebook by changing the model name from distilbert to bert ## Eval results ```json { 'test_accuracy': 0.9405, 'test_f1': 0.9405920712282673, 'test_loss': 0.15769127011299133, 'test_runtime': 10.5179, 'test_samples_per_second': 190.152, 'test_steps_per_second': 3.042 } ``` ## Reference: * [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)

Trelis/Llama-2-7b-chat-hf-function-calling-GPTQ

Trelis

transformers

text-generation

--- language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 - functions - function calling - sharded - ggml - gptq --- # fLlama 2 - Function Calling Llama 2 - fLlama 2 extends the hugging face Llama 2 models with function calling capabilities. - The model responds with a structured json argument with the function name and arguments Available models: - fLlama-7B ([bitsandbytes NF4](https://huggingface.co/Trelis/Llama-2-7b-chat-hf-function-calling)), ([GGML](https://huggingface.co/Trelis/Llama-2-7b-chat-hf-function-calling-GGML)), ([GPTQ](https://huggingface.co/Trelis/Llama-2-7b-chat-hf-function-calling-GPTQ)) - free - fLlama-13B ([bitsandbytes NF4](https://huggingface.co/Trelis/Llama-2-13b-chat-hf-function-calling)), ([GPTQ](https://huggingface.co/Trelis/Llama-2-13b-chat-hf-function-calling-GPTQ)) - paid ## Inference with Google Colab and HuggingFace 🤗 **GPTQ (fastest + good accuracy)** Get started by saving your own copy of this [function calling chatbot](https://colab.research.google.com/drive/1u8x41Jx8WWtI-nzHOgqTxkS3Q_lcjaSX?usp=sharing). You will be able to run inference using a free Colab notebook if you select a gpu runtime. See the notebook for more details. **Bits and Bytes NF4 (slowest inference)** Try out this notebook [fLlama_Inference notebook](https://colab.research.google.com/drive/1Ow5cQ0JNv-vXsT-apCceH6Na3b4L7JyW?usp=sharing) **GGML (best for running on a laptop, great for Mac)** To run this you'll need to install llamaccp from ggerganov on github. - Download the ggml file from the ggml link above, under available models - I recommend running a command like: ``` ./server -m fLlama-2-7b-chat.ggmlv3.q3_K_M.bin -ngl 32 -c 2048 ``` which will allow you to run a chatbot in your browser. The -ngl offloads layers to the Mac's GPU and gets very good token generation speed. ## Licensing and Usage fLlama-7B: - Llama 2 license fLlama-13B: - For higher precision on function calling. - Purchase acess here: [fLlama-13b: €19.99 per user/seat.](https://buy.stripe.com/9AQ7te3lHdmbdZ68wz) - Licenses are not transferable to other users/entities. - Commercial licenses for larger models are available on request - email ronan [at] trelis [dot] com - Use of fLlama models is further subject to terms in the [Meta license](https://ai.meta.com/resources/models-and-libraries/llama-downloads/). ### Dataset The dataset used for training this model can be found at [Trelis Function Calling Extended Dataset](https://huggingface.co/datasets/Trelis/function_calling_extended). ## Prompt and Response Format To make a function call, you should format your input like this: ``` <s>[INST] <<SYS>> You are a helpful research assistant. The following functions are available for you to fetch further data to answer user questions, if relevant: { "function": "search_bing", "description": "Search the web for content on Bing. This allows users to search online/the internet/the web for content.", "arguments": [ { "name": "query", "type": "string", "description": "The search query string" } ] } { "function": "search_arxiv", "description": "Search for research papers on ArXiv. Make use of AND, OR and NOT operators as appropriate to join terms within the query.", "arguments": [ { "name": "query", "type": "string", "description": "The search query string" } ] } To call a function, respond - immediately and only - with a JSON object of the following format: { "function": "function_name", "arguments": { "argument1": "argument_value", "argument2": "argument_value" } } <</SYS>> Find papers on high pressure batch reverse osmosis [/INST] ``` Notice that functionMetadata should be a string representation of a JSON object, like this: ``` "functionMetaData": { "function": "search_bing", "description": "Search the web for content on Bing. This allows users to search online/the internet/the web for content.", "arguments": [ { "name": "query", "type": "string", "description": "The search query string" } ] } ''' ``` and the language model should respond with a json object formatted like this: ``` { "function": "function_name", "arguments": { "argument1": "argument_value", "argument2": "argument_value" } } ``` It is recommended to handle cases where: - There is no json object in the response - The response contains text in addition to the json response ## Quanitization Configurations The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ~ Below follows information on the original Llama 2 model... ~ # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

LanguageBind/LanguageBind_Image

LanguageBind

transformers

zero-shot-image-classification

--- license: mit --- <p align="center"> <img src="https://s11.ax1x.com/2024/02/01/pFMDAm9.png" width="250" style="margin-bottom: 0.2;"/> <p> <h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</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. * **[2024.01.16]** 🔥🔥🔥 Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) [here](https://openreview.net/forum?id=QmZKc7UZCy&noteId=OgsxQxAleA). * **[2023.12.15]** 💪💪💪 We expand the 💥💥💥 VIDAL dataset and now have **10M video-text data**. We launch **LanguageBind_Video 1.5**, checking our [model zoo](#-model-zoo). * **[2023.12.10]** We expand the 💥💥💥 VIDAL dataset and now have **10M depth and 10M thermal data**. We are in the process of uploading thermal and depth data on [Hugging Face](https://huggingface.co/datasets/LanguageBind/VIDAL-Depth-Thermal) and expect the whole process to last 1-2 months. * **[2023.11.27]** 🔥🔥🔥 We have updated our [paper](https://arxiv.org/abs/2310.01852) with emergency zero-shot results., checking our ✨ [results](#emergency-results). * **[2023.11.26]** 💥💥💥 We have open-sourced all textual sources and corresponding YouTube IDs [here](DATASETS.md). * **[2023.11.26]** 📣📣📣 We have open-sourced fully fine-tuned **Video & Audio**, achieving improved performance once again, checking our [model zoo](#-model-zoo). * **[2023.11.22]** We are about to release a fully fine-tuned version, and the **HUGE** version is currently undergoing training. * **[2023.11.21]** 💥 We are releasing sample data in [DATASETS.md](DATASETS.md) so that individuals who are interested can further modify the code to train it on their own data. * **[2023.11.20]** 🚀🚀🚀 [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) builds a large visual-language model to achieve 🎉SOTA performances based on LanguageBind encoders. * **[2023.10.23]** 🎶 LanguageBind-Audio achieves 🎉🎉🎉**state-of-the-art (SOTA) performance on 5 datasets**, checking our ✨ [results](#multiple-modalities)! * **[2023.10.14]** 😱 Released a stronger LanguageBind-Video, checking our ✨ [results](#video-language)! The video checkpoint **have updated** on Huggingface Model Hub! * **[2023.10.10]** We provide sample data, which can be found in [assets](assets), and [emergency zero-shot usage](#emergency-zero-shot) is described. * **[2023.10.07]** The checkpoints are available on 🤗 [Huggingface Model](https://huggingface.co/LanguageBind). * **[2023.10.04]** Code and [demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) are available now! Welcome to **watch** 👀 this repository for the latest updates. ## 😮 Highlights ### 💡 High performance, but NO intermediate modality required LanguageBind is a **language-centric** multimodal pretraining approach, **taking the language as the bind across different modalities** because the language modality is well-explored and contains rich semantics. * The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities. ### ⚡️ A multimodal, fully aligned and voluminous dataset We propose **VIDAL-10M**, **10 Million data** with **V**ideo, **I**nfrared, **D**epth, **A**udio and their corresponding **L**anguage, which greatly expands the data beyond visual modalities. * The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language. ### 🔥 Multi-view enhanced description for training We make multi-view enhancements to language. We produce multi-view description that combines **meta-data**, **spatial**, and **temporal** to greatly enhance the semantic information of the language. In addition we further **enhance the language with ChatGPT** to create a good semantic space for each modality aligned language. ## 🤗 Demo * **Local demo.** Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind. ```bash python gradio_app.py ``` * **Online demo.** We provide the [online demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image. ## 🛠️ Requirements and Installation * Python >= 3.8 * Pytorch >= 1.13.1 * CUDA Version >= 11.6 * Install required packages: ```bash git clone https://github.com/PKU-YuanGroup/LanguageBind cd LanguageBind pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116 pip install -r requirements.txt ``` ## 🐳 Model Zoo The names in the table represent different encoder models. For example, `LanguageBind/LanguageBind_Video_FT` represents the fully fine-tuned version, while `LanguageBind/LanguageBind_Video` represents the LoRA-tuned version. You can freely replace them in the recommended [API usage](#-api). We recommend using the fully fine-tuned version, as it offers stronger performance. <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th> </tr> <tr align="center"> <td>Video</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">LanguageBind_Video</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">LanguageBind_Video_FT</a></td> </tr> <tr align="center"> <td>Audio</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio">LanguageBind_Audio</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio_FT">LanguageBind_Audio_FT</a></td> </tr> <tr align="center"> <td>Depth</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Depth">LanguageBind_Depth</a></td><td>-</td> </tr> <tr align="center"> <td>Thermal</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Thermal">LanguageBind_Thermal</a></td><td>-</td> </tr> </table> </div> <div align="center"> <table border="1" width="100%"> <tr align="center"> <th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th> </tr> <tr align="center"> <td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">Link</a></td><td>42.6</td><td>37.8</td><td>35.1</td><td>52.2</td> </tr> <tr align="center"> <td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">Link</a></td><td>42.7</td><td>38.1</td><td>36.9</td><td>53.5</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_V1.5_FT">Link</a></td><td>42.8</td><td>39.7</td><td>38.4</td><td>54.1</td> </tr> <tr align="center"> <td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_Huge_V1.5_FT">Link</a></td><td>44.8</td><td>39.9</td><td>41.0</td><td>53.7</td> </tr> <tr align="center"> <td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td> </tr> </table> </div> ## 🤖 API **We open source all modalities preprocessing code.** If you want to load the model (e.g. ```LanguageBind/LanguageBind_Thermal```) from the model hub on Huggingface or on local, you can use the following code snippets! ### Inference for Multi-modal Binding We have provided some sample datasets in [assets](assets) to quickly see how languagebind works. ```python import torch from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer if __name__ == '__main__': device = 'cuda:0' device = torch.device(device) clip_type = { 'video': 'LanguageBind_Video_FT', # also LanguageBind_Video 'audio': 'LanguageBind_Audio_FT', # also LanguageBind_Audio 'thermal': 'LanguageBind_Thermal', 'image': 'LanguageBind_Image', 'depth': 'LanguageBind_Depth', } model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir') model = model.to(device) model.eval() pretrained_ckpt = f'lb203/LanguageBind_Image' tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir') modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type.keys()} image = ['assets/image/0.jpg', 'assets/image/1.jpg'] audio = ['assets/audio/0.wav', 'assets/audio/1.wav'] video = ['assets/video/0.mp4', 'assets/video/1.mp4'] depth = ['assets/depth/0.png', 'assets/depth/1.png'] thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg'] language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.'] inputs = { 'image': to_device(modality_transform['image'](image), device), 'video': to_device(modality_transform['video'](video), device), 'audio': to_device(modality_transform['audio'](audio), device), 'depth': to_device(modality_transform['depth'](depth), device), 'thermal': to_device(modality_transform['thermal'](thermal), device), } inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length', truncation=True, return_tensors='pt'), device) with torch.no_grad(): embeddings = model(inputs) print("Video x Text: \n", torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Image x Text: \n", torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Depth x Text: \n", torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Audio x Text: \n", torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) print("Thermal x Text: \n", torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy()) ``` Then returns the following result. ```bash Video x Text: [[9.9989331e-01 1.0667283e-04] [1.3255903e-03 9.9867439e-01]] Image x Text: [[9.9990666e-01 9.3292067e-05] [4.6132666e-08 1.0000000e+00]] Depth x Text: [[0.9954276 0.00457235] [0.12042473 0.8795753 ]] Audio x Text: [[0.97634876 0.02365119] [0.02917843 0.97082156]] Thermal x Text: [[0.9482511 0.0517489 ] [0.48746133 0.5125386 ]] ``` ### Emergency zero-shot Since languagebind binds each modality together, we also found the **emergency zero-shot**. It's very simple to use. ```python print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy()) print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy()) print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy()) ``` Then, you will get: ``` Video x Audio: [[1.0000000e+00 0.0000000e+00] [3.1150486e-32 1.0000000e+00]] Image x Depth: [[1. 0.] [0. 1.]] Image x Thermal: [[1. 0.] [0. 1.]] ``` ### Different branches for X-Language task Additionally, LanguageBind can be **disassembled into different branches** to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP. #### Thermal ```python import torch from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Thermal' model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') thermal_process = LanguageBindThermalProcessor(model.config, tokenizer) model.eval() data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Depth ```python import torch from languagebind import LanguageBindDepth, LanguageBindDepthTokenizer, LanguageBindDepthProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Depth' model = LanguageBindDepth.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindDepthTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') depth_process = LanguageBindDepthProcessor(model.config, tokenizer) model.eval() data = depth_process([r"your/depth.png"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Video ```python import torch from languagebind import LanguageBindVideo, LanguageBindVideoTokenizer, LanguageBindVideoProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Video_FT' # also 'LanguageBind/LanguageBind_Video' model = LanguageBindVideo.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindVideoTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') video_process = LanguageBindVideoProcessor(model.config, tokenizer) model.eval() data = video_process(["your/video.mp4"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Audio ```python import torch from languagebind import LanguageBindAudio, LanguageBindAudioTokenizer, LanguageBindAudioProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Audio_FT' # also 'LanguageBind/LanguageBind_Audio' model = LanguageBindAudio.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindAudioTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') audio_process = LanguageBindAudioProcessor(model.config, tokenizer) model.eval() data = audio_process([r"your/audio.wav"], ['your audio.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` #### Image Note that our image encoder is the same as OpenCLIP. **Not** as fine-tuned as other modalities. ```python import torch from languagebind import LanguageBindImage, LanguageBindImageTokenizer, LanguageBindImageProcessor pretrained_ckpt = 'LanguageBind/LanguageBind_Image' model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir') image_process = LanguageBindImageProcessor(model.config, tokenizer) model.eval() data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt') with torch.no_grad(): out = model(**data) print(out.text_embeds @ out.image_embeds.T) ``` ## 💥 VIDAL-10M The datasets is in [DATASETS.md](DATASETS.md). ## 🗝️ Training & Validating The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md). ## 👍 Acknowledgement * [OpenCLIP](https://github.com/mlfoundations/open_clip) An open source pretraining framework. * [CLIP4Clip](https://github.com/ArrowLuo/CLIP4Clip) An open source Video-Text retrieval framework. * [sRGB-TIR](https://github.com/rpmsnu/sRGB-TIR) An open source framework to generate infrared (thermal) images. * [GLPN](https://github.com/vinvino02/GLPDepth) An open source framework to generate depth images. ## 🔒 License * The majority of this project is released under the MIT license as found in the [LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/LICENSE) file. * The dataset of this project is released under the CC-BY-NC 4.0 license as found in the [DATASET_LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/DATASET_LICENSE) file. ## ✏️ Citation If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:. ```BibTeX @misc{zhu2023languagebind, title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment}, author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and Wang HongFa and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Cai Wan Zhang and Zhifeng Li and Wei Liu and Li Yuan}, year={2023}, eprint={2310.01852}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## ✨ Star History [](https://star-history.com/#PKU-YuanGroup/LanguageBind&Date) ## 🤝 Contributors <a href="https://github.com/PKU-YuanGroup/LanguageBind/graphs/contributors"> <img src="https://contrib.rocks/image?repo=PKU-YuanGroup/LanguageBind" /> </a>

mpoyraz/wav2vec2-xls-r-300m-cv7-turkish

mpoyraz

transformers

automatic-speech-recognition

--- license: cc-by-4.0 language: tr tags: - automatic-speech-recognition - hf-asr-leaderboard - mozilla-foundation/common_voice_7_0 - robust-speech-event - tr datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: mpoyraz/wav2vec2-xls-r-300m-cv7-turkish results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: tr metrics: - name: Test WER type: wer value: 8.62 - name: Test CER type: cer value: 2.26 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: tr metrics: - name: Test WER type: wer value: 30.87 - name: Test CER type: cer value: 10.69 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: tr metrics: - name: Test WER type: wer value: 32.09 --- # wav2vec2-xls-r-300m-cv7-turkish ## Model description This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language. ## Training and evaluation data The following datasets were used for finetuning: - [Common Voice 7.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) All `validated` split except `test` split was used for training. - [MediaSpeech](https://www.openslr.org/108/) ## Training procedure To support both of the datasets above, custom pre-processing and loading steps was performed and [wav2vec2-turkish](https://github.com/mpoyraz/wav2vec2-turkish) repo was used for that purpose. ### Training hyperparameters The following hypermaters were used for finetuning: - learning_rate 2e-4 - num_train_epochs 10 - warmup_steps 500 - freeze_feature_extractor - mask_time_prob 0.1 - mask_feature_prob 0.05 - feat_proj_dropout 0.05 - attention_dropout 0.05 - final_dropout 0.05 - activation_dropout 0.05 - per_device_train_batch_size 8 - per_device_eval_batch_size 8 - gradient_accumulation_steps 8 ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1 - Datasets 1.17.0 - Tokenizers 0.10.3 ## Language Model N-gram language model is trained on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format. ## Evaluation Commands Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing. 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv7-turkish --dataset mozilla-foundation/common_voice_7_0 --config tr --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv7-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ## Evaluation results: | Dataset | WER | CER | |---|---|---| |Common Voice 7 TR test split| 8.62 | 2.26 | |Speech Recognition Community dev data| 30.87 | 10.69 |

