Datasets:

librarian-bots

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model_cards_with_metadata_test

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cardiffnlp/twitter-roberta-base-sentiment

cardiffnlp

transformers

text-classification

--- datasets: - tweet_eval language: - en --- # Twitter-roBERTa-base for Sentiment Analysis This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)). - Reference Paper: [_TweetEval_ (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). <b>Labels</b>: 0 -> Negative; 1 -> Neutral; 2 -> Positive <b>New!</b> We just released a new sentiment analysis model trained on more recent and a larger quantity of tweets. See [twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) and [TweetNLP](https://tweetnlp.org) for more details. ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Tasks: # emoji, emotion, hate, irony, offensive, sentiment # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary task='sentiment' MODEL = f"cardiffnlp/twitter-roberta-base-{task}" tokenizer = AutoTokenizer.from_pretrained(MODEL) # download label mapping labels=[] mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Good night 😊" # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) positive 0.8466 2) neutral 0.1458 3) negative 0.0076 ``` ### BibTeX entry and citation info Please cite the [reference paper](https://aclanthology.org/2020.findings-emnlp.148/) if you use this model. ```bibtex @inproceedings{barbieri-etal-2020-tweeteval, title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification", author = "Barbieri, Francesco and Camacho-Collados, Jose and Espinosa Anke, Luis and Neves, Leonardo", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.findings-emnlp.148", doi = "10.18653/v1/2020.findings-emnlp.148", pages = "1644--1650" } ```

sonoisa/sentence-bert-base-ja-mean-tokens-v2

sonoisa

sentence-transformers

feature-extraction

--- language: ja license: cc-by-sa-4.0 tags: - sentence-transformers - sentence-bert - feature-extraction - sentence-similarity --- This is a Japanese sentence-BERT model. 日本語用Sentence-BERTモデル（バージョン2）です。 [バージョン1](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens)よりも良いロス関数である[MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss)を用いて学習した改良版です。 手元の非公開データセットでは、バージョン1よりも1.5〜2ポイントほど精度が高い結果が得られました。 事前学習済みモデルとして[cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking)を利用しました。 従って、推論の実行にはfugashiとipadicが必要です（pip install fugashi ipadic）。 # 旧バージョンの解説 https://qiita.com/sonoisa/items/1df94d0a98cd4f209051 モデル名を"sonoisa/sentence-bert-base-ja-mean-tokens-v2"に書き換えれば、本モデルを利用した挙動になります。 # 使い方 ```python from transformers import BertJapaneseTokenizer, BertModel import torch class SentenceBertJapanese: def __init__(self, model_name_or_path, device=None): self.tokenizer = BertJapaneseTokenizer.from_pretrained(model_name_or_path) self.model = BertModel.from_pretrained(model_name_or_path) self.model.eval() if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" self.device = torch.device(device) self.model.to(device) def _mean_pooling(self, 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) @torch.no_grad() def encode(self, sentences, batch_size=8): all_embeddings = [] iterator = range(0, len(sentences), batch_size) for batch_idx in iterator: batch = sentences[batch_idx:batch_idx + batch_size] encoded_input = self.tokenizer.batch_encode_plus(batch, padding="longest", truncation=True, return_tensors="pt").to(self.device) model_output = self.model(**encoded_input) sentence_embeddings = self._mean_pooling(model_output, encoded_input["attention_mask"]).to('cpu') all_embeddings.extend(sentence_embeddings) # return torch.stack(all_embeddings).numpy() return torch.stack(all_embeddings) MODEL_NAME = "sonoisa/sentence-bert-base-ja-mean-tokens-v2" # <- v2です。 model = SentenceBertJapanese(MODEL_NAME) sentences = ["暴走したAI", "暴走した人工知能"] sentence_embeddings = model.encode(sentences, batch_size=8) print("Sentence embeddings:", sentence_embeddings) ```

Supabase/gte-small

Supabase

transformers.js

feature-extraction

--- pipeline_tag: feature-extraction library_name: "transformers.js" language: - en license: mit --- _Fork of https://huggingface.co/thenlper/gte-small with ONNX weights to be compatible with Transformers.js. See [JavaScript usage](#javascript)._ --- # gte-small General Text Embeddings (GTE) model. The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Metrics Performance of GTE models were compared with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | | [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | | [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | ## Usage This model can be used with both [Python](#python) and [JavaScript](#javascript). ### Python Use with [Transformers](https://huggingface.co/docs/transformers/index) and [PyTorch](https://pytorch.org/docs/stable/index.html): ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("Supabase/gte-small") model = AutoModel.from_pretrained("Supabase/gte-small") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with [sentence-transformers](https://www.sbert.net/): ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('Supabase/gte-small') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### JavaScript This model can be used with JavaScript via [Transformers.js](https://huggingface.co/docs/transformers.js/index). Use with [Deno](https://deno.land/manual/introduction) or [Supabase Edge Functions](https://supabase.com/docs/guides/functions): ```ts import { serve } from 'https://deno.land/std@0.168.0/http/server.ts' import { env, pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.0' // Configuration for Deno runtime env.useBrowserCache = false; env.allowLocalModels = false; const pipe = await pipeline( 'feature-extraction', 'Supabase/gte-small', ); serve(async (req) => { // Extract input string from JSON body const { input } = await req.json(); // Generate the embedding from the user input const output = await pipe(input, { pooling: 'mean', normalize: true, }); // Extract the embedding output const embedding = Array.from(output.data); // Return the embedding return new Response( JSON.stringify({ embedding }), { headers: { 'Content-Type': 'application/json' } } ); }); ``` Use within the browser ([JavaScript Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules)): ```html <script type="module"> import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.0'; const pipe = await pipeline( 'feature-extraction', 'Supabase/gte-small', ); // Generate the embedding from text const output = await pipe('Hello world', { pooling: 'mean', normalize: true, }); // Extract the embedding output const embedding = Array.from(output.data); console.log(embedding); </script> ``` Use within [Node.js](https://nodejs.org/en/docs) or a web bundler ([Webpack](https://webpack.js.org/concepts/), etc): ```js import { pipeline } from '@xenova/transformers'; const pipe = await pipeline( 'feature-extraction', 'Supabase/gte-small', ); // Generate the embedding from text const output = await pipe('Hello world', { pooling: 'mean', normalize: true, }); // Extract the embedding output const embedding = Array.from(output.data); console.log(embedding); ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.

BAAI/bge-small-en-v1.5

BAAI

sentence-transformers

feature-extraction

--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-small-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 37.21923821573361 - type: f1 value: 68.0914945617093 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.75377499999999 - type: ap value: 89.46766124546022 - type: f1 value: 92.73884001331487 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.986 - type: f1 value: 46.55936786727896 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 35.846000000000004 - type: map_at_10 value: 51.388 - type: map_at_100 value: 52.132999999999996 - type: map_at_1000 value: 52.141000000000005 - type: map_at_3 value: 47.037 - type: map_at_5 value: 49.579 - type: mrr_at_1 value: 36.558 - type: mrr_at_10 value: 51.658 - type: mrr_at_100 value: 52.402 - type: mrr_at_1000 value: 52.410000000000004 - type: mrr_at_3 value: 47.345 - type: mrr_at_5 value: 49.797999999999995 - type: ndcg_at_1 value: 35.846000000000004 - type: ndcg_at_10 value: 59.550000000000004 - type: ndcg_at_100 value: 62.596 - type: ndcg_at_1000 value: 62.759 - type: ndcg_at_3 value: 50.666999999999994 - type: ndcg_at_5 value: 55.228 - type: precision_at_1 value: 35.846000000000004 - type: precision_at_10 value: 8.542 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.389 - type: precision_at_5 value: 14.438 - type: recall_at_1 value: 35.846000000000004 - type: recall_at_10 value: 85.42 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 61.166 - type: recall_at_5 value: 72.191 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.402770198163594 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.01545436974177 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.586465273207196 - type: mrr value: 74.42169019038825 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.1891186537969 - type: cos_sim_spearman value: 83.75492046087288 - type: euclidean_pearson value: 84.11766204805357 - type: euclidean_spearman value: 84.01456493126516 - type: manhattan_pearson value: 84.2132950502772 - type: manhattan_spearman value: 83.89227298813377 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.74025974025975 - type: f1 value: 85.71493566466381 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.467181385006434 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.719496037339056 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.587000000000003 - type: map_at_10 value: 41.114 - type: map_at_100 value: 42.532 - type: map_at_1000 value: 42.661 - type: map_at_3 value: 37.483 - type: map_at_5 value: 39.652 - type: mrr_at_1 value: 36.338 - type: mrr_at_10 value: 46.763 - type: mrr_at_100 value: 47.393 - type: mrr_at_1000 value: 47.445 - type: mrr_at_3 value: 43.538 - type: mrr_at_5 value: 45.556000000000004 - type: ndcg_at_1 value: 36.338 - type: ndcg_at_10 value: 47.658 - type: ndcg_at_100 value: 52.824000000000005 - type: ndcg_at_1000 value: 54.913999999999994 - type: ndcg_at_3 value: 41.989 - type: ndcg_at_5 value: 44.944 - type: precision_at_1 value: 36.338 - type: precision_at_10 value: 9.156 - type: precision_at_100 value: 1.4789999999999999 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 20.076 - type: precision_at_5 value: 14.85 - type: recall_at_1 value: 29.587000000000003 - type: recall_at_10 value: 60.746 - type: recall_at_100 value: 82.157 - type: recall_at_1000 value: 95.645 - type: recall_at_3 value: 44.821 - type: recall_at_5 value: 52.819 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.239 - type: map_at_10 value: 39.989000000000004 - type: map_at_100 value: 41.196 - type: map_at_1000 value: 41.325 - type: map_at_3 value: 37.261 - type: map_at_5 value: 38.833 - type: mrr_at_1 value: 37.516 - type: mrr_at_10 value: 46.177 - type: mrr_at_100 value: 46.806 - type: mrr_at_1000 value: 46.849000000000004 - type: mrr_at_3 value: 44.002 - type: mrr_at_5 value: 45.34 - type: ndcg_at_1 value: 37.516 - type: ndcg_at_10 value: 45.586 - type: ndcg_at_100 value: 49.897000000000006 - type: ndcg_at_1000 value: 51.955 - type: ndcg_at_3 value: 41.684 - type: ndcg_at_5 value: 43.617 - type: precision_at_1 value: 37.516 - type: precision_at_10 value: 8.522 - type: precision_at_100 value: 1.374 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 20.105999999999998 - type: precision_at_5 value: 14.152999999999999 - type: recall_at_1 value: 30.239 - type: recall_at_10 value: 55.03 - type: recall_at_100 value: 73.375 - type: recall_at_1000 value: 86.29599999999999 - type: recall_at_3 value: 43.269000000000005 - type: recall_at_5 value: 48.878 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.338 - type: map_at_10 value: 50.468999999999994 - type: map_at_100 value: 51.553000000000004 - type: map_at_1000 value: 51.608 - type: map_at_3 value: 47.107 - type: map_at_5 value: 49.101 - type: mrr_at_1 value: 44.201 - type: mrr_at_10 value: 54.057 - type: mrr_at_100 value: 54.764 - type: mrr_at_1000 value: 54.791000000000004 - type: mrr_at_3 value: 51.56699999999999 - type: mrr_at_5 value: 53.05 - type: ndcg_at_1 value: 44.201 - type: ndcg_at_10 value: 56.379000000000005 - type: ndcg_at_100 value: 60.645 - type: ndcg_at_1000 value: 61.73499999999999 - type: ndcg_at_3 value: 50.726000000000006 - type: ndcg_at_5 value: 53.58500000000001 - type: precision_at_1 value: 44.201 - type: precision_at_10 value: 9.141 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.654 - type: precision_at_5 value: 15.723999999999998 - type: recall_at_1 value: 38.338 - type: recall_at_10 value: 70.30499999999999 - type: recall_at_100 value: 88.77199999999999 - type: recall_at_1000 value: 96.49799999999999 - type: recall_at_3 value: 55.218 - type: recall_at_5 value: 62.104000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.682 - type: map_at_10 value: 33.498 - type: map_at_100 value: 34.461000000000006 - type: map_at_1000 value: 34.544000000000004 - type: map_at_3 value: 30.503999999999998 - type: map_at_5 value: 32.216 - type: mrr_at_1 value: 27.683999999999997 - type: mrr_at_10 value: 35.467999999999996 - type: mrr_at_100 value: 36.32 - type: mrr_at_1000 value: 36.386 - type: mrr_at_3 value: 32.618 - type: mrr_at_5 value: 34.262 - type: ndcg_at_1 value: 27.683999999999997 - type: ndcg_at_10 value: 38.378 - type: ndcg_at_100 value: 43.288 - type: ndcg_at_1000 value: 45.413 - type: ndcg_at_3 value: 32.586 - type: ndcg_at_5 value: 35.499 - type: precision_at_1 value: 27.683999999999997 - type: precision_at_10 value: 5.864 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 13.446 - type: precision_at_5 value: 9.718 - type: recall_at_1 value: 25.682 - type: recall_at_10 value: 51.712 - type: recall_at_100 value: 74.446 - type: recall_at_1000 value: 90.472 - type: recall_at_3 value: 36.236000000000004 - type: recall_at_5 value: 43.234 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.073999999999998 - type: map_at_10 value: 24.352999999999998 - type: map_at_100 value: 25.438 - type: map_at_1000 value: 25.545 - type: map_at_3 value: 21.614 - type: map_at_5 value: 23.104 - type: mrr_at_1 value: 19.776 - type: mrr_at_10 value: 28.837000000000003 - type: mrr_at_100 value: 29.755 - type: mrr_at_1000 value: 29.817 - type: mrr_at_3 value: 26.201999999999998 - type: mrr_at_5 value: 27.714 - type: ndcg_at_1 value: 19.776 - type: ndcg_at_10 value: 29.701 - type: ndcg_at_100 value: 35.307 - type: ndcg_at_1000 value: 37.942 - type: ndcg_at_3 value: 24.764 - type: ndcg_at_5 value: 27.025 - type: precision_at_1 value: 19.776 - type: precision_at_10 value: 5.659 - type: precision_at_100 value: 0.971 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 12.065 - type: precision_at_5 value: 8.905000000000001 - type: recall_at_1 value: 16.073999999999998 - type: recall_at_10 value: 41.647 - type: recall_at_100 value: 66.884 - type: recall_at_1000 value: 85.91499999999999 - type: recall_at_3 value: 27.916 - type: recall_at_5 value: 33.729 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.444999999999997 - type: map_at_10 value: 38.218999999999994 - type: map_at_100 value: 39.595 - type: map_at_1000 value: 39.709 - type: map_at_3 value: 35.586 - type: map_at_5 value: 36.895 - type: mrr_at_1 value: 34.841 - type: mrr_at_10 value: 44.106 - type: mrr_at_100 value: 44.98 - type: mrr_at_1000 value: 45.03 - type: mrr_at_3 value: 41.979 - type: mrr_at_5 value: 43.047999999999995 - type: ndcg_at_1 value: 34.841 - type: ndcg_at_10 value: 43.922 - type: ndcg_at_100 value: 49.504999999999995 - type: ndcg_at_1000 value: 51.675000000000004 - type: ndcg_at_3 value: 39.858 - type: ndcg_at_5 value: 41.408 - type: precision_at_1 value: 34.841 - type: precision_at_10 value: 7.872999999999999 - type: precision_at_100 value: 1.2449999999999999 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 18.993 - type: precision_at_5 value: 13.032 - type: recall_at_1 value: 28.444999999999997 - type: recall_at_10 value: 54.984 - type: recall_at_100 value: 78.342 - type: recall_at_1000 value: 92.77 - type: recall_at_3 value: 42.842999999999996 - type: recall_at_5 value: 47.247 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.072 - type: map_at_10 value: 32.354 - type: map_at_100 value: 33.800000000000004 - type: map_at_1000 value: 33.908 - type: map_at_3 value: 29.232000000000003 - type: map_at_5 value: 31.049 - type: mrr_at_1 value: 29.110000000000003 - type: mrr_at_10 value: 38.03 - type: mrr_at_100 value: 39.032 - type: mrr_at_1000 value: 39.086999999999996 - type: mrr_at_3 value: 35.407 - type: mrr_at_5 value: 36.76 - type: ndcg_at_1 value: 29.110000000000003 - type: ndcg_at_10 value: 38.231 - type: ndcg_at_100 value: 44.425 - type: ndcg_at_1000 value: 46.771 - type: ndcg_at_3 value: 33.095 - type: ndcg_at_5 value: 35.459 - type: precision_at_1 value: 29.110000000000003 - type: precision_at_10 value: 7.215000000000001 - type: precision_at_100 value: 1.2109999999999999 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 16.058 - type: precision_at_5 value: 11.644 - type: recall_at_1 value: 23.072 - type: recall_at_10 value: 50.285999999999994 - type: recall_at_100 value: 76.596 - type: recall_at_1000 value: 92.861 - type: recall_at_3 value: 35.702 - type: recall_at_5 value: 42.152 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.937916666666666 - type: map_at_10 value: 33.755250000000004 - type: map_at_100 value: 34.955999999999996 - type: map_at_1000 value: 35.070499999999996 - type: map_at_3 value: 30.98708333333333 - type: map_at_5 value: 32.51491666666666 - type: mrr_at_1 value: 29.48708333333333 - type: mrr_at_10 value: 37.92183333333334 - type: mrr_at_100 value: 38.76583333333333 - type: mrr_at_1000 value: 38.82466666666667 - type: mrr_at_3 value: 35.45125 - type: mrr_at_5 value: 36.827000000000005 - type: ndcg_at_1 value: 29.48708333333333 - type: ndcg_at_10 value: 39.05225 - type: ndcg_at_100 value: 44.25983333333334 - type: ndcg_at_1000 value: 46.568333333333335 - type: ndcg_at_3 value: 34.271583333333325 - type: ndcg_at_5 value: 36.483916666666666 - type: precision_at_1 value: 29.48708333333333 - type: precision_at_10 value: 6.865749999999999 - type: precision_at_100 value: 1.1195833333333332 - type: precision_at_1000 value: 0.15058333333333335 - type: precision_at_3 value: 15.742083333333333 - type: precision_at_5 value: 11.221916666666667 - type: recall_at_1 value: 24.937916666666666 - type: recall_at_10 value: 50.650416666666665 - type: recall_at_100 value: 73.55383333333334 - type: recall_at_1000 value: 89.61691666666667 - type: recall_at_3 value: 37.27808333333334 - type: recall_at_5 value: 42.99475 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.947 - type: map_at_10 value: 30.575000000000003 - type: map_at_100 value: 31.465 - type: map_at_1000 value: 31.558000000000003 - type: map_at_3 value: 28.814 - type: map_at_5 value: 29.738999999999997 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 33.415 - type: mrr_at_100 value: 34.18 - type: mrr_at_1000 value: 34.245 - type: mrr_at_3 value: 31.621 - type: mrr_at_5 value: 32.549 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 34.482 - type: ndcg_at_100 value: 38.915 - type: ndcg_at_1000 value: 41.355 - type: ndcg_at_3 value: 31.139 - type: ndcg_at_5 value: 32.589 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 5.322 - type: precision_at_100 value: 0.8160000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 13.344000000000001 - type: precision_at_5 value: 8.988 - type: recall_at_1 value: 23.947 - type: recall_at_10 value: 43.647999999999996 - type: recall_at_100 value: 63.851 - type: recall_at_1000 value: 82.0 - type: recall_at_3 value: 34.288000000000004 - type: recall_at_5 value: 38.117000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.197 - type: map_at_10 value: 22.968 - type: map_at_100 value: 24.095 - type: map_at_1000 value: 24.217 - type: map_at_3 value: 20.771 - type: map_at_5 value: 21.995 - type: mrr_at_1 value: 19.511 - type: mrr_at_10 value: 26.55 - type: mrr_at_100 value: 27.500999999999998 - type: mrr_at_1000 value: 27.578999999999997 - type: mrr_at_3 value: 24.421 - type: mrr_at_5 value: 25.604 - type: ndcg_at_1 value: 19.511 - type: ndcg_at_10 value: 27.386 - type: ndcg_at_100 value: 32.828 - type: ndcg_at_1000 value: 35.739 - type: ndcg_at_3 value: 23.405 - type: ndcg_at_5 value: 25.255 - type: precision_at_1 value: 19.511 - type: precision_at_10 value: 5.017 - type: precision_at_100 value: 0.91 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 11.023 - type: precision_at_5 value: 8.025 - type: recall_at_1 value: 16.197 - type: recall_at_10 value: 37.09 - type: recall_at_100 value: 61.778 - type: recall_at_1000 value: 82.56599999999999 - type: recall_at_3 value: 26.034000000000002 - type: recall_at_5 value: 30.762 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.41 - type: map_at_10 value: 33.655 - type: map_at_100 value: 34.892 - type: map_at_1000 value: 34.995 - type: map_at_3 value: 30.94 - type: map_at_5 value: 32.303 - type: mrr_at_1 value: 29.477999999999998 - type: mrr_at_10 value: 37.443 - type: mrr_at_100 value: 38.383 - type: mrr_at_1000 value: 38.440000000000005 - type: mrr_at_3 value: 34.949999999999996 - type: mrr_at_5 value: 36.228 - type: ndcg_at_1 value: 29.477999999999998 - type: ndcg_at_10 value: 38.769 - type: ndcg_at_100 value: 44.245000000000005 - type: ndcg_at_1000 value: 46.593 - type: ndcg_at_3 value: 33.623 - type: ndcg_at_5 value: 35.766 - type: precision_at_1 value: 29.477999999999998 - type: precision_at_10 value: 6.455 - type: precision_at_100 value: 1.032 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 14.893999999999998 - type: precision_at_5 value: 10.485 - type: recall_at_1 value: 25.41 - type: recall_at_10 value: 50.669 - type: recall_at_100 value: 74.084 - type: recall_at_1000 value: 90.435 - type: recall_at_3 value: 36.679 - type: recall_at_5 value: 41.94 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.339 - type: map_at_10 value: 31.852000000000004 - type: map_at_100 value: 33.411 - type: map_at_1000 value: 33.62 - type: map_at_3 value: 28.929 - type: map_at_5 value: 30.542 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.301 - type: mrr_at_100 value: 37.288 - type: mrr_at_1000 value: 37.349 - type: mrr_at_3 value: 33.663 - type: mrr_at_5 value: 35.165 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 37.462 - type: ndcg_at_100 value: 43.620999999999995 - type: ndcg_at_1000 value: 46.211 - type: ndcg_at_3 value: 32.68 - type: ndcg_at_5 value: 34.981 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.1739999999999995 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 15.217 - type: precision_at_5 value: 11.265 - type: recall_at_1 value: 23.339 - type: recall_at_10 value: 48.376999999999995 - type: recall_at_100 value: 76.053 - type: recall_at_1000 value: 92.455 - type: recall_at_3 value: 34.735 - type: recall_at_5 value: 40.71 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.925 - type: map_at_10 value: 26.017000000000003 - type: map_at_100 value: 27.034000000000002 - type: map_at_1000 value: 27.156000000000002 - type: map_at_3 value: 23.604 - type: map_at_5 value: 24.75 - type: mrr_at_1 value: 20.333000000000002 - type: mrr_at_10 value: 27.915 - type: mrr_at_100 value: 28.788000000000004 - type: mrr_at_1000 value: 28.877999999999997 - type: mrr_at_3 value: 25.446999999999996 - type: mrr_at_5 value: 26.648 - type: ndcg_at_1 value: 20.333000000000002 - type: ndcg_at_10 value: 30.673000000000002 - type: ndcg_at_100 value: 35.618 - type: ndcg_at_1000 value: 38.517 - type: ndcg_at_3 value: 25.71 - type: ndcg_at_5 value: 27.679 - type: precision_at_1 value: 20.333000000000002 - type: precision_at_10 value: 4.9910000000000005 - type: precision_at_100 value: 0.8130000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 11.029 - type: precision_at_5 value: 7.8740000000000006 - type: recall_at_1 value: 18.925 - type: recall_at_10 value: 43.311 - type: recall_at_100 value: 66.308 - type: recall_at_1000 value: 87.49 - type: recall_at_3 value: 29.596 - type: recall_at_5 value: 34.245 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 13.714 - type: map_at_10 value: 23.194 - type: map_at_100 value: 24.976000000000003 - type: map_at_1000 value: 25.166 - type: map_at_3 value: 19.709 - type: map_at_5 value: 21.523999999999997 - type: mrr_at_1 value: 30.619000000000003 - type: mrr_at_10 value: 42.563 - type: mrr_at_100 value: 43.386 - type: mrr_at_1000 value: 43.423 - type: mrr_at_3 value: 39.555 - type: mrr_at_5 value: 41.268 - type: ndcg_at_1 value: 30.619000000000003 - type: ndcg_at_10 value: 31.836 - type: ndcg_at_100 value: 38.652 - type: ndcg_at_1000 value: 42.088 - type: ndcg_at_3 value: 26.733 - type: ndcg_at_5 value: 28.435 - type: precision_at_1 value: 30.619000000000003 - type: precision_at_10 value: 9.751999999999999 - type: precision_at_100 value: 1.71 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 19.935 - type: precision_at_5 value: 14.984 - type: recall_at_1 value: 13.714 - type: recall_at_10 value: 37.26 - type: recall_at_100 value: 60.546 - type: recall_at_1000 value: 79.899 - type: recall_at_3 value: 24.325 - type: recall_at_5 value: 29.725 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.462 - type: map_at_10 value: 18.637 - type: map_at_100 value: 26.131999999999998 - type: map_at_1000 value: 27.607 - type: map_at_3 value: 13.333 - type: map_at_5 value: 15.654000000000002 - type: mrr_at_1 value: 66.25 - type: mrr_at_10 value: 74.32600000000001 - type: mrr_at_100 value: 74.60900000000001 - type: mrr_at_1000 value: 74.62 - type: mrr_at_3 value: 72.667 - type: mrr_at_5 value: 73.817 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.028999999999996 - type: ndcg_at_100 value: 44.199 - type: ndcg_at_1000 value: 51.629999999999995 - type: ndcg_at_3 value: 44.113 - type: ndcg_at_5 value: 41.731 - type: precision_at_1 value: 66.25 - type: precision_at_10 value: 31.900000000000002 - type: precision_at_100 value: 10.043000000000001 - type: precision_at_1000 value: 1.926 - type: precision_at_3 value: 47.417 - type: precision_at_5 value: 40.65 - type: recall_at_1 value: 8.462 - type: recall_at_10 value: 24.293 - type: recall_at_100 value: 50.146 - type: recall_at_1000 value: 74.034 - type: recall_at_3 value: 14.967 - type: recall_at_5 value: 18.682000000000002 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.84499999999999 - type: f1 value: 42.48106691979349 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 74.034 - type: map_at_10 value: 82.76 - type: map_at_100 value: 82.968 - type: map_at_1000 value: 82.98299999999999 - type: map_at_3 value: 81.768 - type: map_at_5 value: 82.418 - type: mrr_at_1 value: 80.048 - type: mrr_at_10 value: 87.64999999999999 - type: mrr_at_100 value: 87.712 - type: mrr_at_1000 value: 87.713 - type: mrr_at_3 value: 87.01100000000001 - type: mrr_at_5 value: 87.466 - type: ndcg_at_1 value: 80.048 - type: ndcg_at_10 value: 86.643 - type: ndcg_at_100 value: 87.361 - type: ndcg_at_1000 value: 87.606 - type: ndcg_at_3 value: 85.137 - type: ndcg_at_5 value: 86.016 - type: precision_at_1 value: 80.048 - type: precision_at_10 value: 10.372 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 32.638 - type: precision_at_5 value: 20.177 - type: recall_at_1 value: 74.034 - type: recall_at_10 value: 93.769 - type: recall_at_100 value: 96.569 - type: recall_at_1000 value: 98.039 - type: recall_at_3 value: 89.581 - type: recall_at_5 value: 91.906 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.5 - type: map_at_10 value: 32.857 - type: map_at_100 value: 34.589 - type: map_at_1000 value: 34.778 - type: map_at_3 value: 29.160999999999998 - type: map_at_5 value: 31.033 - type: mrr_at_1 value: 40.123 - type: mrr_at_10 value: 48.776 - type: mrr_at_100 value: 49.495 - type: mrr_at_1000 value: 49.539 - type: mrr_at_3 value: 46.605000000000004 - type: mrr_at_5 value: 47.654 - type: ndcg_at_1 value: 40.123 - type: ndcg_at_10 value: 40.343 - type: ndcg_at_100 value: 46.56 - type: ndcg_at_1000 value: 49.777 - type: ndcg_at_3 value: 37.322 - type: ndcg_at_5 value: 37.791000000000004 - type: precision_at_1 value: 40.123 - type: precision_at_10 value: 11.08 - type: precision_at_100 value: 1.752 - type: precision_at_1000 value: 0.232 - type: precision_at_3 value: 24.897 - type: precision_at_5 value: 17.809 - type: recall_at_1 value: 20.5 - type: recall_at_10 value: 46.388 - type: recall_at_100 value: 69.552 - type: recall_at_1000 value: 89.011 - type: recall_at_3 value: 33.617999999999995 - type: recall_at_5 value: 38.211 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 39.135999999999996 - type: map_at_10 value: 61.673 - type: map_at_100 value: 62.562 - type: map_at_1000 value: 62.62 - type: map_at_3 value: 58.467999999999996 - type: map_at_5 value: 60.463 - type: mrr_at_1 value: 78.271 - type: mrr_at_10 value: 84.119 - type: mrr_at_100 value: 84.29299999999999 - type: mrr_at_1000 value: 84.299 - type: mrr_at_3 value: 83.18900000000001 - type: mrr_at_5 value: 83.786 - type: ndcg_at_1 value: 78.271 - type: ndcg_at_10 value: 69.935 - type: ndcg_at_100 value: 73.01299999999999 - type: ndcg_at_1000 value: 74.126 - type: ndcg_at_3 value: 65.388 - type: ndcg_at_5 value: 67.906 - type: precision_at_1 value: 78.271 - type: precision_at_10 value: 14.562 - type: precision_at_100 value: 1.6969999999999998 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 41.841 - type: precision_at_5 value: 27.087 - type: recall_at_1 value: 39.135999999999996 - type: recall_at_10 value: 72.809 - type: recall_at_100 value: 84.86200000000001 - type: recall_at_1000 value: 92.208 - type: recall_at_3 value: 62.76199999999999 - type: recall_at_5 value: 67.718 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.60600000000001 - type: ap value: 86.6579587804335 - type: f1 value: 90.5938853929307 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.852 - type: map_at_10 value: 33.982 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.167 - type: map_at_3 value: 30.134 - type: map_at_5 value: 32.340999999999994 - type: mrr_at_1 value: 22.479 - type: mrr_at_10 value: 34.594 - type: mrr_at_100 value: 35.672 - type: mrr_at_1000 value: 35.716 - type: mrr_at_3 value: 30.84 - type: mrr_at_5 value: 32.998 - type: ndcg_at_1 value: 22.493 - type: ndcg_at_10 value: 40.833000000000006 - type: ndcg_at_100 value: 46.357 - type: ndcg_at_1000 value: 47.637 - type: ndcg_at_3 value: 32.995999999999995 - type: ndcg_at_5 value: 36.919000000000004 - type: precision_at_1 value: 22.493 - type: precision_at_10 value: 6.465999999999999 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.030999999999999 - type: precision_at_5 value: 10.413 - type: recall_at_1 value: 21.852 - type: recall_at_10 value: 61.934999999999995 - type: recall_at_100 value: 87.611 - type: recall_at_1000 value: 97.441 - type: recall_at_3 value: 40.583999999999996 - type: recall_at_5 value: 49.992999999999995 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.36069311445507 - type: f1 value: 93.16456330371453 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 74.74692202462381 - type: f1 value: 58.17903579421599 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.80833893745796 - type: f1 value: 72.70786592684664 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.69872225958305 - type: f1 value: 78.61626934504731 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.058658628717694 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.85561739360599 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.290259910144385 - type: mrr value: 32.44223046102856 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.288 - type: map_at_10 value: 12.267999999999999 - type: map_at_100 value: 15.557000000000002 - type: map_at_1000 value: 16.98 - type: map_at_3 value: 8.866 - type: map_at_5 value: 10.418 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 52.681 - type: mrr_at_100 value: 53.315999999999995 - type: mrr_at_1000 value: 53.357 - type: mrr_at_3 value: 51.393 - type: mrr_at_5 value: 51.903999999999996 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.305 - type: ndcg_at_100 value: 30.825999999999997 - type: ndcg_at_1000 value: 39.393 - type: ndcg_at_3 value: 39.931 - type: ndcg_at_5 value: 37.519999999999996 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.728 - type: precision_at_100 value: 7.932 - type: precision_at_1000 value: 2.07 - type: precision_at_3 value: 38.184000000000005 - type: precision_at_5 value: 32.879000000000005 - type: recall_at_1 value: 5.288 - type: recall_at_10 value: 16.195 - type: recall_at_100 value: 31.135 - type: recall_at_1000 value: 61.531000000000006 - type: recall_at_3 value: 10.313 - type: recall_at_5 value: 12.754999999999999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 28.216 - type: map_at_10 value: 42.588 - type: map_at_100 value: 43.702999999999996 - type: map_at_1000 value: 43.739 - type: map_at_3 value: 38.177 - type: map_at_5 value: 40.754000000000005 - type: mrr_at_1 value: 31.866 - type: mrr_at_10 value: 45.189 - type: mrr_at_100 value: 46.056000000000004 - type: mrr_at_1000 value: 46.081 - type: mrr_at_3 value: 41.526999999999994 - type: mrr_at_5 value: 43.704 - type: ndcg_at_1 value: 31.837 - type: ndcg_at_10 value: 50.178 - type: ndcg_at_100 value: 54.98800000000001 - type: ndcg_at_1000 value: 55.812 - type: ndcg_at_3 value: 41.853 - type: ndcg_at_5 value: 46.153 - type: precision_at_1 value: 31.837 - type: precision_at_10 value: 8.43 - type: precision_at_100 value: 1.1119999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 19.023 - type: precision_at_5 value: 13.911000000000001 - type: recall_at_1 value: 28.216 - type: recall_at_10 value: 70.8 - type: recall_at_100 value: 91.857 - type: recall_at_1000 value: 97.941 - type: recall_at_3 value: 49.196 - type: recall_at_5 value: 59.072 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.22800000000001 - type: map_at_10 value: 85.115 - type: map_at_100 value: 85.72 - type: map_at_1000 value: 85.737 - type: map_at_3 value: 82.149 - type: map_at_5 value: 84.029 - type: mrr_at_1 value: 81.96 - type: mrr_at_10 value: 88.00200000000001 - type: mrr_at_100 value: 88.088 - type: mrr_at_1000 value: 88.089 - type: mrr_at_3 value: 87.055 - type: mrr_at_5 value: 87.715 - type: ndcg_at_1 value: 82.01 - type: ndcg_at_10 value: 88.78 - type: ndcg_at_100 value: 89.91 - type: ndcg_at_1000 value: 90.013 - type: ndcg_at_3 value: 85.957 - type: ndcg_at_5 value: 87.56 - type: precision_at_1 value: 82.01 - type: precision_at_10 value: 13.462 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.732000000000003 - type: recall_at_1 value: 71.22800000000001 - type: recall_at_10 value: 95.69 - type: recall_at_100 value: 99.531 - type: recall_at_1000 value: 99.98 - type: recall_at_3 value: 87.632 - type: recall_at_5 value: 92.117 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 52.31768034366916 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.640266772723606 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.7780000000000005 - type: map_at_10 value: 12.299 - type: map_at_100 value: 14.363000000000001 - type: map_at_1000 value: 14.71 - type: map_at_3 value: 8.738999999999999 - type: map_at_5 value: 10.397 - type: mrr_at_1 value: 23.599999999999998 - type: mrr_at_10 value: 34.845 - type: mrr_at_100 value: 35.916 - type: mrr_at_1000 value: 35.973 - type: mrr_at_3 value: 31.7 - type: mrr_at_5 value: 33.535 - type: ndcg_at_1 value: 23.599999999999998 - type: ndcg_at_10 value: 20.522000000000002 - type: ndcg_at_100 value: 28.737000000000002 - type: ndcg_at_1000 value: 34.596 - type: ndcg_at_3 value: 19.542 - type: ndcg_at_5 value: 16.958000000000002 - type: precision_at_1 value: 23.599999999999998 - type: precision_at_10 value: 10.67 - type: precision_at_100 value: 2.259 - type: precision_at_1000 value: 0.367 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 14.879999999999999 - type: recall_at_1 value: 4.7780000000000005 - type: recall_at_10 value: 21.617 - type: recall_at_100 value: 45.905 - type: recall_at_1000 value: 74.42 - type: recall_at_3 value: 11.148 - type: recall_at_5 value: 15.082999999999998 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.22372750297885 - type: cos_sim_spearman value: 79.40972617119405 - type: euclidean_pearson value: 80.6101072020434 - type: euclidean_spearman value: 79.53844217225202 - type: manhattan_pearson value: 80.57265975286111 - type: manhattan_spearman value: 79.46335611792958 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.43713315520749 - type: cos_sim_spearman value: 77.44128693329532 - type: euclidean_pearson value: 81.63869928101123 - type: euclidean_spearman value: 77.29512977961515 - type: manhattan_pearson value: 81.63704185566183 - type: manhattan_spearman value: 77.29909412738657 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 81.59451537860527 - type: cos_sim_spearman value: 82.97994638856723 - type: euclidean_pearson value: 82.89478688288412 - type: euclidean_spearman value: 83.58740751053104 - type: manhattan_pearson value: 82.69140840941608 - type: manhattan_spearman value: 83.33665956040555 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.00756527711764 - type: cos_sim_spearman value: 81.83560996841379 - type: euclidean_pearson value: 82.07684151976518 - type: euclidean_spearman value: 82.00913052060511 - type: manhattan_pearson value: 82.05690778488794 - type: manhattan_spearman value: 82.02260252019525 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.13710262895447 - type: cos_sim_spearman value: 87.26412811156248 - type: euclidean_pearson value: 86.94151453230228 - type: euclidean_spearman value: 87.5363796699571 - type: manhattan_pearson value: 86.86989424083748 - type: manhattan_spearman value: 87.47315940781353 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.0230597603627 - type: cos_sim_spearman value: 84.93344499318864 - type: euclidean_pearson value: 84.23754743431141 - type: euclidean_spearman value: 85.09707376597099 - type: manhattan_pearson value: 84.04325160987763 - type: manhattan_spearman value: 84.89353071339909 - 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: 86.75620824563921 - type: cos_sim_spearman value: 87.15065513706398 - type: euclidean_pearson value: 88.26281533633521 - type: euclidean_spearman value: 87.51963738643983 - type: manhattan_pearson value: 88.25599267618065 - type: manhattan_spearman value: 87.58048736047483 - 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: 64.74645319195137 - type: cos_sim_spearman value: 65.29996325037214 - type: euclidean_pearson value: 67.04297794086443 - type: euclidean_spearman value: 65.43841726694343 - type: manhattan_pearson value: 67.39459955690904 - type: manhattan_spearman value: 65.92864704413651 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.31291020270801 - type: cos_sim_spearman value: 85.86473738688068 - type: euclidean_pearson value: 85.65537275064152 - type: euclidean_spearman value: 86.13087454209642 - type: manhattan_pearson value: 85.43946955047609 - type: manhattan_spearman value: 85.91568175344916 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.93798118350695 - type: mrr value: 95.93536274908824 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 57.594 - type: map_at_10 value: 66.81899999999999 - type: map_at_100 value: 67.368 - type: map_at_1000 value: 67.4 - type: map_at_3 value: 64.061 - type: map_at_5 value: 65.47 - type: mrr_at_1 value: 60.667 - type: mrr_at_10 value: 68.219 - type: mrr_at_100 value: 68.655 - type: mrr_at_1000 value: 68.684 - type: mrr_at_3 value: 66.22200000000001 - type: mrr_at_5 value: 67.289 - type: ndcg_at_1 value: 60.667 - type: ndcg_at_10 value: 71.275 - type: ndcg_at_100 value: 73.642 - type: ndcg_at_1000 value: 74.373 - type: ndcg_at_3 value: 66.521 - type: ndcg_at_5 value: 68.581 - type: precision_at_1 value: 60.667 - type: precision_at_10 value: 9.433 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.556 - type: precision_at_5 value: 16.8 - type: recall_at_1 value: 57.594 - type: recall_at_10 value: 83.622 - type: recall_at_100 value: 94.167 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 70.64399999999999 - type: recall_at_5 value: 75.983 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85841584158416 - type: cos_sim_ap value: 96.66996142314342 - type: cos_sim_f1 value: 92.83208020050125 - type: cos_sim_precision value: 93.06532663316584 - type: cos_sim_recall value: 92.60000000000001 - type: dot_accuracy value: 99.85841584158416 - type: dot_ap value: 96.6775307676576 - type: dot_f1 value: 92.69289729177312 - type: dot_precision value: 94.77533960292581 - type: dot_recall value: 90.7 - type: euclidean_accuracy value: 99.86138613861387 - type: euclidean_ap value: 96.6338454403108 - type: euclidean_f1 value: 92.92214357937311 - type: euclidean_precision value: 93.96728016359918 - type: euclidean_recall value: 91.9 - type: manhattan_accuracy value: 99.86237623762376 - type: manhattan_ap value: 96.60370449645053 - type: manhattan_f1 value: 92.91177970423253 - type: manhattan_precision value: 94.7970863683663 - type: manhattan_recall value: 91.10000000000001 - type: max_accuracy value: 99.86237623762376 - type: max_ap value: 96.6775307676576 - type: max_f1 value: 92.92214357937311 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 60.77977058695198 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.2725272535638 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.64052466362125 - type: mrr value: 54.533067014684654 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.677624219206578 - type: cos_sim_spearman value: 30.121368518123447 - type: dot_pearson value: 30.69870088041608 - type: dot_spearman value: 29.61284927093751 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22 - type: map_at_10 value: 1.855 - type: map_at_100 value: 9.885 - type: map_at_1000 value: 23.416999999999998 - type: map_at_3 value: 0.637 - type: map_at_5 value: 1.024 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.067 - type: mrr_at_100 value: 93.067 - type: mrr_at_1000 value: 93.067 - type: mrr_at_3 value: 92.667 - type: mrr_at_5 value: 93.067 - type: ndcg_at_1 value: 82.0 - type: ndcg_at_10 value: 75.899 - type: ndcg_at_100 value: 55.115 - type: ndcg_at_1000 value: 48.368 - type: ndcg_at_3 value: 79.704 - type: ndcg_at_5 value: 78.39699999999999 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 79.60000000000001 - type: precision_at_100 value: 56.06 - type: precision_at_1000 value: 21.206 - type: precision_at_3 value: 84.667 - type: precision_at_5 value: 83.2 - type: recall_at_1 value: 0.22 - type: recall_at_10 value: 2.078 - type: recall_at_100 value: 13.297 - type: recall_at_1000 value: 44.979 - type: recall_at_3 value: 0.6689999999999999 - type: recall_at_5 value: 1.106 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.258 - type: map_at_10 value: 10.439 - type: map_at_100 value: 16.89 - type: map_at_1000 value: 18.407999999999998 - type: map_at_3 value: 5.668 - type: map_at_5 value: 7.718 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 51.159 - type: mrr_at_100 value: 51.714000000000006 - type: mrr_at_1000 value: 51.714000000000006 - type: mrr_at_3 value: 47.959 - type: mrr_at_5 value: 50.407999999999994 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 26.037 - type: ndcg_at_100 value: 37.924 - type: ndcg_at_1000 value: 49.126999999999995 - type: ndcg_at_3 value: 30.631999999999998 - type: ndcg_at_5 value: 28.571 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 22.857 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.529 - type: precision_at_3 value: 34.014 - type: precision_at_5 value: 29.796 - type: recall_at_1 value: 2.258 - type: recall_at_10 value: 16.554 - type: recall_at_100 value: 48.439 - type: recall_at_1000 value: 82.80499999999999 - type: recall_at_3 value: 7.283 - type: recall_at_5 value: 10.732 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.8858 - type: ap value: 13.835684144362109 - type: f1 value: 53.803351693244586 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.50650820599886 - type: f1 value: 60.84357825979259 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 48.52131044852134 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.59337187816654 - type: cos_sim_ap value: 73.23925826533437 - type: cos_sim_f1 value: 67.34693877551021 - type: cos_sim_precision value: 62.40432237730752 - type: cos_sim_recall value: 73.13984168865434 - type: dot_accuracy value: 85.31322644096085 - type: dot_ap value: 72.30723963807422 - type: dot_f1 value: 66.47051612112296 - type: dot_precision value: 62.0792305930845 - type: dot_recall value: 71.53034300791556 - type: euclidean_accuracy value: 85.61125350181797 - type: euclidean_ap value: 73.32843720487845 - type: euclidean_f1 value: 67.36549633745895 - type: euclidean_precision value: 64.60755813953489 - type: euclidean_recall value: 70.36939313984169 - type: manhattan_accuracy value: 85.63509566668654 - type: manhattan_ap value: 73.16658488311325 - type: manhattan_f1 value: 67.20597386434349 - type: manhattan_precision value: 63.60424028268551 - type: manhattan_recall value: 71.2401055408971 - type: max_accuracy value: 85.63509566668654 - type: max_ap value: 73.32843720487845 - type: max_f1 value: 67.36549633745895 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.33779640625606 - type: cos_sim_ap value: 84.83868375898157 - type: cos_sim_f1 value: 77.16506154017773 - type: cos_sim_precision value: 74.62064005753327 - type: cos_sim_recall value: 79.88912842623961 - type: dot_accuracy value: 88.02732176815307 - type: dot_ap value: 83.95089283763002 - type: dot_f1 value: 76.29635101196631 - type: dot_precision value: 73.31771720613288 - type: dot_recall value: 79.52725592854944 - type: euclidean_accuracy value: 88.44452206310397 - type: euclidean_ap value: 84.98384576824827 - type: euclidean_f1 value: 77.29311047696697 - type: euclidean_precision value: 74.51232583065381 - type: euclidean_recall value: 80.28949799815214 - type: manhattan_accuracy value: 88.47362906042613 - type: manhattan_ap value: 84.91421462218432 - type: manhattan_f1 value: 77.05107637204792 - type: manhattan_precision value: 74.74484256243214 - type: manhattan_recall value: 79.50415768401602 - type: max_accuracy value: 88.47362906042613 - type: max_ap value: 84.98384576824827 - type: max_f1 value: 77.29311047696697 license: mit language: - en --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` #### Usage of the ONNX files ```python from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5') model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5') model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx") # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') model_output_ort = model_ort(**encoded_input) # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # model_output and model_output_ort are identical ``` #### Usage via infinity Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", device="cpu", engine="optimum" # or engine="torch" )) async def main(): async with engine: embeddings, usage = await engine.embed(sentences=sentences) asyncio.run(main()) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