Systran/faster-whisper-large-v2

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 large-v2 model for CTranslate2 This repository contains the conversion of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) 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("large-v2") 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-large-v2 --output_dir faster-whisper-large-v2 \ --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-large-v2).**

ai4bharat/indic-bert

ai4bharat

transformers

--- language: - as - bn - en - gu - hi - kn - ml - mr - or - pa - ta - te license: mit datasets: - AI4Bharat IndicNLP Corpora --- # IndicBERT IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBERT has much fewer parameters than other multilingual models (mBERT, XLM-R etc.) while it also achieves a performance on-par or better than these models. The 12 languages covered by IndicBERT are: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu. The code can be found [here](https://github.com/divkakwani/indic-bert). For more information, checkout our [project page](https://indicnlp.ai4bharat.org/) or our [paper](https://indicnlp.ai4bharat.org/papers/arxiv2020_indicnlp_corpus.pdf). ## Pretraining Corpus We pre-trained indic-bert on AI4Bharat's monolingual corpus. The corpus has the following distribution of languages: | Language | as | bn | en | gu | hi | kn | | | ----------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------- | | **No. of Tokens** | 36.9M | 815M | 1.34B | 724M | 1.84B | 712M | | | **Language** | **ml** | **mr** | **or** | **pa** | **ta** | **te** | **all** | | **No. of Tokens** | 767M | 560M | 104M | 814M | 549M | 671M | 8.9B | ## Evaluation Results IndicBERT is evaluated on IndicGLUE and some additional tasks. The results are summarized below. For more details about the tasks, refer our [official repo](https://github.com/divkakwani/indic-bert) #### IndicGLUE Task | mBERT | XLM-R | IndicBERT -----| ----- | ----- | ------ News Article Headline Prediction | 89.58 | 95.52 | **95.87** Wikipedia Section Title Prediction| **73.66** | 66.33 | 73.31 Cloze-style multiple-choice QA | 39.16 | 27.98 | **41.87** Article Genre Classification | 90.63 | 97.03 | **97.34** Named Entity Recognition (F1-score) | **73.24** | 65.93 | 64.47 Cross-Lingual Sentence Retrieval Task | 21.46 | 13.74 | **27.12** Average | 64.62 | 61.09 | **66.66** #### Additional Tasks Task | Task Type | mBERT | XLM-R | IndicBERT -----| ----- | ----- | ------ | ----- BBC News Classification | Genre Classification | 60.55 | **75.52** | 74.60 IIT Product Reviews | Sentiment Analysis | 74.57 | **78.97** | 71.32 IITP Movie Reviews | Sentiment Analaysis | 56.77 | **61.61** | 59.03 Soham News Article | Genre Classification | 80.23 | **87.6** | 78.45 Midas Discourse | Discourse Analysis | 71.20 | **79.94** | 78.44 iNLTK Headlines Classification | Genre Classification | 87.95 | 93.38 | **94.52** ACTSA Sentiment Analysis | Sentiment Analysis | 48.53 | 59.33 | **61.18** Winograd NLI | Natural Language Inference | 56.34 | 55.87 | **56.34** Choice of Plausible Alternative (COPA) | Natural Language Inference | 54.92 | 51.13 | **58.33** Amrita Exact Paraphrase | Paraphrase Detection | **93.81** | 93.02 | 93.75 Amrita Rough Paraphrase | Paraphrase Detection | 83.38 | 82.20 | **84.33** Average | | 69.84 | **74.42** | 73.66 \* Note: all models have been restricted to a max_seq_length of 128. ## Downloads The model can be downloaded [here](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/models/indic-bert-v1.tar.gz). Both tf checkpoints and pytorch binaries are included in the archive. Alternatively, you can also download it from [Huggingface](https://huggingface.co/ai4bharat/indic-bert). ## Citing If you are using any of the resources, please cite the following article: ``` @inproceedings{kakwani2020indicnlpsuite, title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}}, author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar}, year={2020}, booktitle={Findings of EMNLP}, } ``` We would like to hear from you if: - You are using our resources. Please let us know how you are putting these resources to use. - You have any feedback on these resources. ## License The IndicBERT code (and models) are released under the MIT License. ## Contributors - Divyanshu Kakwani - Anoop Kunchukuttan - Gokul NC - Satish Golla - Avik Bhattacharyya - Mitesh Khapra - Pratyush Kumar This work is the outcome of a volunteer effort as part of [AI4Bharat initiative](https://ai4bharat.org). ## Contact - Anoop Kunchukuttan ([anoop.kunchukuttan@gmail.com](mailto:anoop.kunchukuttan@gmail.com)) - Mitesh Khapra ([miteshk@cse.iitm.ac.in](mailto:miteshk@cse.iitm.ac.in)) - Pratyush Kumar ([pratyush@cse.iitm.ac.in](mailto:pratyush@cse.iitm.ac.in))

JungleLee/bert-toxic-comment-classification

JungleLee

transformers

text-classification

--- license: afl-3.0 language: - en metrics: - accuracy library_name: transformers pipeline_tag: text-classification --- ## Model description This model is a fine-tuned version of the [bert-base-uncased](https://huggingface.co/transformers/model_doc/bert.html) model to classify toxic comments. ## How to use You can use the model with the following code. ```python from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline model_path = "JungleLee/bert-toxic-comment-classification" tokenizer = BertTokenizer.from_pretrained(model_path) model = BertForSequenceClassification.from_pretrained(model_path, num_labels=2) pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer) print(pipeline("You're a fucking nerd.")) ``` ## Training data The training data comes from this [Kaggle competition](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data). We use 90% of the `train.csv` data to train the model. ## Evaluation results The model achieves 0.95 AUC in a 1500 rows held-out test set.

sentence-transformers/paraphrase-distilroberta-base-v1

sentence-transformers

sentence-transformers

sentence-similarity

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- # sentence-transformers/paraphrase-distilroberta-base-v1 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-distilroberta-base-v1') 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-distilroberta-base-v1') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-distilroberta-base-v1') # 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-distilroberta-base-v1) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel (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", } ```

google-bert/bert-base-german-dbmdz-uncased

google-bert

transformers

fill-mask

--- language: de license: mit --- This model is the same as [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-uncased) for details on the model.

vibhorag101/roberta-base-suicide-prediction-phr

vibhorag101

transformers

text-classification

--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy - recall - precision - f1 model-index: - name: roberta-base-suicide-prediction-phr results: - task: type: text-classification name: Suicidal Tendency Prediction in text dataset: type: vibhorag101/roberta-base-suicide-prediction-phr name: Suicide Prediction Dataset split: test metrics: - type: accuracy value: 0.9652972367116438 - type: f1 value: 0.9651921995935487 - type: recall value: 0.966571403827834 - type: precision value: 0.9638169257340242 datasets: - vibhorag101/suicide_prediction_dataset_phr language: - en library_name: transformers --- # roberta-base-suicide-prediction-phr This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on this [dataset](https://huggingface.co/datasets/vibhorag101/suicide_prediction_dataset_phr) sourced from Reddit. It achieves the following results on the evaluation/validation set: - Loss: 0.1543 - Accuracy: 0.9652972367116438 - Recall: 0.966571403827834 - Precision: 0.9638169257340242 - F1: 0.9651921995935487 It achieves the following result on validation partition of this updated [dataset](vibhorag101/phr_suicide_prediction_dataset_clean_light) - Loss: 0.08761 - Accuracy: 0.97065 - Recall: 0.96652 - Precision: 0.97732 - F1: 0.97189 ## Model description This model is a finetune of roberta-base to detect suicidal tendencies in a given text. ## Training and evaluation data - The dataset is sourced from Reddit and is available on [Kaggle](https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch). - The dataset contains text with binary labels for suicide or non-suicide. - The dataset was cleaned, and following steps were applied - Converted to lowercase - Removed numbers and special characters. - Removed URLs, Emojis and accented characters. - Removed any word contractions. - Remove any extra white spaces and any extra spaces after a single space. - Removed any consecutive characters repeated more than 3 times. - Tokenised the text, then lemmatized it and then removed the stopwords (excluding not). - The cleaned dataset can be found [here](https://huggingface.co/datasets/vibhorag101/suicide_prediction_dataset_phr) - The evaluation set had ~23000 samples, while the training set had ~186k samples, i.e. a 80:10:10 (train:test:val) split. ## Training procedure - The model was trained on an RTXA5000 GPU. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------------------------------:|:------------------------------:|:---------------------------------:|:--------------------------:| | 0.2023 | 0.09 | 1000 | 0.1868 | {'accuracy': 0.9415010561710566} | {'recall': 0.9389451805663809} | {'precision': 0.943274752044545} | {'f1': 0.9411049867627274} | | 0.1792 | 0.17 | 2000 | 0.1465 | {'accuracy': 0.9528387291460103} | {'recall': 0.9615484541439335} | {'precision': 0.9446949714966392} | {'f1': 0.9530472103004292} | | 0.1596 | 0.26 | 3000 | 0.1871 | {'accuracy': 0.9523645298961072} | {'recall': 0.9399844115354637} | {'precision': 0.9634297887448962} | {'f1': 0.9515627054749485} | | 0.1534 | 0.34 | 4000 | 0.1563 | {'accuracy': 0.9518041126007674} | {'recall': 0.974971854161254} | {'precision': 0.9314139157772814} | {'f1': 0.9526952695269527} | | 0.1553 | 0.43 | 5000 | 0.1691 | {'accuracy': 0.9513730223735828} | {'recall': 0.93141075604053} | {'precision': 0.9697051663510955} | {'f1': 0.950172276702889} | | 0.1537 | 0.52 | 6000 | 0.1347 | {'accuracy': 0.9568478682588266} | {'recall': 0.9644063393089114} | {'precision': 0.9496844618795839} | {'f1': 0.9569887852876723} | | 0.1515 | 0.6 | 7000 | 0.1276 | {'accuracy': 0.9565461050997974} | {'recall': 0.9426690915389279} | {'precision': 0.9691924138545098} | {'f1': 0.9557467732022126} | | 0.1453 | 0.69 | 8000 | 0.1351 | {'accuracy': 0.960210372030866} | {'recall': 0.9589503767212263} | {'precision': 0.961031070994619} | {'f1': 0.959989596428107} | | 0.1526 | 0.78 | 9000 | 0.1423 | {'accuracy': 0.9610725524852352} | {'recall': 0.9612020438209059} | {'precision': 0.9606196988056085} | {'f1': 0.9609107830829834} | | 0.1437 | 0.86 | 10000 | 0.1365 | {'accuracy': 0.9599948269172738} | {'recall': 0.9625010825322594} | {'precision': 0.9573606684468946} | {'f1': 0.9599239937813093} | | 0.1317 | 0.95 | 11000 | 0.1275 | {'accuracy': 0.9616760788032935} | {'recall': 0.9653589676972374} | {'precision': 0.9579752492265383} | {'f1': 0.9616529353405513} | | 0.125 | 1.03 | 12000 | 0.1428 | {'accuracy': 0.9608138983489244} | {'recall': 0.9522819780029445} | {'precision': 0.9684692619341201} | {'f1': 0.9603074101567617} | | 0.1135 | 1.12 | 13000 | 0.1627 | {'accuracy': 0.960770789326206} | {'recall': 0.9544470425218672} | {'precision': 0.966330556773345} | {'f1': 0.9603520390379923} | | 0.1096 | 1.21 | 14000 | 0.1240 | {'accuracy': 0.9624520412122257} | {'recall': 0.9566987096215467} | {'precision': 0.9675074443860571} | {'f1': 0.962072719355541} | | 0.1213 | 1.29 | 15000 | 0.1502 | {'accuracy': 0.9616760788032935} | {'recall': 0.9659651857625358} | {'precision': 0.9574248927038627} | {'f1': 0.9616760788032936} | | 0.1166 | 1.38 | 16000 | 0.1574 | {'accuracy': 0.958873992326594} | {'recall': 0.9438815276695246} | {'precision': 0.9726907630522088} | {'f1': 0.9580696202531646} | | 0.1214 | 1.47 | 17000 | 0.1626 | {'accuracy': 0.9562443419407682} | {'recall': 0.9773101238416905} | {'precision': 0.9374480810765908} | {'f1': 0.9569641721433114} | | 0.1064 | 1.55 | 18000 | 0.1653 | {'accuracy': 0.9624089321895073} | {'recall': 0.9622412747899888} | {'precision': 0.9622412747899888} | {'f1': 0.9622412747899888} | | 0.1046 | 1.64 | 19000 | 0.1608 | {'accuracy': 0.9640039660300901} | {'recall': 0.9697756993158396} | {'precision': 0.9584046559397467} | {'f1': 0.9640566484438896} | | 0.1043 | 1.72 | 20000 | 0.1556 | {'accuracy': 0.960770789326206} | {'recall': 0.9493374902572097} | {'precision': 0.9712058119961017} | {'f1': 0.9601471489883507} | | 0.0995 | 1.81 | 21000 | 0.1646 | {'accuracy': 0.9602534810535845} | {'recall': 0.9752316619035247} | {'precision': 0.9465411448264268} | {'f1': 0.9606722402320423} | | 0.1065 | 1.9 | 22000 | 0.1721 | {'accuracy': 0.9627106953485365} | {'recall': 0.9710747380271932} | {'precision': 0.9547854223433242} | {'f1': 0.9628611910179897} | | 0.1204 | 1.98 | 23000 | 0.1214 | {'accuracy': 0.9629693494848471} | {'recall': 0.961028838659392} | {'precision': 0.9644533286980705} | {'f1': 0.9627380384331756} | | 0.0852 | 2.07 | 24000 | 0.1583 | {'accuracy': 0.9643919472345562} | {'recall': 0.9624144799515025} | {'precision': 0.9659278574532811} | {'f1': 0.9641679680721846} | | 0.0812 | 2.16 | 25000 | 0.1594 | {'accuracy': 0.9635728758029055} | {'recall': 0.9572183251060882} | {'precision': 0.9692213258505787} | {'f1': 0.9631824321380331} | | 0.0803 | 2.24 | 26000 | 0.1629 | {'accuracy': 0.9639177479846532} | {'recall': 0.9608556334978783} | {'precision': 0.9664634146341463} | {'f1': 0.963651365787988} | | 0.0832 | 2.33 | 27000 | 0.1570 | {'accuracy': 0.9631417855757209} | {'recall': 0.9658785831817788} | {'precision': 0.9603065266058206} | {'f1': 0.9630844954881052} | | 0.0887 | 2.41 | 28000 | 0.1551 | {'accuracy': 0.9623227141440703} | {'recall': 0.9669178141508616} | {'precision': 0.9577936004117698} | {'f1': 0.9623340803309774} | | 0.084 | 2.5 | 29000 | 0.1585 | {'accuracy': 0.9644350562572747} | {'recall': 0.9613752489824197} | {'precision': 0.96698606271777} | {'f1': 0.9641724931602031} | | 0.0807 | 2.59 | 30000 | 0.1601 | {'accuracy': 0.9639177479846532} | {'recall': 0.9699489044773534} | {'precision': 0.9580838323353293} | {'f1': 0.9639798597065025} | | 0.079 | 2.67 | 31000 | 0.1645 | {'accuracy': 0.9628400224166919} | {'recall': 0.9558326838139777} | {'precision': 0.9690929844586882} | {'f1': 0.9624171607952564} | | 0.0913 | 2.76 | 32000 | 0.1560 | {'accuracy': 0.9642626201664009} | {'recall': 0.964752749631939} | {'precision': 0.9635011243729459} | {'f1': 0.9641265307888701} | | 0.0927 | 2.85 | 33000 | 0.1491 | {'accuracy': 0.9649523645298961} | {'recall': 0.9659651857625358} | {'precision': 0.9637117677553136} | {'f1': 0.9648371610224472} | | 0.0882 | 2.93 | 34000 | 0.1543 | {'accuracy': 0.9652972367116438} | {'recall': 0.966571403827834} | {'precision': 0.9638169257340242} | {'f1': 0.9651921995935487} | ### Framework versions - Transformers 4.31.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3