papluca/xlm-roberta-base-language-detection

papluca

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 tags: - generated_from_trainer datasets: papluca/language-identification metrics: - accuracy - f1 base_model: xlm-roberta-base model-index: - name: xlm-roberta-base-language-detection results: [] --- # xlm-roberta-base-language-detection 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. ## Model description 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)` ## Training and evaluation data The model was fine-tuned on the [Language Identification](https://huggingface.co/datasets/papluca/language-identification#additional-information) dataset, which consists of text sequences in 20 languages. The training set contains 70k samples, while the validation and test sets 10k each. The average accuracy on the test set is **99.6%** (this matches the average macro/weighted F1-score being the test set perfectly balanced). A more detailed evaluation is provided by the following table. | Language | Precision | Recall | F1-score | support | |:--------:|:---------:|:------:|:--------:|:-------:| |ar |0.998 |0.996 |0.997 |500 | |bg |0.998 |0.964 |0.981 |500 | |de |0.998 |0.996 |0.997 |500 | |el |0.996 |1.000 |0.998 |500 | |en |1.000 |1.000 |1.000 |500 | |es |0.967 |1.000 |0.983 |500 | |fr |1.000 |1.000 |1.000 |500 | |hi |0.994 |0.992 |0.993 |500 | |it |1.000 |0.992 |0.996 |500 | |ja |0.996 |0.996 |0.996 |500 | |nl |1.000 |1.000 |1.000 |500 | |pl |1.000 |1.000 |1.000 |500 | |pt |0.988 |1.000 |0.994 |500 | |ru |1.000 |0.994 |0.997 |500 | |sw |1.000 |1.000 |1.000 |500 | |th |1.000 |0.998 |0.999 |500 | |tr |0.994 |0.992 |0.993 |500 | |ur |1.000 |1.000 |1.000 |500 | |vi |0.992 |1.000 |0.996 |500 | |zh |1.000 |1.000 |1.000 |500 | ### Benchmarks As a baseline to compare `xlm-roberta-base-language-detection` against, we have used the Python [langid](https://github.com/saffsd/langid.py) library. Since it comes pre-trained on 97 languages, we have used its `.set_languages()` method to constrain the language set to our 20 languages. The average accuracy of langid on the test set is **98.5%**. More details are provided by the table below. | Language | Precision | Recall | F1-score | support | |:--------:|:---------:|:------:|:--------:|:-------:| |ar |0.990 |0.970 |0.980 |500 | |bg |0.998 |0.964 |0.981 |500 | |de |0.992 |0.944 |0.967 |500 | |el |1.000 |0.998 |0.999 |500 | |en |1.000 |1.000 |1.000 |500 | |es |1.000 |0.968 |0.984 |500 | |fr |0.996 |1.000 |0.998 |500 | |hi |0.949 |0.976 |0.963 |500 | |it |0.990 |0.980 |0.985 |500 | |ja |0.927 |0.988 |0.956 |500 | |nl |0.980 |1.000 |0.990 |500 | |pl |0.986 |0.996 |0.991 |500 | |pt |0.950 |0.996 |0.973 |500 | |ru |0.996 |0.974 |0.985 |500 | |sw |1.000 |1.000 |1.000 |500 | |th |1.000 |0.996 |0.998 |500 | |tr |0.990 |0.968 |0.979 |500 | |ur |0.998 |0.996 |0.997 |500 | |vi |0.971 |0.990 |0.980 |500 | |zh |1.000 |1.000 |1.000 |500 | ## How to get started with the model The easiest way to use the model is via the high-level `pipeline` API: ```python from transformers import pipeline text = [ "Brevity is the soul of wit.", "Amor, ch'a nullo amato amar perdona." ] model_ckpt = "papluca/xlm-roberta-base-language-detection" pipe = pipeline("text-classification", model=model_ckpt) pipe(text, top_k=1, truncation=True) ``` Or one can proceed with the tokenizer and model separately: ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer text = [ "Brevity is the soul of wit.", "Amor, ch'a nullo amato amar perdona." ] model_ckpt = "papluca/xlm-roberta-base-language-detection" tokenizer = AutoTokenizer.from_pretrained(model_ckpt) model = AutoModelForSequenceClassification.from_pretrained(model_ckpt) inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits preds = torch.softmax(logits, dim=-1) # Map raw predictions to languages id2lang = model.config.id2label vals, idxs = torch.max(preds, dim=1) {id2lang[k.item()]: v.item() for k, v in zip(idxs, vals)} ``` ## Training procedure Fine-tuning was done via the `Trainer` API. Here is the [Colab notebook](https://colab.research.google.com/drive/15LJTckS6gU3RQOmjLqxVNBmbsBdnUEvl?usp=sharing) with the training code. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results The validation results on the `valid` split of the Language Identification dataset are summarised here below. | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2492 | 1.0 | 1094 | 0.0149 | 0.9969 | 0.9969 | | 0.0101 | 2.0 | 2188 | 0.0103 | 0.9977 | 0.9977 | In short, it achieves the following results on the validation set: - Loss: 0.0101 - Accuracy: 0.9977 - F1: 0.9977 ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

google/flan-t5-base

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 base <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-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base") 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-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", 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-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", 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-base") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", 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-Base, 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} } ``` ## Model Recycling [Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=9.16&mnli_lp=nan&20_newsgroup=3.34&ag_news=1.49&amazon_reviews_multi=0.21&anli=13.91&boolq=16.75&cb=23.12&cola=9.97&copa=34.50&dbpedia=6.90&esnli=5.37&financial_phrasebank=18.66&imdb=0.33&isear=1.37&mnli=11.74&mrpc=16.63&multirc=6.24&poem_sentiment=14.62&qnli=3.41&qqp=6.18&rotten_tomatoes=2.98&rte=24.26&sst2=0.67&sst_5bins=5.44&stsb=20.68&trec_coarse=3.95&trec_fine=10.73&tweet_ev_emoji=13.39&tweet_ev_emotion=4.62&tweet_ev_hate=3.46&tweet_ev_irony=9.04&tweet_ev_offensive=1.69&tweet_ev_sentiment=0.75&wic=14.22&wnli=9.44&wsc=5.53&yahoo_answers=4.14&model_name=google%2Fflan-t5-base&base_name=google%2Ft5-v1_1-base) using google/flan-t5-base as a base model yields average score of 77.98 in comparison to 68.82 by google/t5-v1_1-base. The model is ranked 1st among all tested models for the google/t5-v1_1-base architecture as of 06/02/2023 Results: | 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers | |---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|-------:|--------:|----------------:| | 86.2188 | 89.6667 | 67.12 | 51.9688 | 82.3242 | 78.5714 | 80.1534 | 75 | 77.6667 | 90.9507 | 85.4 | 93.324 | 72.425 | 87.2457 | 89.4608 | 62.3762 | 82.6923 | 92.7878 | 89.7724 | 89.0244 | 84.8375 | 94.3807 | 57.2851 | 89.4759 | 97.2 | 92.8 | 46.848 | 80.2252 | 54.9832 | 76.6582 | 84.3023 | 70.6366 | 70.0627 | 56.338 | 53.8462 | 73.4 | For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)

dslim/bert-base-NER

dslim

transformers

token-classification

--- language: en datasets: - conll2003 license: mit model-index: - name: dslim/bert-base-NER results: - task: type: token-classification name: Token Classification dataset: name: conll2003 type: conll2003 config: conll2003 split: test metrics: - name: Accuracy type: accuracy value: 0.9118041001560013 verified: true - name: Precision type: precision value: 0.9211550382257732 verified: true - name: Recall type: recall value: 0.9306415698281261 verified: true - name: F1 type: f1 value: 0.9258740048459675 verified: true - name: loss type: loss value: 0.48325642943382263 verified: true --- # bert-base-NER ## Model description **bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a *bert-base-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a [**bert-large-NER**](https://huggingface.co/dslim/bert-large-NER/) version is also available. ### Available NER models | Model Name | Description | Parameters | |-------------------|-------------|------------------| | [distilbert-NER](https://huggingface.co/dslim/distilbert-NER) **(NEW!)** | Fine-tuned DistilBERT - a smaller, faster, lighter version of BERT | 66M | | [bert-large-NER](https://huggingface.co/dslim/bert-large-NER/) | Fine-tuned bert-large-cased - larger model with slightly better performance | 340M | | [bert-base-NER](https://huggingface.co/dslim/bert-base-NER)-([uncased](https://huggingface.co/dslim/bert-base-NER-uncased)) | Fine-tuned bert-base, available in both cased and uncased versions | 110M | ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "My name is Wolfgang and I live in Berlin" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. ## Training data This model was fine-tuned on English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-MISC |Beginning of a miscellaneous entity right after another miscellaneous entity I-MISC | Miscellaneous entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organization right after another organization I-ORG |organization B-LOC |Beginning of a location right after another location I-LOC |Location ### CoNLL-2003 English Dataset Statistics This dataset was derived from the Reuters corpus which consists of Reuters news stories. You can read more about how this dataset was created in the CoNLL-2003 paper. #### # of training examples per entity type Dataset|LOC|MISC|ORG|PER -|-|-|-|- Train|7140|3438|6321|6600 Dev|1837|922|1341|1842 Test|1668|702|1661|1617 #### # of articles/sentences/tokens per dataset Dataset |Articles |Sentences |Tokens -|-|-|- Train |946 |14,987 |203,621 Dev |216 |3,466 |51,362 Test |231 |3,684 |46,435 ## Training procedure This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original BERT paper](https://arxiv.org/pdf/1810.04805) which trained & evaluated the model on CoNLL-2003 NER task. ## Eval results metric|dev|test -|-|- f1 |95.1 |91.3 precision |95.0 |90.7 recall |95.3 |91.9 The test metrics are a little lower than the official Google BERT results which encoded document context & experimented with CRF. More on replicating the original results [here](https://github.com/google-research/bert/issues/223). ### BibTeX entry and citation info ``` @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} } ``` ``` @inproceedings{tjong-kim-sang-de-meulder-2003-introduction, title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition", author = "Tjong Kim Sang, Erik F. and De Meulder, Fien", booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", year = "2003", url = "https://www.aclweb.org/anthology/W03-0419", pages = "142--147", } ```

google-bert/bert-large-uncased

google-bert

transformers

fill-mask

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT large model (uncased) 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. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.1886913776397705, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a professional model. [SEP]", 'score': 0.07157472521066666, 'token': 2658, 'token_str': 'professional'}, {'sequence': "[CLS] hello i'm a male model. [SEP]", 'score': 0.04053466394543648, 'token': 3287, 'token_str': 'male'}, {'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.03891477733850479, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a fitness model. [SEP]", 'score': 0.03038121573626995, 'token': 10516, 'token_str': 'fitness'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-large-uncased') model = BertModel.from_pretrained("bert-large-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-large-uncased') model = TFBertModel.from_pretrained("bert-large-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-uncased') >>> unmasker("The man worked as a [MASK].") [{'sequence': '[CLS] the man worked as a bartender. [SEP]', 'score': 0.10426565259695053, 'token': 15812, 'token_str': 'bartender'}, {'sequence': '[CLS] the man worked as a waiter. [SEP]', 'score': 0.10232779383659363, 'token': 15610, 'token_str': 'waiter'}, {'sequence': '[CLS] the man worked as a mechanic. [SEP]', 'score': 0.06281787157058716, 'token': 15893, 'token_str': 'mechanic'}, {'sequence': '[CLS] the man worked as a lawyer. [SEP]', 'score': 0.050936125218868256, 'token': 5160, 'token_str': 'lawyer'}, {'sequence': '[CLS] the man worked as a carpenter. [SEP]', 'score': 0.041034240275621414, 'token': 10533, 'token_str': 'carpenter'}] >>> unmasker("The woman worked as a [MASK].") [{'sequence': '[CLS] the woman worked as a waitress. [SEP]', 'score': 0.28473711013793945, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the woman worked as a nurse. [SEP]', 'score': 0.11336520314216614, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the woman worked as a bartender. [SEP]', 'score': 0.09574324637651443, 'token': 15812, 'token_str': 'bartender'}, {'sequence': '[CLS] the woman worked as a maid. [SEP]', 'score': 0.06351090222597122, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the woman worked as a secretary. [SEP]', 'score': 0.048970773816108704, 'token': 3187, 'token_str': 'secretary'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Model | SQUAD 1.1 F1/EM | Multi NLI Accuracy ---------------------------------------- | :-------------: | :----------------: BERT-Large, Uncased (Original) | 91.0/84.3 | 86.05 ### 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} } ```

CompVis/stable-diffusion-v1-4

CompVis

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image widget: - text: "A high tech solarpunk utopia in the Amazon rainforest" example_title: Amazon rainforest - text: "A pikachu fine dining with a view to the Eiffel Tower" example_title: Pikachu in Paris - text: "A mecha robot in a favela in expressionist style" example_title: Expressionist robot - text: "an insect robot preparing a delicious meal" example_title: Insect robot - text: "A small cabin on top of a snowy mountain in the style of Disney, artstation" example_title: Snowy disney cabin extra_gated_prompt: |- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1-4 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). This weights here are intended to be used with the 🧨 Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples We recommend using [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion. ### PyTorch ```bash pip install --upgrade diffusers transformers scipy ``` Running the pipeline with the default PNDM scheduler: ```python import torch from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-4" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` **Note**: If you are limited by GPU memory and have less than 4GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to expect the weights to be in float16 precision: ```py import torch pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to(device) pipe.enable_attention_slicing() prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` To swap out the noise scheduler, pass it to `from_pretrained`: ```python from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler model_id = "CompVis/stable-diffusion-v1-4" # Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### JAX/Flax To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax. Running the pipeline with default PNDMScheduler ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="flax", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, num_samples) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` **Note**: If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch. ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, num_samples) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-4 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide four checkpoints, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

Babelscape/wikineural-multilingual-ner

Babelscape

transformers

token-classification

--- annotations_creators: - machine-generated language_creators: - machine-generated widget: - text: My name is Wolfgang and I live in Berlin. - text: George Washington went to Washington. - text: Mi nombre es Sarah y vivo en Londres. - text: Меня зовут Симона, и я живу в Риме. tags: - named-entity-recognition - sequence-tagger-model datasets: - Babelscape/wikineural language: - de - en - es - fr - it - nl - pl - pt - ru - multilingual license: - cc-by-nc-sa-4.0 pretty_name: wikineural-dataset source_datasets: - original task_categories: - structure-prediction task_ids: - named-entity-recognition --- # WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER This is the model card for the EMNLP 2021 paper [WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER](https://aclanthology.org/2021.findings-emnlp.215/). We fine-tuned a multilingual language model (mBERT) for 3 epochs on our [WikiNEuRal dataset](https://huggingface.co/datasets/Babelscape/wikineural) for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. **If you use the model, please reference this work in your paper**: ```bibtex @inproceedings{tedeschi-etal-2021-wikineural-combined, title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}", author = "Tedeschi, Simone and Maiorca, Valentino and Campolungo, Niccol{\`o} and Cecconi, Francesco and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.215", pages = "2521--2533", abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.", } ``` The original repository for the paper can be found at [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural). ## How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Babelscape/wikineural-multilingual-ner") model = AutoModelForTokenClassification.from_pretrained("Babelscape/wikineural-multilingual-ner") nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True) example = "My name is Wolfgang and I live in Berlin" ner_results = nlp(example) print(ner_results) ``` ## Limitations and bias This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL). ## Licensing Information Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents and models belongs to the original copyright holders.

amazon/chronos-t5-small

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 (Small) 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-small", 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.