whaleloops/phrase-bert

whaleloops

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # whaleloops/phrase-bert This is the official repository for the EMNLP 2021 long paper [Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration](https://arxiv.org/abs/2109.06304). We provide [code](https://github.com/sf-wa-326/phrase-bert-topic-model) for training and evaluating Phrase-BERT in addition to the datasets used in the paper. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Our model is tested on pytorch=1.9.0, tranformers=4.8.1, sentence-tranformers = 2.1.0 TODO Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer phrase_list = [ 'play an active role', 'participate actively', 'active lifestyle'] model = SentenceTransformer('whaleloops/phrase-bert') phrase_embs = model.encode( phrase_list ) [p1, p2, p3] = phrase_embs ``` As in sentence-BERT, the default output is a list of numpy arrays: ```` for phrase, embedding in zip(phrase_list, phrase_embs): print("Phrase:", phrase) print("Embedding:", embedding) print("") ```` An example of computing the dot product of phrase embeddings: ```` import numpy as np print(f'The dot product between phrase 1 and 2 is: {np.dot(p1, p2)}') print(f'The dot product between phrase 1 and 3 is: {np.dot(p1, p3)}') print(f'The dot product between phrase 2 and 3 is: {np.dot(p2, p3)}') ```` An example of computing cosine similarity of phrase embeddings: ```` import torch from torch import nn cos_sim = nn.CosineSimilarity(dim=0) print(f'The cosine similarity between phrase 1 and 2 is: {cos_sim( torch.tensor(p1), torch.tensor(p2))}') print(f'The cosine similarity between phrase 1 and 3 is: {cos_sim( torch.tensor(p1), torch.tensor(p3))}') print(f'The cosine similarity between phrase 2 and 3 is: {cos_sim( torch.tensor(p2), torch.tensor(p3))}') ```` The output should look like: ```` The dot product between phrase 1 and 2 is: 218.43600463867188 The dot product between phrase 1 and 3 is: 165.48483276367188 The dot product between phrase 2 and 3 is: 160.51708984375 The cosine similarity between phrase 1 and 2 is: 0.8142536282539368 The cosine similarity between phrase 1 and 3 is: 0.6130303144454956 The cosine similarity between phrase 2 and 3 is: 0.584893524646759 ```` ## Evaluation Given the lack of a unified phrase embedding evaluation benchmark, we collect the following five phrase semantics evaluation tasks, which are described further in our paper: * Turney [[Download](https://storage.googleapis.com/phrase-bert/turney/data.txt) ] * BiRD [[Download](https://storage.googleapis.com/phrase-bert/bird/data.txt)] * PPDB [[Download](https://storage.googleapis.com/phrase-bert/ppdb/examples.json)] * PPDB-filtered [[Download](https://storage.googleapis.com/phrase-bert/ppdb_exact/examples.json)] * PAWS-short [[Download Train-split](https://storage.googleapis.com/phrase-bert/paws_short/train_examples.json) ] [[Download Dev-split](https://storage.googleapis.com/phrase-bert/paws_short/dev_examples.json) ] [[Download Test-split](https://storage.googleapis.com/phrase-bert/paws_short/test_examples.json) ] Change `config/model_path.py` with the model path according to your directories and * For evaluation on Turney, run `python eval_turney.py` * For evaluation on BiRD, run `python eval_bird.py` * for evaluation on PPDB / PPDB-filtered / PAWS-short, run `eval_ppdb_paws.py` with: ```` nohup python -u eval_ppdb_paws.py \ --full_run_mode \ --task <task-name> \ --data_dir <input-data-dir> \ --result_dir <result-storage-dr> \ >./output.txt 2>&1 & ```` ## Train your own Phrase-BERT If you would like to go beyond using the pre-trained Phrase-BERT model, you may train your own Phrase-BERT using data from the domain you are interested in. Please refer to `phrase-bert/phrase_bert_finetune.py` The datasets we used to fine-tune Phrase-BERT are here: [training data csv file](https://storage.googleapis.com/phrase-bert/phrase-bert-ft-data/pooled_context_para_triples_p%3D0.8_train.csv) and [validation data csv file](https://storage.googleapis.com/phrase-bert/phrase-bert-ft-data/pooled_context_para_triples_p%3D0.8_valid.csv). To re-produce the trained Phrase-BERT, please run: export INPUT_DATA_PATH=<directory-of-phrasebert-finetuning-data> export TRAIN_DATA_FILE=<training-data-filename.csv> export VALID_DATA_FILE=<validation-data-filename.csv> export INPUT_MODEL_PATH=bert-base-nli-stsb-mean-tokens export OUTPUT_MODEL_PATH=<directory-of-saved-model> python -u phrase_bert_finetune.py \ --input_data_path $INPUT_DATA_PATH \ --train_data_file $TRAIN_DATA_FILE \ --valid_data_file $VALID_DATA_FILE \ --input_model_path $INPUT_MODEL_PATH \ --output_model_path $OUTPUT_MODEL_PATH ## Citation: Please cite us if you find this useful: ```` @inproceedings{phrasebertwang2021, author={Shufan Wang and Laure Thompson and Mohit Iyyer}, Booktitle = {Empirical Methods in Natural Language Processing}, Year = "2021", Title={Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration} } ````

deepseek-ai/deepseek-coder-33b-instruct

deepseek-ai

transformers

text-generation

--- license: other license_name: deepseek license_link: LICENSE --- <p align="center"> <img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek Coder Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks. - **Massive Training Data**: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages. - **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements. - **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks. - **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks. ### 2. Model Summary deepseek-coder-33b-instruct is a 33B parameter model initialized from deepseek-coder-33b-base and fine-tuned on 2B tokens of instruction data. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder) - **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Chat Model Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() messages=[ { 'role': 'user', 'content': "write a quick sort algorithm in python."} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) # tokenizer.eos_token_id is the id of <|EOT|> token outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).

sihaochen/SegmenT5-large

sihaochen

transformers

text2text-generation

--- license: cc language: - en --- This is the proposition segmentation model from "Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations" by Chen et. al. 2023. ## What does the model do? It splits a complex, long-form sentence into a list of propositions -- i.e. self-contained, atomic pieces of meaning in the sentence. For example, the following sentence -- ``` "Dracula is a novel by Bram Stoker featuring Count Dracula as the protagonist." ``` will be split into -- ``` ['Dracula is a novel by Bram Stoker.', 'Count Dracula is the protagonist of Dracula.'] ``` ## Usage The prompt to the model is formatted like: `segment sentence: {input_sentence}`. For each sentence, the model will output the propositions concatenated by `[sep]` as a string. For example, if we use the following example code to segment `"Dracula is a novel by Bram Stoker featuring Count Dracula as the protagonist."`. The model output will be `['Dracula is a novel by Bram Stoker.[sep]Count Dracula is the protagonist of Dracula.']` ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM gen_kwargs = { "length_penalty": 0, "max_new_tokens": 256, "min_length": 10, "no_repeat_ngram_size": 0, "num_beams": 1, } SEGMENT5_PROMPT = "segment sentence: {}" SEGMENT5_SEP_TOKEN = "[sep]" model = AutoModelForSeq2SeqLM.from_pretrained("sihaochen/SegmenT5-large") tokenizer = AutoTokenizer.from_pretrained("sihaochen/SegmenT5-large") model.eval() # define an example input sentence example_sentence = "Dracula is a novel by Bram Stoker featuring Count Dracula as the protagonist." example_input = SEGMENT5_PROMPT.format(example_sentence) input_ids = tokenizer(example_input, return_tensors="pt", padding="max_length", max_length=512, truncation=True).input_ids logits = model.generate(input_ids, **gen_kwargs) outputs = tokenizer.batch_decode(logits, skip_special_tokens=True) output = outputs[0].split(SEGMENT5_SEP_TOKEN) print(output) # Output: ['Dracula is a novel by Bram Stoker.', 'Count Dracula is the protagonist of Dracula.'] ``` ## Sub-Sentence Encoder For model checkpoints + code for the sub-sentence encoders, checkout: https://github.com/schen149/sub-sentence-encoder/ ## Citation ``` @article{chen2023subsentence, title={Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations}, author={Sihao Chen and Hongming Zhang and Tong Chen and Ben Zhou and Wenhao Yu and Dian Yu and Baolin Peng and Hongwei Wang and Dan Roth and Dong Yu}, journal={arXiv preprint arXiv:2311.04335}, year={2023}, URL = {https://arxiv.org/pdf/2311.04335.pdf} } ```

protectai/xlm-roberta-base-language-detection-onnx

protectai

transformers

text-classification

--- language: - multilingual - ar - bg - de - el - en - es - fr - hi - it - ja - nl - pl - pt - ru - sw - th - tr - ur - vi - zh license: mit inference: false tags: - language - language-detection metrics: - accuracy - f1 base_model: papluca/xlm-roberta-base-language-detection model-index: - name: xlm-roberta-base-language-detection results: [] pipeline_tag: text-classification --- # ONNX version of papluca/xlm-roberta-base-language-detection **This model is a conversion of [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) to ONNX** format using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library. ## Model description This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Language Identification](https://huggingface.co/datasets/papluca/language-identification#additional-information) dataset. This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output). For additional information please refer to the [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) model card or to the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. ## Intended uses & limitations You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages: `arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)` ## Usage ### Optimum Loading the model requires the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library installed. ```python from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("laiyer/xlm-roberta-base-language-detection-onnx") model = ORTModelForSequenceClassification.from_pretrained("laiyer/xlm-roberta-base-language-detection-onnx") classifier = pipeline( task="text-classification", model=model, tokenizer=tokenizer, top_k=None, ) classifier_output = ner("It's not toxic comment") print(classifier_output) ``` ### LLM Guard [Language scanner](https://llm-guard.com/input_scanners/language/) ## Community Join our Slack to give us feedback, connect with the maintainers and fellow users, ask questions, or engage in discussions about LLM security! <a href="https://join.slack.com/t/laiyerai/shared_invite/zt-28jv3ci39-sVxXrLs3rQdaN3mIl9IT~w"><img src="https://github.com/laiyer-ai/llm-guard/blob/main/docs/assets/join-our-slack-community.png?raw=true" width="200"></a>

deepset/sentence_bert

deepset

transformers

--- license: apache-2.0 --- This is an upload of the bert-base-nli-stsb-mean-tokens pretrained model from the Sentence Transformers Repo (https://github.com/UKPLab/sentence-transformers)

google/byt5-xl

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 --- # ByT5 - xl ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-xl). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-xl` significantly outperforms [mt5-xl](https://huggingface.co/google/mt5-xl) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel* ## Example Inference ByT5 works on raw UTF-8 bytes and can be used without a tokenizer: ```python from transformers import T5ForConditionalGeneration import torch model = T5ForConditionalGeneration.from_pretrained('google/byt5-xl') input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens loss = model(input_ids, labels=labels).loss # forward pass ``` For batched inference & training it is however recommended using a tokenizer class for padding: ```python from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('google/byt5-xl') tokenizer = AutoTokenizer.from_pretrained('google/byt5-xl') model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt") labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids loss = model(**model_inputs, labels=labels).loss # forward pass ``` ## Abstract Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments. 

Salesforce/blip2-opt-2.7b

Salesforce

transformers

image-to-text

--- language: en license: mit tags: - vision - image-to-text - image-captioning - visual-question-answering pipeline_tag: image-to-text --- # BLIP-2, OPT-2.7b, pre-trained only BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2). Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings, which bridge the gap between the embedding space of the image encoder and the large language model. The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg" alt="drawing" width="600"/> This allows the model to be used for tasks like: - image captioning - visual question answering (VQA) - chat-like conversations by feeding the image and the previous conversation as prompt to the model ## Direct Use and Downstream Use You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for fine-tuned versions on a task that interests you. ## Bias, Risks, Limitations, and Ethical Considerations BLIP2-OPT uses off-the-shelf OPT as the language model. It inherits the same risks and limitations as mentioned in Meta's model card. > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. > BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data. BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example). ### Memory requirements The memory requirements differ based on the precision one uses. One can use 4-bit inference using [Bitsandbytes](https://huggingface.co/blog/4bit-transformers-bitsandbytes), which greatly reduce the memory requirements. | dtype | Largest Layer or Residual Group | Total Size | Training using Adam | |-------------------|---------------------------------|------------|----------------------| | float32 | 490.94 MB | 14.43 GB | 57.72 GB | | float16/bfloat16 | 245.47 MB | 7.21 GB | 28.86 GB | | int8 | 122.73 MB | 3.61 GB | 14.43 GB | | int4 | 61.37 MB | 1.8 GB | 7.21 GB | #### Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True).strip()) ``` </details> #### Running the model on GPU ##### In full precision <details> <summary> Click to expand </summary> ```python # pip install accelerate import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map="auto") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True).strip()) ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python # pip install accelerate import torch import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16, device_map="auto") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True).strip()) ``` </details> ##### In 8-bit precision (`int8`) <details> <summary> Click to expand </summary> ```python # pip install accelerate bitsandbytes import torch import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map="auto") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True).strip()) ``` </details>

Felladrin/Llama-68M-Chat-v1

Felladrin

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation datasets: - THUDM/webglm-qa - databricks/databricks-dolly-15k - cognitivecomputations/wizard_vicuna_70k_unfiltered - totally-not-an-llm/EverythingLM-data-V3 - Amod/mental_health_counseling_conversations - sablo/oasst2_curated - starfishmedical/webGPT_x_dolly - Open-Orca/OpenOrca - mlabonne/chatml_dpo_pairs base_model: JackFram/llama-68m widget: - messages: - role: system content: You are a career counselor. The user will provide you with an individual looking for guidance in their professional life, and your task is to assist them in determining what careers they are most suited for based on their skills, interests, and experience. You should also conduct research into the various options available, explain the job market trends in different industries, and advice on which qualifications would be beneficial for pursuing particular fields. - role: user content: Heya! - role: assistant content: Hi! How may I help you? - role: user content: I am interested in developing a career in software engineering. What would you recommend me to do? - messages: - role: system content: You are a knowledgeable assistant. Help the user as much as you can. - role: user content: How to become healthier? - messages: - role: system content: You are a helpful assistant who provides concise responses. - role: user content: Hi! - role: assistant content: Hello there! How may I help you? - role: user content: I need to build a simple website. Where should I start learning about web development? - messages: - role: system content: You are a very creative assistant. User will give you a task, which you should complete with all your knowledge. - role: user content: Write the background story of an RPG game about wizards and dragons in a sci-fi world. inference: parameters: max_new_tokens: 64 penalty_alpha: 0.5 top_k: 4 model-index: - name: Llama-68M-Chat-v1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 23.29 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 28.27 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 25.18 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 47.27 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 54.3 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1 name: Open LLM Leaderboard --- # A Llama Chat Model of 68M Parameters - Base model: [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) - Datasets: - [THUDM/webglm-qa](https://huggingface.co/datasets/THUDM/webglm-qa) - [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) - [cognitivecomputations/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/cognitivecomputations/wizard_vicuna_70k_unfiltered) - [totally-not-an-llm/EverythingLM-data-V3](https://huggingface.co/datasets/totally-not-an-llm/EverythingLM-data-V3) - [Amod/mental_health_counseling_conversations](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations) - [sablo/oasst2_curated](https://huggingface.co/datasets/sablo/oasst2_curated) - [starfishmedical/webGPT_x_dolly](https://huggingface.co/datasets/starfishmedical/webGPT_x_dolly) - [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) - [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) - Availability in other ML formats: - GGUF: [afrideva/Llama-68M-Chat-v1-GGUF](https://huggingface.co/afrideva/Llama-68M-Chat-v1-GGUF) - ONNX: [Felladrin/onnx-Llama-68M-Chat-v1](https://huggingface.co/Felladrin/onnx-Llama-68M-Chat-v1) ## Recommended Prompt Format ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {user_message}<|im_end|> <|im_start|>assistant ``` ## Recommended Inference Parameters ```yml penalty_alpha: 0.5 top_k: 4 ``` ## [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_Felladrin__Llama-68M-Chat-v1) | Metric |Value| |---------------------------------|----:| |Avg. |29.72| |AI2 Reasoning Challenge (25-Shot)|23.29| |HellaSwag (10-Shot) |28.27| |MMLU (5-Shot) |25.18| |TruthfulQA (0-shot) |47.27| |Winogrande (5-shot) |54.30| |GSM8k (5-shot) | 0.00|

dhcppc0/soyle_onnx

dhcppc0

transformers

automatic-speech-recognition

Entry not found

llava-hf/llava-1.5-7b-hf

llava-hf

transformers

image-to-text

--- language: - en pipeline_tag: image-to-text inference: false arxiv: 2304.08485 datasets: - liuhaotian/LLaVA-Instruct-150K --- # LLaVA Model Card  Below is the model card of Llava model 7b, which is copied from the original Llava model card that you can find [here](https://huggingface.co/liuhaotian/llava-v1.5-13b). Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing) Or check out our Spaces demo! [](https://huggingface.co/spaces/llava-hf/llava-4bit) ## Model details **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. **Model date:** LLaVA-v1.5-7B was trained in September 2023. **Paper or resources for more information:** https://llava-vl.github.io/ ## How to use the model First, make sure to have `transformers >= 4.35.3`. The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template (`USER: xxx\nASSISTANT:`) and add the token `<image>` to the location where you want to query images: ### Using `pipeline`: Below we used [`"llava-hf/llava-1.5-7b-hf"`](https://huggingface.co/llava-hf/llava-1.5-7b-hf) checkpoint. ```python from transformers import pipeline from PIL import Image import requests model_id = "llava-hf/llava-1.5-7b-hf" pipe = pipeline("image-to-text", model=model_id) url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg" image = Image.open(requests.get(url, stream=True).raw) prompt = "USER: <image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT:" outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200}) print(outputs) >>> {"generated_text": "\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT: Lava"} ``` ### Using pure `transformers`: Below is an example script to run generation in `float16` precision on a GPU device: ```python import requests from PIL import Image import torch from transformers import AutoProcessor, LlavaForConditionalGeneration model_id = "llava-hf/llava-1.5-7b-hf" prompt = "USER: <image>\nWhat are these?\nASSISTANT:" image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" model = LlavaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(0) processor = AutoProcessor.from_pretrained(model_id) raw_image = Image.open(requests.get(image_file, stream=True).raw) inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16) output = model.generate(**inputs, max_new_tokens=200, do_sample=False) print(processor.decode(output[0][2:], skip_special_tokens=True)) ``` ### Model optimization #### 4-bit quantization through `bitsandbytes` library First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with: ```diff model = LlavaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, + load_in_4bit=True ) ``` #### Use Flash-Attention 2 to further speed-up generation First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with: ```diff model = LlavaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, + use_flash_attention_2=True ).to(0) ``` ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.