BAAI/bge-large-en-v1.5

BAAI

sentence-transformers

feature-extraction

--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-large-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.8507462686567 - type: ap value: 38.566457320228245 - type: f1 value: 69.69386648043475 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.416675 - type: ap value: 89.1928861155922 - type: f1 value: 92.39477019574215 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.175999999999995 - type: f1 value: 47.80712792870253 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 40.184999999999995 - type: map_at_10 value: 55.654 - type: map_at_100 value: 56.25 - type: map_at_1000 value: 56.255 - type: map_at_3 value: 51.742999999999995 - type: map_at_5 value: 54.129000000000005 - type: mrr_at_1 value: 40.967 - type: mrr_at_10 value: 55.96 - type: mrr_at_100 value: 56.54900000000001 - type: mrr_at_1000 value: 56.554 - type: mrr_at_3 value: 51.980000000000004 - type: mrr_at_5 value: 54.44 - type: ndcg_at_1 value: 40.184999999999995 - type: ndcg_at_10 value: 63.542 - type: ndcg_at_100 value: 65.96499999999999 - type: ndcg_at_1000 value: 66.08699999999999 - type: ndcg_at_3 value: 55.582 - type: ndcg_at_5 value: 59.855000000000004 - type: precision_at_1 value: 40.184999999999995 - type: precision_at_10 value: 8.841000000000001 - type: precision_at_100 value: 0.987 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.238 - type: precision_at_5 value: 15.405 - type: recall_at_1 value: 40.184999999999995 - type: recall_at_10 value: 88.407 - type: recall_at_100 value: 98.72 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 66.714 - type: recall_at_5 value: 77.027 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.567077926750066 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.19453389182364 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.46555939623092 - type: mrr value: 77.82361605768807 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.9554128814735 - type: cos_sim_spearman value: 84.65373612172036 - type: euclidean_pearson value: 83.2905059954138 - type: euclidean_spearman value: 84.52240782811128 - type: manhattan_pearson value: 82.99533802997436 - type: manhattan_spearman value: 84.20673798475734 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.78896103896103 - type: f1 value: 87.77189310964883 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.714538337650495 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.90108349284447 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.795 - type: map_at_10 value: 43.669000000000004 - type: map_at_100 value: 45.151 - type: map_at_1000 value: 45.278 - type: map_at_3 value: 40.006 - type: map_at_5 value: 42.059999999999995 - type: mrr_at_1 value: 39.771 - type: mrr_at_10 value: 49.826 - type: mrr_at_100 value: 50.504000000000005 - type: mrr_at_1000 value: 50.549 - type: mrr_at_3 value: 47.115 - type: mrr_at_5 value: 48.832 - type: ndcg_at_1 value: 39.771 - type: ndcg_at_10 value: 50.217999999999996 - type: ndcg_at_100 value: 55.454 - type: ndcg_at_1000 value: 57.37 - type: ndcg_at_3 value: 44.885000000000005 - type: ndcg_at_5 value: 47.419 - type: precision_at_1 value: 39.771 - type: precision_at_10 value: 9.642000000000001 - type: precision_at_100 value: 1.538 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 21.268 - type: precision_at_5 value: 15.536 - type: recall_at_1 value: 32.795 - type: recall_at_10 value: 62.580999999999996 - type: recall_at_100 value: 84.438 - type: recall_at_1000 value: 96.492 - type: recall_at_3 value: 47.071000000000005 - type: recall_at_5 value: 54.079 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.671 - type: map_at_10 value: 43.334 - type: map_at_100 value: 44.566 - type: map_at_1000 value: 44.702999999999996 - type: map_at_3 value: 40.343 - type: map_at_5 value: 41.983 - type: mrr_at_1 value: 40.764 - type: mrr_at_10 value: 49.382 - type: mrr_at_100 value: 49.988 - type: mrr_at_1000 value: 50.03300000000001 - type: mrr_at_3 value: 47.293 - type: mrr_at_5 value: 48.51 - type: ndcg_at_1 value: 40.764 - type: ndcg_at_10 value: 49.039 - type: ndcg_at_100 value: 53.259 - type: ndcg_at_1000 value: 55.253 - type: ndcg_at_3 value: 45.091 - type: ndcg_at_5 value: 46.839999999999996 - type: precision_at_1 value: 40.764 - type: precision_at_10 value: 9.191 - type: precision_at_100 value: 1.476 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 21.72 - type: precision_at_5 value: 15.299 - type: recall_at_1 value: 32.671 - type: recall_at_10 value: 58.816 - type: recall_at_100 value: 76.654 - type: recall_at_1000 value: 89.05999999999999 - type: recall_at_3 value: 46.743 - type: recall_at_5 value: 51.783 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.328 - type: map_at_10 value: 53.32599999999999 - type: map_at_100 value: 54.37499999999999 - type: map_at_1000 value: 54.429 - type: map_at_3 value: 49.902 - type: map_at_5 value: 52.002 - type: mrr_at_1 value: 46.332 - type: mrr_at_10 value: 56.858 - type: mrr_at_100 value: 57.522 - type: mrr_at_1000 value: 57.54899999999999 - type: mrr_at_3 value: 54.472 - type: mrr_at_5 value: 55.996 - type: ndcg_at_1 value: 46.332 - type: ndcg_at_10 value: 59.313 - type: ndcg_at_100 value: 63.266999999999996 - type: ndcg_at_1000 value: 64.36 - type: ndcg_at_3 value: 53.815000000000005 - type: ndcg_at_5 value: 56.814 - type: precision_at_1 value: 46.332 - type: precision_at_10 value: 9.53 - type: precision_at_100 value: 1.238 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 24.054000000000002 - type: precision_at_5 value: 16.589000000000002 - type: recall_at_1 value: 40.328 - type: recall_at_10 value: 73.421 - type: recall_at_100 value: 90.059 - type: recall_at_1000 value: 97.81 - type: recall_at_3 value: 59.009 - type: recall_at_5 value: 66.352 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.424 - type: map_at_10 value: 36.332 - type: map_at_100 value: 37.347 - type: map_at_1000 value: 37.422 - type: map_at_3 value: 33.743 - type: map_at_5 value: 35.176 - type: mrr_at_1 value: 29.153000000000002 - type: mrr_at_10 value: 38.233 - type: mrr_at_100 value: 39.109 - type: mrr_at_1000 value: 39.164 - type: mrr_at_3 value: 35.876000000000005 - type: mrr_at_5 value: 37.169000000000004 - type: ndcg_at_1 value: 29.153000000000002 - type: ndcg_at_10 value: 41.439 - type: ndcg_at_100 value: 46.42 - type: ndcg_at_1000 value: 48.242000000000004 - type: ndcg_at_3 value: 36.362 - type: ndcg_at_5 value: 38.743 - type: precision_at_1 value: 29.153000000000002 - type: precision_at_10 value: 6.315999999999999 - type: precision_at_100 value: 0.927 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 15.443000000000001 - type: precision_at_5 value: 10.644 - type: recall_at_1 value: 27.424 - type: recall_at_10 value: 55.364000000000004 - type: recall_at_100 value: 78.211 - type: recall_at_1000 value: 91.74600000000001 - type: recall_at_3 value: 41.379 - type: recall_at_5 value: 47.14 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.601 - type: map_at_10 value: 27.826 - type: map_at_100 value: 29.017 - type: map_at_1000 value: 29.137 - type: map_at_3 value: 25.125999999999998 - type: map_at_5 value: 26.765 - type: mrr_at_1 value: 24.005000000000003 - type: mrr_at_10 value: 32.716 - type: mrr_at_100 value: 33.631 - type: mrr_at_1000 value: 33.694 - type: mrr_at_3 value: 29.934 - type: mrr_at_5 value: 31.630999999999997 - type: ndcg_at_1 value: 24.005000000000003 - type: ndcg_at_10 value: 33.158 - type: ndcg_at_100 value: 38.739000000000004 - type: ndcg_at_1000 value: 41.495 - type: ndcg_at_3 value: 28.185 - type: ndcg_at_5 value: 30.796 - type: precision_at_1 value: 24.005000000000003 - type: precision_at_10 value: 5.908 - type: precision_at_100 value: 1.005 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 13.391 - type: precision_at_5 value: 9.876 - type: recall_at_1 value: 19.601 - type: recall_at_10 value: 44.746 - type: recall_at_100 value: 68.82300000000001 - type: recall_at_1000 value: 88.215 - type: recall_at_3 value: 31.239 - type: recall_at_5 value: 37.695 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.130000000000003 - type: map_at_10 value: 40.96 - type: map_at_100 value: 42.282 - type: map_at_1000 value: 42.392 - type: map_at_3 value: 37.889 - type: map_at_5 value: 39.661 - type: mrr_at_1 value: 36.958999999999996 - type: mrr_at_10 value: 46.835 - type: mrr_at_100 value: 47.644 - type: mrr_at_1000 value: 47.688 - type: mrr_at_3 value: 44.562000000000005 - type: mrr_at_5 value: 45.938 - type: ndcg_at_1 value: 36.958999999999996 - type: ndcg_at_10 value: 47.06 - type: ndcg_at_100 value: 52.345 - type: ndcg_at_1000 value: 54.35 - type: ndcg_at_3 value: 42.301 - type: ndcg_at_5 value: 44.635999999999996 - type: precision_at_1 value: 36.958999999999996 - type: precision_at_10 value: 8.479000000000001 - type: precision_at_100 value: 1.284 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 20.244 - type: precision_at_5 value: 14.224999999999998 - type: recall_at_1 value: 30.130000000000003 - type: recall_at_10 value: 59.27 - type: recall_at_100 value: 81.195 - type: recall_at_1000 value: 94.21199999999999 - type: recall_at_3 value: 45.885 - type: recall_at_5 value: 52.016 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.169999999999998 - type: map_at_10 value: 36.451 - type: map_at_100 value: 37.791000000000004 - type: map_at_1000 value: 37.897 - type: map_at_3 value: 33.109 - type: map_at_5 value: 34.937000000000005 - type: mrr_at_1 value: 32.877 - type: mrr_at_10 value: 42.368 - type: mrr_at_100 value: 43.201 - type: mrr_at_1000 value: 43.259 - type: mrr_at_3 value: 39.763999999999996 - type: mrr_at_5 value: 41.260000000000005 - type: ndcg_at_1 value: 32.877 - type: ndcg_at_10 value: 42.659000000000006 - type: ndcg_at_100 value: 48.161 - type: ndcg_at_1000 value: 50.345 - type: ndcg_at_3 value: 37.302 - type: ndcg_at_5 value: 39.722 - type: precision_at_1 value: 32.877 - type: precision_at_10 value: 7.9 - type: precision_at_100 value: 1.236 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.846 - type: precision_at_5 value: 12.9 - type: recall_at_1 value: 26.169999999999998 - type: recall_at_10 value: 55.35 - type: recall_at_100 value: 78.755 - type: recall_at_1000 value: 93.518 - type: recall_at_3 value: 40.176 - type: recall_at_5 value: 46.589000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.15516666666667 - type: map_at_10 value: 36.65741666666667 - type: map_at_100 value: 37.84991666666666 - type: map_at_1000 value: 37.96316666666667 - type: map_at_3 value: 33.74974999999999 - type: map_at_5 value: 35.3765 - type: mrr_at_1 value: 32.08233333333334 - type: mrr_at_10 value: 41.033833333333334 - type: mrr_at_100 value: 41.84524999999999 - type: mrr_at_1000 value: 41.89983333333333 - type: mrr_at_3 value: 38.62008333333333 - type: mrr_at_5 value: 40.03441666666666 - type: ndcg_at_1 value: 32.08233333333334 - type: ndcg_at_10 value: 42.229 - type: ndcg_at_100 value: 47.26716666666667 - type: ndcg_at_1000 value: 49.43466666666667 - type: ndcg_at_3 value: 37.36408333333333 - type: ndcg_at_5 value: 39.6715 - type: precision_at_1 value: 32.08233333333334 - type: precision_at_10 value: 7.382583333333334 - type: precision_at_100 value: 1.16625 - type: precision_at_1000 value: 0.15408333333333332 - type: precision_at_3 value: 17.218 - type: precision_at_5 value: 12.21875 - type: recall_at_1 value: 27.15516666666667 - type: recall_at_10 value: 54.36683333333333 - type: recall_at_100 value: 76.37183333333333 - type: recall_at_1000 value: 91.26183333333333 - type: recall_at_3 value: 40.769916666666674 - type: recall_at_5 value: 46.702333333333335 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.749 - type: map_at_10 value: 33.001999999999995 - type: map_at_100 value: 33.891 - type: map_at_1000 value: 33.993 - type: map_at_3 value: 30.703999999999997 - type: map_at_5 value: 31.959 - type: mrr_at_1 value: 28.834 - type: mrr_at_10 value: 35.955 - type: mrr_at_100 value: 36.709 - type: mrr_at_1000 value: 36.779 - type: mrr_at_3 value: 33.947 - type: mrr_at_5 value: 35.089 - type: ndcg_at_1 value: 28.834 - type: ndcg_at_10 value: 37.329 - type: ndcg_at_100 value: 41.79 - type: ndcg_at_1000 value: 44.169000000000004 - type: ndcg_at_3 value: 33.184999999999995 - type: ndcg_at_5 value: 35.107 - type: precision_at_1 value: 28.834 - type: precision_at_10 value: 5.7669999999999995 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.213000000000001 - type: precision_at_5 value: 9.754999999999999 - type: recall_at_1 value: 25.749 - type: recall_at_10 value: 47.791 - type: recall_at_100 value: 68.255 - type: recall_at_1000 value: 85.749 - type: recall_at_3 value: 36.199 - type: recall_at_5 value: 41.071999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.777 - type: map_at_10 value: 25.201 - type: map_at_100 value: 26.423999999999996 - type: map_at_1000 value: 26.544 - type: map_at_3 value: 22.869 - type: map_at_5 value: 24.023 - type: mrr_at_1 value: 21.473 - type: mrr_at_10 value: 29.12 - type: mrr_at_100 value: 30.144 - type: mrr_at_1000 value: 30.215999999999998 - type: mrr_at_3 value: 26.933 - type: mrr_at_5 value: 28.051 - type: ndcg_at_1 value: 21.473 - type: ndcg_at_10 value: 30.003 - type: ndcg_at_100 value: 35.766 - type: ndcg_at_1000 value: 38.501000000000005 - type: ndcg_at_3 value: 25.773000000000003 - type: ndcg_at_5 value: 27.462999999999997 - type: precision_at_1 value: 21.473 - type: precision_at_10 value: 5.482 - type: precision_at_100 value: 0.975 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.205 - type: precision_at_5 value: 8.692 - type: recall_at_1 value: 17.777 - type: recall_at_10 value: 40.582 - type: recall_at_100 value: 66.305 - type: recall_at_1000 value: 85.636 - type: recall_at_3 value: 28.687 - type: recall_at_5 value: 33.089 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.677 - type: map_at_10 value: 36.309000000000005 - type: map_at_100 value: 37.403999999999996 - type: map_at_1000 value: 37.496 - type: map_at_3 value: 33.382 - type: map_at_5 value: 34.98 - type: mrr_at_1 value: 31.343 - type: mrr_at_10 value: 40.549 - type: mrr_at_100 value: 41.342 - type: mrr_at_1000 value: 41.397 - type: mrr_at_3 value: 38.029 - type: mrr_at_5 value: 39.451 - type: ndcg_at_1 value: 31.343 - type: ndcg_at_10 value: 42.1 - type: ndcg_at_100 value: 47.089999999999996 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 36.836999999999996 - type: ndcg_at_5 value: 39.21 - type: precision_at_1 value: 31.343 - type: precision_at_10 value: 7.164 - type: precision_at_100 value: 1.0959999999999999 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 16.915 - type: precision_at_5 value: 11.940000000000001 - type: recall_at_1 value: 26.677 - type: recall_at_10 value: 55.54599999999999 - type: recall_at_100 value: 77.094 - type: recall_at_1000 value: 92.01 - type: recall_at_3 value: 41.191 - type: recall_at_5 value: 47.006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.501 - type: map_at_10 value: 33.102 - type: map_at_100 value: 34.676 - type: map_at_1000 value: 34.888000000000005 - type: map_at_3 value: 29.944 - type: map_at_5 value: 31.613999999999997 - type: mrr_at_1 value: 29.447000000000003 - type: mrr_at_10 value: 37.996 - type: mrr_at_100 value: 38.946 - type: mrr_at_1000 value: 38.995000000000005 - type: mrr_at_3 value: 35.079 - type: mrr_at_5 value: 36.69 - type: ndcg_at_1 value: 29.447000000000003 - type: ndcg_at_10 value: 39.232 - type: ndcg_at_100 value: 45.247 - type: ndcg_at_1000 value: 47.613 - type: ndcg_at_3 value: 33.922999999999995 - type: ndcg_at_5 value: 36.284 - type: precision_at_1 value: 29.447000000000003 - type: precision_at_10 value: 7.648000000000001 - type: precision_at_100 value: 1.516 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 16.008 - type: precision_at_5 value: 11.779 - type: recall_at_1 value: 24.501 - type: recall_at_10 value: 51.18899999999999 - type: recall_at_100 value: 78.437 - type: recall_at_1000 value: 92.842 - type: recall_at_3 value: 35.808 - type: recall_at_5 value: 42.197 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.039 - type: map_at_10 value: 30.377 - type: map_at_100 value: 31.275 - type: map_at_1000 value: 31.379 - type: map_at_3 value: 27.98 - type: map_at_5 value: 29.358 - type: mrr_at_1 value: 24.03 - type: mrr_at_10 value: 32.568000000000005 - type: mrr_at_100 value: 33.403 - type: mrr_at_1000 value: 33.475 - type: mrr_at_3 value: 30.436999999999998 - type: mrr_at_5 value: 31.796000000000003 - type: ndcg_at_1 value: 24.03 - type: ndcg_at_10 value: 35.198 - type: ndcg_at_100 value: 39.668 - type: ndcg_at_1000 value: 42.296 - type: ndcg_at_3 value: 30.709999999999997 - type: ndcg_at_5 value: 33.024 - type: precision_at_1 value: 24.03 - type: precision_at_10 value: 5.564 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 13.309000000000001 - type: precision_at_5 value: 9.39 - type: recall_at_1 value: 22.039 - type: recall_at_10 value: 47.746 - type: recall_at_100 value: 68.23599999999999 - type: recall_at_1000 value: 87.852 - type: recall_at_3 value: 35.852000000000004 - type: recall_at_5 value: 41.410000000000004 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 15.692999999999998 - type: map_at_10 value: 26.903 - type: map_at_100 value: 28.987000000000002 - type: map_at_1000 value: 29.176999999999996 - type: map_at_3 value: 22.137 - type: map_at_5 value: 24.758 - type: mrr_at_1 value: 35.57 - type: mrr_at_10 value: 47.821999999999996 - type: mrr_at_100 value: 48.608000000000004 - type: mrr_at_1000 value: 48.638999999999996 - type: mrr_at_3 value: 44.452000000000005 - type: mrr_at_5 value: 46.546 - type: ndcg_at_1 value: 35.57 - type: ndcg_at_10 value: 36.567 - type: ndcg_at_100 value: 44.085 - type: ndcg_at_1000 value: 47.24 - type: ndcg_at_3 value: 29.964000000000002 - type: ndcg_at_5 value: 32.511 - type: precision_at_1 value: 35.57 - type: precision_at_10 value: 11.485 - type: precision_at_100 value: 1.9619999999999997 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 22.237000000000002 - type: precision_at_5 value: 17.471999999999998 - type: recall_at_1 value: 15.692999999999998 - type: recall_at_10 value: 43.056 - type: recall_at_100 value: 68.628 - type: recall_at_1000 value: 86.075 - type: recall_at_3 value: 26.918999999999997 - type: recall_at_5 value: 34.14 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.53 - type: map_at_10 value: 20.951 - type: map_at_100 value: 30.136000000000003 - type: map_at_1000 value: 31.801000000000002 - type: map_at_3 value: 15.021 - type: map_at_5 value: 17.471999999999998 - type: mrr_at_1 value: 71.0 - type: mrr_at_10 value: 79.176 - type: mrr_at_100 value: 79.418 - type: mrr_at_1000 value: 79.426 - type: mrr_at_3 value: 78.125 - type: mrr_at_5 value: 78.61200000000001 - type: ndcg_at_1 value: 58.5 - type: ndcg_at_10 value: 44.106 - type: ndcg_at_100 value: 49.268 - type: ndcg_at_1000 value: 56.711999999999996 - type: ndcg_at_3 value: 48.934 - type: ndcg_at_5 value: 45.826 - type: precision_at_1 value: 71.0 - type: precision_at_10 value: 35.0 - type: precision_at_100 value: 11.360000000000001 - type: precision_at_1000 value: 2.046 - type: precision_at_3 value: 52.833 - type: precision_at_5 value: 44.15 - type: recall_at_1 value: 9.53 - type: recall_at_10 value: 26.811 - type: recall_at_100 value: 55.916999999999994 - type: recall_at_1000 value: 79.973 - type: recall_at_3 value: 16.413 - type: recall_at_5 value: 19.980999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 51.519999999999996 - type: f1 value: 46.36601294761231 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 74.413 - type: map_at_10 value: 83.414 - type: map_at_100 value: 83.621 - type: map_at_1000 value: 83.635 - type: map_at_3 value: 82.337 - type: map_at_5 value: 83.039 - type: mrr_at_1 value: 80.19800000000001 - type: mrr_at_10 value: 87.715 - type: mrr_at_100 value: 87.778 - type: mrr_at_1000 value: 87.779 - type: mrr_at_3 value: 87.106 - type: mrr_at_5 value: 87.555 - type: ndcg_at_1 value: 80.19800000000001 - type: ndcg_at_10 value: 87.182 - type: ndcg_at_100 value: 87.90299999999999 - type: ndcg_at_1000 value: 88.143 - type: ndcg_at_3 value: 85.60600000000001 - type: ndcg_at_5 value: 86.541 - type: precision_at_1 value: 80.19800000000001 - type: precision_at_10 value: 10.531 - type: precision_at_100 value: 1.113 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.933 - type: precision_at_5 value: 20.429 - type: recall_at_1 value: 74.413 - type: recall_at_10 value: 94.363 - type: recall_at_100 value: 97.165 - type: recall_at_1000 value: 98.668 - type: recall_at_3 value: 90.108 - type: recall_at_5 value: 92.52 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.701 - type: map_at_10 value: 37.122 - type: map_at_100 value: 39.178000000000004 - type: map_at_1000 value: 39.326 - type: map_at_3 value: 32.971000000000004 - type: map_at_5 value: 35.332 - type: mrr_at_1 value: 44.753 - type: mrr_at_10 value: 53.452 - type: mrr_at_100 value: 54.198 - type: mrr_at_1000 value: 54.225 - type: mrr_at_3 value: 50.952 - type: mrr_at_5 value: 52.464 - type: ndcg_at_1 value: 44.753 - type: ndcg_at_10 value: 45.021 - type: ndcg_at_100 value: 52.028 - type: ndcg_at_1000 value: 54.596000000000004 - type: ndcg_at_3 value: 41.622 - type: ndcg_at_5 value: 42.736000000000004 - type: precision_at_1 value: 44.753 - type: precision_at_10 value: 12.284 - type: precision_at_100 value: 1.955 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 27.828999999999997 - type: precision_at_5 value: 20.061999999999998 - type: recall_at_1 value: 22.701 - type: recall_at_10 value: 51.432 - type: recall_at_100 value: 77.009 - type: recall_at_1000 value: 92.511 - type: recall_at_3 value: 37.919000000000004 - type: recall_at_5 value: 44.131 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.189 - type: map_at_10 value: 66.24600000000001 - type: map_at_100 value: 67.098 - type: map_at_1000 value: 67.149 - type: map_at_3 value: 62.684 - type: map_at_5 value: 64.974 - type: mrr_at_1 value: 80.378 - type: mrr_at_10 value: 86.127 - type: mrr_at_100 value: 86.29299999999999 - type: mrr_at_1000 value: 86.297 - type: mrr_at_3 value: 85.31400000000001 - type: mrr_at_5 value: 85.858 - type: ndcg_at_1 value: 80.378 - type: ndcg_at_10 value: 74.101 - type: ndcg_at_100 value: 76.993 - type: ndcg_at_1000 value: 77.948 - type: ndcg_at_3 value: 69.232 - type: ndcg_at_5 value: 72.04599999999999 - type: precision_at_1 value: 80.378 - type: precision_at_10 value: 15.595999999999998 - type: precision_at_100 value: 1.7840000000000003 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 44.884 - type: precision_at_5 value: 29.145 - type: recall_at_1 value: 40.189 - type: recall_at_10 value: 77.981 - type: recall_at_100 value: 89.21 - type: recall_at_1000 value: 95.48299999999999 - type: recall_at_3 value: 67.326 - type: recall_at_5 value: 72.863 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.84599999999999 - type: ap value: 89.4710787567357 - type: f1 value: 92.83752676932258 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 23.132 - type: map_at_10 value: 35.543 - type: map_at_100 value: 36.702 - type: map_at_1000 value: 36.748999999999995 - type: map_at_3 value: 31.737 - type: map_at_5 value: 33.927 - type: mrr_at_1 value: 23.782 - type: mrr_at_10 value: 36.204 - type: mrr_at_100 value: 37.29 - type: mrr_at_1000 value: 37.330999999999996 - type: mrr_at_3 value: 32.458999999999996 - type: mrr_at_5 value: 34.631 - type: ndcg_at_1 value: 23.782 - type: ndcg_at_10 value: 42.492999999999995 - type: ndcg_at_100 value: 47.985 - type: ndcg_at_1000 value: 49.141 - type: ndcg_at_3 value: 34.748000000000005 - type: ndcg_at_5 value: 38.651 - type: precision_at_1 value: 23.782 - type: precision_at_10 value: 6.665 - type: precision_at_100 value: 0.941 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.776 - type: precision_at_5 value: 10.84 - type: recall_at_1 value: 23.132 - type: recall_at_10 value: 63.794 - type: recall_at_100 value: 89.027 - type: recall_at_1000 value: 97.807 - type: recall_at_3 value: 42.765 - type: recall_at_5 value: 52.11 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.59188326493388 - type: f1 value: 94.3842594786827 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 79.49384404924761 - type: f1 value: 59.7580539534629 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 77.56220578345663 - type: f1 value: 75.27228165561478 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 80.53463349024884 - type: f1 value: 80.4893958236536 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 32.56100273484962 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.470380028839607 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.06102792457849 - type: mrr value: 33.30709199672238 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.776999999999999 - type: map_at_10 value: 14.924000000000001 - type: map_at_100 value: 18.955 - type: map_at_1000 value: 20.538999999999998 - type: map_at_3 value: 10.982 - type: map_at_5 value: 12.679000000000002 - type: mrr_at_1 value: 47.988 - type: mrr_at_10 value: 57.232000000000006 - type: mrr_at_100 value: 57.818999999999996 - type: mrr_at_1000 value: 57.847 - type: mrr_at_3 value: 54.901999999999994 - type: mrr_at_5 value: 56.481 - type: ndcg_at_1 value: 46.594 - type: ndcg_at_10 value: 38.129000000000005 - type: ndcg_at_100 value: 35.54 - type: ndcg_at_1000 value: 44.172 - type: ndcg_at_3 value: 43.025999999999996 - type: ndcg_at_5 value: 41.052 - type: precision_at_1 value: 47.988 - type: precision_at_10 value: 28.111000000000004 - type: precision_at_100 value: 8.929 - type: precision_at_1000 value: 2.185 - type: precision_at_3 value: 40.144000000000005 - type: precision_at_5 value: 35.232 - type: recall_at_1 value: 6.776999999999999 - type: recall_at_10 value: 19.289 - type: recall_at_100 value: 36.359 - type: recall_at_1000 value: 67.54 - type: recall_at_3 value: 11.869 - type: recall_at_5 value: 14.999 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 31.108000000000004 - type: map_at_10 value: 47.126000000000005 - type: map_at_100 value: 48.171 - type: map_at_1000 value: 48.199 - type: map_at_3 value: 42.734 - type: map_at_5 value: 45.362 - type: mrr_at_1 value: 34.936 - type: mrr_at_10 value: 49.571 - type: mrr_at_100 value: 50.345 - type: mrr_at_1000 value: 50.363 - type: mrr_at_3 value: 45.959 - type: mrr_at_5 value: 48.165 - type: ndcg_at_1 value: 34.936 - type: ndcg_at_10 value: 55.028999999999996 - type: ndcg_at_100 value: 59.244 - type: ndcg_at_1000 value: 59.861 - type: ndcg_at_3 value: 46.872 - type: ndcg_at_5 value: 51.217999999999996 - type: precision_at_1 value: 34.936 - type: precision_at_10 value: 9.099 - type: precision_at_100 value: 1.145 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 21.456 - type: precision_at_5 value: 15.411 - type: recall_at_1 value: 31.108000000000004 - type: recall_at_10 value: 76.53999999999999 - type: recall_at_100 value: 94.39 - type: recall_at_1000 value: 98.947 - type: recall_at_3 value: 55.572 - type: recall_at_5 value: 65.525 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.56400000000001 - type: map_at_10 value: 85.482 - type: map_at_100 value: 86.114 - type: map_at_1000 value: 86.13 - type: map_at_3 value: 82.607 - type: map_at_5 value: 84.405 - type: mrr_at_1 value: 82.42 - type: mrr_at_10 value: 88.304 - type: mrr_at_100 value: 88.399 - type: mrr_at_1000 value: 88.399 - type: mrr_at_3 value: 87.37 - type: mrr_at_5 value: 88.024 - type: ndcg_at_1 value: 82.45 - type: ndcg_at_10 value: 89.06500000000001 - type: ndcg_at_100 value: 90.232 - type: ndcg_at_1000 value: 90.305 - type: ndcg_at_3 value: 86.375 - type: ndcg_at_5 value: 87.85300000000001 - type: precision_at_1 value: 82.45 - type: precision_at_10 value: 13.486999999999998 - type: precision_at_100 value: 1.534 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.813 - type: precision_at_5 value: 24.773999999999997 - type: recall_at_1 value: 71.56400000000001 - type: recall_at_10 value: 95.812 - type: recall_at_100 value: 99.7 - type: recall_at_1000 value: 99.979 - type: recall_at_3 value: 87.966 - type: recall_at_5 value: 92.268 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 57.241876648614145 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.66212576446223 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.308 - type: map_at_10 value: 13.803 - type: map_at_100 value: 16.176 - type: map_at_1000 value: 16.561 - type: map_at_3 value: 9.761000000000001 - type: map_at_5 value: 11.802 - type: mrr_at_1 value: 26.200000000000003 - type: mrr_at_10 value: 37.621 - type: mrr_at_100 value: 38.767 - type: mrr_at_1000 value: 38.815 - type: mrr_at_3 value: 34.117 - type: mrr_at_5 value: 36.107 - type: ndcg_at_1 value: 26.200000000000003 - type: ndcg_at_10 value: 22.64 - type: ndcg_at_100 value: 31.567 - type: ndcg_at_1000 value: 37.623 - type: ndcg_at_3 value: 21.435000000000002 - type: ndcg_at_5 value: 18.87 - type: precision_at_1 value: 26.200000000000003 - type: precision_at_10 value: 11.74 - type: precision_at_100 value: 2.465 - type: precision_at_1000 value: 0.391 - type: precision_at_3 value: 20.033 - type: precision_at_5 value: 16.64 - type: recall_at_1 value: 5.308 - type: recall_at_10 value: 23.794999999999998 - type: recall_at_100 value: 50.015 - type: recall_at_1000 value: 79.283 - type: recall_at_3 value: 12.178 - type: recall_at_5 value: 16.882 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.93231134675553 - type: cos_sim_spearman value: 81.68319292603205 - type: euclidean_pearson value: 81.8396814380367 - type: euclidean_spearman value: 81.24641903349945 - type: manhattan_pearson value: 81.84698799204274 - type: manhattan_spearman value: 81.24269997904105 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 86.73241671587446 - type: cos_sim_spearman value: 79.05091082971826 - type: euclidean_pearson value: 83.91146869578044 - type: euclidean_spearman value: 79.87978465370936 - type: manhattan_pearson value: 83.90888338917678 - type: manhattan_spearman value: 79.87482848584241 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 85.14970731146177 - type: cos_sim_spearman value: 86.37363490084627 - type: euclidean_pearson value: 83.02154218530433 - type: euclidean_spearman value: 83.80258761957367 - type: manhattan_pearson value: 83.01664495119347 - type: manhattan_spearman value: 83.77567458007952 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.40474139886784 - type: cos_sim_spearman value: 82.77768789165984 - type: euclidean_pearson value: 80.7065877443695 - type: euclidean_spearman value: 81.375940662505 - type: manhattan_pearson value: 80.6507552270278 - type: manhattan_spearman value: 81.32782179098741 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.08585968722274 - type: cos_sim_spearman value: 88.03110031451399 - type: euclidean_pearson value: 85.74012019602384 - type: euclidean_spearman value: 86.13592849438209 - type: manhattan_pearson value: 85.74404842369206 - type: manhattan_spearman value: 86.14492318960154 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.95069052788875 - type: cos_sim_spearman value: 86.4867991595147 - type: euclidean_pearson value: 84.31013325754635 - type: euclidean_spearman value: 85.01529258006482 - type: manhattan_pearson value: 84.26995570085374 - type: manhattan_spearman value: 84.96982104986162 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.54617647971897 - type: cos_sim_spearman value: 87.49834181751034 - type: euclidean_pearson value: 86.01015322577122 - type: euclidean_spearman value: 84.63362652063199 - type: manhattan_pearson value: 86.13807574475706 - type: manhattan_spearman value: 84.7772370721132 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 67.20047755786615 - type: cos_sim_spearman value: 67.05324077987636 - 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type: mrr value: 56.97151398438164 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.541657650692905 - type: cos_sim_spearman value: 31.605804192286303 - type: dot_pearson value: 28.26905996736398 - type: dot_spearman value: 27.864801765851187 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.22599999999999998 - type: map_at_10 value: 1.8870000000000002 - type: map_at_100 value: 9.78 - type: map_at_1000 value: 22.514 - type: map_at_3 value: 0.6669999999999999 - type: map_at_5 value: 1.077 - type: mrr_at_1 value: 82.0 - type: mrr_at_10 value: 89.86699999999999 - type: mrr_at_100 value: 89.86699999999999 - type: mrr_at_1000 value: 89.86699999999999 - type: mrr_at_3 value: 89.667 - type: mrr_at_5 value: 89.667 - type: ndcg_at_1 value: 79.0 - type: ndcg_at_10 value: 74.818 - type: ndcg_at_100 value: 53.715999999999994 - type: ndcg_at_1000 value: 47.082 - type: ndcg_at_3 value: 82.134 - type: ndcg_at_5 value: 79.81899999999999 - type: precision_at_1 value: 82.0 - type: precision_at_10 value: 78.0 - type: precision_at_100 value: 54.48 - type: precision_at_1000 value: 20.518 - type: precision_at_3 value: 87.333 - type: precision_at_5 value: 85.2 - type: recall_at_1 value: 0.22599999999999998 - type: recall_at_10 value: 2.072 - type: recall_at_100 value: 13.013 - type: recall_at_1000 value: 43.462 - type: recall_at_3 value: 0.695 - type: recall_at_5 value: 1.139 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.328 - type: map_at_10 value: 9.795 - type: map_at_100 value: 15.801000000000002 - type: map_at_1000 value: 17.23 - type: map_at_3 value: 4.734 - type: map_at_5 value: 6.644 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 46.902 - type: mrr_at_100 value: 47.495 - type: mrr_at_1000 value: 47.495 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 44.218 - type: ndcg_at_1 value: 28.571 - type: ndcg_at_10 value: 24.806 - type: ndcg_at_100 value: 36.419000000000004 - type: ndcg_at_1000 value: 47.272999999999996 - type: ndcg_at_3 value: 25.666 - type: ndcg_at_5 value: 25.448999999999998 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 23.061 - type: precision_at_100 value: 7.714 - type: precision_at_1000 value: 1.484 - type: precision_at_3 value: 26.531 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.328 - type: recall_at_10 value: 16.524 - type: recall_at_100 value: 47.179 - type: recall_at_1000 value: 81.22200000000001 - type: recall_at_3 value: 5.745 - type: recall_at_5 value: 9.339 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - 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type: max_f1 value: 77.6957163958641 license: mit language: - en --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` #### Usage of the ONNX files ```python from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13") model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx") # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') model_output_ort = model_ort(**encoded_input) # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # model_output and model_output_ort are identical ``` Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch" )) async def main(): async with engine: embeddings, usage = await engine.embed(sentences=sentences) asyncio.run(main()) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

liuhaotian/llava-v1.6-34b

liuhaotian

transformers

text-generation

--- inference: false license: apache-2.0 --- <br> <br> # LLaVA Model Card ## Model details **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) **Model date:** LLaVA-v1.6-34B was trained in December 2023. **Paper or resources for more information:** https://llava-vl.github.io/ ## License [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) license. **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues ## Intended use **Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. - 158K GPT-generated multimodal instruction-following data. - 500K academic-task-oriented VQA data mixture. - 50K GPT-4V data mixture. - 40K ShareGPT data. ## Evaluation dataset A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

yiyanghkust/finbert-tone

yiyanghkust

transformers

text-classification

--- language: "en" tags: - financial-sentiment-analysis - sentiment-analysis widget: - text: "growth is strong and we have plenty of liquidity" --- `FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. - Corporate Reports 10-K & 10-Q: 2.5B tokens - Earnings Call Transcripts: 1.3B tokens - Analyst Reports: 1.1B tokens More technical details on `FinBERT`: [Click Link](https://github.com/yya518/FinBERT) This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try. If you use the model in your academic work, please cite the following paper: Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022). # How to use You can use this model with Transformers pipeline for sentiment analysis. ```python from transformers import BertTokenizer, BertForSequenceClassification from transformers import pipeline finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3) tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone') nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer) sentences = ["there is a shortage of capital, and we need extra financing", "growth is strong and we have plenty of liquidity", "there are doubts about our finances", "profits are flat"] results = nlp(sentences) print(results) #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative ```

Helsinki-NLP/opus-mt-sv-en

Helsinki-NLP

transformers

translation

--- pipeline_tag: translation license: apache-2.0 --- ### opus-mt-sv-en * source languages: sv * target languages: en * OPUS readme: [sv-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.test.txt) * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-en/opus-2020-02-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.sv.en | 64.5 | 0.763 |

dslim/bert-large-NER

dslim

transformers

token-classification

--- language: en datasets: - conll2003 license: mit model-index: - name: dslim/bert-large-NER results: - task: type: token-classification name: Token Classification dataset: name: conll2003 type: conll2003 config: conll2003 split: test metrics: - name: Accuracy type: accuracy value: 0.9031688753722759 verified: true - name: Precision type: precision value: 0.920025068328604 verified: true - name: Recall type: recall value: 0.9193688678588825 verified: true - name: F1 type: f1 value: 0.9196968510445761 verified: true - name: loss type: loss value: 0.5085050463676453 verified: true --- # bert-large-NER ## Model description **bert-large-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a *bert-large-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. If you'd like to use a smaller BERT model fine-tuned on the same dataset, a [**bert-base-NER**](https://huggingface.co/dslim/bert-base-NER/) version is also available. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("dslim/bert-large-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-large-NER") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "My name is Wolfgang and I live in Berlin" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. ## Training data This model was fine-tuned on English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity I-MIS | Miscellaneous entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organization right after another organization I-ORG |organization B-LOC |Beginning of a location right after another location I-LOC |Location ### CoNLL-2003 English Dataset Statistics This dataset was derived from the Reuters corpus which consists of Reuters news stories. You can read more about how this dataset was created in the CoNLL-2003 paper. #### # of training examples per entity type Dataset|LOC|MISC|ORG|PER -|-|-|-|- Train|7140|3438|6321|6600 Dev|1837|922|1341|1842 Test|1668|702|1661|1617 #### # of articles/sentences/tokens per dataset Dataset |Articles |Sentences |Tokens -|-|-|- Train |946 |14,987 |203,621 Dev |216 |3,466 |51,362 Test |231 |3,684 |46,435 ## Training procedure This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original BERT paper](https://arxiv.org/pdf/1810.04805) which trained & evaluated the model on CoNLL-2003 NER task. ## Eval results metric|dev|test -|-|- f1 |95.7 |91.7 precision |95.3 |91.2 recall |96.1 |92.3 The test metrics are a little lower than the official Google BERT results which encoded document context & experimented with CRF. More on replicating the original results [here](https://github.com/google-research/bert/issues/223). ### BibTeX entry and citation info ``` @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} } ``` ``` @inproceedings{tjong-kim-sang-de-meulder-2003-introduction, title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition", author = "Tjong Kim Sang, Erik F. and De Meulder, Fien", booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", year = "2003", url = "https://www.aclweb.org/anthology/W03-0419", pages = "142--147", } ```

dmis-lab/biobert-v1.1

dmis-lab

transformers

feature-extraction

Entry not found

Helsinki-NLP/opus-mt-it-en

Helsinki-NLP

transformers

translation

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

guillaumekln/faster-whisper-large-v2

guillaumekln

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/guillaumekln/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).**

amazon/chronos-t5-tiny

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 (Tiny) 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-tiny", 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.

ckiplab/bert-base-chinese-ner

ckiplab

transformers

token-classification

--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - bert - zh license: gpl-3.0 --- # CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-ner') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。

google/bert_uncased_L-2_H-128_A-2

google

transformers

--- thumbnail: https://huggingface.co/front/thumbnails/google.png license: apache-2.0 --- BERT Miniatures === This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity. You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below: | |H=128|H=256|H=512|H=768| |---|:---:|:---:|:---:|:---:| | **L=2** |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]| | **L=4** |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]| | **L=6** |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]| | **L=8** |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]| | **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]| | **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]| Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model. Here are the corresponding GLUE scores on the test set: |Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0| |BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1| |BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6| |BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5| For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs: - batch sizes: 8, 16, 32, 64, 128 - learning rates: 3e-4, 1e-4, 5e-5, 3e-5 If you use these models, please cite the following paper: ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ``` [2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2 [2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4 [2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8 [2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12 [4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2 [4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4 [4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8 [4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12 [6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2 [6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4 [6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8 [6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12 [8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2 [8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4 [8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8 [8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12 [10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2 [10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4 [10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8 [10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12 [12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2 [12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4 [12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8 [12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12

lfoppiano/MatTPUSciBERT

lfoppiano

transformers

fill-mask

# Material SciBERT (TPU): Improving language understanding in materials science **Work in progress** ## Introduction SciBERT-based model pre-trained with materials science scientific fulltext ## Authors Luca Foppiano Pedro Ortiz Suarez ## TLDR - Collected full-text from ~700000 articles provided by the National Institute for Materials Science (NIMS) TDM platform (https://dice.nims.go.jp/services/TDM-PF/en/), dataset called ScienceCorpus (SciCorpus) - We added to the SciBERT vocabulary (32k tokens), 100 domain-specific unknown words extracted from SciCorpus with a keywords modeler (KeyBERT) - Starting conditions: original SciBERT weights - Pre-train the model MatTpuSciBERT from on the Google Cloud with the TPU (Tensor Processing Unit) as follow: - 800000 steps with batch_size: 256, max_seq_length:512 - 100000 steps with batch_size: 2048, max_seq_length:128 - Fine-tuning and testing on NER on superconductors (https://github.com/lfoppiano/grobid-superconductors) and physical quantities (https://github.com/kermitt2/grobid-quantities) ## Related work ### BERT Implementations - BERT (the original) https://arxiv.org/abs/1810.04805 - RoBERTa (Re-implementation by Facebook) https://arxiv.org/abs/1907.11692 ### Relevant models - SciBERT: BERT, from scratch, scientific articles (biology + CS) https://github.com/allenai/scibert - MatSciBERT (Gupta): RoBERTa, from scratch, SciBERT vocab and weights, ~150 K paper limited to 4 MS families http://github.com/m3rg-iitd/matscibert - MaterialBERT: Not yet published - MatBERT (CEDER): BERT, from scratch, 2M documents on materials science (~60M paragraphs) https://github.com/lbnlp/MatBERT - BatteryBERT (Cole): BERT, mixed from scratch and with predefined weights https://github.com/ShuHuang/batterybert/ ### Results Results obtained via 10-fold cross-validation, using DeLFT (https://github.com/kermitt2/delft) #### NER Superconductors | Model | Precision | Recall | F1 | |----------------------|-----------|---------|--------| | SciBERT (baseline) | 81.62% | 84.23% | 82.90% | | MatSciBERT (Gupta) | 81.45% | 84.36% | 82.88% | | MatTPUSciBERT | 82.13% | 85.15% | 83.61% | | MatBERT (Ceder) | 81.25% | 83.99% | 82.60% | | BatteryScibert-cased | 81.09% | 84.14% | 82.59% | #### NER Quantities | Model | Precision | Recall | F1 | |----------------------|-----------|---------|----------| | SciBERT (baseline) | 88.73% | 86.76% | 87.73% | | MatSciBERT (Gupta) | 84.98% | 90.12% | 87.47% | | MatTPUSciBERT | 88.62% | 86.33% | 87.46% | | MatBERT (Ceder) | 85.08% | 89.93% | 87.44% | | BatteryScibert-cased | 85.02% | 89.30% | 87.11% | | BatteryScibert-cased | 81.09% | 84.14% | 82.59% | ## References This work was supported by Google, through the researchers program https://cloud.google.com/edu/researchers ## Acknowledgements TBA

Ashishkr/query_wellformedness_score

Ashishkr

transformers

text-classification

--- license: apache-2.0 inference: false datasets: google_wellformed_query --- ```DOI @misc {ashish_kumar_2024, author = { {Ashish Kumar} }, title = { query_wellformedness_score (Revision 55a424c) }, year = 2024, url = { https://huggingface.co/Ashishkr/query_wellformedness_score }, doi = { 10.57967/hf/1980 }, publisher = { Hugging Face } } ``` **Intended Use Cases** *Content Creation*: Validate the well-formedness of written content. *Educational Platforms*: Helps students check the grammaticality of their sentences. *Chatbots & Virtual Assistants*: To validate user queries or generate well-formed responses. **contact: kua613@g.harvard.edu** **Model name**: Query Wellformedness Scoring **Description** : Evaluate the well-formedness of sentences by checking grammatical correctness and completeness. Sensitive to case and penalizes sentences for incorrect grammar and case. **Features**: - *Wellformedness Score*: Provides a score indicating grammatical correctness and completeness. - *Case Sensitivity*: Recognizes and penalizes incorrect casing in sentences. - *Broad Applicability*: Can be used on a wide range of sentences. **Example**: 1. Dogs are mammals. 2. she loves to read books on history. 3. When the rain in Spain. 4. Eating apples are healthy for you. 5. The Eiffel Tower is in Paris. Among these sentences: Sentences 1 and 5 are well-formed and have correct grammar and case. Sentence 2 starts with a lowercase letter. Sentence 3 is a fragment and is not well-formed. Sentence 4 has a subject-verb agreement error. **example_usage:** *library: HuggingFace transformers* ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ashishkr/query_wellformedness_score") model = AutoModelForSequenceClassification.from_pretrained("Ashishkr/query_wellformedness_score") sentences = [ "The quarterly financial report are showing an increase.", # Incorrect "Him has completed the audit for last fiscal year.", # Incorrect "Please to inform the board about the recent developments.", # Incorrect "The team successfully achieved all its targets for the last quarter.", # Correct "Our company is exploring new ventures in the European market." # Correct ] features = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits print(scores) ``` Cite Ashishkr/query_wellformedness_score

ibm/MoLFormer-XL-both-10pct

ibm

transformers

feature-extraction

--- license: apache-2.0 library_name: transformers pipeline_tag: feature-extraction tags: - chemistry --- # MoLFormer-XL-both-10% MoLFormer is a class of models pretrained on SMILES string representations of up to 1.1B molecules from ZINC and PubChem. This repository is for the model pretrained on 10% of both datasets. It was introduced in the paper [Large-Scale Chemical Language Representations Capture Molecular Structure and Properties](https://arxiv.org/abs/2106.09553) by Ross et al. and first released in [this repository](https://github.com/IBM/molformer). ## Model Details ### Model Description MoLFormer is a large-scale chemical language model designed with the intention of learning a model trained on small molecules which are represented as SMILES strings. MoLFormer leverges masked language modeling and employs a linear attention Transformer combined with rotary embeddings.  An overview of the MoLFormer pipeline is seen in the image above. One can see that the transformer-based neural network model is trained on a large collection of chemical molecules represented by SMILES sequences from two public chemical datasets PubChem and ZINC in a self-supervised fashion. The MoLFormer architecture was designed with an efficient linear attention mechanism and relative positional embeddings with the goal of learning a meaningful and compressed representation of chemical molecules. After training the MoLFormer foundation model was then adopted to different downstream molecular property prediction tasks via fine-tuning on task-specific data. To further test the representative power of MoLFormer, the MoLFormer encodings were used to recover molecular similarity, and analysis on the correspondence between the interatomic spatial distance and attention value for a given molecule was performed. ## Intended use and limitations You can use the model for masked language modeling, but it is mainly intended to be used as a feature extractor or to be fine-tuned for a prediction task. The "frozen" model embeddings may be used for similarity measurements, visualization, or training predictor models. The model may also be fine-tuned for sequence classification tasks (e.g., solubility, toxicity, etc.). This model is not intended for molecule generation. It is also not tested for molecules larger than ~200 atoms (i.e., macromolecules). Furthermore, using invalid or noncanonical SMILES may result in worse performance. ## Example code Use the code below to get started with the model. ```py import torch from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("ibm/MoLFormer-XL-both-10pct", deterministic_eval=True, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("ibm/MoLFormer-XL-both-10pct", trust_remote_code=True) smiles = ["Cn1c(=O)c2c(ncn2C)n(C)c1=O", "CC(=O)Oc1ccccc1C(=O)O"] inputs = tokenizer(smiles, padding=True, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) outputs.pooler_output ``` ## Training Details ### Data We trained MoLFormer-XL on a combination of molecules from the ZINC15 and PubChem datasets. This repository contains the version trained on 10% ZINC + 10% PubChem. Molecules were canonicalized with RDKit prior to training and isomeric information was removed. Also, molecules longer than 202 tokens were dropped. ### Hardware - 16 x NVIDIA V100 GPUs ## Evaluation We evaluated MoLFormer by fine-tuning on 11 benchmark tasks from MoleculeNet. The tables below show the performance of different MoLFormer variants: | | BBBP | HIV | BACE | SIDER | ClinTox | Tox21 | |-------------------------|----------|----------|----------|----------|----------|----------| | 10% ZINC + 10% PubChem | 91.5 | 81.3 | 86.6 | 68.9 | 94.6 | 84.5 | | 10% ZINC + 100% PubChem | 92.2 | 79.2 | 86.3 | 69.0 | 94.7 | 84.5 | | 100% ZINC | 89.9 | 78.4 | 87.7 | 66.8 | 82.2 | 83.2 | | MoLFormer-Base | 90.9 | 77,7 | 82.8 | 64.8 | 61.3 | 43.1 | | MoLFormer-XL | **93.7** | **82.2** | **88.2** | **69.0** | **94.8** | **84.7** | | | QM9 | QM8 | ESOL | FreeSolv | Lipophilicity | |-------------------------|------------|------------|--------|------------|---------------| | 10% ZINC + 10% PubChem | 1.7754 | 0.0108 | 0.3295 | 0.2221 | 0.5472 | | 10% ZINC + 100% PubChem | 1.9093 | **0.0102** | 0.2775 | **0.2050** | 0.5331 | | 100% ZINC | 1.9403 | 0.0124 | 0.3023 | 0.2981 | 0.5440 | | MoLFormer-Base | 2.2500 | 0.0111 | 0.2798 | 0.2596 | 0.6492 | | MoLFormer-XL | **1.5984** | **0.0102** | 0.2787 | 0.2308 | **0.5298** | We report AUROC for all classification tasks, average MAE for QM9/8, and RMSE for the remaining regression tasks. ## Citation ``` @article{10.1038/s42256-022-00580-7, year = {2022}, title = {{Large-scale chemical language representations capture molecular structure and properties}}, author = {Ross, Jerret and Belgodere, Brian and Chenthamarakshan, Vijil and Padhi, Inkit and Mroueh, Youssef and Das, Payel}, journal = {Nature Machine Intelligence}, doi = {10.1038/s42256-022-00580-7}, pages = {1256--1264}, number = {12}, volume = {4} } ``` ``` @misc{https://doi.org/10.48550/arxiv.2106.09553, doi = {10.48550/ARXIV.2106.09553}, url = {https://arxiv.org/abs/2106.09553}, author = {Ross, Jerret and Belgodere, Brian and Chenthamarakshan, Vijil and Padhi, Inkit and Mroueh, Youssef and Das, Payel}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Biomolecules (q-bio.BM), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Biological sciences, FOS: Biological sciences}, title = {Large-Scale Chemical Language Representations Capture Molecular Structure and Properties}, publisher = {arXiv}, year = {2021}, copyright = {arXiv.org perpetual, non-exclusive license} } ```

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

meta-llama

transformers

text-generation

--- extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: >- ### LLAMA 2 COMMUNITY LICENSE AGREEMENT "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Llama 2" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/. "Llama Materials" means, collectively, Meta's proprietary Llama 2 and documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). By clicking "I Accept" below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1. License Rights and Redistribution. a. Grant of Rights. 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Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy). #### Prohibited Uses We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. 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Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. 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Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com) extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 license: llama2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B 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/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)| |70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)|

benjamin/wtp-canine-s-1l

benjamin

transformers

token-classification

--- license: mit language: - multilingual - am - ar - az - be - bg - bn - ca - ceb - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lt - lv - mg - mk - ml - mn - mr - ms - mt - my - ne - nl - no - pa - pl - ps - pt - ro - ru - si - sk - sl - sq - sr - sv - ta - te - tg - th - tr - uk - ur - uz - vi - xh - yi - yo - zh - zu --- # wtp-canine-s-1l Model for [`wtpsplit`](https://github.com/bminixhofer/wtpsplit).