sentence-transformers/paraphrase-mpnet-base-v2

sentence-transformers

sentence-transformers

sentence-similarity

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- # sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-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-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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", } ```

stabilityai/sdxl-vae

stabilityai

diffusers

--- license: mit tags: - stable-diffusion - stable-diffusion-diffusers inference: false --- # SDXL - VAE #### 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 = "stabilityai/your-stable-diffusion-model" vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae") pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae) ``` ## Model [SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9) is a [latent diffusion model](https://arxiv.org/abs/2112.10752), where the diffusion operates in a pretrained, learned (and fixed) latent space of an autoencoder. While the bulk of the semantic composition is done by the latent diffusion model, we can improve _local_, high-frequency details in generated images by improving the quality of the autoencoder. To this end, we train the same autoencoder architecture used for the original [Stable Diffusion](https://github.com/CompVis/stable-diffusion) at a larger batch-size (256 vs 9) and additionally track the weights with an exponential moving average (EMA). The resulting autoencoder outperforms the original model in all evaluated reconstruction metrics, see the table below. ## Evaluation _SDXL-VAE vs original kl-f8 VAE vs f8-ft-MSE_ ### COCO 2017 (256x256, val, 5000 images) | Model | rFID | PSNR | SSIM | PSIM | Link | Comments |----------|------|--------------|---------------|---------------|------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| | | | | | | | | | SDXL-VAE | 4.42 | 24.7 +/- 3.9 | 0.73 +/- 0.13 | 0.88 +/- 0.27 | https://huggingface.co/stabilityai/sdxl-vae/blob/main/sdxl_vae.safetensors | as used in SDXL | | original | 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-MSE | 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 |

google/t5-v1_1-xl

google

transformers

text2text-generation

--- language: en datasets: - c4 license: apache-2.0 --- [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 ## Version 1.1 [T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202). - Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning. - Pre-trained on C4 only without mixing in the downstream tasks. - no parameter sharing between embedding and classifier layer - "xl" and "xxl" replace "3B" and "11B". The model shapes are a bit different - larger `d_model` and smaller `num_heads` and `d_ff`. **Note**: T5 Version 1.1 was only pre-trained on C4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) Other Community Checkpoints: [here](https://huggingface.co/models?search=t5-v1_1) Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* ## Abstract Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. 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. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. 

bigcode/starpii

bigcode

transformers

token-classification

--- datasets: - bigcode/pii-annotated-toloka-donwsample-emails - bigcode/pseudo-labeled-python-data-pii-detection-filtered metrics: - f1 pipeline_tag: token-classification language: - code extra_gated_prompt: >- ## Terms of Use for the model This is an NER model trained to detect Personal Identifiable Information (PII) in code datasets. We ask that you read and agree to the following Terms of Use before using the model: 1. You agree that you will not use the model for any purpose other than PII detection for the purpose of removing PII from datasets. 2. You agree that you will not share the model or any modified versions for whatever purpose. 3. Unless required by applicable law or agreed to in writing, the model is provided on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using the model, and assume any risks associated with your exercise of permissions under these Terms of Use. 4. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL OR THE USE OR OTHER DEALINGS IN THE MODEL. extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox --- # StarPII ## Model description This is an NER model trained to detect Personal Identifiable Information (PII) in code datasets. We fine-tuned [bigcode-encoder](https://huggingface.co/bigcode/bigcode-encoder) on a PII dataset we annotated, available with gated access at [bigcode-pii-dataset](https://huggingface.co/datasets/bigcode/pii-annotated-toloka-donwsample-emails) (see [bigcode-pii-dataset-training](https://huggingface.co/datasets/bigcode/bigcode-pii-dataset-training) for the exact data splits). We added a linear layer as a token classification head on top of the encoder model, with 6 target classes: Names, Emails, Keys, Passwords, IP addresses and Usernames. ## Dataset ### Fine-tuning on the annotated dataset The finetuning dataset contains 20961 secrets and 31 programming languages, but the base encoder model was pre-trained on 88 programming languages from [The Stack](https://huggingface.co/datasets/bigcode/the-stack) dataset. ### Initial training on a pseudo-labelled dataset To enhance model performance on some rare PII entities like keys, we initially trained on a pseudo-labeled dataset before fine-tuning on the annotated dataset. The method involves training a model on a small set of labeled data and subsequently generating predictions for a larger set of unlabeled data. Specifically, we annotated 18,000 files available at [bigcode-pii-ppseudo-labeled](https://huggingface.co/datasets/bigcode/pseudo-labeled-python-data-pii-detection-filtered) using an ensemble of two encoder models [Deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) and [stanford-deidentifier-base](StanfordAIMI/stanford-deidentifier-base) which were fine-tuned on an intern previously labeled PII [dataset](https://huggingface.co/datasets/bigcode/pii-for-code) for code with 400 files from this [work](https://arxiv.org/abs/2301.03988). To select good-quality pseudo-labels, we computed the average probability logits between the models and filtered based on a minimum score. After inspection, we observed a high rate of false positives for Keys and Passwords, hence we retained only the entities that had a trigger word like `key`, `auth` and `pwd` in the surrounding context. Training on this synthetic dataset prior to fine-tuning on the annotated one yielded superior results for all PII categories, as demonstrated in the table in the following section. ### Performance This model is respresented in the last row (NER + pseudo labels ) - Emails, IP addresses and Keys | Method | Email address | | | IP address | | | Key | | | | ------------------ | -------------- | ---- | ---- | ---------- | ---- | ---- | ----- | ---- | ---- | | | Prec. | Recall | F1 | Prec. | Recall | F1 | Prec. | Recall | F1 | | Regex | 69.8% | 98.8% | 81.8% | 65.9% | 78% | 71.7% | 2.8% | 46.9% | 5.3% | | NER | 94.01% | 98.10% | 96.01% | 88.95% | *94.43%* | 91.61% | 60.37% | 53.38% | 56.66% | | + pseudo labels | **97.73%** | **98.94%** | **98.15%** | **90.10%** | 93.86% | **91.94%** | **62.38%** | **80.81%** | **70.41%** | - Names, Usernames and Passwords | Method | Name | | | Username | | | Password | | | | ------------------ | -------- | ---- | ---- | -------- | ---- | ---- | -------- | ---- | ---- | | | Prec. | Recall | F1 | Prec. | Recall | F1 | Prec. | Recall | F1 | | NER | 83.66% | 95.52% | 89.19% | 48.93% | *75.55%* | 59.39% | 59.16% | *96.62%* | 73.39%| | + pseudo labels | **86.45%** | **97.38%** | **91.59%** | **52.20%** | 74.81% | **61.49%** | **70.94%** | 95.96% | **81.57%** | We used this model to mask PII in the bigcode large model training. We dropped usernames since they resulted in many false positives and negatives. For the other PII types, we added the following post-processing that we recommend for future uses of the model (the code is also available on GitHub): - Ignore secrets with less than 4 characters. - Detect full names only. - Ignore detected keys with less than 9 characters or that are not gibberish using a [gibberish-detector](https://github.com/domanchi/gibberish-detector). - Ignore IP addresses that aren't valid or are private (non-internet facing) using the `ipaddress` python package. We also ignore IP addresses from popular DNS servers. We use the same list as in this [paper](https://huggingface.co/bigcode/santacoder). # Considerations for Using the Model While using this model, please be aware that there may be potential risks associated with its application. There is a possibility of false positives and negatives, which could lead to unintended consequences when processing sensitive data. Moreover, the model's performance may vary across different data types and programming languages, necessitating validation and fine-tuning for specific use cases. Researchers and developers are expected to uphold ethical standards and data protection measures when using the model. By making it openly accessible, our aim is to encourage the development of privacy-preserving AI technologies while remaining vigilant of potential risks associated with PII.

google/owlvit-base-patch32

google

transformers

zero-shot-object-detection

--- license: apache-2.0 tags: - vision - zero-shot-object-detection inference: false --- # Model Card: OWL-ViT ## Model Details The OWL-ViT (short for Vision Transformer for Open-World Localization) was proposed in [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby. OWL-ViT is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries. OWL-ViT uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection. ### Model Date May 2022 ### Model Type The model uses a CLIP backbone with a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The CLIP backbone is trained from scratch and fine-tuned together with the box and class prediction heads with an object detection objective. ### Documents - [OWL-ViT Paper](https://arxiv.org/abs/2205.06230) ### Use with Transformers ```python3 import requests from PIL import Image import torch from transformers import OwlViTProcessor, OwlViTForObjectDetection processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32") model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) texts = [["a photo of a cat", "a photo of a dog"]] inputs = processor(text=texts, images=image, return_tensors="pt") outputs = model(**inputs) # Target image sizes (height, width) to rescale box predictions [batch_size, 2] target_sizes = torch.Tensor([image.size[::-1]]) # Convert outputs (bounding boxes and class logits) to COCO API results = processor.post_process_object_detection(outputs=outputs, threshold=0.1, target_sizes=target_sizes) i = 0 # Retrieve predictions for the first image for the corresponding text queries text = texts[i] boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"] # Print detected objects and rescaled box coordinates for box, score, label in zip(boxes, scores, labels): box = [round(i, 2) for i in box.tolist()] print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}") ``` ## Model Use ### Intended Use The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, text-conditioned object detection. We also hope it can be used for interdisciplinary studies of the potential impact of such models, especially in areas that commonly require identifying objects whose label is unavailable during training. #### Primary intended uses The primary intended users of these models are AI researchers. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. ## Data The CLIP backbone of the model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet. The prediction heads of OWL-ViT, along with the CLIP backbone, are fine-tuned on publicly available object detection datasets such as [COCO](https://cocodataset.org/#home) and [OpenImages](https://storage.googleapis.com/openimages/web/index.html). ### BibTeX entry and citation info ```bibtex @article{minderer2022simple, title={Simple Open-Vocabulary Object Detection with Vision Transformers}, author={Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, Neil Houlsby}, journal={arXiv preprint arXiv:2205.06230}, year={2022}, } ```

Salesforce/blip-vqa-base

Salesforce

transformers

visual-question-answering

--- pipeline_tag: 'visual-question-answering' tags: - visual-question-answering inference: false languages: - en license: bsd-3-clause --- # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for BLIP trained on visual question answering- base architecture (with ViT base backbone). |  | |:--:| | <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>| ## TL;DR Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract: *Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.* ## Usage You can use this model for conditional and un-conditional image captioning ### Using the Pytorch model #### Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForQuestionAnswering processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> 1 ``` </details> #### Running the model on GPU ##### In full precision <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForQuestionAnswering processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> 1 ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForQuestionAnswering processor = BlipProcessor.from_pretrained("ybelkada/blip-vqa-base") model = BlipForQuestionAnswering.from_pretrained("ybelkada/blip-vqa-base", torch_dtype=torch.float16).to("cuda") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> 1 ``` </details> ## BibTex and citation info ``` @misc{https://doi.org/10.48550/arxiv.2201.12086, doi = {10.48550/ARXIV.2201.12086}, url = {https://arxiv.org/abs/2201.12086}, author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

facebook/encodec_32khz

facebook

transformers

feature-extraction

--- inference: false ---  # Model Card for EnCodec This model card provides details and information about EnCodec 32kHz, a state-of-the-art real-time audio codec developed by Meta AI. This EnCodec checkpoint was trained specifically as part of the [MusicGen project](https://huggingface.co/docs/transformers/main/model_doc/musicgen), and is intended to be used in conjuction with the MusicGen models. ## Model Details ### Model Description EnCodec is a high-fidelity audio codec leveraging neural networks. It introduces a streaming encoder-decoder architecture with quantized latent space, trained in an end-to-end fashion. The model simplifies and speeds up training using a single multiscale spectrogram adversary that efficiently reduces artifacts and produces high-quality samples. It also includes a novel loss balancer mechanism that stabilizes training by decoupling the choice of hyperparameters from the typical scale of the loss. Additionally, lightweight Transformer models are used to further compress the obtained representation while maintaining real-time performance. This variant of EnCodec is trained on 20k of music data, consisting of an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music datasets. - **Developed by:** Meta AI - **Model type:** Audio Codec ### Model Sources - **Repository:** [GitHub Repository](https://github.com/facebookresearch/audiocraft) - **Paper:** [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) ## 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. --> ### Direct Use EnCodec can be used directly as an audio codec for real-time compression and decompression of audio signals. It provides high-quality audio compression and efficient decoding. The model was trained on various bandwiths, which can be specified when encoding (compressing) and decoding (decompressing). Two different setup exist for EnCodec: - Non-streamable: the input audio is split into chunks of 1 seconds, with an overlap of 10 ms, which are then encoded. - Streamable: weight normalizationis used on the convolution layers, and the input is not split into chunks but rather padded on the left. ### Downstream Use This variant of EnCodec is designed to be used in conjunction with the official [MusicGen checkpoints](https://huggingface.co/models?search=facebook/musicgen-). However, it can also be used standalone to encode audio files. ## How to Get Started with the Model Use the following code to get started with the EnCodec model using a dummy example from the LibriSpeech dataset (~9MB). First, install the required Python packages: ``` pip install --upgrade pip pip install --upgrade transformers datasets[audio] ``` Then load an audio sample, and run a forward pass of the model: ```python from datasets import load_dataset, Audio from transformers import EncodecModel, AutoProcessor # load a demonstration datasets librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # load the model + processor (for pre-processing the audio) model = EncodecModel.from_pretrained("facebook/encodec_48khz") processor = AutoProcessor.from_pretrained("facebook/encodec_48khz") # cast the audio data to the correct sampling rate for the model librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate)) audio_sample = librispeech_dummy[0]["audio"]["array"] # pre-process the inputs inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt") # explicitly encode then decode the audio inputs encoder_outputs = model.encode(inputs["input_values"], inputs["padding_mask"]) audio_values = model.decode(encoder_outputs.audio_codes, encoder_outputs.audio_scales, inputs["padding_mask"])[0] # or the equivalent with a forward pass audio_values = model(inputs["input_values"], inputs["padding_mask"]).audio_values ``` ## Evaluation For evaluation results, refer to the [MusicGen evaluation scores](https://huggingface.co/facebook/musicgen-large#evaluation-results). ## Summary EnCodec is a state-of-the-art real-time neural audio compression model that excels in producing high-fidelity audio samples at various sample rates and bandwidths. The model's performance was evaluated across different settings, ranging from 24kHz monophonic at 1.5 kbps to 48kHz stereophonic, showcasing both subjective and objective results. Notably, EnCodec incorporates a novel spectrogram-only adversarial loss, effectively reducing artifacts and enhancing sample quality. Training stability and interpretability were further enhanced through the introduction of a gradient balancer for the loss weights. Additionally, the study demonstrated that a compact Transformer model can be employed to achieve an additional bandwidth reduction of up to 40% without compromising quality, particularly in applications where low latency is not critical (e.g., music streaming). ## Citation **BibTeX:** ``` @misc{copet2023simple, title={Simple and Controllable Music Generation}, author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, year={2023}, eprint={2306.05284}, archivePrefix={arXiv}, primaryClass={cs.SD} } ```

lgodwangl/new_01m

lgodwangl

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## 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. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### 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. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset 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. --> [More Information Needed] ### 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] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### 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 Dataset 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 --> [More Information Needed] ## 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 [More Information Needed] ### 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:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

facebook/hubert-large-ls960-ft

facebook

transformers

automatic-speech-recognition

--- language: en datasets: - libri-light - librispeech_asr tags: - speech - audio - automatic-speech-recognition - hf-asr-leaderboard license: apache-2.0 model-index: - name: hubert-large-ls960-ft 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: 1.9 --- # Hubert-Large-Finetuned [Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression) The large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. The model is a fine-tuned version of [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k). [Paper](https://arxiv.org/abs/2106.07447) Authors: Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed **Abstract** Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/hubert . # Usage The model can be used for automatic-speech-recognition as follows: ```python import torch from transformers import Wav2Vec2Processor, HubertForCTC from datasets import load_dataset processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft") model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft") ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values # Batch size 1 logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.decode(predicted_ids[0]) # ->"A MAN SAID TO THE UNIVERSE SIR I EXIST" ```

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).

dataautogpt3/OpenDalleV1.1

dataautogpt3

diffusers

text-to-image

--- license: cc-by-nc-nd-4.0 pipeline_tag: text-to-image widget: - text: >- black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed output: url: ComfyUI_01611_.png - text: >- (impressionistic realism by csybgh), a 50 something male, working in banking, very short dyed dark curly balding hair, Afro-Asiatic ancestry, talks a lot but listens poorly, stuck in the past, wearing a suit, he has a certain charm, bronze skintone, sitting in a bar at night, he is smoking and feeling cool, drunk on plum wine, masterpiece, 8k, hyper detailed, smokey ambiance, perfect hands AND fingers output: url: ComfyUI_01609_.jpeg - text: >- an anime female general laughing, with a military cap, evil smile, sadistic, grim output: url: ComfyUI_01556_.jpeg - text: >- John Berkey Style page,ral-oilspill, There is no road ahead,no land, Strangely,the river is still flowing,crossing the void into the mysterious unknown, The end of nothingness,a huge ripple,it is a kind of wave,and it is the law of time that lasts forever in that void, At the end of the infinite void,there is a colorful world,very hazy and mysterious,and it cannot be seen clearly,but it is real, And that's where the river goes output: url: ComfyUI_01519_.jpeg - text: >- Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd output: url: ComfyUI_01817_(1).png - text: >- cinematic film still of Kodak Motion Picture Film: (Sharp Detailed Image) An Oscar winning movie for Best Cinematography a woman in a kimono standing on a subway train in Japan Kodak Motion Picture Film Style, shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy output: url: ComfyUI_01882_.png - text: >- in the style of artgerm, comic style,3D model, mythical seascape, negative space, space quixotic dreams, temporal hallucination, psychedelic, mystical, intricate details, very bright neon colors, (vantablack background:1.5), pointillism, pareidolia, melting, symbolism, very high contrast, chiaroscuro parameters: negative_prompt: >- bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image output: url: ComfyUI_01542_.jpeg - text: ((OpenDAlle!)text logo:1), ~*~aesthetic~*~ output: url: ComfyUI_01528_.jpeg --- # OpenDalleV1.1 my newest model and best current model is located here: https://huggingface.co/dataautogpt3/ProteusV0.2 <Gallery /> OpenDalle v1.1 on Hugging Face - It's Here! Realism & Style: improved We're talking about a major glow-up in the realism and style department. Expect images that not only hit the bullseye with your prompts but also bring that extra zing of artistic flair. It's like your prompts went to art school! Prompt Loyalty: Our Heartbeat The soul of OpenDalle? Sticking to your prompts like glue. v1.1 takes your words and turns them into visual masterpieces that are just what you pictured – maybe even better. Where We Stand: The Cool Middle Kid Here's the scoop: OpenDalle v1.1 is proudly strutting a notch above SDXL. While DALLE-3 is still the big cheese, we're hot on its heels. Think of us as the cool, savvy middle sibling, rocking both brains and beauty. ## Settings for OpenDalle v1.1 Use these settings for the best results with OpenDalle v1.1: CFG Scale: Use a CFG scale of 8 to 7 Steps: 60 to 70 steps for more detail, 35 steps for faster results. Sampler: DPM2 Scheduler: Normal or Karras ## Use it with 🧨 diffusers ```python from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('dataautogpt3/OpenDalleV1.1', torch_dtype=torch.float16).to('cuda') image = pipeline('black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed').images[0] ``` Non-Commercial Personal Use License Agreement For dataautogpt3/OpenDalleV1.1 1. Introduction This Non-Commercial Personal Use License Agreement ("Agreement") is between Alexander Izquierdo ("Licensor") and the individual or entity ("Licensee") using the Stable Diffusion model with unique merging method and tuning ("Model") hosted on the Hugging Face repository named OpenDalleV1.1. 2. Grant of License a. Licensor hereby grants to Licensee a non-exclusive, non-transferable, non-sublicensable license to use the Model for personal, non-commercial purposes. b. "Personal, non-commercial purposes" are defined as use that does not involve any form of compensation or monetary gain. This includes, but is not limited to, academic research, educational use, and hobbyist projects. c. The Licensee is permitted to modify, merge, and use the Model for personal projects, provided that such use adheres to the terms of this Agreement. 3. Ownership and Intellectual Property Rights a. The Licensor explicitly retains all rights, title, and interest in and to the unique merging method used in the Model. This merging method is the proprietary creation and intellectual property of the Licensor. b. The Licensee shall not claim ownership, reverse engineer, or attempt to recreate the merging method for any purpose. c. The Licensor retains all rights, title, and interest in and to the Model, including any modifications or improvements made by the Licensee. d. The Licensee agrees to attribute the Licensor in any academic or public display of the Model or derivative works. 4. Restrictions a. The Licensee shall not use the Model or the merging method for any commercial purposes. b. The Licensee shall not distribute, sublicense, lease, or lend the Model or the merging method to any third party. c. The Licensee shall not publicly display, perform, or communicate the Model, the merging method, or any derivative works thereof without the prior written consent of the Licensor. 5. Termination This Agreement will terminate automatically if the Licensee breaches any of its terms and conditions. 6. Disclaimer of Warranties The Model and the merging method are provided "as is," and the Licensor makes no warranties, express or implied, regarding their performance, reliability, or suitability for any purpose. 7. Limitation of Liability The Licensor shall not be liable for any damages arising out of or related to the use or inability to use the Model or the merging method. 8. General Provisions a. This Agreement constitutes the entire agreement between the parties and supersedes all prior agreements and understandings, whether written or oral, relating to its subject matter. b. Any amendment to this Agreement must be in writing and signed by both parties. c. This Agreement shall be governed by the laws of Maryland. IN WITNESS WHEREOF, the parties have executed this Agreement as of the Effective Date.

Deci/DeciLM-7B-instruct

Deci

transformers

text-generation

--- license: apache-2.0 language: - en datasets: - Open-Orca/SlimOrca --- # DeciLM-7B-instruct DeciLM-7B-instruct is a model for short-form instruction following. It is built by LoRA fine-tuning on the [SlimOrca dataset](https://huggingface.co/datasets/Open-Orca/SlimOrca). ## Model Details ### Model Description DeciLM-7B-instruct is a derivative of the recently released [DeciLM-7B](https://huggingface.co/Deci/DeciLM-7B) language model, a pre-trained, high-efficiency generative text model with 7 billion parameters. DeciLM-7B-instruct is one the best 7B instruct models obtained using simple LoRA fine-tuning, without relying on preference optimization techniques such as RLHF and DPO. - **Developed by:** [Deci](https://deci.ai/?utm_campaign=repos&utm_source=hugging-face&utm_medium=model-card&utm_content=decilm-7b-instruct) - **Model type:** DeciLM is an auto-regressive language model using an optimized transformer decoder architecture that includes variable Grouped-Query Attention. - **Language(s) (NLP):** English - **License:** Apache 2.0 ## Model Architecture | Parameters | Layers | Heads | Sequence Length | GQA num_key_value_heads* | |:----------|:----------|:----------|:----------|:----------| | 7.04 billion | 32 | 32 | 8192 | Variable | *AutoNAC was employed to optimize the selection of the GQA num_key_value_heads for each model layer. ### Model Sources - **Blog:** [DeciLM-7B Technical Blog](https://deci.ai/blog/introducing-DeciLM-7B-the-fastest-and-most-accurate-7b-large-language-model-to-date/?utm_campaign=repos&utm_source=hugging-face&utm_medium=model-card&utm_content=decilm-7b-instruct) - **Demo:** [DeciLM-7B-instruct Demo](https://huggingface.co/spaces/Deci/DeciLM-7B-instruct) - **Finetuning Notebook:** [DeciLM-7B Finetuning Notebook](https://colab.research.google.com/drive/1kEV6i96AQ94xTCvSd11TxkEaksTb5o3U?usp=sharing) - **Text Generation Notebook:** [DeciLM-7B-instruct Text Generation Notebook](https://bit.ly/declm-7b-instruct) ### Prompt Template ``` ### System: {system_prompt} ### User: {user_prompt} ### Assistant: ``` ## Uses The model is intended for commercial and research use in English. ## How to Get Started with the Model Use the code below to get started with the model. ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline model_name = "Deci/DeciLM-7B-instruct" device = "cuda" # for GPU usage or "cpu" for CPU usage quantize = False # Optional. Useful for GPUs with less than 24GB memory if quantize: dtype_kwargs = dict(quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16 )) else: dtype_kwargs = dict(torch_dtype="auto") model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", trust_remote_code=True, **dtype_kwargs ) tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.pad_token = tokenizer.eos_token deci_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, temperature=0.1, device_map="auto", max_length=4096, return_full_text=False) system_prompt = "You are an AI assistant that follows instruction extremely well. Help as much as you can." user_prompt = "How do I make the most delicious pancakes the world has ever tasted?" prompt = tokenizer.apply_chat_template([ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ], tokenize=False, add_generation_prompt=True) response = deci_generator(prompt)[0]['generated_text'] print(prompt + response) ``` ## Evaluation Below are DeciLM-7B and DeciLM-7B-instruct's evaluation results. | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |:----------|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:| | DecilLM-7B | 61.55 | 59.39 | 82.51 | 59.76 | 40.33 | 79.95 | 47.38 | | DecilLM-7B-instruct | 63.19 | 61.01 | 82.37 | 60.24 | 49.75 | 79.72 | 46.02 | ### Runtime Benchmarks | Inference Tool | Hardware | Prompt length | Generation length | Generated tokens/sec | Batch Size | Number of Prompts | |:----------|:----------|:---------:|:---------:|:---------:|:---------:|:---------:| | HuggingFace (PyTorch) | A100 (SXM4-80GB-400W) | 512 | 512 | **1174** | 352 | 352 | | HuggingFace (PyTorch) | A100 (SXM4-80GB-400W) | 2048 | 2048 | **328** | 72 | 72 | | Infery-LLM | A100 (SXM4-80GB-400W)| 512 | 512 | **4559** | 1024 | 4096 | | Infery-LLM | A100 (SXM4-80GB-400W) | 2048 | 2048 | **3997** | 512 | 2048 | | Infery-LLM | A10 | 512 | 512 | **1345** | 128 | 512 | | Infery-LLM | A10 | 2048 | 2048 | **599** | 32 | 128 | - In order to replicate the results of the Hugging Face benchmarks, you can use this [code example](https://huggingface.co/Deci/DeciLM-7B/blob/main/benchmark_hf_model.py). - Infery-LLM, Deci's inference engine, features a suite of optimization algorithms, including selective quantization, optimized beam search, continuous batching, and custom CUDA kernels. To explore the full capabilities of Infery-LLM, [schedule a live demo](https://deci.ai/infery-llm-book-a-demo/?utm_campaign=DeciLM%207B%20Launch&utm_source=HF&utm_medium=decilm7b-model-card&utm_term=infery-demo). ## Ethical Considerations and Limitations DeciLM-7B-instruct is a new technology that comes with inherent risks associated with its use. The testing conducted so far has been primarily in English and does not encompass all possible scenarios. Like those of all large language models, DeciLM-7B's outputs are unpredictable, and the model may generate responses that are inaccurate, biased, or otherwise objectionable. Consequently, developers planning to use DeciLM-7B should undertake thorough safety testing and tuning designed explicitly for their intended applications of the model before deployment. ## How to Cite Please cite this model using this format. ```bibtex @misc{DeciFoundationModels, title = {DeciLM-7B-instruct}, author = {DeciAI Research Team}, year = {2023} url={https://huggingface.co/Deci/DeciLM-7B-instruct}, } ```

facebook/bart-large

facebook

transformers

feature-extraction

--- license: apache-2.0 language: en --- # BART (large-sized model) BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/bart). Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). ## Intended uses & limitations You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) 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 BartTokenizer, BartModel tokenizer = BartTokenizer.from_pretrained('facebook/bart-large') model = BartModel.from_pretrained('facebook/bart-large') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer}, title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, journal = {CoRR}, volume = {abs/1910.13461}, year = {2019}, url = {http://arxiv.org/abs/1910.13461}, eprinttype = {arXiv}, eprint = {1910.13461}, timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

sentence-transformers/nli-mpnet-base-v2

sentence-transformers

sentence-transformers

sentence-similarity

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- # sentence-transformers/nli-mpnet-base-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/nli-mpnet-base-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/nli-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/nli-mpnet-base-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/nli-mpnet-base-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: MPNetModel (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", } ```

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.

Helsinki-NLP/opus-mt-da-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-da-en * source languages: da * target languages: en * OPUS readme: [da-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/da-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/da-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/da-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/da-en/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.da.en | 63.6 | 0.769 |

Jean-Baptiste/roberta-large-ner-english

Jean-Baptiste

transformers

token-classification

--- language: en datasets: - conll2003 widget: - text: "My name is jean-baptiste and I live in montreal" - text: "My name is clara and I live in berkeley, california." - text: "My name is wolfgang and I live in berlin" train-eval-index: - config: conll2003 task: token-classification task_id: entity_extraction splits: eval_split: validation col_mapping: tokens: tokens ner_tags: tags license: mit --- # roberta-large-ner-english: model fine-tuned from roberta-large for NER task ## Introduction [roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset. Model was validated on emails/chat data and outperformed other models on this type of data specifically. In particular the model seems to work better on entity that don't start with an upper case. ## Training data Training data was classified as follow: Abbreviation|Description -|- O |Outside of a named entity MISC |Miscellaneous entity PER |Person’s name ORG |Organization LOC |Location In order to simplify, the prefix B- or I- from original conll2003 was removed. I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size: Train | Validation -|- 17494 | 3250 ## How to use roberta-large-ner-english with HuggingFace ##### Load roberta-large-ner-english and its sub-word tokenizer : ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-ner-english") model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner-english") ##### Process text sample (from wikipedia) from transformers import pipeline nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") nlp("Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne to develop and sell Wozniak's Apple I personal computer") [{'entity_group': 'ORG', 'score': 0.99381506, 'word': ' Apple', 'start': 0, 'end': 5}, {'entity_group': 'PER', 'score': 0.99970853, 'word': ' Steve Jobs', 'start': 29, 'end': 39}, {'entity_group': 'PER', 'score': 0.99981767, 'word': ' Steve Wozniak', 'start': 41, 'end': 54}, {'entity_group': 'PER', 'score': 0.99956465, 'word': ' Ronald Wayne', 'start': 59, 'end': 71}, {'entity_group': 'PER', 'score': 0.9997918, 'word': ' Wozniak', 'start': 92, 'end': 99}, {'entity_group': 'MISC', 'score': 0.99956393, 'word': ' Apple I', 'start': 102, 'end': 109}] ``` ## Model performances Model performances computed on conll2003 validation dataset (computed on the tokens predictions) entity|precision|recall|f1 -|-|-|- PER|0.9914|0.9927|0.9920 ORG|0.9627|0.9661|0.9644 LOC|0.9795|0.9862|0.9828 MISC|0.9292|0.9262|0.9277 Overall|0.9740|0.9766|0.9753 On private dataset (email, chat, informal discussion), computed on word predictions: entity|precision|recall|f1 -|-|-|- PER|0.8823|0.9116|0.8967 ORG|0.7694|0.7292|0.7487 LOC|0.8619|0.7768|0.8171 By comparison on the same private dataset, Spacy (en_core_web_trf-3.2.0) was giving: entity|precision|recall|f1 -|-|-|- PER|0.9146|0.8287|0.8695 ORG|0.7655|0.6437|0.6993 LOC|0.8727|0.6180|0.7236 For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails: https://medium.com/@jean-baptiste.polle/lstm-model-for-email-signature-detection-8e990384fefa