timm/vit_base_patch16_224.augreg2_in21k_ft_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k - imagenet-21k --- # Model card for vit_base_patch16_224.augreg2_in21k_ft_in1k A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k by paper authors and (re) fine-tuned on ImageNet-1k with additional augmentation and regularization by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 86.6 - GMACs: 16.9 - Activations (M): 16.5 - Image size: 224 x 224 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_base_patch16_224.augreg2_in21k_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_base_patch16_224.augreg2_in21k_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 197, 768) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @article{steiner2021augreg, title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers}, author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas}, journal={arXiv preprint arXiv:2106.10270}, year={2021} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

stabilityai/stable-diffusion-xl-refiner-1.0

stabilityai

diffusers

image-to-image

--- license: openrail++ tags: - stable-diffusion - image-to-image --- # SD-XL 1.0-refiner Model Card  ## Model  [SDXL](https://arxiv.org/abs/2307.01952) consists of an [ensemble of experts](https://arxiv.org/abs/2211.01324) pipeline for latent diffusion: In a first step, the base model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) is used to generate (noisy) latents, which are then further processed with a refinement model specialized for the final denoising steps. Note that the base model can be used as a standalone module. Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations. Source code is available at https://github.com/Stability-AI/generative-models . ### Model Description - **Developed by:** Stability AI - **Model type:** Diffusion-based text-to-image generative model - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/LICENSE.md) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses two fixed, pretrained text encoders ([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main)). - **Resources for more information:** Check out our [GitHub Repository](https://github.com/Stability-AI/generative-models) and the [SDXL report on arXiv](https://arxiv.org/abs/2307.01952). ### Model Sources For research purposes, we recommned our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popoular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. [Clipdrop](https://clipdrop.co/stable-diffusion) provides free SDXL inference. - **Repository:** https://github.com/Stability-AI/generative-models - **Demo:** https://clipdrop.co/stable-diffusion ## Evaluation  The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. ### 🧨 Diffusers Make sure to upgrade diffusers to >= 0.18.0: ``` pip install diffusers --upgrade ``` In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` Yon can then use the refiner to improve images. ```py import torch from diffusers import StableDiffusionXLImg2ImgPipeline from diffusers.utils import load_image pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) pipe = pipe.to("cuda") url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png" init_image = load_image(url).convert("RGB") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt, image=init_image).images ``` When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline: ```py pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) ``` If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload` instead of `.to("cuda")`: ```diff - pipe.to("cuda") + pipe.enable_model_cpu_offload() ``` For more advanced use cases, please have a look at [the docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl). ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.

facebook/m2m100_418M

facebook

transformers

text2text-generation

--- language: - multilingual - af - am - ar - ast - az - ba - be - bg - bn - br - bs - ca - ceb - cs - cy - da - de - el - en - es - et - fa - ff - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - ht - hu - hy - id - ig - ilo - is - it - ja - jv - ka - kk - km - kn - ko - lb - lg - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - ns - oc - or - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - th - tl - tn - tr - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu license: mit --- # M2M100 418M M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository. The model that can directly translate between the 9,900 directions of 100 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. *Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example.* To install `sentencepiece` run `pip install sentencepiece` ```python from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।" chinese_text = "生活就像一盒巧克力。" model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M") tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M") # translate Hindi to French tokenizer.src_lang = "hi" encoded_hi = tokenizer(hi_text, return_tensors="pt") generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "La vie est comme une boîte de chocolat." # translate Chinese to English tokenizer.src_lang = "zh" encoded_zh = tokenizer(chinese_text, return_tensors="pt") generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Life is like a box of chocolate." ``` See the [model hub](https://huggingface.co/models?filter=m2m_100) to look for more fine-tuned versions. ## Languages covered Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu) ## BibTeX entry and citation info ``` @misc{fan2020englishcentric, title={Beyond English-Centric Multilingual Machine Translation}, author={Angela Fan and Shruti Bhosale and Holger Schwenk and Zhiyi Ma and Ahmed El-Kishky and Siddharth Goyal and Mandeep Baines and Onur Celebi and Guillaume Wenzek and Vishrav Chaudhary and Naman Goyal and Tom Birch and Vitaliy Liptchinsky and Sergey Edunov and Edouard Grave and Michael Auli and Armand Joulin}, year={2020}, eprint={2010.11125}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

jinaai/jina-embeddings-v2-base-en

jinaai

sentence-transformers

feature-extraction

--- tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb datasets: - allenai/c4 language: en inference: false license: apache-2.0 model-index: - name: jina-embedding-b-en-v2 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.73134328358209 - type: ap value: 37.765427081831035 - type: f1 value: 68.79367444339518 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 88.544275 - type: ap value: 84.61328675662887 - type: f1 value: 88.51879035862375 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.263999999999996 - type: f1 value: 43.778759656699435 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 21.693 - type: map_at_10 value: 35.487 - type: map_at_100 value: 36.862 - type: map_at_1000 value: 36.872 - type: map_at_3 value: 30.049999999999997 - type: map_at_5 value: 32.966 - type: mrr_at_1 value: 21.977 - type: mrr_at_10 value: 35.565999999999995 - type: mrr_at_100 value: 36.948 - type: mrr_at_1000 value: 36.958 - type: mrr_at_3 value: 30.121 - type: mrr_at_5 value: 33.051 - type: ndcg_at_1 value: 21.693 - type: ndcg_at_10 value: 44.181 - type: ndcg_at_100 value: 49.982 - type: ndcg_at_1000 value: 50.233000000000004 - type: ndcg_at_3 value: 32.830999999999996 - type: ndcg_at_5 value: 38.080000000000005 - type: precision_at_1 value: 21.693 - type: precision_at_10 value: 7.248 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 13.632 - type: precision_at_5 value: 10.725 - type: recall_at_1 value: 21.693 - type: recall_at_10 value: 72.475 - type: recall_at_100 value: 97.653 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 40.896 - type: recall_at_5 value: 53.627 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.39242428696777 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 36.675626784714 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.247725694904034 - type: mrr value: 74.91359978894604 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.68003802970496 - type: cos_sim_spearman value: 81.23438110096286 - type: euclidean_pearson value: 81.87462986142582 - type: euclidean_spearman value: 81.23438110096286 - type: manhattan_pearson value: 81.61162566600755 - type: manhattan_spearman value: 81.11329400456184 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.01298701298701 - type: f1 value: 83.31690714969382 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.050108150972086 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 30.15731442819715 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.391999999999996 - type: map_at_10 value: 42.597 - type: map_at_100 value: 44.07 - type: map_at_1000 value: 44.198 - type: map_at_3 value: 38.957 - type: map_at_5 value: 40.961 - type: mrr_at_1 value: 37.196 - type: mrr_at_10 value: 48.152 - type: mrr_at_100 value: 48.928 - type: mrr_at_1000 value: 48.964999999999996 - type: mrr_at_3 value: 45.446 - type: mrr_at_5 value: 47.205999999999996 - type: ndcg_at_1 value: 37.196 - type: ndcg_at_10 value: 49.089 - type: ndcg_at_100 value: 54.471000000000004 - type: ndcg_at_1000 value: 56.385 - type: ndcg_at_3 value: 43.699 - type: ndcg_at_5 value: 46.22 - type: precision_at_1 value: 37.196 - type: precision_at_10 value: 9.313 - type: precision_at_100 value: 1.478 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 20.839 - type: precision_at_5 value: 14.936 - type: recall_at_1 value: 31.391999999999996 - type: recall_at_10 value: 61.876 - type: recall_at_100 value: 84.214 - type: recall_at_1000 value: 95.985 - type: recall_at_3 value: 46.6 - type: recall_at_5 value: 53.588 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.083 - type: map_at_10 value: 38.812999999999995 - type: map_at_100 value: 40.053 - type: map_at_1000 value: 40.188 - type: map_at_3 value: 36.111 - type: map_at_5 value: 37.519000000000005 - type: mrr_at_1 value: 36.497 - type: mrr_at_10 value: 44.85 - type: mrr_at_100 value: 45.546 - type: mrr_at_1000 value: 45.593 - type: mrr_at_3 value: 42.686 - type: mrr_at_5 value: 43.909 - type: ndcg_at_1 value: 36.497 - type: ndcg_at_10 value: 44.443 - type: ndcg_at_100 value: 48.979 - type: ndcg_at_1000 value: 51.154999999999994 - type: ndcg_at_3 value: 40.660000000000004 - type: ndcg_at_5 value: 42.193000000000005 - type: precision_at_1 value: 36.497 - type: precision_at_10 value: 8.433 - type: precision_at_100 value: 1.369 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 19.894000000000002 - type: precision_at_5 value: 13.873 - type: recall_at_1 value: 29.083 - type: recall_at_10 value: 54.313 - type: recall_at_100 value: 73.792 - type: recall_at_1000 value: 87.629 - type: recall_at_3 value: 42.257 - type: recall_at_5 value: 47.066 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.556000000000004 - type: map_at_10 value: 50.698 - type: map_at_100 value: 51.705 - type: map_at_1000 value: 51.768 - type: map_at_3 value: 47.848 - type: map_at_5 value: 49.358000000000004 - type: mrr_at_1 value: 43.95 - type: mrr_at_10 value: 54.191 - type: mrr_at_100 value: 54.852999999999994 - type: mrr_at_1000 value: 54.885 - type: mrr_at_3 value: 51.954 - type: mrr_at_5 value: 53.13 - type: ndcg_at_1 value: 43.95 - type: ndcg_at_10 value: 56.516 - type: ndcg_at_100 value: 60.477000000000004 - type: ndcg_at_1000 value: 61.746 - type: ndcg_at_3 value: 51.601 - type: ndcg_at_5 value: 53.795 - type: precision_at_1 value: 43.95 - type: precision_at_10 value: 9.009 - type: precision_at_100 value: 1.189 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.989 - type: precision_at_5 value: 15.473 - type: recall_at_1 value: 38.556000000000004 - type: recall_at_10 value: 70.159 - type: recall_at_100 value: 87.132 - type: recall_at_1000 value: 96.16 - type: recall_at_3 value: 56.906 - type: recall_at_5 value: 62.332 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.238 - type: map_at_10 value: 32.5 - type: map_at_100 value: 33.637 - type: map_at_1000 value: 33.719 - type: map_at_3 value: 30.026999999999997 - type: map_at_5 value: 31.555 - type: mrr_at_1 value: 26.328000000000003 - type: mrr_at_10 value: 34.44 - type: mrr_at_100 value: 35.455999999999996 - type: mrr_at_1000 value: 35.521 - type: mrr_at_3 value: 32.034 - type: mrr_at_5 value: 33.565 - type: ndcg_at_1 value: 26.328000000000003 - type: ndcg_at_10 value: 37.202 - type: ndcg_at_100 value: 42.728 - type: ndcg_at_1000 value: 44.792 - type: ndcg_at_3 value: 32.368 - type: ndcg_at_5 value: 35.008 - type: precision_at_1 value: 26.328000000000003 - type: precision_at_10 value: 5.7059999999999995 - type: precision_at_100 value: 0.8880000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 13.672 - type: precision_at_5 value: 9.74 - type: recall_at_1 value: 24.238 - type: recall_at_10 value: 49.829 - type: recall_at_100 value: 75.21 - type: recall_at_1000 value: 90.521 - type: recall_at_3 value: 36.867 - type: recall_at_5 value: 43.241 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.378 - type: map_at_10 value: 22.817999999999998 - type: map_at_100 value: 23.977999999999998 - type: map_at_1000 value: 24.108 - type: map_at_3 value: 20.719 - type: map_at_5 value: 21.889 - type: mrr_at_1 value: 19.03 - type: mrr_at_10 value: 27.022000000000002 - type: mrr_at_100 value: 28.011999999999997 - type: mrr_at_1000 value: 28.096 - type: mrr_at_3 value: 24.855 - type: mrr_at_5 value: 26.029999999999998 - type: ndcg_at_1 value: 19.03 - type: ndcg_at_10 value: 27.526 - type: ndcg_at_100 value: 33.040000000000006 - type: ndcg_at_1000 value: 36.187000000000005 - type: ndcg_at_3 value: 23.497 - type: ndcg_at_5 value: 25.334 - type: precision_at_1 value: 19.03 - type: precision_at_10 value: 4.963 - type: precision_at_100 value: 0.893 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 11.360000000000001 - type: precision_at_5 value: 8.134 - type: recall_at_1 value: 15.378 - type: recall_at_10 value: 38.061 - type: recall_at_100 value: 61.754 - type: recall_at_1000 value: 84.259 - type: recall_at_3 value: 26.788 - type: recall_at_5 value: 31.326999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.511999999999997 - type: map_at_10 value: 37.429 - type: map_at_100 value: 38.818000000000005 - type: map_at_1000 value: 38.924 - type: map_at_3 value: 34.625 - type: map_at_5 value: 36.064 - type: mrr_at_1 value: 33.300999999999995 - type: mrr_at_10 value: 43.036 - type: mrr_at_100 value: 43.894 - type: mrr_at_1000 value: 43.936 - type: mrr_at_3 value: 40.825 - type: mrr_at_5 value: 42.028 - type: ndcg_at_1 value: 33.300999999999995 - type: ndcg_at_10 value: 43.229 - type: ndcg_at_100 value: 48.992000000000004 - type: ndcg_at_1000 value: 51.02100000000001 - type: ndcg_at_3 value: 38.794000000000004 - type: ndcg_at_5 value: 40.65 - type: precision_at_1 value: 33.300999999999995 - type: precision_at_10 value: 7.777000000000001 - type: precision_at_100 value: 1.269 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 18.351 - type: precision_at_5 value: 12.762 - type: recall_at_1 value: 27.511999999999997 - type: recall_at_10 value: 54.788000000000004 - type: recall_at_100 value: 79.105 - type: recall_at_1000 value: 92.49199999999999 - type: recall_at_3 value: 41.924 - type: recall_at_5 value: 47.026 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.117 - type: map_at_10 value: 33.32 - type: map_at_100 value: 34.677 - type: map_at_1000 value: 34.78 - type: map_at_3 value: 30.233999999999998 - type: map_at_5 value: 31.668000000000003 - type: mrr_at_1 value: 29.566 - type: mrr_at_10 value: 38.244 - type: mrr_at_100 value: 39.245000000000005 - type: mrr_at_1000 value: 39.296 - type: mrr_at_3 value: 35.864000000000004 - type: mrr_at_5 value: 36.919999999999995 - type: ndcg_at_1 value: 29.566 - type: ndcg_at_10 value: 39.127 - type: ndcg_at_100 value: 44.989000000000004 - type: ndcg_at_1000 value: 47.189 - type: ndcg_at_3 value: 34.039 - type: ndcg_at_5 value: 35.744 - type: precision_at_1 value: 29.566 - type: precision_at_10 value: 7.385999999999999 - type: precision_at_100 value: 1.204 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 16.286 - type: precision_at_5 value: 11.484 - type: recall_at_1 value: 24.117 - type: recall_at_10 value: 51.559999999999995 - type: recall_at_100 value: 77.104 - type: recall_at_1000 value: 91.79899999999999 - type: recall_at_3 value: 36.82 - type: recall_at_5 value: 41.453 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.17625 - type: map_at_10 value: 34.063916666666664 - type: map_at_100 value: 35.255500000000005 - type: map_at_1000 value: 35.37275 - type: map_at_3 value: 31.351666666666667 - type: map_at_5 value: 32.80608333333333 - type: mrr_at_1 value: 29.59783333333333 - type: mrr_at_10 value: 38.0925 - type: mrr_at_100 value: 38.957249999999995 - type: mrr_at_1000 value: 39.01608333333333 - type: mrr_at_3 value: 35.77625 - type: mrr_at_5 value: 37.04991666666667 - type: ndcg_at_1 value: 29.59783333333333 - type: ndcg_at_10 value: 39.343666666666664 - type: ndcg_at_100 value: 44.488249999999994 - type: ndcg_at_1000 value: 46.83358333333334 - type: ndcg_at_3 value: 34.69708333333333 - type: ndcg_at_5 value: 36.75075 - type: precision_at_1 value: 29.59783333333333 - type: precision_at_10 value: 6.884083333333332 - type: precision_at_100 value: 1.114 - type: precision_at_1000 value: 0.15108333333333332 - type: precision_at_3 value: 15.965250000000003 - type: precision_at_5 value: 11.246500000000001 - type: recall_at_1 value: 25.17625 - type: recall_at_10 value: 51.015999999999984 - type: recall_at_100 value: 73.60174999999998 - type: recall_at_1000 value: 89.849 - type: recall_at_3 value: 37.88399999999999 - type: recall_at_5 value: 43.24541666666666 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.537 - type: map_at_10 value: 31.081999999999997 - type: map_at_100 value: 32.042 - type: map_at_1000 value: 32.141 - type: map_at_3 value: 29.137 - type: map_at_5 value: 30.079 - type: mrr_at_1 value: 27.454 - type: mrr_at_10 value: 33.694 - type: mrr_at_100 value: 34.579 - type: mrr_at_1000 value: 34.649 - type: mrr_at_3 value: 32.004 - type: mrr_at_5 value: 32.794000000000004 - type: ndcg_at_1 value: 27.454 - type: ndcg_at_10 value: 34.915 - type: ndcg_at_100 value: 39.641 - type: ndcg_at_1000 value: 42.105 - type: ndcg_at_3 value: 31.276 - type: ndcg_at_5 value: 32.65 - type: precision_at_1 value: 27.454 - type: precision_at_10 value: 5.337 - type: precision_at_100 value: 0.8250000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 13.241 - type: precision_at_5 value: 8.895999999999999 - type: recall_at_1 value: 24.537 - type: recall_at_10 value: 44.324999999999996 - type: recall_at_100 value: 65.949 - type: recall_at_1000 value: 84.017 - type: recall_at_3 value: 33.857 - type: recall_at_5 value: 37.316 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.122 - type: map_at_10 value: 24.32 - type: map_at_100 value: 25.338 - type: map_at_1000 value: 25.462 - type: map_at_3 value: 22.064 - type: map_at_5 value: 23.322000000000003 - type: mrr_at_1 value: 20.647 - type: mrr_at_10 value: 27.858 - type: mrr_at_100 value: 28.743999999999996 - type: mrr_at_1000 value: 28.819 - type: mrr_at_3 value: 25.769 - type: mrr_at_5 value: 26.964 - type: ndcg_at_1 value: 20.647 - type: ndcg_at_10 value: 28.849999999999998 - type: ndcg_at_100 value: 33.849000000000004 - type: ndcg_at_1000 value: 36.802 - type: ndcg_at_3 value: 24.799 - type: ndcg_at_5 value: 26.682 - type: precision_at_1 value: 20.647 - type: precision_at_10 value: 5.2170000000000005 - type: precision_at_100 value: 0.906 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 11.769 - type: precision_at_5 value: 8.486 - type: recall_at_1 value: 17.122 - type: recall_at_10 value: 38.999 - type: recall_at_100 value: 61.467000000000006 - type: recall_at_1000 value: 82.716 - type: recall_at_3 value: 27.601 - type: recall_at_5 value: 32.471 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.396 - type: map_at_10 value: 33.415 - type: map_at_100 value: 34.521 - type: map_at_1000 value: 34.631 - type: map_at_3 value: 30.703999999999997 - type: map_at_5 value: 32.166 - type: mrr_at_1 value: 28.825 - type: mrr_at_10 value: 37.397000000000006 - type: mrr_at_100 value: 38.286 - type: mrr_at_1000 value: 38.346000000000004 - type: mrr_at_3 value: 35.028 - type: mrr_at_5 value: 36.32 - type: ndcg_at_1 value: 28.825 - type: ndcg_at_10 value: 38.656 - type: ndcg_at_100 value: 43.856 - type: ndcg_at_1000 value: 46.31 - type: ndcg_at_3 value: 33.793 - type: ndcg_at_5 value: 35.909 - type: precision_at_1 value: 28.825 - type: precision_at_10 value: 6.567 - type: precision_at_100 value: 1.0330000000000001 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 15.516 - type: precision_at_5 value: 10.914 - type: recall_at_1 value: 24.396 - type: recall_at_10 value: 50.747 - type: recall_at_100 value: 73.477 - type: recall_at_1000 value: 90.801 - type: recall_at_3 value: 37.1 - type: recall_at_5 value: 42.589 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.072 - type: map_at_10 value: 34.307 - type: map_at_100 value: 35.725 - type: map_at_1000 value: 35.943999999999996 - type: map_at_3 value: 30.906 - type: map_at_5 value: 32.818000000000005 - type: mrr_at_1 value: 29.644 - type: mrr_at_10 value: 38.673 - type: mrr_at_100 value: 39.459 - type: mrr_at_1000 value: 39.527 - type: mrr_at_3 value: 35.771 - type: mrr_at_5 value: 37.332 - type: ndcg_at_1 value: 29.644 - type: ndcg_at_10 value: 40.548 - type: ndcg_at_100 value: 45.678999999999995 - type: ndcg_at_1000 value: 48.488 - type: ndcg_at_3 value: 34.887 - type: ndcg_at_5 value: 37.543 - type: precision_at_1 value: 29.644 - type: precision_at_10 value: 7.688000000000001 - type: precision_at_100 value: 1.482 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 16.206 - type: precision_at_5 value: 12.016 - type: recall_at_1 value: 25.072 - type: recall_at_10 value: 53.478 - type: recall_at_100 value: 76.07300000000001 - type: recall_at_1000 value: 93.884 - type: recall_at_3 value: 37.583 - type: recall_at_5 value: 44.464 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 20.712 - type: map_at_10 value: 27.467999999999996 - type: map_at_100 value: 28.502 - type: map_at_1000 value: 28.610000000000003 - type: map_at_3 value: 24.887999999999998 - type: map_at_5 value: 26.273999999999997 - type: mrr_at_1 value: 22.736 - type: mrr_at_10 value: 29.553 - type: mrr_at_100 value: 30.485 - type: mrr_at_1000 value: 30.56 - type: mrr_at_3 value: 27.078999999999997 - type: mrr_at_5 value: 28.401 - type: ndcg_at_1 value: 22.736 - type: ndcg_at_10 value: 32.023 - type: ndcg_at_100 value: 37.158 - type: ndcg_at_1000 value: 39.823 - type: ndcg_at_3 value: 26.951999999999998 - type: ndcg_at_5 value: 29.281000000000002 - type: precision_at_1 value: 22.736 - type: precision_at_10 value: 5.213 - type: precision_at_100 value: 0.832 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 11.459999999999999 - type: precision_at_5 value: 8.244 - type: recall_at_1 value: 20.712 - type: recall_at_10 value: 44.057 - type: recall_at_100 value: 67.944 - type: recall_at_1000 value: 87.925 - type: recall_at_3 value: 30.305 - type: recall_at_5 value: 36.071999999999996 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.181999999999999 - type: map_at_10 value: 16.66 - type: map_at_100 value: 18.273 - type: map_at_1000 value: 18.45 - type: map_at_3 value: 14.141 - type: map_at_5 value: 15.455 - type: mrr_at_1 value: 22.15 - type: mrr_at_10 value: 32.062000000000005 - type: mrr_at_100 value: 33.116 - type: mrr_at_1000 value: 33.168 - type: mrr_at_3 value: 28.827 - type: mrr_at_5 value: 30.892999999999997 - type: ndcg_at_1 value: 22.15 - type: ndcg_at_10 value: 23.532 - type: ndcg_at_100 value: 30.358 - type: ndcg_at_1000 value: 33.783 - type: ndcg_at_3 value: 19.222 - type: ndcg_at_5 value: 20.919999999999998 - type: precision_at_1 value: 22.15 - type: precision_at_10 value: 7.185999999999999 - type: precision_at_100 value: 1.433 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 13.941 - type: precision_at_5 value: 10.906 - type: recall_at_1 value: 10.181999999999999 - type: recall_at_10 value: 28.104000000000003 - type: recall_at_100 value: 51.998999999999995 - type: recall_at_1000 value: 71.311 - type: recall_at_3 value: 17.698 - type: recall_at_5 value: 22.262999999999998 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 6.669 - type: map_at_10 value: 15.552 - type: map_at_100 value: 21.865000000000002 - type: map_at_1000 value: 23.268 - type: map_at_3 value: 11.309 - type: map_at_5 value: 13.084000000000001 - type: mrr_at_1 value: 55.50000000000001 - type: mrr_at_10 value: 66.46600000000001 - type: mrr_at_100 value: 66.944 - type: mrr_at_1000 value: 66.956 - type: mrr_at_3 value: 64.542 - type: mrr_at_5 value: 65.717 - type: ndcg_at_1 value: 44.75 - type: ndcg_at_10 value: 35.049 - type: ndcg_at_100 value: 39.073 - type: ndcg_at_1000 value: 46.208 - type: ndcg_at_3 value: 39.525 - type: ndcg_at_5 value: 37.156 - type: precision_at_1 value: 55.50000000000001 - type: precision_at_10 value: 27.800000000000004 - type: precision_at_100 value: 9.013 - type: precision_at_1000 value: 1.8800000000000001 - type: precision_at_3 value: 42.667 - type: precision_at_5 value: 36.0 - type: recall_at_1 value: 6.669 - type: recall_at_10 value: 21.811 - type: recall_at_100 value: 45.112 - type: recall_at_1000 value: 67.806 - type: recall_at_3 value: 13.373 - type: recall_at_5 value: 16.615 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.769999999999996 - type: f1 value: 42.91448356376592 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 54.013 - type: map_at_10 value: 66.239 - type: map_at_100 value: 66.62599999999999 - type: map_at_1000 value: 66.644 - type: map_at_3 value: 63.965 - type: map_at_5 value: 65.45400000000001 - type: mrr_at_1 value: 58.221000000000004 - type: mrr_at_10 value: 70.43700000000001 - type: mrr_at_100 value: 70.744 - type: mrr_at_1000 value: 70.75099999999999 - type: mrr_at_3 value: 68.284 - type: mrr_at_5 value: 69.721 - type: ndcg_at_1 value: 58.221000000000004 - type: ndcg_at_10 value: 72.327 - type: ndcg_at_100 value: 73.953 - type: ndcg_at_1000 value: 74.312 - type: ndcg_at_3 value: 68.062 - type: ndcg_at_5 value: 70.56400000000001 - type: precision_at_1 value: 58.221000000000004 - type: precision_at_10 value: 9.521 - type: precision_at_100 value: 1.045 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 27.348 - type: precision_at_5 value: 17.794999999999998 - type: recall_at_1 value: 54.013 - type: recall_at_10 value: 86.957 - type: recall_at_100 value: 93.911 - type: recall_at_1000 value: 96.38 - type: recall_at_3 value: 75.555 - type: recall_at_5 value: 81.671 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 21.254 - type: map_at_10 value: 33.723 - type: map_at_100 value: 35.574 - type: map_at_1000 value: 35.730000000000004 - type: map_at_3 value: 29.473 - type: map_at_5 value: 31.543 - type: mrr_at_1 value: 41.358 - type: mrr_at_10 value: 49.498 - type: mrr_at_100 value: 50.275999999999996 - type: mrr_at_1000 value: 50.308 - type: mrr_at_3 value: 47.016000000000005 - type: mrr_at_5 value: 48.336 - type: ndcg_at_1 value: 41.358 - type: ndcg_at_10 value: 41.579 - type: ndcg_at_100 value: 48.455 - type: ndcg_at_1000 value: 51.165000000000006 - type: ndcg_at_3 value: 37.681 - type: ndcg_at_5 value: 38.49 - type: precision_at_1 value: 41.358 - type: precision_at_10 value: 11.543000000000001 - type: precision_at_100 value: 1.87 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 24.743000000000002 - type: precision_at_5 value: 17.994 - type: recall_at_1 value: 21.254 - type: recall_at_10 value: 48.698 - type: recall_at_100 value: 74.588 - type: recall_at_1000 value: 91.00200000000001 - type: recall_at_3 value: 33.939 - type: recall_at_5 value: 39.367000000000004 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 35.922 - type: map_at_10 value: 52.32599999999999 - type: map_at_100 value: 53.18000000000001 - type: map_at_1000 value: 53.245 - type: map_at_3 value: 49.294 - type: map_at_5 value: 51.202999999999996 - type: mrr_at_1 value: 71.843 - type: mrr_at_10 value: 78.24600000000001 - type: mrr_at_100 value: 78.515 - type: mrr_at_1000 value: 78.527 - type: mrr_at_3 value: 77.17500000000001 - type: mrr_at_5 value: 77.852 - type: ndcg_at_1 value: 71.843 - type: ndcg_at_10 value: 61.379 - type: ndcg_at_100 value: 64.535 - type: ndcg_at_1000 value: 65.888 - type: ndcg_at_3 value: 56.958 - type: ndcg_at_5 value: 59.434 - type: precision_at_1 value: 71.843 - type: precision_at_10 value: 12.686 - type: precision_at_100 value: 1.517 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 35.778 - type: precision_at_5 value: 23.422 - type: recall_at_1 value: 35.922 - type: recall_at_10 value: 63.43 - type: recall_at_100 value: 75.868 - type: recall_at_1000 value: 84.88900000000001 - type: recall_at_3 value: 53.666000000000004 - type: recall_at_5 value: 58.555 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 79.4408 - type: ap value: 73.52820871620366 - type: f1 value: 79.36240238685001 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.826999999999998 - type: map_at_10 value: 34.04 - type: map_at_100 value: 35.226 - type: map_at_1000 value: 35.275 - type: map_at_3 value: 30.165999999999997 - type: map_at_5 value: 32.318000000000005 - type: mrr_at_1 value: 22.464000000000002 - type: mrr_at_10 value: 34.631 - type: mrr_at_100 value: 35.752 - type: mrr_at_1000 value: 35.795 - type: mrr_at_3 value: 30.798 - type: mrr_at_5 value: 32.946999999999996 - type: ndcg_at_1 value: 22.464000000000002 - type: ndcg_at_10 value: 40.919 - type: ndcg_at_100 value: 46.632 - type: ndcg_at_1000 value: 47.833 - type: ndcg_at_3 value: 32.992 - type: ndcg_at_5 value: 36.834 - type: precision_at_1 value: 22.464000000000002 - type: precision_at_10 value: 6.494 - type: precision_at_100 value: 0.9369999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.021 - type: precision_at_5 value: 10.347000000000001 - type: recall_at_1 value: 21.826999999999998 - type: recall_at_10 value: 62.132 - type: recall_at_100 value: 88.55199999999999 - type: recall_at_1000 value: 97.707 - type: recall_at_3 value: 40.541 - type: recall_at_5 value: 49.739 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 95.68399452804377 - type: f1 value: 95.25490609832268 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 83.15321477428182 - type: f1 value: 60.35476439087966 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.92669804976462 - type: f1 value: 69.22815107207565 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.4855413584398 - type: f1 value: 72.92107516103387 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 32.412679360205544 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 28.09211869875204 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.540919056982545 - type: mrr value: 31.529904607063536 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.745 - type: map_at_10 value: 12.013 - type: map_at_100 value: 15.040000000000001 - type: map_at_1000 value: 16.427 - type: map_at_3 value: 8.841000000000001 - type: map_at_5 value: 10.289 - type: mrr_at_1 value: 45.201 - type: mrr_at_10 value: 53.483999999999995 - type: mrr_at_100 value: 54.20700000000001 - type: mrr_at_1000 value: 54.252 - type: mrr_at_3 value: 51.29 - type: mrr_at_5 value: 52.73 - type: ndcg_at_1 value: 43.808 - type: ndcg_at_10 value: 32.445 - type: ndcg_at_100 value: 30.031000000000002 - type: ndcg_at_1000 value: 39.007 - type: ndcg_at_3 value: 37.204 - type: ndcg_at_5 value: 35.07 - type: precision_at_1 value: 45.201 - type: precision_at_10 value: 23.684 - type: precision_at_100 value: 7.600999999999999 - type: precision_at_1000 value: 2.043 - type: precision_at_3 value: 33.953 - type: precision_at_5 value: 29.412 - type: recall_at_1 value: 5.745 - type: recall_at_10 value: 16.168 - type: recall_at_100 value: 30.875999999999998 - type: recall_at_1000 value: 62.686 - type: recall_at_3 value: 9.75 - type: recall_at_5 value: 12.413 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 37.828 - type: map_at_10 value: 53.239000000000004 - type: map_at_100 value: 54.035999999999994 - type: map_at_1000 value: 54.067 - type: map_at_3 value: 49.289 - type: map_at_5 value: 51.784 - type: mrr_at_1 value: 42.497 - type: mrr_at_10 value: 55.916999999999994 - type: mrr_at_100 value: 56.495 - type: mrr_at_1000 value: 56.516999999999996 - type: mrr_at_3 value: 52.800000000000004 - type: mrr_at_5 value: 54.722 - type: ndcg_at_1 value: 42.468 - type: ndcg_at_10 value: 60.437 - type: ndcg_at_100 value: 63.731 - type: ndcg_at_1000 value: 64.41799999999999 - type: ndcg_at_3 value: 53.230999999999995 - type: ndcg_at_5 value: 57.26 - type: precision_at_1 value: 42.468 - type: precision_at_10 value: 9.47 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 23.724999999999998 - type: precision_at_5 value: 16.593 - type: recall_at_1 value: 37.828 - type: recall_at_10 value: 79.538 - type: recall_at_100 value: 93.646 - type: recall_at_1000 value: 98.72999999999999 - type: recall_at_3 value: 61.134 - type: recall_at_5 value: 70.377 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.548 - type: map_at_10 value: 84.466 - type: map_at_100 value: 85.10600000000001 - type: map_at_1000 value: 85.123 - type: map_at_3 value: 81.57600000000001 - type: map_at_5 value: 83.399 - type: mrr_at_1 value: 81.24 - type: mrr_at_10 value: 87.457 - type: mrr_at_100 value: 87.574 - type: mrr_at_1000 value: 87.575 - type: mrr_at_3 value: 86.507 - type: mrr_at_5 value: 87.205 - type: ndcg_at_1 value: 81.25 - type: ndcg_at_10 value: 88.203 - type: ndcg_at_100 value: 89.457 - type: ndcg_at_1000 value: 89.563 - type: ndcg_at_3 value: 85.465 - type: ndcg_at_5 value: 87.007 - type: precision_at_1 value: 81.25 - type: precision_at_10 value: 13.373 - type: precision_at_100 value: 1.5270000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.417 - type: precision_at_5 value: 24.556 - type: recall_at_1 value: 70.548 - type: recall_at_10 value: 95.208 - type: recall_at_100 value: 99.514 - type: recall_at_1000 value: 99.988 - type: recall_at_3 value: 87.214 - type: recall_at_5 value: 91.696 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 53.04822095496839 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.30778476474675 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.692 - type: map_at_10 value: 11.766 - type: map_at_100 value: 13.904 - type: map_at_1000 value: 14.216999999999999 - type: map_at_3 value: 8.245 - type: map_at_5 value: 9.92 - type: mrr_at_1 value: 23.0 - type: mrr_at_10 value: 33.78 - type: mrr_at_100 value: 34.922 - type: mrr_at_1000 value: 34.973 - type: mrr_at_3 value: 30.2 - type: mrr_at_5 value: 32.565 - type: ndcg_at_1 value: 23.0 - type: ndcg_at_10 value: 19.863 - type: ndcg_at_100 value: 28.141 - type: ndcg_at_1000 value: 33.549 - type: ndcg_at_3 value: 18.434 - type: ndcg_at_5 value: 16.384 - type: precision_at_1 value: 23.0 - type: precision_at_10 value: 10.39 - type: precision_at_100 value: 2.235 - type: precision_at_1000 value: 0.35300000000000004 - type: precision_at_3 value: 17.133000000000003 - type: precision_at_5 value: 14.44 - type: recall_at_1 value: 4.692 - type: recall_at_10 value: 21.025 - type: recall_at_100 value: 45.324999999999996 - type: recall_at_1000 value: 71.675 - type: recall_at_3 value: 10.440000000000001 - type: recall_at_5 value: 14.64 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.96178184892842 - type: cos_sim_spearman value: 79.6487740813199 - type: euclidean_pearson value: 82.06661161625023 - type: euclidean_spearman value: 79.64876769031183 - type: manhattan_pearson value: 82.07061164575131 - type: manhattan_spearman value: 79.65197039464537 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.15305604100027 - type: cos_sim_spearman value: 74.27447427941591 - type: euclidean_pearson value: 80.52737337565307 - type: euclidean_spearman value: 74.27416077132192 - type: manhattan_pearson value: 80.53728571140387 - type: manhattan_spearman value: 74.28853605753457 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.44386080639279 - type: cos_sim_spearman value: 84.17947648159536 - type: euclidean_pearson value: 83.34145388129387 - type: euclidean_spearman value: 84.17947648159536 - type: manhattan_pearson value: 83.30699061927966 - type: manhattan_spearman value: 84.18125737380451 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.57392220985612 - type: cos_sim_spearman value: 78.80745014464101 - type: euclidean_pearson value: 80.01660371487199 - type: euclidean_spearman value: 78.80741240102256 - type: manhattan_pearson value: 79.96810779507953 - type: manhattan_spearman value: 78.75600400119448 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.85421063026625 - type: cos_sim_spearman value: 87.55320285299192 - type: euclidean_pearson value: 86.69750143323517 - type: euclidean_spearman value: 87.55320284326378 - type: manhattan_pearson value: 86.63379169960379 - type: manhattan_spearman value: 87.4815029877984 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.31314130411842 - type: cos_sim_spearman value: 85.3489588181433 - type: euclidean_pearson value: 84.13240933463535 - type: euclidean_spearman value: 85.34902871403281 - type: manhattan_pearson value: 84.01183086503559 - type: manhattan_spearman value: 85.19316703166102 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.09979781689536 - type: cos_sim_spearman value: 88.87813323759015 - type: euclidean_pearson value: 88.65413031123792 - type: euclidean_spearman value: 88.87813323759015 - type: manhattan_pearson value: 88.61818758256024 - type: manhattan_spearman value: 88.81044100494604 - 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: 62.30693258111531 - type: cos_sim_spearman value: 62.195516523251946 - type: euclidean_pearson value: 62.951283701049476 - type: euclidean_spearman value: 62.195516523251946 - type: manhattan_pearson value: 63.068322281439535 - type: manhattan_spearman value: 62.10621171028406 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.27092833763909 - type: cos_sim_spearman value: 84.84429717949759 - type: euclidean_pearson value: 84.8516966060792 - type: euclidean_spearman value: 84.84429717949759 - type: manhattan_pearson value: 84.82203139242881 - type: manhattan_spearman value: 84.8358503952945 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 83.10290863981409 - type: mrr value: 95.31168450286097 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 52.161 - type: map_at_10 value: 62.138000000000005 - type: map_at_100 value: 62.769 - type: map_at_1000 value: 62.812 - type: map_at_3 value: 59.111000000000004 - type: map_at_5 value: 60.995999999999995 - type: mrr_at_1 value: 55.333 - type: mrr_at_10 value: 63.504000000000005 - type: mrr_at_100 value: 64.036 - type: mrr_at_1000 value: 64.08 - type: mrr_at_3 value: 61.278 - type: mrr_at_5 value: 62.778 - type: ndcg_at_1 value: 55.333 - type: ndcg_at_10 value: 66.678 - type: ndcg_at_100 value: 69.415 - type: ndcg_at_1000 value: 70.453 - type: ndcg_at_3 value: 61.755 - type: ndcg_at_5 value: 64.546 - type: precision_at_1 value: 55.333 - type: precision_at_10 value: 9.033 - type: precision_at_100 value: 1.043 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 24.221999999999998 - type: precision_at_5 value: 16.333000000000002 - type: recall_at_1 value: 52.161 - type: recall_at_10 value: 79.156 - type: recall_at_100 value: 91.333 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 66.43299999999999 - type: recall_at_5 value: 73.272 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81287128712871 - type: cos_sim_ap value: 95.30034785910676 - type: cos_sim_f1 value: 90.28629856850716 - type: cos_sim_precision value: 92.36401673640168 - type: cos_sim_recall value: 88.3 - type: dot_accuracy value: 99.81287128712871 - type: dot_ap value: 95.30034785910676 - type: dot_f1 value: 90.28629856850716 - type: dot_precision value: 92.36401673640168 - type: dot_recall value: 88.3 - type: euclidean_accuracy value: 99.81287128712871 - type: euclidean_ap value: 95.30034785910676 - type: euclidean_f1 value: 90.28629856850716 - type: euclidean_precision value: 92.36401673640168 - type: euclidean_recall value: 88.3 - type: manhattan_accuracy value: 99.80990099009901 - type: manhattan_ap value: 95.26880751950654 - type: manhattan_f1 value: 90.22177419354838 - type: manhattan_precision value: 90.95528455284553 - type: manhattan_recall value: 89.5 - type: max_accuracy value: 99.81287128712871 - type: max_ap value: 95.30034785910676 - type: max_f1 value: 90.28629856850716 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 58.518662504351184 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.96168178378587 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.04862593471896 - type: mrr value: 52.97238402936932 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.092545236479946 - type: cos_sim_spearman value: 31.599851000175498 - type: dot_pearson value: 30.092542723901676 - type: dot_spearman value: 31.599851000175498 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.189 - type: map_at_10 value: 1.662 - type: map_at_100 value: 9.384 - type: map_at_1000 value: 22.669 - type: map_at_3 value: 0.5559999999999999 - type: map_at_5 value: 0.9039999999999999 - type: mrr_at_1 value: 68.0 - type: mrr_at_10 value: 81.01899999999999 - type: mrr_at_100 value: 81.01899999999999 - type: mrr_at_1000 value: 81.01899999999999 - type: mrr_at_3 value: 79.333 - type: mrr_at_5 value: 80.733 - type: ndcg_at_1 value: 63.0 - type: ndcg_at_10 value: 65.913 - type: ndcg_at_100 value: 51.895 - type: ndcg_at_1000 value: 46.967 - type: ndcg_at_3 value: 65.49199999999999 - type: ndcg_at_5 value: 66.69699999999999 - type: precision_at_1 value: 68.0 - type: precision_at_10 value: 71.6 - type: precision_at_100 value: 53.66 - type: precision_at_1000 value: 21.124000000000002 - type: precision_at_3 value: 72.667 - type: precision_at_5 value: 74.0 - type: recall_at_1 value: 0.189 - type: recall_at_10 value: 1.913 - type: recall_at_100 value: 12.601999999999999 - type: recall_at_1000 value: 44.296 - type: recall_at_3 value: 0.605 - type: recall_at_5 value: 1.018 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.701 - type: map_at_10 value: 10.445 - type: map_at_100 value: 17.324 - type: map_at_1000 value: 19.161 - type: map_at_3 value: 5.497 - type: map_at_5 value: 7.278 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 45.534 - type: mrr_at_100 value: 45.792 - type: mrr_at_1000 value: 45.806999999999995 - type: mrr_at_3 value: 37.755 - type: mrr_at_5 value: 43.469 - type: ndcg_at_1 value: 26.531 - type: ndcg_at_10 value: 26.235000000000003 - type: ndcg_at_100 value: 39.17 - type: ndcg_at_1000 value: 51.038 - type: ndcg_at_3 value: 23.625 - type: ndcg_at_5 value: 24.338 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 24.285999999999998 - type: precision_at_100 value: 8.224 - type: precision_at_1000 value: 1.6179999999999999 - type: precision_at_3 value: 24.490000000000002 - type: precision_at_5 value: 24.898 - type: recall_at_1 value: 2.701 - type: recall_at_10 value: 17.997 - type: recall_at_100 value: 51.766999999999996 - type: recall_at_1000 value: 87.863 - type: recall_at_3 value: 6.295000000000001 - type: recall_at_5 value: 9.993 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 73.3474 - type: ap value: 15.393431414459924 - type: f1 value: 56.466681887882416 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 62.062818336163 - type: f1 value: 62.11230840463252 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 42.464892820845115 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.15962329379508 - type: cos_sim_ap value: 74.73674057919256 - type: cos_sim_f1 value: 68.81245642574947 - type: cos_sim_precision value: 61.48255813953488 - type: cos_sim_recall value: 78.12664907651715 - type: dot_accuracy value: 86.15962329379508 - type: dot_ap value: 74.7367634988281 - type: dot_f1 value: 68.81245642574947 - type: dot_precision value: 61.48255813953488 - type: dot_recall value: 78.12664907651715 - type: euclidean_accuracy value: 86.15962329379508 - type: euclidean_ap value: 74.7367761466634 - type: euclidean_f1 value: 68.81245642574947 - type: euclidean_precision value: 61.48255813953488 - type: euclidean_recall value: 78.12664907651715 - type: manhattan_accuracy value: 86.21326816474935 - type: manhattan_ap value: 74.64416473733951 - type: manhattan_f1 value: 68.80924855491331 - type: manhattan_precision value: 61.23456790123457 - type: manhattan_recall value: 78.52242744063325 - type: max_accuracy value: 86.21326816474935 - type: max_ap value: 74.7367761466634 - type: max_f1 value: 68.81245642574947 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.97620988085536 - type: cos_sim_ap value: 86.08680845745758 - type: cos_sim_f1 value: 78.02793637114438 - type: cos_sim_precision value: 73.11082699683736 - type: cos_sim_recall value: 83.65414228518632 - type: dot_accuracy value: 88.97620988085536 - type: dot_ap value: 86.08681149437946 - type: dot_f1 value: 78.02793637114438 - type: dot_precision value: 73.11082699683736 - type: dot_recall value: 83.65414228518632 - type: euclidean_accuracy value: 88.97620988085536 - type: euclidean_ap value: 86.08681215460771 - type: euclidean_f1 value: 78.02793637114438 - type: euclidean_precision value: 73.11082699683736 - type: euclidean_recall value: 83.65414228518632 - type: manhattan_accuracy value: 88.88888888888889 - type: manhattan_ap value: 86.02916327562438 - type: manhattan_f1 value: 78.02063045516843 - type: manhattan_precision value: 73.38851947346994 - type: manhattan_recall value: 83.2768709578072 - type: max_accuracy value: 88.97620988085536 - type: max_ap value: 86.08681215460771 - type: max_f1 value: 78.02793637114438 --- <!-- TODO: add evaluation results here --> <br><br> <p align="center"> <img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> </p> <p align="center"> <b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> </p> ## Quick Start The easiest way to starting using `jina-embeddings-v2-base-en` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/). ## Intended Usage & Model Info `jina-embeddings-v2-base-en` is an English, monolingual **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length. The backbone `jina-bert-v2-base-en` is pretrained on the C4 dataset. The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc. With a standard size of 137 million parameters, the model enables fast inference while delivering better performance than our small model. It is recommended to use a single GPU for inference. Additionally, we provide the following embedding models: - [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters. - [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters **(you are here)**. - [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): Chinese-English Bilingual embeddings. - [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): German-English Bilingual embeddings. - [`jina-embeddings-v2-base-es`](https://huggingface.co/jinaai/jina-embeddings-v2-base-es): Spanish-English Bilingual embeddings. ## Data & Parameters Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923) ## Usage **<details><summary>Please apply mean pooling when integrating the model.</summary>** <p> ### Why mean pooling? `mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. It has been proved to be the most effective way to produce high-quality sentence embeddings. We offer an `encode` function to deal with this. However, if you would like to do it without using the default `encode` function: ```python import torch import torch.nn.functional as F from transformers import AutoTokenizer, AutoModel def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] 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 = ['How is the weather today?', 'What is the current weather like today?'] tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en') model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True) encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') with torch.no_grad(): model_output = model(**encoded_input) embeddings = mean_pooling(model_output, encoded_input['attention_mask']) embeddings = F.normalize(embeddings, p=2, dim=1) ``` </p> </details> You can use Jina Embedding models directly from transformers package. ```python !pip install transformers from transformers import AutoModel from numpy.linalg import norm cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b)) model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?']) print(cos_sim(embeddings[0], embeddings[1])) ``` If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function: ```python embeddings = model.encode( ['Very long ... document'], max_length=2048 ) ``` Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well): ```python !pip install -U sentence-transformers from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( "jinaai/jina-embeddings-v2-base-en", # switch to en/zh for English or Chinese trust_remote_code=True ) # control your input sequence length up to 8192 model.max_seq_length = 1024 embeddings = model.encode([ 'How is the weather today?', 'What is the current weather like today?' ]) print(cos_sim(embeddings[0], embeddings[1])) ``` ## Alternatives to Using Transformers (or SentencTransformers) Package 1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). 2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy). ## Use Jina Embeddings for RAG According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83), > In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out. <img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px"> ## Plans 1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese. 2. Multimodal embedding models enable Multimodal RAG applications. 3. High-performt rerankers. ## Trouble Shooting **Loading of Model Code failed** If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized. This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model: ```bash Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ... ``` **User is not logged into Huggingface** The model is only availabe under [gated access](https://huggingface.co/docs/hub/models-gated). This means you need to be logged into huggingface load load it. If you receive the following error, you need to provide an access token, either by using the huggingface-cli or providing the token via an environment variable as described above: ```bash OSError: jinaai/jina-embeddings-v2-base-en is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models' If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`. ``` ## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## Citation If you find Jina Embeddings useful in your research, please cite the following paper: ``` @misc{günther2023jina, title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao}, year={2023}, eprint={2310.19923}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