facebook/w2v-bert-2.0

facebook

transformers

feature-extraction

--- license: mit 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 inference: false --- # W2v-BERT 2.0 speech encoder We are open-sourcing our Conformer-based [W2v-BERT 2.0 speech encoder](#w2v-bert-20-speech-encoder) as described in Section 3.2.1 of the [paper](https://arxiv.org/pdf/2312.05187.pdf), which is at the core of our Seamless models. This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification. | Model Name | #params | checkpoint | | ----------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | W2v-BERT 2.0 | 600M | [checkpoint](https://huggingface.co/reach-vb/conformer-shaw/resolve/main/conformer_shaw.pt) **This model and its training are supported by 🤗 Transformers, more on it in the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/wav2vec2-bert).** # 🤗 Transformers usage This is a bare checkpoint without any modeling head, and thus requires finetuning to be used for downstream tasks such as ASR. You can however use it to extract audio embeddings from the top layer with this code snippet: ```python from transformers import AutoFeatureExtractor, Wav2Vec2BertModel import torch from datasets import load_dataset dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") dataset = dataset.sort("id") sampling_rate = dataset.features["audio"].sampling_rate processor = AutoProcessor.from_pretrained("facebook/w2v-bert-2.0") model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0") # 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) ``` To learn more about the model use, refer to the following resources: - [its docs](https://huggingface.co/docs/transformers/main/en/model_doc/wav2vec2-bert) - [a blog post showing how to fine-tune it on Mongolian ASR](https://huggingface.co/blog/fine-tune-w2v2-bert) - [a training script example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) # Seamless Communication usage This model can be used in [Seamless Communication](https://github.com/facebookresearch/seamless_communication), where it was released. Here's how to make a forward pass through the voice encoder, after having completed the [installation steps](https://github.com/facebookresearch/seamless_communication?tab=readme-ov-file#installation): ```python import torch from fairseq2.data.audio import AudioDecoder, WaveformToFbankConverter from fairseq2.memory import MemoryBlock from fairseq2.nn.padding import get_seqs_and_padding_mask from pathlib import Path from seamless_communication.models.conformer_shaw import load_conformer_shaw_model audio_wav_path, device, dtype = ... audio_decoder = AudioDecoder(dtype=torch.float32, device=device) fbank_converter = WaveformToFbankConverter( num_mel_bins=80, waveform_scale=2**15, channel_last=True, standardize=True, device=device, dtype=dtype, ) collater = Collater(pad_value=1) model = load_conformer_shaw_model("conformer_shaw", device=device, dtype=dtype) model.eval() with Path(audio_wav_path).open("rb") as fb: block = MemoryBlock(fb.read()) decoded_audio = audio_decoder(block) src = collater(fbank_converter(decoded_audio))["fbank"] seqs, padding_mask = get_seqs_and_padding_mask(src) with torch.inference_mode(): seqs, padding_mask = model.encoder_frontend(seqs, padding_mask) seqs, padding_mask = model.encoder(seqs, padding_mask) ```

DionTimmer/controlnet_qrcode-control_v1p_sd15

DionTimmer

diffusers

image-to-image

--- tags: - stable-diffusion - controlnet - image-to-image license: openrail++ language: - en library_name: diffusers pipeline_tag: image-to-image --- # QR Code Conditioned ControlNet Models for Stable Diffusion 1.5  ## Model Description This repo holds the safetensors & diffusers versions of the QR code conditioned ControlNet for Stable Diffusion v1.5. The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, this 1.5 version model was also trained on the same dataset for those who are using the older version. ## How to use with Diffusers ```bash pip -q install diffusers transformers accelerate torch xformers ``` ```python import torch from PIL import Image from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler from diffusers.utils import load_image controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16) pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) pipe.enable_xformers_memory_efficient_attention() pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() def resize_for_condition_image(input_image: Image, resolution: int): input_image = input_image.convert("RGB") W, H = input_image.size k = float(resolution) / min(H, W) H *= k W *= k H = int(round(H / 64.0)) * 64 W = int(round(W / 64.0)) * 64 img = input_image.resize((W, H), resample=Image.LANCZOS) return img # play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image # qr code image source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png") # initial image, anything init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg") condition_image = resize_for_condition_image(source_image, 768) init_image = resize_for_condition_image(init_image, 768) generator = torch.manual_seed(123121231) image = pipe(prompt="a bilboard in NYC with a qrcode", negative_prompt="ugly, disfigured, low quality, blurry, nsfw", image=init_image, control_image=condition_image, width=768, height=768, guidance_scale=20, controlnet_conditioning_scale=1.5, generator=generator, strength=0.9, num_inference_steps=150, ) image.images[0] ``` ## Performance and Limitations These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.**To optimize for scanning, please generate your QR codes with correction mode 'H' (30%).** To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork. ## Installation The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application. For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail. Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell.

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] ```

StanfordAIMI/stanford-deidentifier-base

StanfordAIMI

transformers

token-classification

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

princeton-nlp/sup-simcse-roberta-base

princeton-nlp

transformers

feature-extraction

Entry not found

monologg/koelectra-small-v2-distilled-korquad-384

monologg

transformers

question-answering

Entry not found

neggles/animatediff-modules

neggles

diffusers

Entry not found

TheBloke/Llama-2-13B-chat-GPTQ

TheBloke

transformers

text-generation

--- language: - en license: llama2 tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 13B Chat base_model: meta-llama/Llama-2-13b-chat-hf inference: false model_creator: Meta Llama 2 model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don''t know the answer to a question, please don''t share false information. <</SYS>> {prompt}[/INST] ' quantized_by: TheBloke --- <!-- 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 --> # Llama 2 13B Chat - GPTQ - Model creator: [Meta Llama 2](https://huggingface.co/meta-llama) - Original model: [Llama 2 13B Chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) <!-- description start --> ## Description This repo contains GPTQ model files for [Meta's Llama 2 13B-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-13B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) * [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-13B-chat-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/main) | 4 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.95 GB | No | 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. | | [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_False) | 8 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Llama-2-13B-chat-GPTQ:main` - With Git, you can clone a branch with: ``` git clone --single-branch --branch main https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). 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/Llama-2-13B-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Llama-2-13B-chat-GPTQ:main` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Llama-2-13B-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Llama-2-13B-chat-GPTQ" # To use a different branch, change revision # For example: revision="main" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Meta's Llama 2 13B-chat # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

openai/whisper-small.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-small.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: - 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: 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-small.en") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small.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 small.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-small.en") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small.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"])) 3.053161596922323 ``` ## Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from datasets import load_dataset >>> device = "cuda:0" if torch.cuda.is_available() else "cpu" >>> pipe = pipeline( >>> "automatic-speech-recognition", >>> model="openai/whisper-small.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} } ```

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

jonatasgrosman

transformers

automatic-speech-recognition

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

vinai/phobert-base-v2

vinai

transformers

fill-mask

#### Table of contents 1. [Introduction](#introduction) 2. [Using PhoBERT with `transformers`](#transformers) - [Installation](#install2) - [Pre-trained models](#models2) - [Example usage](#usage2) 3. [Using PhoBERT with `fairseq`](#fairseq) 4. [Notes](#vncorenlp) # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam): - Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance. - PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. The general architecture and experimental results of PhoBERT can be found in our [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.92/): @inproceedings{phobert, title = {{PhoBERT: Pre-trained language models for Vietnamese}}, author = {Dat Quoc Nguyen and Anh Tuan Nguyen}, booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020}, year = {2020}, pages = {1037--1042} } **Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software. ## <a name="transformers"></a> Using PhoBERT with `transformers` ### Installation <a name="install2"></a> - Install `transformers` with pip: `pip install transformers`, or [install `transformers` from source](https://huggingface.co/docs/transformers/installation#installing-from-source). <br /> Note that we merged a slow tokenizer for PhoBERT into the main `transformers` branch. The process of merging a fast tokenizer for PhoBERT is in the discussion, as mentioned in [this pull request](https://github.com/huggingface/transformers/pull/17254#issuecomment-1133932067). If users would like to utilize the fast tokenizer, the users might install `transformers` as follows: ``` git clone --single-branch --branch fast_tokenizers_BARTpho_PhoBERT_BERTweet https://github.com/datquocnguyen/transformers.git cd transformers pip3 install -e . ``` - Install `tokenizers` with pip: `pip3 install tokenizers` ### Pre-trained models <a name="models2"></a> Model | #params | Arch. | Max length | Pre-training data ---|---|---|---|--- `vinai/phobert-base` | 135M | base | 256 | 20GB of Wikipedia and News texts `vinai/phobert-large` | 370M | large | 256 | 20GB of Wikipedia and News texts `vinai/phobert-base-v2` | 135M | base | 256 | 20GB of Wikipedia and News texts + 120GB of texts from OSCAR-2301 ### Example usage <a name="usage2"></a> ```python import torch from transformers import AutoModel, AutoTokenizer phobert = AutoModel.from_pretrained("vinai/phobert-base-v2") tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base-v2") # INPUT TEXT MUST BE ALREADY WORD-SEGMENTED! sentence = 'Chúng_tôi là những nghiên_cứu_viên .' input_ids = torch.tensor([tokenizer.encode(sentence)]) with torch.no_grad(): features = phobert(input_ids) # Models outputs are now tuples ## With TensorFlow 2.0+: # from transformers import TFAutoModel # phobert = TFAutoModel.from_pretrained("vinai/phobert-base") ``` ## <a name="fairseq"></a> Using PhoBERT with `fairseq` Please see details at [HERE](https://github.com/VinAIResearch/PhoBERT/blob/master/README_fairseq.md)! ## <a name="vncorenlp"></a> Notes In case the input texts are `raw`, i.e. without word segmentation, a word segmenter must be applied to produce word-segmented texts before feeding to PhoBERT. As PhoBERT employed the [RDRSegmenter](https://github.com/datquocnguyen/RDRsegmenter) from [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) to pre-process the pre-training data (including [Vietnamese tone normalization](https://github.com/VinAIResearch/BARTpho/blob/main/VietnameseToneNormalization.md) and word and sentence segmentation), it is recommended to also use the same word segmenter for PhoBERT-based downstream applications w.r.t. the input raw texts. #### Installation pip install py_vncorenlp #### Example usage <a name="example"></a> ```python import py_vncorenlp # Automatically download VnCoreNLP components from the original repository # and save them in some local machine folder py_vncorenlp.download_model(save_dir='/absolute/path/to/vncorenlp') # Load the word and sentence segmentation component rdrsegmenter = py_vncorenlp.VnCoreNLP(annotators=["wseg"], save_dir='/absolute/path/to/vncorenlp') text = "Ông Nguyễn Khắc Chúc đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây." output = rdrsegmenter.word_segment(text) print(output) # ['Ông Nguyễn_Khắc_Chúc đang làm_việc tại Đại_học Quốc_gia Hà_Nội .', 'Bà Lan , vợ ông Chúc , cũng làm_việc tại đây .'] ``` ## License Copyright (c) 2023 VinAI Research This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have received a copy of the GNU Affero General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

microsoft/deberta-v3-large

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 large model comes with 24 layers and a hidden size of 1024. It has 304M backbone parameters with a vocabulary containing 128K tokens which introduces 131M 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-large |50 |304 | 89.4/86.5 | 90.2 | | XLNet-large |32 |- | 90.6/87.9 | 90.8 | | DeBERTa-large |50 |- | 90.7/88.0 | 91.3 | | **DeBERTa-v3-large**|128|304 | **91.5/89.0**| **91.8/91.9**| #### 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-large \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --evaluation_strategy steps \ --max_seq_length 256 \ --warmup_steps 50 \ --per_device_train_batch_size ${batch_size} \ --learning_rate 6e-6 \ --num_train_epochs 2 \ --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} } ```

TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF

TheBloke

transformers

--- base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 inference: false language: - fr - it - de - es - en license: apache-2.0 model_creator: Mistral AI_ model_name: Mixtral 8X7B Instruct v0.1 model_type: mixtral prompt_template: '[INST] {prompt} [/INST] ' quantized_by: TheBloke widget: - output: text: 'Arr, shiver me timbers! Ye have a llama on yer lawn, ye say? Well, that be a new one for me! Here''s what I''d suggest, arr: 1. Firstly, ensure yer safety. Llamas may look gentle, but they can be protective if they feel threatened. 2. Try to make the area less appealing to the llama. Remove any food sources or water that might be attracting it. 3. Contact local animal control or a wildlife rescue organization. They be the experts and can provide humane ways to remove the llama from yer property. 4. If ye have any experience with animals, you could try to gently herd the llama towards a nearby field or open space. But be careful, arr! Remember, arr, it be important to treat the llama with respect and care. It be a creature just trying to survive, like the rest of us.' text: '[INST] You are a pirate chatbot who always responds with Arr and pirate speak! There''s a llama on my lawn, how can I get rid of him? [/INST]' --- <!-- 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 --> # Mixtral 8X7B Instruct v0.1 - GGUF - Model creator: [Mistral AI_](https://huggingface.co/mistralai) - Original model: [Mixtral 8X7B Instruct v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) <!-- description start --> ## Description This repo contains GGUF format model files for [Mistral AI_'s Mixtral 8X7B Instruct v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### 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. ### Mixtral GGUF Support for Mixtral was merged into Llama.cpp on December 13th. These Mixtral GGUFs are known to work in: * llama.cpp as of December 13th * KoboldCpp 1.52 as later * LM Studio 0.2.9 and later * llama-cpp-python 0.2.23 and later Other clients/libraries, not listed above, may not yet work. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF) * [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Mistral ``` [INST] {prompt} [/INST] ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These Mixtral GGUFs are compatible with llama.cpp from December 13th onwards. Other clients/libraries may not work yet. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [mixtral-8x7b-instruct-v0.1.Q2_K.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/blob/main/mixtral-8x7b-instruct-v0.1.Q2_K.gguf) | Q2_K | 2 | 15.64 GB| 18.14 GB | smallest, significant quality loss - not recommended for most purposes | | [mixtral-8x7b-instruct-v0.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/blob/main/mixtral-8x7b-instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 3 | 20.36 GB| 22.86 GB | very small, high quality loss | | [mixtral-8x7b-instruct-v0.1.Q4_0.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/blob/main/mixtral-8x7b-instruct-v0.1.Q4_0.gguf) | Q4_0 | 4 | 26.44 GB| 28.94 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/blob/main/mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 4 | 26.44 GB| 28.94 GB | medium, balanced quality - recommended | | [mixtral-8x7b-instruct-v0.1.Q5_0.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/blob/main/mixtral-8x7b-instruct-v0.1.Q5_0.gguf) | Q5_0 | 5 | 32.23 GB| 34.73 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/blob/main/mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 5 | 32.23 GB| 34.73 GB | large, very low quality loss - recommended | | [mixtral-8x7b-instruct-v0.1.Q6_K.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/blob/main/mixtral-8x7b-instruct-v0.1.Q6_K.gguf) | Q6_K | 6 | 38.38 GB| 40.88 GB | very large, extremely low quality loss | | [mixtral-8x7b-instruct-v0.1.Q8_0.gguf](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/blob/main/mixtral-8x7b-instruct-v0.1.Q8_0.gguf) | Q8_0 | 8 | 49.62 GB| 52.12 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF and below it, a specific filename to download, such as: mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] {prompt} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Note that text-generation-webui may not yet be compatible with Mixtral GGUFs. Please check compatibility first. Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) version 0.2.23 and later. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf", # Download the model file first n_ctx=2048, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "[INST] {prompt} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) <!-- README_GGUF.md-how-to-run 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Mistral AI_'s Mixtral 8X7B Instruct v0.1 # Model Card for Mixtral-8x7B The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested. For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/). ## Warning This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF. ## Instruction format This format must be strictly respected, otherwise the model will generate sub-optimal outputs. The template used to build a prompt for the Instruct model is defined as follows: ``` <s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST] ``` Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings. As reference, here is the pseudo-code used to tokenize instructions during fine-tuning: ```python def tokenize(text): return tok.encode(text, add_special_tokens=False) [BOS_ID] + tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_1) + [EOS_ID] + … tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") + tokenize(BOT_MESSAGE_N) + [EOS_ID] ``` In the pseudo-code above, note that the `tokenize` method should not add a BOS or EOS token automatically, but should add a prefix space. ## Run the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem: ### In half-precision Note `float16` precision only works on GPU devices <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Lower precision using (8-bit & 4-bit) using `bitsandbytes` <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Load the model with Flash Attention 2 <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ## Limitations The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. # The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. <!-- original-model-card end -->