myshell-ai/MeloTTS-English

myshell-ai

transformers

text-to-speech

--- license: mit language: - ko pipeline_tag: text-to-speech --- # MeloTTS MeloTTS is a **high-quality multi-lingual** text-to-speech library by [MyShell.ai](https://myshell.ai). Supported languages include: | Model card | Example | | --- | --- | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (American) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-US/speed_1.0/sent_000.wav) | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (British) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-BR/speed_1.0/sent_000.wav) | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Indian) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN_INDIA/speed_1.0/sent_000.wav) | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Australian) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-AU/speed_1.0/sent_000.wav) | | [English](https://huggingface.co/myshell-ai/MeloTTS-English-v2) (Default) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/en/EN-Default/speed_1.0/sent_000.wav) | | [Spanish](https://huggingface.co/myshell-ai/MeloTTS-Spanish) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/es/ES/speed_1.0/sent_000.wav) | | [French](https://huggingface.co/myshell-ai/MeloTTS-French) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/fr/FR/speed_1.0/sent_000.wav) | | [Chinese](https://huggingface.co/myshell-ai/MeloTTS-Chinese) (mix EN) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/zh/ZH/speed_1.0/sent_008.wav) | | [Japanese](https://huggingface.co/myshell-ai/MeloTTS-Japanese) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/jp/JP/speed_1.0/sent_000.wav) | | [Korean](https://huggingface.co/myshell-ai/MeloTTS-Korean/) | [Link](https://myshell-public-repo-hosting.s3.amazonaws.com/myshellttsbase/examples/kr/KR/speed_1.0/sent_000.wav) | Some other features include: - The Chinese speaker supports `mixed Chinese and English`. - Fast enough for `CPU real-time inference`. ## Usage ### Without Installation An unofficial [live demo](https://huggingface.co/spaces/mrfakename/MeloTTS) is hosted on Hugging Face Spaces. #### Use it on MyShell There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples [here](https://github.com/myshell-ai/MeloTTS/blob/main/docs/quick_use.md#use-melotts-without-installation). More can be found at the widget center of [MyShell.ai](https://app.myshell.ai/robot-workshop). ### Install and Use Locally Follow the installation steps [here](https://github.com/myshell-ai/MeloTTS/blob/main/docs/install.md#linux-and-macos-install) before using the following snippet: ```python from melo.api import TTS # Speed is adjustable speed = 1.0 # CPU is sufficient for real-time inference. # You can set it manually to 'cpu' or 'cuda' or 'cuda:0' or 'mps' device = 'auto' # Will automatically use GPU if available # English text = "Did you ever hear a folk tale about a giant turtle?" model = TTS(language='EN', device=device) speaker_ids = model.hps.data.spk2id # American accent output_path = 'en-us.wav' model.tts_to_file(text, speaker_ids['EN-US'], output_path, speed=speed) # British accent output_path = 'en-br.wav' model.tts_to_file(text, speaker_ids['EN-BR'], output_path, speed=speed) # Indian accent output_path = 'en-india.wav' model.tts_to_file(text, speaker_ids['EN_INDIA'], output_path, speed=speed) # Australian accent output_path = 'en-au.wav' model.tts_to_file(text, speaker_ids['EN-AU'], output_path, speed=speed) # Default accent output_path = 'en-default.wav' model.tts_to_file(text, speaker_ids['EN-Default'], output_path, speed=speed) ``` ## Join the Community **Open Source AI Grant** We are actively sponsoring open-source AI projects. The sponsorship includes GPU resources, fundings and intellectual support (collaboration with top research labs). We welcome both reseach and engineering projects, as long as the open-source community needs them. Please contact [Zengyi Qin](https://www.qinzy.tech/) if you are interested. **Contributing** If you find this work useful, please consider contributing to the GitHub [repo](https://github.com/myshell-ai/MeloTTS). - Many thanks to [@fakerybakery](https://github.com/fakerybakery) for adding the Web UI and CLI part. ## License This library is under MIT License, which means it is free for both commercial and non-commercial use. ## Acknowledgements This implementation is based on [TTS](https://github.com/coqui-ai/TTS), [VITS](https://github.com/jaywalnut310/vits), [VITS2](https://github.com/daniilrobnikov/vits2) and [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2). We appreciate their awesome work.

meta-llama/Llama-2-7b-hf

meta-llama

transformers

text-generation

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

emilyalsentzer/Bio_ClinicalBERT

emilyalsentzer

transformers

fill-mask

--- language: "en" tags: - fill-mask license: mit --- # ClinicalBERT - Bio + Clinical BERT Model The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries. This model card describes the Bio+Clinical BERT model, which was initialized from [BioBERT](https://arxiv.org/abs/1901.08746) & trained on all MIMIC notes. ## Pretraining Data The `Bio_ClinicalBERT` model was trained on all notes from [MIMIC III](https://www.nature.com/articles/sdata201635), a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see [here](https://mimic.physionet.org/). All notes from the `NOTEEVENTS` table were included (~880M words). ## Model Pretraining ### Note Preprocessing Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (`en core sci md` tokenizer). ### Pretraining Procedures The model was trained using code from [Google's BERT repository](https://github.com/google-research/bert) on a GeForce GTX TITAN X 12 GB GPU. Model parameters were initialized with BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`). ### Pretraining Hyperparameters We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15 and max predictions per sequence = 20). ## How to use the model Load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT") model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT") ``` ## More Information Refer to the original paper, [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) (NAACL Clinical NLP Workshop 2019) for additional details and performance on NLI and NER tasks. ## Questions? Post a Github issue on the [clinicalBERT repo](https://github.com/EmilyAlsentzer/clinicalBERT) or email emilya@mit.edu with any questions.

lmsys/fastchat-t5-3b-v1.0

lmsys

transformers

text2text-generation

--- license: apache-2.0 inference: false --- # FastChat-T5 Model Card ## Model details **Model type:** FastChat-T5 is an open-source chatbot trained by fine-tuning Flan-t5-xl (3B parameters) on user-shared conversations collected from ShareGPT. It is based on an encoder-decoder transformer architecture, and can autoregressively generate responses to users' inputs. **Model date:** FastChat-T5 was trained on April 2023. **Organizations developing the model:** The FastChat developers, primarily Dacheng Li, Lianmin Zheng and Hao Zhang. **Paper or resources for more information:** https://github.com/lm-sys/FastChat#FastChat-T5 **License:** Apache License 2.0 **Where to send questions or comments about the model:** https://github.com/lm-sys/FastChat/issues ## Intended use **Primary intended uses:** The primary use of FastChat-T5 is the commercial usage of large language models and chatbots. It can also be used for research purposes. **Primary intended users:** The primary intended users of the model are entrepreneurs and researchers in natural language processing, machine learning, and artificial intelligence. ## Training dataset 70K conversations collected from ShareGPT.com. ## Training details It processes the ShareGPT data in the form of question answering. Each ChatGPT response is processed as an answer, and previous conversations between the user and the ChatGPT are processed as the question. The encoder bi-directionally encodes a question into a hidden representation. The decoder uses cross-attention to attend to this representation while generating an answer uni-directionally from a start token. This model is fine-tuned for 3 epochs, with a max learning rate 2e-5, warmup ratio 0.03, and a cosine learning rate schedule. ## Evaluation dataset A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.

microsoft/phi-2

microsoft

transformers

text-generation

--- license: mit license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE language: - en pipeline_tag: text-generation tags: - nlp - code --- ## Model Summary Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as [Phi-1.5](https://huggingface.co/microsoft/phi-1.5), augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters. Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more. ## How to Use Phi-2 was integrated in `transformers` version 4.37. If you need to use an earlier version, you need to pass `trust_remote_code=True` to the `from_pretrained()` function. Phi-2 is known for having an attention overflow issue (with FP16). If you are facing this issue, please enable/disable autocast on the [PhiAttention.forward()](https://huggingface.co/microsoft/phi-2/blob/main/modeling_phi.py#L306) function. ## Intended Uses Given the nature of the training data, the Phi-2 model is best suited for prompts using the QA format, the chat format, and the code format. ### QA Format: You can provide the prompt as a standalone question as follows: ```markdown Write a detailed analogy between mathematics and a lighthouse. ``` where the model generates the text after "." . To encourage the model to write more concise answers, you can also try the following QA format using "Instruct: \<prompt\>\nOutput:" ```markdown Instruct: Write a detailed analogy between mathematics and a lighthouse. Output: Mathematics is like a lighthouse. Just as a lighthouse guides ships safely to shore, mathematics provides a guiding light in the world of numbers and logic. It helps us navigate through complex problems and find solutions. Just as a lighthouse emits a steady beam of light, mathematics provides a consistent framework for reasoning and problem-solving. It illuminates the path to understanding and helps us make sense of the world around us. ``` where the model generates the text after "Output:". ### Chat Format: ```markdown Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions? Bob: Well, have you tried creating a study schedule and sticking to it? Alice: Yes, I have, but it doesn't seem to help much. Bob: Hmm, maybe you should try studying in a quiet environment, like the library. Alice: ... ``` where the model generates the text after the first "Bob:". ### Code Format: ```python def print_prime(n): """ Print all primes between 1 and n """ primes = [] for num in range(2, n+1): is_prime = True for i in range(2, int(math.sqrt(num))+1): if num % i == 0: is_prime = False break if is_prime: primes.append(num) print(primes) ``` where the model generates the text after the comments. **Notes:** * Phi-2 is intended for QA, chat, and code purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications. * Direct adoption for production tasks without evaluation is out of scope of this project. As a result, the Phi-2 model has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details. * If you are using `transformers<4.37.0`, always load the model with `trust_remote_code=True` to prevent side-effects. ## Sample Code ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer torch.set_default_device("cuda") model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True) inputs = tokenizer('''def print_prime(n): """ Print all primes between 1 and n """''', return_tensors="pt", return_attention_mask=False) outputs = model.generate(**inputs, max_length=200) text = tokenizer.batch_decode(outputs)[0] print(text) ``` ## Limitations of Phi-2 * Generate Inaccurate Code and Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions. * Limited Scope for code: Majority of Phi-2 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. * Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users. * Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response. * Potential Societal Biases: Phi-2 is not entirely free from societal biases despite efforts in assuring training data safety. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs. * Toxicity: Despite being trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining. * Verbosity: Phi-2 being a base model often produces irrelevant or extra text and responses following its first answer to user prompts within a single turn. This is due to its training dataset being primarily textbooks, which results in textbook-like responses. ## Training ### Model * Architecture: a Transformer-based model with next-word prediction objective * Context length: 2048 tokens * Dataset size: 250B tokens, combination of NLP synthetic data created by AOAI GPT-3.5 and filtered web data from Falcon RefinedWeb and SlimPajama, which was assessed by AOAI GPT-4. * Training tokens: 1.4T tokens * GPUs: 96xA100-80G * Training time: 14 days ### Software * [PyTorch](https://github.com/pytorch/pytorch) * [DeepSpeed](https://github.com/microsoft/DeepSpeed) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ### License The model is licensed under the [MIT license](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

SZTAKI-HLT/hubert-base-cc

SZTAKI-HLT

transformers

--- language: hu license: apache-2.0 datasets: - common_crawl - wikipedia --- # huBERT base model (cased) ## Model description Cased BERT model for Hungarian, trained on the (filtered, deduplicated) Hungarian subset of the Common Crawl and a snapshot of the Hungarian Wikipedia. ## Intended uses & limitations The model can be used as any other (cased) BERT model. It has been tested on the chunking and named entity recognition tasks and set a new state-of-the-art on the former. ## Training Details of the training data and procedure can be found in the PhD thesis linked below. (With the caveat that it only contains preliminary results based on the Wikipedia subcorpus. Evaluation of the full model will appear in a future paper.) ## Eval results When fine-tuned (via `BertForTokenClassification`) on chunking and NER, the model outperforms multilingual BERT, achieves state-of-the-art results on both tasks. The exact scores are | NER | Minimal NP | Maximal NP | |-----|------------|------------| | **97.62%** | **97.14%** | **96.97%** | ### BibTeX entry and citation info If you use the model, please cite the following papers: [Nemeskey, Dávid Márk (2020). "Natural Language Processing Methods for Language Modeling." PhD Thesis. Eötvös Loránd University.](https://hlt.bme.hu/en/publ/nemeskey_2020) Bibtex: ```bibtex @PhDThesis{ Nemeskey:2020, author = {Nemeskey, Dávid Márk}, title = {Natural Language Processing Methods for Language Modeling}, year = {2020}, school = {E\"otv\"os Lor\'and University} } ``` [Nemeskey, Dávid Márk (2021). "Introducing huBERT." In: XVII. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2021). Szeged, pp. 3-14](https://hlt.bme.hu/en/publ/hubert_2021) Bibtex: ```bibtex @InProceedings{ Nemeskey:2021a, author = {Nemeskey, Dávid Márk}, title = {Introducing \texttt{huBERT}}, booktitle = {{XVII}.\ Magyar Sz{\'a}m{\'i}t{\'o}g{\'e}pes Nyelv{\'e}szeti Konferencia ({MSZNY}2021)}, year = 2021, pages = {TBA}, address = {Szeged}, } ```

tiiuae/falcon-40b-instruct

tiiuae

transformers

text-generation

--- datasets: - tiiuae/falcon-refinedweb language: - en inference: false license: apache-2.0 --- # ✨ Falcon-40B-Instruct **Falcon-40B-Instruct is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) and finetuned on a mixture of [Baize](https://github.com/project-baize/baize-chatbot). 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-40B-Instruct? * **You are looking for a ready-to-use chat/instruct model based on [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b).** * **Falcon-40B is the best open-source model available.** It outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. 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-40B](https://huggingface.co/tiiuae/falcon-40b). 💸 **Looking for a smaller, less expensive model?** [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) is Falcon-40B-Instruct's little brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-40b-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']}") ``` 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 85-100GB of memory** to swiftly run inference with Falcon-40B. # Model Card for Falcon-40B-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-40B](https://huggingface.co/tiiuae/falcon-40b). ### Model Source - **Paper:** *coming soon*. ## Uses ### Direct Use Falcon-40B-Instruct has been finetuned on a chat dataset. ### 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-40B-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-40B-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-40b-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-40B-Instruct was finetuned on a 150M tokens from [Bai ze](https://github.com/project-baize/baize-chatbot) mixed with 5% of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) data. 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. ## Technical Specifications For more information about pretraining, see [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b). ### Model Architecture and Objective Falcon-40B 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. For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree. | **Hyperparameter** | **Value** | **Comment** | |--------------------|-----------|----------------------------------------| | Layers | 60 | | | `d_model` | 8192 | | | `head_dim` | 64 | Reduced to optimise for FlashAttention | | Vocabulary | 65024 | | | Sequence length | 2048 | | ### Compute Infrastructure #### Hardware Falcon-40B-Instruct was trained on AWS SageMaker, on 64 A100 40GB GPUs in P4d instances. #### Software Falcon-40B-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} } ``` To cite the [Baize](https://github.com/project-baize/baize-chatbot) instruction dataset used for this model: ``` @article{xu2023baize, title={Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data}, author={Xu, Canwen and Guo, Daya and Duan, Nan and McAuley, Julian}, journal={arXiv preprint arXiv:2304.01196}, year={2023} } ``` ## License Falcon-40B-Instruct is made available under the Apache 2.0 license. ## Contact falconllm@tii.ae

microsoft/deberta-v3-base

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 base model comes with 12 layers and a hidden size of 768. It has only 86M backbone parameters with a vocabulary containing 128K tokens which introduces 98M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2. #### Fine-tuning on NLU tasks We present the dev results on SQuAD 2.0 and MNLI tasks. | Model |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)| |-------------------|----------|-------------------|-----------|----------| | RoBERTa-base |50 |86 | 83.7/80.5 | 87.6/- | | XLNet-base |32 |92 | -/80.2 | 86.8/- | | ELECTRA-base |30 |86 | -/80.5 | 88.8/ | | DeBERTa-base |50 |100 | 86.2/83.1| 88.8/88.5| | DeBERTa-v3-base |128|86 | **88.4/85.4** | **90.6/90.7**| | DeBERTa-v3-base + SiFT |128|86 | -/- | 91.0/-| We present the dev results on SQuAD 1.1/2.0 and MNLI tasks. #### 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-base \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --evaluation_strategy steps \ --max_seq_length 256 \ --warmup_steps 500 \ --per_device_train_batch_size ${batch_size} \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --output_dir $output_dir \ --overwrite_output_dir \ --logging_steps 1000 \ --logging_dir $output_dir ``` ### Citation If you find DeBERTa useful for your work, please cite the following papers: ``` latex @misc{he2021debertav3, title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing}, author={Pengcheng He and Jianfeng Gao and Weizhu Chen}, year={2021}, eprint={2111.09543}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` latex @inproceedings{ he2021deberta, title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION}, author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=XPZIaotutsD} } ```

deepset/roberta-base-squad2

deepset

transformers

question-answering

--- language: en license: cc-by-4.0 datasets: - squad_v2 model-index: - name: deepset/roberta-base-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: 79.9309 name: Exact Match verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhNjg5YzNiZGQ1YTIyYTAwZGUwOWEzZTRiYzdjM2QzYjA3ZTUxNDM1NjE1MTUyMjE1MGY1YzEzMjRjYzVjYiIsInZlcnNpb24iOjF9.EH5JJo8EEFwU7osPz3s7qanw_tigeCFhCXjSfyN0Y1nWVnSfulSxIk_DbAEI5iE80V4EKLyp5-mYFodWvL2KDA - type: f1 value: 82.9501 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjk5ZDYwOGQyNjNkMWI0OTE4YzRmOTlkY2JjNjQ0YTZkNTMzMzNkYTA0MDFmNmI3NjA3NjNlMjhiMDQ2ZjJjNSIsInZlcnNpb24iOjF9.DDm0LNTkdLbGsue58bg1aH_s67KfbcmkvL-6ZiI2s8IoxhHJMSf29H_uV2YLyevwx900t-MwTVOW3qfFnMMEAQ - type: total value: 11869 name: total verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFkMmI2ODM0NmY5NGNkNmUxYWViOWYxZDNkY2EzYWFmOWI4N2VhYzY5MGEzMTVhOTU4Zjc4YWViOGNjOWJjMCIsInZlcnNpb24iOjF9.fexrU1icJK5_MiifBtZWkeUvpmFISqBLDXSQJ8E6UnrRof-7cU0s4tX_dIsauHWtUpIHMPZCf5dlMWQKXZuAAA - task: type: question-answering name: Question Answering dataset: name: squad type: squad config: plain_text split: validation metrics: - type: exact_match value: 85.289 name: Exact Match - type: f1 value: 91.841 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: 29.500 name: Exact Match - type: f1 value: 40.367 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: 78.567 name: Exact Match - type: f1 value: 84.469 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts amazon type: squadshifts config: amazon split: test metrics: - type: exact_match value: 69.924 name: Exact Match - type: f1 value: 83.284 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: 81.204 name: Exact Match - type: f1 value: 90.595 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts nyt type: squadshifts config: nyt split: test metrics: - type: exact_match value: 82.931 name: Exact Match - type: f1 value: 90.756 name: F1 - task: type: question-answering name: Question Answering dataset: name: squadshifts reddit type: squadshifts config: reddit split: test metrics: - type: exact_match value: 71.550 name: Exact Match - type: f1 value: 82.939 name: F1 --- # roberta-base for QA This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. ## Overview **Language model:** roberta-base **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) **Infrastructure**: 4x Tesla v100 ## Hyperparameters ``` batch_size = 96 n_epochs = 2 base_LM_model = "roberta-base" max_seq_len = 386 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` ## Using a distilled model instead Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model. ## 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/roberta-base-squad2") # or reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") ``` For a complete example of ``roberta-base-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system) ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/roberta-base-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) ``` ## Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 79.87029394424324, "f1": 82.91251169582613, "total": 11873, "HasAns_exact": 77.93522267206478, "HasAns_f1": 84.02838248389763, "HasAns_total": 5928, "NoAns_exact": 81.79983179142137, "NoAns_f1": 81.79983179142137, "NoAns_total": 5945 ``` ## Authors **Branden Chan:** branden.chan@deepset.ai **Timo Möller:** timo.moeller@deepset.ai **Malte Pietsch:** malte.pietsch@deepset.ai **Tanay Soni:** tanay.soni@deepset.ai ## 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) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)

microsoft/trocr-base-handwritten

microsoft

transformers

image-to-text

--- tags: - trocr - image-to-text widget: - src: https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg example_title: Note 1 - src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU example_title: Note 2 - src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRNYtTuSBpZPV_nkBYPMFwVVD9asZOPgHww4epu9EqWgDmXW--sE2o8og40ZfDGo87j5w&usqp=CAU example_title: Note 3 --- # TrOCR (base-sized model, fine-tuned on IAM) TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr). Disclaimer: The team releasing TrOCR did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens. ## Intended uses & limitations You can use the raw model for optical character recognition (OCR) on single text-line images. See the [model hub](https://huggingface.co/models?search=microsoft/trocr) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image import requests # load image from the IAM database url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg' image = Image.open(requests.get(url, stream=True).raw).convert("RGB") processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten') model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten') pixel_values = processor(images=image, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### BibTeX entry and citation info ```bibtex @misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Salesforce/blip-image-captioning-large

Salesforce

transformers

image-to-text

--- pipeline_tag: image-to-text tags: - image-captioning languages: - en license: bsd-3-clause --- # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large 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, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") 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') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning inputs = processor(raw_image, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </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, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").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') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", 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') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </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} } ```

ckiplab/albert-tiny-chinese-ws

ckiplab

transformers

token-classification

--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - albert - zh license: gpl-3.0 --- # CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-ws') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。

marieke93/MiniLM-evidence-types

marieke93

transformers

text-classification

--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: MiniLM-evidence-types results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MiniLM-evidence-types This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the evidence types dataset. It achieved the following results on the evaluation set: - Loss: 1.8672 - Macro f1: 0.3726 - Weighted f1: 0.7030 - Accuracy: 0.7161 - Balanced accuracy: 0.3616 ## Training and evaluation data The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies) ### 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: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:| | 1.4106 | 1.0 | 250 | 1.2698 | 0.1966 | 0.6084 | 0.6735 | 0.2195 | | 1.1437 | 2.0 | 500 | 1.0985 | 0.3484 | 0.6914 | 0.7116 | 0.3536 | | 0.9714 | 3.0 | 750 | 1.0901 | 0.2606 | 0.6413 | 0.6446 | 0.2932 | | 0.8382 | 4.0 | 1000 | 1.0197 | 0.2764 | 0.7024 | 0.7237 | 0.2783 | | 0.7192 | 5.0 | 1250 | 1.0895 | 0.2847 | 0.6824 | 0.6963 | 0.2915 | | 0.6249 | 6.0 | 1500 | 1.1296 | 0.3487 | 0.6888 | 0.6948 | 0.3377 | | 0.5336 | 7.0 | 1750 | 1.1515 | 0.3591 | 0.6982 | 0.7024 | 0.3496 | | 0.4694 | 8.0 | 2000 | 1.1962 | 0.3626 | 0.7185 | 0.7314 | 0.3415 | | 0.4058 | 9.0 | 2250 | 1.3313 | 0.3121 | 0.6920 | 0.7085 | 0.3033 | | 0.3746 | 10.0 | 2500 | 1.3993 | 0.3628 | 0.6976 | 0.7047 | 0.3495 | | 0.3267 | 11.0 | 2750 | 1.5078 | 0.3560 | 0.6958 | 0.7055 | 0.3464 | | 0.2939 | 12.0 | 3000 | 1.5875 | 0.3685 | 0.6968 | 0.7062 | 0.3514 | | 0.2677 | 13.0 | 3250 | 1.6470 | 0.3606 | 0.6976 | 0.7070 | 0.3490 | | 0.2425 | 14.0 | 3500 | 1.7164 | 0.3714 | 0.7069 | 0.7207 | 0.3551 | | 0.2301 | 15.0 | 3750 | 1.8151 | 0.3597 | 0.6975 | 0.7123 | 0.3466 | | 0.2268 | 16.0 | 4000 | 1.7838 | 0.3940 | 0.7034 | 0.7123 | 0.3869 | | 0.201 | 17.0 | 4250 | 1.8328 | 0.3725 | 0.6964 | 0.7062 | 0.3704 | | 0.1923 | 18.0 | 4500 | 1.8788 | 0.3708 | 0.7019 | 0.7154 | 0.3591 | | 0.1795 | 19.0 | 4750 | 1.8574 | 0.3752 | 0.7031 | 0.7161 | 0.3619 | | 0.1713 | 20.0 | 5000 | 1.8672 | 0.3726 | 0.7030 | 0.7161 | 0.3616 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

sentence-transformers/msmarco-MiniLM-L-12-v3

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/msmarco-MiniLM-L-12-v3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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/msmarco-MiniLM-L-12-v3') 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/msmarco-MiniLM-L-12-v3') model = AutoModel.from_pretrained('sentence-transformers/msmarco-MiniLM-L-12-v3') # 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/msmarco-MiniLM-L-12-v3) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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", } ```

Salesforce/blip-image-captioning-base

Salesforce

transformers

image-to-text

--- pipeline_tag: image-to-text tags: - image-captioning languages: - en license: bsd-3-clause --- # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for image captioning pretrained on COCO dataset - 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, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-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') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </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, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-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') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-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') # conditional image captioning text = "a photography of" inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # >>> a photography of a woman and her dog # unconditional image captioning inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) >>> a woman sitting on the beach with her dog ``` </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} } ```

jonatasgrosman/wav2vec2-large-xlsr-53-russian

jonatasgrosman

transformers

automatic-speech-recognition

--- language: ru license: apache-2.0 datasets: - common_voice - mozilla-foundation/common_voice_6_0 metrics: - wer - cer tags: - audio - automatic-speech-recognition - hf-asr-leaderboard - mozilla-foundation/common_voice_6_0 - robust-speech-event - ru - speech - xlsr-fine-tuning-week model-index: - name: XLSR Wav2Vec2 Russian by Jonatas Grosman results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ru type: common_voice args: ru metrics: - name: Test WER type: wer value: 13.3 - name: Test CER type: cer value: 2.88 - name: Test WER (+LM) type: wer value: 9.57 - name: Test CER (+LM) type: cer value: 2.24 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: ru metrics: - name: Dev WER type: wer value: 40.22 - name: Dev CER type: cer value: 14.8 - name: Dev WER (+LM) type: wer value: 33.61 - name: Dev CER (+LM) type: cer value: 13.5 --- # Fine-tuned XLSR-53 large model for speech recognition in Russian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Russian using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint ## Usage The model can be used directly (without a language model) as follows... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-russian") 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 = "ru" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-russian" SAMPLES = 5 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) for i, predicted_sentence in enumerate(predicted_sentences): print("-" * 100) print("Reference:", test_dataset[i]["sentence"]) print("Prediction:", predicted_sentence) ``` | Reference | Prediction | | ------------- | ------------- | | ОН РАБОТАТЬ, А ЕЕ НЕ УДЕРЖАТЬ НИКАК — БЕГАЕТ ЗА КЛЁШЕМ КАЖДОГО БУЛЬВАРНИКА. | ОН РАБОТАТЬ А ЕЕ НЕ УДЕРЖАТ НИКАК БЕГАЕТ ЗА КЛЕШОМ КАЖДОГО БУЛЬБАРНИКА | | ЕСЛИ НЕ БУДЕТ ВОЗРАЖЕНИЙ, Я БУДУ СЧИТАТЬ, ЧТО АССАМБЛЕЯ СОГЛАСНА С ЭТИМ ПРЕДЛОЖЕНИЕМ. | ЕСЛИ НЕ БУДЕТ ВОЗРАЖЕНИЙ Я БУДУ СЧИТАТЬ ЧТО АССАМБЛЕЯ СОГЛАСНА С ЭТИМ ПРЕДЛОЖЕНИЕМ | | ПАЛЕСТИНЦАМ НЕОБХОДИМО СНАЧАЛА УСТАНОВИТЬ МИР С ИЗРАИЛЕМ, А ЗАТЕМ ДОБИВАТЬСЯ ПРИЗНАНИЯ ГОСУДАРСТВЕННОСТИ. | ПАЛЕСТИНЦАМ НЕОБХОДИМО СНАЧАЛА УСТАНОВИТЬ С НИ МИР ФЕЗРЕЛЕМ А ЗАТЕМ ДОБИВАТЬСЯ ПРИЗНАНИЯ ГОСУДАРСТВЕНСКИ | | У МЕНЯ БЫЛО ТАКОЕ ЧУВСТВО, ЧТО ЧТО-ТО ТАКОЕ ОЧЕНЬ ВАЖНОЕ Я ПРИБАВЛЯЮ. | У МЕНЯ БЫЛО ТАКОЕ ЧУВСТВО ЧТО ЧТО-ТО ТАКОЕ ОЧЕНЬ ВАЖНОЕ Я ПРЕДБАВЛЯЕТ | | ТОЛЬКО ВРЯД ЛИ ПОЙМЕТ. | ТОЛЬКО ВРЯД ЛИ ПОЙМЕТ | | ВРОНСКИЙ, СЛУШАЯ ОДНИМ УХОМ, ПЕРЕВОДИЛ БИНОКЛЬ С БЕНУАРА НА БЕЛЬ-ЭТАЖ И ОГЛЯДЫВАЛ ЛОЖИ. | ЗЛАЗКИ СЛУШАЮ ОТ ОДНИМ УХАМ ТЫ ВОТИ В ВИНОКОТ СПИЛА НА ПЕРЕТАЧ И ОКЛЯДЫВАЛ БОСУ | | К СОЖАЛЕНИЮ, СИТУАЦИЯ ПРОДОЛЖАЕТ УХУДШАТЬСЯ. | К СОЖАЛЕНИЮ СИТУАЦИИ ПРОДОЛЖАЕТ УХУЖАТЬСЯ | | ВСЁ ЖАЛОВАНИЕ УХОДИЛО НА ДОМАШНИЕ РАСХОДЫ И НА УПЛАТУ МЕЛКИХ НЕПЕРЕВОДИВШИХСЯ ДОЛГОВ. | ВСЕ ЖАЛОВАНИЕ УХОДИЛО НА ДОМАШНИЕ РАСХОДЫ И НА УПЛАТУ МЕЛКИХ НЕ ПЕРЕВОДИВШИХСЯ ДОЛГОВ | | ТЕПЕРЬ ДЕЛО, КОНЕЧНО, ЗА ТЕМ, ЧТОБЫ ПРЕВРАТИТЬ СЛОВА В ДЕЛА. | ТЕПЕРЬ ДЕЛАЮ КОНЕЧНО ЗАТЕМ ЧТОБЫ ПРЕВРАТИТЬ СЛОВА В ДЕЛА | | ДЕВЯТЬ | ЛЕВЕТЬ | ## Evaluation 1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-russian --dataset mozilla-foundation/common_voice_6_0 --config ru --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-russian --dataset speech-recognition-community-v2/dev_data --config ru --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-russian, title={Fine-tuned {XLSR}-53 large model for speech recognition in {R}ussian}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-russian}}, year={2021} } ```