microsoft/DialoGPT-medium

microsoft

transformers

text-generation

--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit --- ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT) DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test. The model is trained on 147M multi-turn dialogue from Reddit discussion thread. * Multi-turn generation examples from an interactive environment: |Role | Response | |---------|--------| |User | Does money buy happiness? | | Bot | Depends how much money you spend on it .| |User | What is the best way to buy happiness ? | | Bot | You just have to be a millionaire by your early 20s, then you can be happy . | |User |This is so difficult ! | | Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money | Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536) ### How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") # Let's chat for 5 lines for step in range(5): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) # pretty print last ouput tokens from bot print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```

sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking

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

intfloat/multilingual-e5-base

intfloat

sentence-transformers

sentence-similarity

--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: multilingual-e5-base results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.97014925373135 - type: ap value: 43.69351129103008 - type: f1 value: 73.38075030070492 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.7237687366167 - type: ap value: 82.22089859962671 - type: f1 value: 69.95532758884401 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.65517241379312 - type: ap value: 28.507918657094738 - type: f1 value: 66.84516013726119 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.32976445396146 - type: ap value: 20.720481637566014 - type: f1 value: 59.78002763416003 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.63775 - type: ap value: 87.22277903861716 - type: f1 value: 90.60378636386807 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.546 - type: f1 value: 44.05666638370923 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.828 - type: f1 value: 41.2710255644252 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.534 - type: f1 value: 39.820743174270326 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.684 - type: f1 value: 39.11052682815307 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.436 - type: f1 value: 37.07082931930871 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.226000000000006 - type: f1 value: 36.65372077739185 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 22.831000000000003 - type: map_at_10 value: 36.42 - type: map_at_100 value: 37.699 - type: map_at_1000 value: 37.724000000000004 - type: map_at_3 value: 32.207 - type: map_at_5 value: 34.312 - type: mrr_at_1 value: 23.257 - type: mrr_at_10 value: 36.574 - type: mrr_at_100 value: 37.854 - type: mrr_at_1000 value: 37.878 - type: mrr_at_3 value: 32.385000000000005 - type: mrr_at_5 value: 34.48 - type: ndcg_at_1 value: 22.831000000000003 - type: ndcg_at_10 value: 44.230000000000004 - type: ndcg_at_100 value: 49.974000000000004 - type: ndcg_at_1000 value: 50.522999999999996 - type: ndcg_at_3 value: 35.363 - type: ndcg_at_5 value: 39.164 - type: precision_at_1 value: 22.831000000000003 - type: precision_at_10 value: 6.935 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.841 - type: precision_at_5 value: 10.754 - type: recall_at_1 value: 22.831000000000003 - type: recall_at_10 value: 69.346 - type: recall_at_100 value: 95.235 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 44.523 - type: recall_at_5 value: 53.769999999999996 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.27789869854063 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 35.41979463347428 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.22752045109304 - type: mrr value: 71.51112430198303 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.71147646622866 - type: cos_sim_spearman value: 85.059167046486 - type: euclidean_pearson value: 75.88421613600647 - type: euclidean_spearman value: 75.12821787150585 - type: manhattan_pearson value: 75.22005646957604 - type: manhattan_spearman value: 74.42880434453272 - 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: 99.23799582463465 - type: f1 value: 99.12665274878218 - type: precision value: 99.07098121085595 - type: recall value: 99.23799582463465 - 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: 97.88685890380806 - type: f1 value: 97.59336708489249 - type: precision value: 97.44662117543473 - type: recall value: 97.88685890380806 - 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: 97.47142362313821 - type: f1 value: 97.1989377670015 - type: precision value: 97.06384944001847 - type: recall value: 97.47142362313821 - 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: 98.4728804634018 - type: f1 value: 98.2973494821836 - type: precision value: 98.2095839915745 - type: recall value: 98.4728804634018 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 82.74025974025975 - type: f1 value: 82.67420447730439 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.0380848063507 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.45956405670166 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.122 - type: map_at_10 value: 42.03 - type: map_at_100 value: 43.364000000000004 - type: map_at_1000 value: 43.474000000000004 - type: map_at_3 value: 38.804 - type: map_at_5 value: 40.585 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 48.227 - type: mrr_at_100 value: 49.018 - type: mrr_at_1000 value: 49.064 - type: mrr_at_3 value: 45.994 - type: mrr_at_5 value: 47.396 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 47.825 - type: ndcg_at_100 value: 52.852 - type: ndcg_at_1000 value: 54.891 - type: ndcg_at_3 value: 43.517 - type: ndcg_at_5 value: 45.493 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 8.956 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 20.791999999999998 - type: precision_at_5 value: 14.821000000000002 - type: recall_at_1 value: 32.122 - type: recall_at_10 value: 58.294999999999995 - type: recall_at_100 value: 79.726 - type: recall_at_1000 value: 93.099 - type: recall_at_3 value: 45.017 - type: recall_at_5 value: 51.002 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.677999999999997 - type: map_at_10 value: 38.684000000000005 - type: map_at_100 value: 39.812999999999995 - type: map_at_1000 value: 39.945 - type: map_at_3 value: 35.831 - type: map_at_5 value: 37.446 - type: mrr_at_1 value: 37.771 - type: mrr_at_10 value: 44.936 - type: mrr_at_100 value: 45.583 - type: mrr_at_1000 value: 45.634 - type: mrr_at_3 value: 42.771 - type: mrr_at_5 value: 43.994 - type: ndcg_at_1 value: 37.771 - type: ndcg_at_10 value: 44.059 - type: ndcg_at_100 value: 48.192 - type: ndcg_at_1000 value: 50.375 - type: ndcg_at_3 value: 40.172000000000004 - type: ndcg_at_5 value: 41.899 - type: precision_at_1 value: 37.771 - type: precision_at_10 value: 8.286999999999999 - type: precision_at_100 value: 1.322 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 19.406000000000002 - type: precision_at_5 value: 13.745 - type: recall_at_1 value: 29.677999999999997 - type: recall_at_10 value: 53.071 - type: recall_at_100 value: 70.812 - type: recall_at_1000 value: 84.841 - type: recall_at_3 value: 41.016000000000005 - type: recall_at_5 value: 46.22 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 42.675000000000004 - type: map_at_10 value: 53.93599999999999 - type: map_at_100 value: 54.806999999999995 - type: map_at_1000 value: 54.867 - type: map_at_3 value: 50.934000000000005 - type: map_at_5 value: 52.583 - type: mrr_at_1 value: 48.339 - type: mrr_at_10 value: 57.265 - type: mrr_at_100 value: 57.873 - type: mrr_at_1000 value: 57.906 - type: mrr_at_3 value: 55.193000000000005 - type: mrr_at_5 value: 56.303000000000004 - type: ndcg_at_1 value: 48.339 - type: ndcg_at_10 value: 59.19799999999999 - type: ndcg_at_100 value: 62.743 - type: ndcg_at_1000 value: 63.99399999999999 - type: ndcg_at_3 value: 54.367 - type: ndcg_at_5 value: 56.548 - type: precision_at_1 value: 48.339 - type: precision_at_10 value: 9.216000000000001 - type: precision_at_100 value: 1.1809999999999998 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 23.72 - type: precision_at_5 value: 16.025 - type: recall_at_1 value: 42.675000000000004 - type: recall_at_10 value: 71.437 - type: recall_at_100 value: 86.803 - type: recall_at_1000 value: 95.581 - type: recall_at_3 value: 58.434 - type: recall_at_5 value: 63.754 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.518 - type: map_at_10 value: 30.648999999999997 - type: map_at_100 value: 31.508999999999997 - type: map_at_1000 value: 31.604 - type: map_at_3 value: 28.247 - type: map_at_5 value: 29.65 - type: mrr_at_1 value: 25.650000000000002 - type: mrr_at_10 value: 32.771 - type: mrr_at_100 value: 33.554 - type: mrr_at_1000 value: 33.629999999999995 - type: mrr_at_3 value: 30.433 - type: mrr_at_5 value: 31.812 - type: ndcg_at_1 value: 25.650000000000002 - type: ndcg_at_10 value: 34.929 - type: ndcg_at_100 value: 39.382 - type: ndcg_at_1000 value: 41.913 - type: ndcg_at_3 value: 30.292 - type: ndcg_at_5 value: 32.629999999999995 - type: precision_at_1 value: 25.650000000000002 - type: precision_at_10 value: 5.311 - type: precision_at_100 value: 0.792 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 12.58 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 23.518 - type: recall_at_10 value: 46.19 - type: recall_at_100 value: 67.123 - type: recall_at_1000 value: 86.442 - type: recall_at_3 value: 33.678000000000004 - type: recall_at_5 value: 39.244 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.891 - type: map_at_10 value: 22.464000000000002 - type: map_at_100 value: 23.483 - type: map_at_1000 value: 23.613 - type: map_at_3 value: 20.080000000000002 - type: map_at_5 value: 21.526 - type: mrr_at_1 value: 20.025000000000002 - type: mrr_at_10 value: 26.712999999999997 - type: mrr_at_100 value: 27.650000000000002 - type: mrr_at_1000 value: 27.737000000000002 - type: mrr_at_3 value: 24.274 - type: mrr_at_5 value: 25.711000000000002 - type: ndcg_at_1 value: 20.025000000000002 - type: ndcg_at_10 value: 27.028999999999996 - type: ndcg_at_100 value: 32.064 - type: ndcg_at_1000 value: 35.188 - type: ndcg_at_3 value: 22.512999999999998 - type: ndcg_at_5 value: 24.89 - type: precision_at_1 value: 20.025000000000002 - type: precision_at_10 value: 4.776 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.531 - type: precision_at_5 value: 7.811 - type: recall_at_1 value: 15.891 - type: recall_at_10 value: 37.261 - type: recall_at_100 value: 59.12 - type: recall_at_1000 value: 81.356 - type: recall_at_3 value: 24.741 - type: recall_at_5 value: 30.753999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.544 - type: map_at_10 value: 36.283 - type: map_at_100 value: 37.467 - type: map_at_1000 value: 37.574000000000005 - type: map_at_3 value: 33.528999999999996 - type: map_at_5 value: 35.028999999999996 - type: mrr_at_1 value: 34.166999999999994 - type: mrr_at_10 value: 41.866 - type: mrr_at_100 value: 42.666 - type: mrr_at_1000 value: 42.716 - type: mrr_at_3 value: 39.541 - type: mrr_at_5 value: 40.768 - type: ndcg_at_1 value: 34.166999999999994 - type: ndcg_at_10 value: 41.577 - type: ndcg_at_100 value: 46.687 - type: ndcg_at_1000 value: 48.967 - type: ndcg_at_3 value: 37.177 - type: ndcg_at_5 value: 39.097 - type: precision_at_1 value: 34.166999999999994 - type: precision_at_10 value: 7.420999999999999 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 17.291999999999998 - type: precision_at_5 value: 12.166 - type: recall_at_1 value: 27.544 - type: recall_at_10 value: 51.99399999999999 - type: recall_at_100 value: 73.738 - type: recall_at_1000 value: 89.33 - type: recall_at_3 value: 39.179 - type: recall_at_5 value: 44.385999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.661 - type: map_at_10 value: 35.475 - type: map_at_100 value: 36.626999999999995 - type: map_at_1000 value: 36.741 - type: map_at_3 value: 32.818000000000005 - type: map_at_5 value: 34.397 - type: mrr_at_1 value: 32.647999999999996 - type: mrr_at_10 value: 40.784 - type: mrr_at_100 value: 41.602 - type: mrr_at_1000 value: 41.661 - type: mrr_at_3 value: 38.68 - type: mrr_at_5 value: 39.838 - type: ndcg_at_1 value: 32.647999999999996 - type: ndcg_at_10 value: 40.697 - type: ndcg_at_100 value: 45.799 - type: ndcg_at_1000 value: 48.235 - type: ndcg_at_3 value: 36.516 - type: ndcg_at_5 value: 38.515 - type: precision_at_1 value: 32.647999999999996 - type: precision_at_10 value: 7.202999999999999 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 17.314 - type: precision_at_5 value: 12.145999999999999 - type: recall_at_1 value: 26.661 - type: recall_at_10 value: 50.995000000000005 - type: recall_at_100 value: 73.065 - type: recall_at_1000 value: 89.781 - type: recall_at_3 value: 39.073 - type: recall_at_5 value: 44.395 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.946583333333333 - type: map_at_10 value: 33.79725 - type: map_at_100 value: 34.86408333333333 - type: map_at_1000 value: 34.9795 - type: map_at_3 value: 31.259999999999998 - type: map_at_5 value: 32.71541666666666 - type: mrr_at_1 value: 30.863749999999996 - type: mrr_at_10 value: 37.99183333333333 - type: mrr_at_100 value: 38.790499999999994 - type: mrr_at_1000 value: 38.85575000000001 - type: mrr_at_3 value: 35.82083333333333 - type: mrr_at_5 value: 37.07533333333333 - type: ndcg_at_1 value: 30.863749999999996 - type: ndcg_at_10 value: 38.52141666666667 - type: ndcg_at_100 value: 43.17966666666667 - type: ndcg_at_1000 value: 45.64608333333333 - type: ndcg_at_3 value: 34.333000000000006 - type: ndcg_at_5 value: 36.34975 - type: precision_at_1 value: 30.863749999999996 - type: precision_at_10 value: 6.598999999999999 - type: precision_at_100 value: 1.0502500000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 15.557583333333334 - type: precision_at_5 value: 11.020000000000001 - type: recall_at_1 value: 25.946583333333333 - type: recall_at_10 value: 48.36991666666666 - type: recall_at_100 value: 69.02408333333334 - type: recall_at_1000 value: 86.43858333333331 - type: recall_at_3 value: 36.4965 - type: recall_at_5 value: 41.76258333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.431 - type: map_at_10 value: 28.889 - type: map_at_100 value: 29.642000000000003 - type: map_at_1000 value: 29.742 - type: map_at_3 value: 26.998 - type: map_at_5 value: 28.172000000000004 - type: mrr_at_1 value: 25.307000000000002 - type: mrr_at_10 value: 31.763 - type: mrr_at_100 value: 32.443 - type: mrr_at_1000 value: 32.531 - type: mrr_at_3 value: 29.959000000000003 - type: mrr_at_5 value: 31.063000000000002 - type: ndcg_at_1 value: 25.307000000000002 - type: ndcg_at_10 value: 32.586999999999996 - type: ndcg_at_100 value: 36.5 - type: ndcg_at_1000 value: 39.133 - type: ndcg_at_3 value: 29.25 - type: ndcg_at_5 value: 31.023 - type: precision_at_1 value: 25.307000000000002 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 0.747 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 12.577 - type: precision_at_5 value: 8.741999999999999 - type: recall_at_1 value: 22.431 - type: recall_at_10 value: 41.134 - type: recall_at_100 value: 59.28600000000001 - type: recall_at_1000 value: 78.857 - type: recall_at_3 value: 31.926 - type: recall_at_5 value: 36.335 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.586 - type: map_at_10 value: 23.304 - type: map_at_100 value: 24.159 - type: map_at_1000 value: 24.281 - type: map_at_3 value: 21.316 - type: map_at_5 value: 22.383 - type: mrr_at_1 value: 21.645 - type: mrr_at_10 value: 27.365000000000002 - type: mrr_at_100 value: 28.108 - type: mrr_at_1000 value: 28.192 - type: mrr_at_3 value: 25.482 - type: mrr_at_5 value: 26.479999999999997 - type: ndcg_at_1 value: 21.645 - type: ndcg_at_10 value: 27.306 - type: ndcg_at_100 value: 31.496000000000002 - type: ndcg_at_1000 value: 34.53 - type: ndcg_at_3 value: 23.73 - type: ndcg_at_5 value: 25.294 - type: precision_at_1 value: 21.645 - type: precision_at_10 value: 4.797 - type: precision_at_100 value: 0.8059999999999999 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 10.850999999999999 - type: precision_at_5 value: 7.736 - type: recall_at_1 value: 17.586 - type: recall_at_10 value: 35.481 - type: recall_at_100 value: 54.534000000000006 - type: recall_at_1000 value: 76.456 - type: recall_at_3 value: 25.335 - type: recall_at_5 value: 29.473 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.095 - type: map_at_10 value: 32.374 - type: map_at_100 value: 33.537 - type: map_at_1000 value: 33.634 - type: map_at_3 value: 30.089 - type: map_at_5 value: 31.433 - type: mrr_at_1 value: 29.198 - type: mrr_at_10 value: 36.01 - type: mrr_at_100 value: 37.022 - type: mrr_at_1000 value: 37.083 - type: mrr_at_3 value: 33.94 - type: mrr_at_5 value: 35.148 - type: ndcg_at_1 value: 29.198 - type: ndcg_at_10 value: 36.729 - type: ndcg_at_100 value: 42.114000000000004 - type: ndcg_at_1000 value: 44.592 - type: ndcg_at_3 value: 32.644 - type: ndcg_at_5 value: 34.652 - type: precision_at_1 value: 29.198 - type: precision_at_10 value: 5.970000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 14.396999999999998 - type: precision_at_5 value: 10.093 - type: recall_at_1 value: 25.095 - type: recall_at_10 value: 46.392 - type: recall_at_100 value: 69.706 - type: recall_at_1000 value: 87.738 - type: recall_at_3 value: 35.303000000000004 - type: recall_at_5 value: 40.441 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.857999999999997 - type: map_at_10 value: 34.066 - type: map_at_100 value: 35.671 - type: map_at_1000 value: 35.881 - type: map_at_3 value: 31.304 - type: map_at_5 value: 32.885 - type: mrr_at_1 value: 32.411 - type: mrr_at_10 value: 38.987 - type: mrr_at_100 value: 39.894 - type: mrr_at_1000 value: 39.959 - type: mrr_at_3 value: 36.626999999999995 - type: mrr_at_5 value: 38.011 - type: ndcg_at_1 value: 32.411 - type: ndcg_at_10 value: 39.208 - type: ndcg_at_100 value: 44.626 - type: ndcg_at_1000 value: 47.43 - type: ndcg_at_3 value: 35.091 - type: ndcg_at_5 value: 37.119 - type: precision_at_1 value: 32.411 - type: precision_at_10 value: 7.51 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 16.14 - type: precision_at_5 value: 11.976 - type: recall_at_1 value: 26.857999999999997 - type: recall_at_10 value: 47.407 - type: recall_at_100 value: 72.236 - type: recall_at_1000 value: 90.77 - type: recall_at_3 value: 35.125 - type: recall_at_5 value: 40.522999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.3 - type: map_at_10 value: 27.412999999999997 - type: map_at_100 value: 28.29 - type: map_at_1000 value: 28.398 - type: map_at_3 value: 25.169999999999998 - type: map_at_5 value: 26.496 - type: mrr_at_1 value: 23.29 - type: mrr_at_10 value: 29.215000000000003 - type: mrr_at_100 value: 30.073 - type: mrr_at_1000 value: 30.156 - type: mrr_at_3 value: 26.956000000000003 - type: mrr_at_5 value: 28.38 - type: ndcg_at_1 value: 23.29 - type: ndcg_at_10 value: 31.113000000000003 - type: ndcg_at_100 value: 35.701 - type: ndcg_at_1000 value: 38.505 - type: ndcg_at_3 value: 26.727 - type: ndcg_at_5 value: 29.037000000000003 - type: precision_at_1 value: 23.29 - type: precision_at_10 value: 4.787 - type: precision_at_100 value: 0.763 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 11.091 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 21.3 - type: recall_at_10 value: 40.782000000000004 - type: recall_at_100 value: 62.13999999999999 - type: recall_at_1000 value: 83.012 - type: recall_at_3 value: 29.131 - type: recall_at_5 value: 34.624 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.631 - type: map_at_10 value: 16.634999999999998 - type: map_at_100 value: 18.23 - type: map_at_1000 value: 18.419 - type: map_at_3 value: 13.66 - type: map_at_5 value: 15.173 - type: mrr_at_1 value: 21.368000000000002 - type: mrr_at_10 value: 31.56 - type: mrr_at_100 value: 32.58 - type: mrr_at_1000 value: 32.633 - type: mrr_at_3 value: 28.241 - type: mrr_at_5 value: 30.225 - type: ndcg_at_1 value: 21.368000000000002 - type: ndcg_at_10 value: 23.855999999999998 - type: ndcg_at_100 value: 30.686999999999998 - type: ndcg_at_1000 value: 34.327000000000005 - type: ndcg_at_3 value: 18.781 - type: ndcg_at_5 value: 20.73 - type: precision_at_1 value: 21.368000000000002 - type: precision_at_10 value: 7.564 - type: precision_at_100 value: 1.496 - type: precision_at_1000 value: 0.217 - type: precision_at_3 value: 13.876 - type: precision_at_5 value: 11.062 - type: recall_at_1 value: 9.631 - type: recall_at_10 value: 29.517 - type: recall_at_100 value: 53.452 - type: recall_at_1000 value: 74.115 - type: recall_at_3 value: 17.605999999999998 - type: recall_at_5 value: 22.505 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.885 - type: map_at_10 value: 18.798000000000002 - type: map_at_100 value: 26.316 - type: map_at_1000 value: 27.869 - type: map_at_3 value: 13.719000000000001 - type: map_at_5 value: 15.716 - type: mrr_at_1 value: 66 - type: mrr_at_10 value: 74.263 - type: mrr_at_100 value: 74.519 - type: mrr_at_1000 value: 74.531 - type: mrr_at_3 value: 72.458 - type: mrr_at_5 value: 73.321 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.355999999999995 - type: ndcg_at_100 value: 44.366 - type: ndcg_at_1000 value: 51.771 - type: ndcg_at_3 value: 45.195 - type: ndcg_at_5 value: 42.187000000000005 - type: precision_at_1 value: 66 - type: precision_at_10 value: 31.75 - type: precision_at_100 value: 10.11 - type: precision_at_1000 value: 1.9800000000000002 - type: precision_at_3 value: 48.167 - type: precision_at_5 value: 40.050000000000004 - type: recall_at_1 value: 8.885 - type: recall_at_10 value: 24.471999999999998 - type: recall_at_100 value: 49.669000000000004 - type: recall_at_1000 value: 73.383 - type: recall_at_3 value: 14.872 - type: recall_at_5 value: 18.262999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 45.18 - type: f1 value: 40.26878691789978 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 62.751999999999995 - type: map_at_10 value: 74.131 - type: map_at_100 value: 74.407 - type: map_at_1000 value: 74.423 - type: map_at_3 value: 72.329 - type: map_at_5 value: 73.555 - type: mrr_at_1 value: 67.282 - type: mrr_at_10 value: 78.292 - type: mrr_at_100 value: 78.455 - type: mrr_at_1000 value: 78.458 - type: mrr_at_3 value: 76.755 - type: mrr_at_5 value: 77.839 - type: ndcg_at_1 value: 67.282 - type: ndcg_at_10 value: 79.443 - type: ndcg_at_100 value: 80.529 - type: ndcg_at_1000 value: 80.812 - type: ndcg_at_3 value: 76.281 - 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type: max_f1 value: 77.87816307403935 language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit --- ## Multilingual-E5-base [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 768. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ", even for non-English texts. # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-base') model = AutoModel.from_pretrained('intfloat/multilingual-e5-base') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) **First stage**: contrastive pre-training with weak supervision | Dataset | Weak supervision | # of text pairs | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | **Second stage**: supervised fine-tuning | Dataset | Language | # of text pairs | |----------------------------------------------------------------------------------------|--------------|-----------------| | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | For all labeled datasets, we only use its training set for fine-tuning. For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | | | | | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/multilingual-e5-base') input_texts = [ 'query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.

openai/whisper-medium.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-medium.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: 4.120542365210176 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (other) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 7.431640255663553 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-medium.en") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium.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 medium.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-medium.en") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium.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"])) 3.0154449620004904 ``` ## 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.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} } ```

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

stabilityai/stable-diffusion-2-inpainting

stabilityai

diffusers

--- license: openrail++ tags: - stable-diffusion inference: false --- # Stable Diffusion v2 Model Card This model card focuses on the model associated with the Stable Diffusion v2, available [here](https://github.com/Stability-AI/stablediffusion). This `stable-diffusion-2-inpainting` model is resumed from [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`512-base-ema.ckpt`) and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.  - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-inpainting-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/resolve/main/512-inpainting-ema.ckpt). - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting#examples) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 inpainting in a simple and efficient manner. ```bash pip install diffusers transformers accelerate scipy safetensors ``` ```python from diffusers import StableDiffusionInpaintPipeline pipe = StableDiffusionInpaintPipeline.from_pretrained( "stabilityai/stable-diffusion-2-inpainting", torch_dtype=torch.float16, ) pipe.to("cuda") prompt = "Face of a yellow cat, high resolution, sitting on a park bench" #image and mask_image should be PIL images. #The mask structure is white for inpainting and black for keeping as is image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0] image.save("./yellow_cat_on_park_bench.png") ``` **Notes**: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed) **How it works:** `image` | `mask_image` :-------------------------:|:-------------------------:| <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="300"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="300"/> `prompt` | `Output` :-------------------------:|:-------------------------:| <span style="position: relative;bottom: 150px;">Face of a yellow cat, high resolution, sitting on a park bench</span> | <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/test.png" alt="drawing" width="300"/> # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. We currently provide the following checkpoints: - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`. - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset. - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama). - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752). In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 1 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 200000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

timm/convnext_small.fb_in22k

timm

timm

image-classification

--- license: apache-2.0 library_name: timm tags: - image-classification - timm datasets: - imagenet-22k --- # Model card for convnext_small.fb_in22k A ConvNeXt image classification model. Pretrained on ImageNet-22k by paper authors. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 66.3 - GMACs: 8.7 - Activations (M): 21.6 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - **Dataset:** ImageNet-22k ## 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('convnext_small.fb_in22k', 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( 'convnext_small.fb_in22k', 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, 96, 56, 56]) # torch.Size([1, 192, 28, 28]) # torch.Size([1, 384, 14, 14]) # torch.Size([1, 768, 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( 'convnext_small.fb_in22k', 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, 768, 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). All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP. | model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size| |------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------| | [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 | | [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 | | [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 | | [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 | | [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 | | [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 | | [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 | | [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 | | [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 | | [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 | | [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 | | [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 | | [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 | | [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 | | [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 | | [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 | | [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 | | [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 | | [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 | | [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 | | [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 | | [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 | | [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 | | [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 | | [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 | | [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 | | [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 | | [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 | | [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 | | [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 | | [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 | | [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 | | [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 | | [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 | | [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 | | [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 | | [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 | | [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 | | [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 | | [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 | | [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 | | [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 | | [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 | | [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 | | [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 | | [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 | | [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 | | [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 | | [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 | | [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 | | [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 | | [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 | | [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 | | [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 | ## Citation ```bibtex @article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ``` ```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}} } ```

tohoku-nlp/bert-base-japanese-whole-word-masking

tohoku-nlp

transformers

fill-mask

--- language: ja license: cc-by-sa-4.0 datasets: - wikipedia widget: - text: 東北大学で[MASK]の研究をしています。 --- # BERT base Japanese (IPA dictionary, whole word masking enabled) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For the training of the MLM (masked language modeling) objective, we introduced the **Whole Word Masking** in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.

tiiuae/falcon-7b-instruct

tiiuae

transformers

text-generation

--- datasets: - tiiuae/falcon-refinedweb language: - en inference: true widget: - text: "Hey Falcon! Any recommendations for my holidays in Abu Dhabi?" example_title: "Abu Dhabi Trip" - text: "What's the Everett interpretation of quantum mechanics?" example_title: "Q/A: Quantum & Answers" - text: "Give me a list of the top 10 dive sites you would recommend around the world." example_title: "Diving Top 10" - text: "Can you tell me more about deep-water soloing?" example_title: "Extreme sports" - text: "Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM?" example_title: "Twitter Helper" - text: "What are the responsabilities of a Chief Llama Officer?" example_title: "Trendy Jobs" license: apache-2.0 --- # ✨ Falcon-7B-Instruct **Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.** *Paper coming soon 😊.* 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-7B-Instruct? * **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).** * **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). 💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). 🔥 **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!** For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B-Instruct. # Model Card for Falcon-7B-Instruct ## Model Details ### Model Description - **Developed by:** [https://www.tii.ae](https://www.tii.ae); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English and French; - **License:** Apache 2.0; - **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Source - **Paper:** *coming soon*. ## Uses ### Direct Use Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets. ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets. | **Data source** | **Fraction** | **Tokens** | **Description** | |--------------------|--------------|------------|-----------------------------------| | [Bai ze](https://github.com/project-baize/baize-chatbot) | 65% | 164M | chat | | [GPT4All](https://github.com/nomic-ai/gpt4all) | 25% | 62M | instruct | | [GPTeacher](https://github.com/teknium1/GPTeacher) | 5% | 11M | instruct | | [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5% | 13M | massive web crawl | The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ## Evaluation *Paper coming soon.* See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. Note that this model variant is not optimized for NLP benchmarks. ## Technical Specifications For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Architecture and Objective Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * **Decoder-block:** parallel attention/MLP with a single layer norm. | **Hyperparameter** | **Value** | **Comment** | |--------------------|-----------|----------------------------------------| | Layers | 32 | | | `d_model` | 4544 | Increased to compensate for multiquery | | `head_dim` | 64 | Reduced to optimise for FlashAttention | | Vocabulary | 65024 | | | Sequence length | 2048 | | ### Compute Infrastructure #### Hardware Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances. #### Software Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation *Paper coming soon* 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-7B-Instruct is made available under the Apache 2.0 license. ## Contact falconllm@tii.ae

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}, } ```

microsoft/xclip-base-patch32

microsoft

transformers

video-classification

--- language: en license: mit tags: - vision - video-classification model-index: - name: nielsr/xclip-base-patch32 results: - task: type: video-classification dataset: name: Kinetics 400 type: kinetics-400 metrics: - type: top-1 accuracy value: 80.4 - type: top-5 accuracy value: 95.0 --- # X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP). This model was trained using 8 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.  This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval. ## Intended uses & limitations You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#). ## Training data This model was trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). ### Preprocessing The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247). The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285). During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation. ## Evaluation results This model achieves a top-1 accuracy of 80.4% and a top-5 accuracy of 95.0%.

tner/roberta-large-ontonotes5

tner

transformers

token-classification

--- datasets: - tner/ontonotes5 metrics: - f1 - precision - recall model-index: - name: tner/roberta-large-ontonotes5 results: - task: name: Token Classification type: token-classification dataset: name: tner/ontonotes5 type: tner/ontonotes5 args: tner/ontonotes5 metrics: - name: F1 type: f1 value: 0.908632361399938 - name: Precision type: precision value: 0.905148095909732 - name: Recall type: recall value: 0.9121435551212579 - name: F1 (macro) type: f1_macro value: 0.8265477704565624 - name: Precision (macro) type: precision_macro value: 0.8170668848546687 - name: Recall (macro) type: recall_macro value: 0.8387672780349001 - name: F1 (entity span) type: f1_entity_span value: 0.9284544931640193 - name: Precision (entity span) type: precision_entity_span value: 0.9248942172073342 - name: Recall (entity span) type: recall_entity_span value: 0.9320422848005685 pipeline_tag: token-classification widget: - text: "Jacob Collier is a Grammy awarded artist from England." example_title: "NER Example 1" --- # tner/roberta-large-ontonotes5 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) dataset. Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set: - F1 (micro): 0.908632361399938 - Precision (micro): 0.905148095909732 - Recall (micro): 0.9121435551212579 - F1 (macro): 0.8265477704565624 - Precision (macro): 0.8170668848546687 - Recall (macro): 0.8387672780349001 The per-entity breakdown of the F1 score on the test set are below: - cardinal_number: 0.8605277329025309 - date: 0.872996300863132 - event: 0.7424242424242424 - facility: 0.7732342007434945 - geopolitical_area: 0.9687148323205043 - group: 0.9470588235294117 - language: 0.7499999999999999 - law: 0.6666666666666666 - location: 0.7593582887700535 - money: 0.901098901098901 - ordinal_number: 0.85785536159601 - organization: 0.9227360841872057 - percent: 0.9171428571428571 - person: 0.9556004036326943 - product: 0.7857142857142858 - quantity: 0.7945205479452055 - time: 0.6870588235294116 - work_of_art: 0.7151515151515151 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.9039454247544766, 0.9128956119702822] - 95%: [0.9030263216115454, 0.9138350859566045] - F1 (macro): - 90%: [0.9039454247544766, 0.9128956119702822] - 95%: [0.9030263216115454, 0.9138350859566045] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip ```shell pip install tner ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/roberta-large-ontonotes5") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. ### Training hyperparameters The following hyperparameters were used during training: - dataset: ['tner/ontonotes5'] - dataset_split: train - dataset_name: None - local_dataset: None - model: roberta-large - crf: True - max_length: 128 - epoch: 15 - batch_size: 64 - lr: 1e-05 - random_seed: 42 - gradient_accumulation_steps: 1 - weight_decay: None - lr_warmup_step_ratio: 0.1 - max_grad_norm: 10.0 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-camacho-collados-2021-ner, title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", author = "Ushio, Asahi and Camacho-Collados, Jose", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-demos.7", doi = "10.18653/v1/2021.eacl-demos.7", pages = "53--62", abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.", } ```