thenlper/gte-base

thenlper

sentence-transformers

sentence-similarity

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-base results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.17910447761193 - type: ap value: 36.827146398068926 - type: f1 value: 68.11292888046363 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.77345000000001 - type: ap value: 88.33530426691347 - type: f1 value: 91.76549906404642 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.964 - type: f1 value: 48.22995586184998 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.147999999999996 - type: map_at_10 value: 48.253 - type: map_at_100 value: 49.038 - type: map_at_1000 value: 49.042 - type: map_at_3 value: 43.433 - type: map_at_5 value: 46.182 - type: mrr_at_1 value: 32.717 - type: mrr_at_10 value: 48.467 - type: mrr_at_100 value: 49.252 - type: mrr_at_1000 value: 49.254999999999995 - type: mrr_at_3 value: 43.599 - type: mrr_at_5 value: 46.408 - type: ndcg_at_1 value: 32.147999999999996 - type: ndcg_at_10 value: 57.12199999999999 - type: ndcg_at_100 value: 60.316 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 47.178 - type: ndcg_at_5 value: 52.146 - type: precision_at_1 value: 32.147999999999996 - type: precision_at_10 value: 8.542 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.346 - type: precision_at_5 value: 14.026 - type: recall_at_1 value: 32.147999999999996 - type: recall_at_10 value: 85.42 - type: recall_at_100 value: 99.004 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 58.037000000000006 - type: recall_at_5 value: 70.128 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.59706013699614 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.01463593002057 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.80250355752458 - type: mrr value: 74.79455216989844 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.87448576082345 - type: cos_sim_spearman value: 87.64235843637468 - type: euclidean_pearson value: 88.4901825511062 - type: euclidean_spearman value: 87.74537283182033 - type: manhattan_pearson value: 88.39040638362911 - type: manhattan_spearman value: 87.62669542888003 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.06818181818183 - type: f1 value: 85.02524460098233 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.20471092679967 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.58967592147641 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.411 - type: map_at_10 value: 45.162 - type: map_at_100 value: 46.717 - type: map_at_1000 value: 46.836 - type: map_at_3 value: 41.428 - type: map_at_5 value: 43.54 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 51.534 - type: mrr_at_100 value: 52.185 - type: mrr_at_1000 value: 52.22 - type: mrr_at_3 value: 49.046 - type: mrr_at_5 value: 50.548 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 52.235 - type: ndcg_at_100 value: 57.4 - type: ndcg_at_1000 value: 58.982 - type: ndcg_at_3 value: 47.332 - type: ndcg_at_5 value: 49.62 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 10.258000000000001 - type: precision_at_100 value: 1.6219999999999999 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 23.462 - type: precision_at_5 value: 16.71 - type: recall_at_1 value: 32.411 - type: recall_at_10 value: 65.408 - type: recall_at_100 value: 87.248 - type: recall_at_1000 value: 96.951 - type: recall_at_3 value: 50.349999999999994 - type: recall_at_5 value: 57.431 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.911 - type: map_at_10 value: 42.608000000000004 - type: map_at_100 value: 43.948 - type: map_at_1000 value: 44.089 - type: map_at_3 value: 39.652 - type: map_at_5 value: 41.236 - type: mrr_at_1 value: 40.064 - type: mrr_at_10 value: 48.916 - type: mrr_at_100 value: 49.539 - type: mrr_at_1000 value: 49.583 - type: mrr_at_3 value: 46.741 - type: mrr_at_5 value: 48.037 - type: ndcg_at_1 value: 40.064 - type: ndcg_at_10 value: 48.442 - type: ndcg_at_100 value: 52.798 - type: ndcg_at_1000 value: 54.871 - type: ndcg_at_3 value: 44.528 - type: ndcg_at_5 value: 46.211 - type: precision_at_1 value: 40.064 - type: precision_at_10 value: 9.178 - type: precision_at_100 value: 1.452 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 21.614 - type: precision_at_5 value: 15.185 - type: recall_at_1 value: 31.911 - type: recall_at_10 value: 58.155 - type: recall_at_100 value: 76.46300000000001 - type: recall_at_1000 value: 89.622 - type: recall_at_3 value: 46.195 - type: recall_at_5 value: 51.288999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.597 - type: map_at_10 value: 54.290000000000006 - type: map_at_100 value: 55.340999999999994 - type: map_at_1000 value: 55.388999999999996 - type: map_at_3 value: 50.931000000000004 - type: map_at_5 value: 52.839999999999996 - type: mrr_at_1 value: 46.646 - type: mrr_at_10 value: 57.524 - type: mrr_at_100 value: 58.225 - type: mrr_at_1000 value: 58.245999999999995 - type: mrr_at_3 value: 55.235 - type: mrr_at_5 value: 56.589 - type: ndcg_at_1 value: 46.646 - type: ndcg_at_10 value: 60.324999999999996 - type: ndcg_at_100 value: 64.30900000000001 - type: ndcg_at_1000 value: 65.19 - type: ndcg_at_3 value: 54.983000000000004 - type: ndcg_at_5 value: 57.621 - type: precision_at_1 value: 46.646 - type: precision_at_10 value: 9.774 - type: precision_at_100 value: 1.265 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 24.911 - type: precision_at_5 value: 16.977999999999998 - type: recall_at_1 value: 40.597 - type: recall_at_10 value: 74.773 - type: recall_at_100 value: 91.61200000000001 - type: recall_at_1000 value: 97.726 - type: recall_at_3 value: 60.458 - type: recall_at_5 value: 66.956 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.122 - type: map_at_10 value: 36.711 - type: map_at_100 value: 37.775 - type: map_at_1000 value: 37.842999999999996 - type: map_at_3 value: 33.693 - type: map_at_5 value: 35.607 - type: mrr_at_1 value: 29.153000000000002 - type: mrr_at_10 value: 38.873999999999995 - type: mrr_at_100 value: 39.739000000000004 - type: mrr_at_1000 value: 39.794000000000004 - type: mrr_at_3 value: 36.102000000000004 - type: mrr_at_5 value: 37.876 - type: ndcg_at_1 value: 29.153000000000002 - type: ndcg_at_10 value: 42.048 - type: ndcg_at_100 value: 47.144999999999996 - type: ndcg_at_1000 value: 48.901 - type: ndcg_at_3 value: 36.402 - type: ndcg_at_5 value: 39.562999999999995 - type: precision_at_1 value: 29.153000000000002 - type: precision_at_10 value: 6.4750000000000005 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 15.479999999999999 - type: precision_at_5 value: 11.028 - type: recall_at_1 value: 27.122 - type: recall_at_10 value: 56.279999999999994 - type: recall_at_100 value: 79.597 - type: recall_at_1000 value: 92.804 - type: recall_at_3 value: 41.437000000000005 - type: recall_at_5 value: 49.019 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.757 - type: map_at_10 value: 26.739 - type: map_at_100 value: 28.015 - type: map_at_1000 value: 28.127999999999997 - type: map_at_3 value: 23.986 - type: map_at_5 value: 25.514 - type: mrr_at_1 value: 22.015 - type: mrr_at_10 value: 31.325999999999997 - type: mrr_at_100 value: 32.368 - type: mrr_at_1000 value: 32.426 - type: mrr_at_3 value: 28.897000000000002 - type: mrr_at_5 value: 30.147000000000002 - type: ndcg_at_1 value: 22.015 - type: ndcg_at_10 value: 32.225 - type: ndcg_at_100 value: 38.405 - type: ndcg_at_1000 value: 40.932 - type: ndcg_at_3 value: 27.403 - type: ndcg_at_5 value: 29.587000000000003 - type: precision_at_1 value: 22.015 - type: precision_at_10 value: 5.9830000000000005 - type: precision_at_100 value: 1.051 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 13.391 - type: precision_at_5 value: 9.602 - type: recall_at_1 value: 17.757 - type: recall_at_10 value: 44.467 - type: recall_at_100 value: 71.53699999999999 - type: recall_at_1000 value: 89.281 - type: recall_at_3 value: 31.095 - type: recall_at_5 value: 36.818 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.354 - type: map_at_10 value: 42.134 - type: map_at_100 value: 43.429 - type: map_at_1000 value: 43.532 - type: map_at_3 value: 38.491 - type: map_at_5 value: 40.736 - type: mrr_at_1 value: 37.247 - type: mrr_at_10 value: 47.775 - type: mrr_at_100 value: 48.522999999999996 - type: mrr_at_1000 value: 48.567 - type: mrr_at_3 value: 45.059 - type: mrr_at_5 value: 46.811 - type: ndcg_at_1 value: 37.247 - type: ndcg_at_10 value: 48.609 - type: ndcg_at_100 value: 53.782 - type: ndcg_at_1000 value: 55.666000000000004 - type: ndcg_at_3 value: 42.866 - type: ndcg_at_5 value: 46.001 - type: precision_at_1 value: 37.247 - type: precision_at_10 value: 8.892999999999999 - type: precision_at_100 value: 1.341 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 20.5 - type: precision_at_5 value: 14.976 - type: recall_at_1 value: 30.354 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 83.65599999999999 - type: recall_at_1000 value: 95.82000000000001 - type: recall_at_3 value: 46.464 - type: recall_at_5 value: 54.225 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.949 - type: map_at_10 value: 37.230000000000004 - type: map_at_100 value: 38.644 - type: map_at_1000 value: 38.751999999999995 - type: map_at_3 value: 33.816 - type: map_at_5 value: 35.817 - type: mrr_at_1 value: 33.446999999999996 - type: mrr_at_10 value: 42.970000000000006 - type: mrr_at_100 value: 43.873 - type: mrr_at_1000 value: 43.922 - type: mrr_at_3 value: 40.467999999999996 - type: mrr_at_5 value: 41.861 - type: ndcg_at_1 value: 33.446999999999996 - type: ndcg_at_10 value: 43.403000000000006 - type: ndcg_at_100 value: 49.247 - type: ndcg_at_1000 value: 51.361999999999995 - type: ndcg_at_3 value: 38.155 - type: ndcg_at_5 value: 40.643 - type: precision_at_1 value: 33.446999999999996 - type: precision_at_10 value: 8.128 - type: precision_at_100 value: 1.274 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 18.493000000000002 - type: precision_at_5 value: 13.333 - type: recall_at_1 value: 26.949 - type: recall_at_10 value: 56.006 - type: recall_at_100 value: 80.99199999999999 - type: recall_at_1000 value: 95.074 - type: recall_at_3 value: 40.809 - type: recall_at_5 value: 47.57 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.243583333333333 - type: map_at_10 value: 37.193250000000006 - type: map_at_100 value: 38.44833333333334 - type: map_at_1000 value: 38.56083333333333 - type: map_at_3 value: 34.06633333333333 - type: map_at_5 value: 35.87858333333334 - type: mrr_at_1 value: 32.291583333333335 - type: mrr_at_10 value: 41.482749999999996 - type: mrr_at_100 value: 42.33583333333333 - type: mrr_at_1000 value: 42.38683333333333 - type: mrr_at_3 value: 38.952999999999996 - type: mrr_at_5 value: 40.45333333333333 - type: ndcg_at_1 value: 32.291583333333335 - type: ndcg_at_10 value: 42.90533333333334 - type: ndcg_at_100 value: 48.138666666666666 - type: ndcg_at_1000 value: 50.229083333333335 - type: ndcg_at_3 value: 37.76133333333334 - type: ndcg_at_5 value: 40.31033333333334 - type: precision_at_1 value: 32.291583333333335 - type: precision_at_10 value: 7.585583333333333 - type: precision_at_100 value: 1.2045000000000001 - type: precision_at_1000 value: 0.15733333333333335 - type: precision_at_3 value: 17.485416666666666 - type: precision_at_5 value: 12.5145 - type: recall_at_1 value: 27.243583333333333 - type: recall_at_10 value: 55.45108333333334 - type: recall_at_100 value: 78.25858333333335 - type: recall_at_1000 value: 92.61716666666665 - type: recall_at_3 value: 41.130583333333334 - type: recall_at_5 value: 47.73133333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.325 - type: map_at_10 value: 32.795 - type: map_at_100 value: 33.96 - type: map_at_1000 value: 34.054 - type: map_at_3 value: 30.64 - type: map_at_5 value: 31.771 - type: mrr_at_1 value: 29.908 - type: mrr_at_10 value: 35.83 - type: mrr_at_100 value: 36.868 - type: mrr_at_1000 value: 36.928 - type: mrr_at_3 value: 33.896 - type: mrr_at_5 value: 34.893 - type: ndcg_at_1 value: 29.908 - type: ndcg_at_10 value: 36.746 - type: ndcg_at_100 value: 42.225 - type: ndcg_at_1000 value: 44.523 - type: ndcg_at_3 value: 32.82 - type: ndcg_at_5 value: 34.583000000000006 - type: precision_at_1 value: 29.908 - type: precision_at_10 value: 5.6129999999999995 - type: precision_at_100 value: 0.9079999999999999 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 13.753000000000002 - type: precision_at_5 value: 9.417 - type: recall_at_1 value: 26.325 - type: recall_at_10 value: 45.975 - type: recall_at_100 value: 70.393 - type: recall_at_1000 value: 87.217 - type: recall_at_3 value: 35.195 - type: recall_at_5 value: 39.69 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.828 - type: map_at_10 value: 25.759 - type: map_at_100 value: 26.961000000000002 - type: map_at_1000 value: 27.094 - type: map_at_3 value: 23.166999999999998 - type: map_at_5 value: 24.610000000000003 - type: mrr_at_1 value: 21.61 - type: mrr_at_10 value: 29.605999999999998 - type: mrr_at_100 value: 30.586000000000002 - type: mrr_at_1000 value: 30.664 - type: mrr_at_3 value: 27.214 - type: mrr_at_5 value: 28.571 - type: ndcg_at_1 value: 21.61 - type: ndcg_at_10 value: 30.740000000000002 - type: ndcg_at_100 value: 36.332 - type: ndcg_at_1000 value: 39.296 - type: ndcg_at_3 value: 26.11 - type: ndcg_at_5 value: 28.297 - type: precision_at_1 value: 21.61 - type: precision_at_10 value: 5.643 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 12.4 - type: precision_at_5 value: 9.119 - type: recall_at_1 value: 17.828 - type: recall_at_10 value: 41.876000000000005 - type: recall_at_100 value: 66.648 - type: recall_at_1000 value: 87.763 - type: recall_at_3 value: 28.957 - type: recall_at_5 value: 34.494 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.921000000000003 - type: map_at_10 value: 37.156 - type: map_at_100 value: 38.399 - type: map_at_1000 value: 38.498 - type: map_at_3 value: 34.134 - type: map_at_5 value: 35.936 - type: mrr_at_1 value: 32.649 - type: mrr_at_10 value: 41.19 - type: mrr_at_100 value: 42.102000000000004 - type: mrr_at_1000 value: 42.157 - type: mrr_at_3 value: 38.464 - type: mrr_at_5 value: 40.148 - type: ndcg_at_1 value: 32.649 - type: ndcg_at_10 value: 42.679 - type: ndcg_at_100 value: 48.27 - type: ndcg_at_1000 value: 50.312 - type: ndcg_at_3 value: 37.269000000000005 - type: ndcg_at_5 value: 40.055 - type: precision_at_1 value: 32.649 - type: precision_at_10 value: 7.155 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 16.791 - type: precision_at_5 value: 12.015 - type: recall_at_1 value: 27.921000000000003 - type: recall_at_10 value: 55.357 - type: recall_at_100 value: 79.476 - type: recall_at_1000 value: 93.314 - type: recall_at_3 value: 40.891 - type: recall_at_5 value: 47.851 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.524 - type: map_at_10 value: 35.135 - type: map_at_100 value: 36.665 - type: map_at_1000 value: 36.886 - type: map_at_3 value: 31.367 - type: map_at_5 value: 33.724 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.616 - type: mrr_at_100 value: 40.54 - type: mrr_at_1000 value: 40.585 - type: mrr_at_3 value: 36.462 - type: mrr_at_5 value: 38.507999999999996 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 41.61 - type: ndcg_at_100 value: 47.249 - type: ndcg_at_1000 value: 49.662 - type: ndcg_at_3 value: 35.421 - type: ndcg_at_5 value: 38.811 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 8.123 - type: precision_at_100 value: 1.5810000000000002 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 16.337 - type: precision_at_5 value: 12.568999999999999 - type: recall_at_1 value: 25.524 - type: recall_at_10 value: 54.994 - type: recall_at_100 value: 80.03099999999999 - type: recall_at_1000 value: 95.25099999999999 - type: recall_at_3 value: 37.563 - type: recall_at_5 value: 46.428999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.224 - type: map_at_10 value: 30.599999999999998 - type: map_at_100 value: 31.526 - type: map_at_1000 value: 31.629 - type: map_at_3 value: 27.491 - type: map_at_5 value: 29.212 - type: mrr_at_1 value: 24.214 - type: mrr_at_10 value: 32.632 - type: mrr_at_100 value: 33.482 - type: mrr_at_1000 value: 33.550000000000004 - type: mrr_at_3 value: 29.852 - type: mrr_at_5 value: 31.451 - type: ndcg_at_1 value: 24.214 - type: ndcg_at_10 value: 35.802 - type: ndcg_at_100 value: 40.502 - type: ndcg_at_1000 value: 43.052 - type: ndcg_at_3 value: 29.847 - type: ndcg_at_5 value: 32.732 - type: precision_at_1 value: 24.214 - type: precision_at_10 value: 5.804 - type: precision_at_100 value: 0.885 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 12.692999999999998 - type: precision_at_5 value: 9.242 - type: recall_at_1 value: 22.224 - type: recall_at_10 value: 49.849 - type: recall_at_100 value: 71.45 - type: recall_at_1000 value: 90.583 - type: recall_at_3 value: 34.153 - type: recall_at_5 value: 41.004000000000005 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 12.386999999999999 - type: map_at_10 value: 20.182 - type: map_at_100 value: 21.86 - type: map_at_1000 value: 22.054000000000002 - type: map_at_3 value: 17.165 - type: map_at_5 value: 18.643 - type: mrr_at_1 value: 26.906000000000002 - type: mrr_at_10 value: 37.907999999999994 - type: mrr_at_100 value: 38.868 - type: mrr_at_1000 value: 38.913 - type: mrr_at_3 value: 34.853 - type: mrr_at_5 value: 36.567 - type: ndcg_at_1 value: 26.906000000000002 - type: ndcg_at_10 value: 28.103 - type: ndcg_at_100 value: 35.073 - type: ndcg_at_1000 value: 38.653 - type: ndcg_at_3 value: 23.345 - type: ndcg_at_5 value: 24.828 - type: precision_at_1 value: 26.906000000000002 - type: precision_at_10 value: 8.547 - type: precision_at_100 value: 1.617 - type: precision_at_1000 value: 0.22799999999999998 - type: precision_at_3 value: 17.025000000000002 - type: precision_at_5 value: 12.834000000000001 - type: recall_at_1 value: 12.386999999999999 - type: recall_at_10 value: 33.306999999999995 - type: recall_at_100 value: 57.516 - type: recall_at_1000 value: 77.74799999999999 - type: recall_at_3 value: 21.433 - type: recall_at_5 value: 25.915 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.322 - type: map_at_10 value: 20.469 - type: map_at_100 value: 28.638 - type: map_at_1000 value: 30.433 - type: map_at_3 value: 14.802000000000001 - type: map_at_5 value: 17.297 - type: mrr_at_1 value: 68.75 - type: mrr_at_10 value: 76.29599999999999 - type: mrr_at_100 value: 76.62400000000001 - type: mrr_at_1000 value: 76.633 - type: mrr_at_3 value: 75.083 - type: mrr_at_5 value: 75.771 - type: ndcg_at_1 value: 54.87499999999999 - type: ndcg_at_10 value: 41.185 - type: ndcg_at_100 value: 46.400000000000006 - type: ndcg_at_1000 value: 54.223 - type: ndcg_at_3 value: 45.489000000000004 - type: ndcg_at_5 value: 43.161 - type: precision_at_1 value: 68.75 - type: precision_at_10 value: 32.300000000000004 - type: precision_at_100 value: 10.607999999999999 - type: precision_at_1000 value: 2.237 - type: precision_at_3 value: 49.083 - type: precision_at_5 value: 41.6 - type: recall_at_1 value: 9.322 - type: recall_at_10 value: 25.696 - type: recall_at_100 value: 52.898 - type: recall_at_1000 value: 77.281 - type: recall_at_3 value: 15.943 - type: recall_at_5 value: 19.836000000000002 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.650000000000006 - type: f1 value: 43.528467245539396 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.56 - type: map_at_10 value: 76.767 - type: map_at_100 value: 77.054 - type: map_at_1000 value: 77.068 - type: map_at_3 value: 75.29299999999999 - type: map_at_5 value: 76.24 - type: mrr_at_1 value: 71.842 - type: mrr_at_10 value: 81.459 - type: mrr_at_100 value: 81.58800000000001 - type: mrr_at_1000 value: 81.59100000000001 - type: mrr_at_3 value: 80.188 - type: mrr_at_5 value: 81.038 - type: ndcg_at_1 value: 71.842 - type: ndcg_at_10 value: 81.51899999999999 - type: ndcg_at_100 value: 82.544 - type: ndcg_at_1000 value: 82.829 - type: ndcg_at_3 value: 78.92 - type: ndcg_at_5 value: 80.406 - type: precision_at_1 value: 71.842 - type: precision_at_10 value: 10.066 - type: precision_at_100 value: 1.076 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 30.703000000000003 - type: precision_at_5 value: 19.301 - type: recall_at_1 value: 66.56 - type: recall_at_10 value: 91.55 - type: recall_at_100 value: 95.67099999999999 - type: recall_at_1000 value: 97.539 - type: recall_at_3 value: 84.46900000000001 - type: recall_at_5 value: 88.201 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 20.087 - type: map_at_10 value: 32.830999999999996 - type: map_at_100 value: 34.814 - type: map_at_1000 value: 34.999 - type: map_at_3 value: 28.198 - type: map_at_5 value: 30.779 - type: mrr_at_1 value: 38.889 - type: mrr_at_10 value: 48.415 - type: mrr_at_100 value: 49.187 - type: mrr_at_1000 value: 49.226 - type: mrr_at_3 value: 45.705 - type: mrr_at_5 value: 47.225 - type: ndcg_at_1 value: 38.889 - type: ndcg_at_10 value: 40.758 - type: ndcg_at_100 value: 47.671 - type: ndcg_at_1000 value: 50.744 - type: ndcg_at_3 value: 36.296 - type: ndcg_at_5 value: 37.852999999999994 - type: precision_at_1 value: 38.889 - type: precision_at_10 value: 11.466 - type: precision_at_100 value: 1.8499999999999999 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 24.126 - type: precision_at_5 value: 18.21 - type: recall_at_1 value: 20.087 - type: recall_at_10 value: 48.042 - type: recall_at_100 value: 73.493 - type: recall_at_1000 value: 91.851 - type: recall_at_3 value: 32.694 - type: recall_at_5 value: 39.099000000000004 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.096000000000004 - type: map_at_10 value: 56.99999999999999 - type: map_at_100 value: 57.914 - type: map_at_1000 value: 57.984 - type: map_at_3 value: 53.900999999999996 - type: map_at_5 value: 55.827000000000005 - type: mrr_at_1 value: 76.19200000000001 - type: mrr_at_10 value: 81.955 - type: mrr_at_100 value: 82.164 - type: mrr_at_1000 value: 82.173 - type: mrr_at_3 value: 80.963 - type: mrr_at_5 value: 81.574 - type: ndcg_at_1 value: 76.19200000000001 - type: ndcg_at_10 value: 65.75 - type: ndcg_at_100 value: 68.949 - type: ndcg_at_1000 value: 70.342 - type: ndcg_at_3 value: 61.29 - type: ndcg_at_5 value: 63.747 - type: precision_at_1 value: 76.19200000000001 - type: precision_at_10 value: 13.571 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 38.663 - type: precision_at_5 value: 25.136999999999997 - type: recall_at_1 value: 38.096000000000004 - type: recall_at_10 value: 67.853 - type: recall_at_100 value: 80.365 - type: recall_at_1000 value: 89.629 - type: recall_at_3 value: 57.995 - type: recall_at_5 value: 62.843 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.95200000000001 - type: ap value: 80.73847277002109 - type: f1 value: 85.92406135678594 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 20.916999999999998 - type: map_at_10 value: 33.23 - type: map_at_100 value: 34.427 - type: map_at_1000 value: 34.477000000000004 - type: map_at_3 value: 29.292 - type: map_at_5 value: 31.6 - type: mrr_at_1 value: 21.547 - type: mrr_at_10 value: 33.839999999999996 - type: mrr_at_100 value: 34.979 - type: mrr_at_1000 value: 35.022999999999996 - type: mrr_at_3 value: 29.988 - type: mrr_at_5 value: 32.259 - type: ndcg_at_1 value: 21.519 - type: ndcg_at_10 value: 40.209 - type: ndcg_at_100 value: 45.954 - type: ndcg_at_1000 value: 47.187 - type: ndcg_at_3 value: 32.227 - type: ndcg_at_5 value: 36.347 - type: precision_at_1 value: 21.519 - type: precision_at_10 value: 6.447 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 13.877999999999998 - type: precision_at_5 value: 10.404 - type: recall_at_1 value: 20.916999999999998 - type: recall_at_10 value: 61.7 - type: recall_at_100 value: 88.202 - type: recall_at_1000 value: 97.588 - type: recall_at_3 value: 40.044999999999995 - type: recall_at_5 value: 49.964999999999996 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.02781577747379 - type: f1 value: 92.83653922768306 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 72.04286365709075 - type: f1 value: 53.43867658525793 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.47276395427035 - type: f1 value: 69.77017399597342 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.3819771351715 - type: f1 value: 76.8484533435409 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.16515993299593 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.77145323314774 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.53637706586391 - type: mrr value: 33.7312926288863 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.063999999999999 - type: map_at_10 value: 15.046999999999999 - type: map_at_100 value: 19.116 - type: map_at_1000 value: 20.702 - type: map_at_3 value: 10.932 - type: map_at_5 value: 12.751999999999999 - type: mrr_at_1 value: 50.464 - type: mrr_at_10 value: 58.189 - type: mrr_at_100 value: 58.733999999999995 - type: mrr_at_1000 value: 58.769000000000005 - type: mrr_at_3 value: 56.24400000000001 - type: mrr_at_5 value: 57.68299999999999 - type: ndcg_at_1 value: 48.142 - type: ndcg_at_10 value: 37.897 - type: ndcg_at_100 value: 35.264 - type: ndcg_at_1000 value: 44.033 - type: ndcg_at_3 value: 42.967 - type: ndcg_at_5 value: 40.815 - type: precision_at_1 value: 50.15500000000001 - type: precision_at_10 value: 28.235 - type: precision_at_100 value: 8.994 - type: precision_at_1000 value: 2.218 - type: precision_at_3 value: 40.041 - type: precision_at_5 value: 35.046 - type: recall_at_1 value: 7.063999999999999 - type: recall_at_10 value: 18.598 - type: recall_at_100 value: 35.577999999999996 - type: recall_at_1000 value: 67.43 - type: recall_at_3 value: 11.562999999999999 - type: recall_at_5 value: 14.771 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 29.046 - type: map_at_10 value: 44.808 - type: map_at_100 value: 45.898 - type: map_at_1000 value: 45.927 - type: map_at_3 value: 40.19 - type: map_at_5 value: 42.897 - type: mrr_at_1 value: 32.706 - type: mrr_at_10 value: 47.275 - type: mrr_at_100 value: 48.075 - type: mrr_at_1000 value: 48.095 - type: mrr_at_3 value: 43.463 - type: mrr_at_5 value: 45.741 - type: ndcg_at_1 value: 32.706 - type: ndcg_at_10 value: 52.835 - type: ndcg_at_100 value: 57.345 - type: ndcg_at_1000 value: 57.985 - type: ndcg_at_3 value: 44.171 - type: ndcg_at_5 value: 48.661 - type: precision_at_1 value: 32.706 - type: precision_at_10 value: 8.895999999999999 - type: precision_at_100 value: 1.143 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 20.238999999999997 - type: precision_at_5 value: 14.728 - type: recall_at_1 value: 29.046 - type: recall_at_10 value: 74.831 - type: recall_at_100 value: 94.192 - type: recall_at_1000 value: 98.897 - type: recall_at_3 value: 52.37500000000001 - type: recall_at_5 value: 62.732 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.38799999999999 - type: map_at_10 value: 84.315 - type: map_at_100 value: 84.955 - type: map_at_1000 value: 84.971 - type: map_at_3 value: 81.33399999999999 - type: map_at_5 value: 83.21300000000001 - type: mrr_at_1 value: 81.03 - type: mrr_at_10 value: 87.395 - type: mrr_at_100 value: 87.488 - type: mrr_at_1000 value: 87.48899999999999 - type: mrr_at_3 value: 86.41499999999999 - type: mrr_at_5 value: 87.074 - type: ndcg_at_1 value: 81.04 - type: ndcg_at_10 value: 88.151 - type: ndcg_at_100 value: 89.38199999999999 - type: ndcg_at_1000 value: 89.479 - type: ndcg_at_3 value: 85.24000000000001 - type: ndcg_at_5 value: 86.856 - type: precision_at_1 value: 81.04 - type: precision_at_10 value: 13.372 - type: precision_at_100 value: 1.526 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.217 - type: precision_at_5 value: 24.502 - type: recall_at_1 value: 70.38799999999999 - type: recall_at_10 value: 95.452 - type: recall_at_100 value: 99.59700000000001 - type: recall_at_1000 value: 99.988 - type: recall_at_3 value: 87.11 - type: recall_at_5 value: 91.662 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 59.334991029213235 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.586500854616666 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.153 - type: map_at_10 value: 14.277000000000001 - type: map_at_100 value: 16.922 - type: map_at_1000 value: 17.302999999999997 - type: map_at_3 value: 9.961 - type: map_at_5 value: 12.257 - type: mrr_at_1 value: 25.4 - type: mrr_at_10 value: 37.458000000000006 - type: mrr_at_100 value: 38.681 - type: mrr_at_1000 value: 38.722 - type: mrr_at_3 value: 34.1 - type: mrr_at_5 value: 36.17 - type: ndcg_at_1 value: 25.4 - type: ndcg_at_10 value: 23.132 - type: ndcg_at_100 value: 32.908 - type: ndcg_at_1000 value: 38.754 - type: ndcg_at_3 value: 21.82 - type: ndcg_at_5 value: 19.353 - type: precision_at_1 value: 25.4 - type: precision_at_10 value: 12.1 - type: precision_at_100 value: 2.628 - type: precision_at_1000 value: 0.402 - type: precision_at_3 value: 20.732999999999997 - type: precision_at_5 value: 17.34 - type: recall_at_1 value: 5.153 - type: recall_at_10 value: 24.54 - type: recall_at_100 value: 53.293 - type: recall_at_1000 value: 81.57 - type: recall_at_3 value: 12.613 - type: recall_at_5 value: 17.577 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.86284404925333 - type: cos_sim_spearman value: 78.85870555294795 - type: euclidean_pearson value: 82.20105295276093 - type: euclidean_spearman value: 78.92125617009592 - type: manhattan_pearson value: 82.15840025289069 - type: manhattan_spearman value: 78.85955732900803 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.98747423389027 - type: cos_sim_spearman value: 75.71298531799367 - type: euclidean_pearson value: 81.59709559192291 - type: euclidean_spearman value: 75.40622749225653 - type: manhattan_pearson value: 81.55553547608804 - type: manhattan_spearman value: 75.39380235424899 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.76861330695503 - type: cos_sim_spearman value: 85.72991921531624 - type: euclidean_pearson value: 84.84504307397536 - type: euclidean_spearman value: 86.02679162824732 - type: manhattan_pearson value: 84.79969439220142 - type: manhattan_spearman value: 85.99238837291625 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.31929747511796 - type: cos_sim_spearman value: 81.50806522502528 - type: euclidean_pearson value: 82.93936686512777 - type: euclidean_spearman value: 81.54403447993224 - type: manhattan_pearson value: 82.89696981900828 - type: manhattan_spearman value: 81.52817825470865 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.14413295332908 - type: cos_sim_spearman value: 88.81032027008195 - type: euclidean_pearson value: 88.19205563407645 - type: euclidean_spearman value: 88.89738339479216 - type: manhattan_pearson value: 88.11075942004189 - type: manhattan_spearman value: 88.8297061675564 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.15980075557017 - type: cos_sim_spearman value: 83.81896308594801 - type: euclidean_pearson value: 83.11195254311338 - type: euclidean_spearman value: 84.10479481755407 - type: manhattan_pearson value: 83.13915225100556 - type: manhattan_spearman value: 84.09895591027859 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.93669480147919 - type: cos_sim_spearman value: 87.89861394614361 - type: euclidean_pearson value: 88.37316413202339 - type: euclidean_spearman value: 88.18033817842569 - type: manhattan_pearson value: 88.39427578879469 - type: manhattan_spearman value: 88.09185009236847 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 66.62215083348255 - type: cos_sim_spearman value: 67.33243665716736 - 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type: recall_at_5 value: 82.428 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82178217821782 - type: cos_sim_ap value: 95.71282508220798 - type: cos_sim_f1 value: 90.73120494335737 - type: cos_sim_precision value: 93.52441613588111 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.73960396039604 - type: dot_ap value: 92.98534606529098 - type: dot_f1 value: 86.83024536805209 - type: dot_precision value: 86.96088264794383 - type: dot_recall value: 86.7 - type: euclidean_accuracy value: 99.82475247524752 - type: euclidean_ap value: 95.72927039014849 - type: euclidean_f1 value: 90.89974293059126 - type: euclidean_precision value: 93.54497354497354 - type: euclidean_recall value: 88.4 - type: manhattan_accuracy value: 99.82574257425742 - type: manhattan_ap value: 95.72142177390405 - type: manhattan_f1 value: 91.00152516522625 - type: manhattan_precision value: 92.55429162357808 - type: manhattan_recall value: 89.5 - type: max_accuracy value: 99.82574257425742 - type: max_ap value: 95.72927039014849 - type: max_f1 value: 91.00152516522625 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.63957663468679 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.003307257923964 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.005825525863905 - type: mrr value: 53.854683919022165 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.503611569974098 - type: cos_sim_spearman value: 31.17155564248449 - type: dot_pearson value: 26.740428413981306 - type: dot_spearman value: 26.55727635469746 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.23600000000000002 - type: map_at_10 value: 1.7670000000000001 - type: map_at_100 value: 10.208 - type: map_at_1000 value: 25.997999999999998 - type: map_at_3 value: 0.605 - type: map_at_5 value: 0.9560000000000001 - type: mrr_at_1 value: 84.0 - type: mrr_at_10 value: 90.167 - type: mrr_at_100 value: 90.167 - type: mrr_at_1000 value: 90.167 - type: mrr_at_3 value: 89.667 - type: mrr_at_5 value: 90.167 - type: ndcg_at_1 value: 77.0 - type: ndcg_at_10 value: 68.783 - type: ndcg_at_100 value: 54.196 - type: ndcg_at_1000 value: 52.077 - type: ndcg_at_3 value: 71.642 - type: ndcg_at_5 value: 70.45700000000001 - type: precision_at_1 value: 84.0 - type: precision_at_10 value: 73.0 - type: precision_at_100 value: 55.48 - type: precision_at_1000 value: 23.102 - type: precision_at_3 value: 76.0 - type: precision_at_5 value: 74.8 - type: recall_at_1 value: 0.23600000000000002 - type: recall_at_10 value: 1.9869999999999999 - type: recall_at_100 value: 13.749 - type: recall_at_1000 value: 50.157 - type: recall_at_3 value: 0.633 - type: recall_at_5 value: 1.0290000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.437 - type: map_at_10 value: 8.791 - type: map_at_100 value: 15.001999999999999 - type: map_at_1000 value: 16.549 - type: map_at_3 value: 3.8080000000000003 - type: map_at_5 value: 5.632000000000001 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 36.96 - type: mrr_at_100 value: 37.912 - type: mrr_at_1000 value: 37.912 - type: mrr_at_3 value: 29.592000000000002 - type: mrr_at_5 value: 34.489999999999995 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 22.554 - type: ndcg_at_100 value: 35.197 - type: ndcg_at_1000 value: 46.58 - type: ndcg_at_3 value: 20.285 - type: ndcg_at_5 value: 21.924 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 21.837 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.537 - type: precision_at_3 value: 21.769 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 1.437 - type: recall_at_10 value: 16.314999999999998 - type: recall_at_100 value: 47.635 - type: recall_at_1000 value: 82.963 - type: recall_at_3 value: 4.955 - type: recall_at_5 value: 8.805 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.6128 - type: ap value: 14.279639861175664 - type: f1 value: 54.922292491204274 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 57.01188455008489 - type: f1 value: 57.377953019225515 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.306769136544254 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.64701674912082 - type: cos_sim_ap value: 72.46600945328552 - type: cos_sim_f1 value: 67.96572367648784 - type: cos_sim_precision value: 61.21801649397336 - type: cos_sim_recall value: 76.38522427440633 - type: dot_accuracy value: 82.33295583238957 - type: dot_ap value: 62.54843443071716 - type: dot_f1 value: 60.38378562507096 - type: dot_precision value: 52.99980067769583 - type: dot_recall value: 70.15831134564644 - type: euclidean_accuracy value: 85.7423854085951 - type: euclidean_ap value: 72.76873850945174 - type: euclidean_f1 value: 68.23556960543262 - type: euclidean_precision value: 61.3344559040202 - type: euclidean_recall value: 76.88654353562005 - type: manhattan_accuracy value: 85.74834594981225 - type: manhattan_ap value: 72.66825372446462 - type: manhattan_f1 value: 68.21539194662853 - type: manhattan_precision value: 62.185056472632496 - type: manhattan_recall value: 75.54089709762533 - type: max_accuracy value: 85.74834594981225 - type: max_ap value: 72.76873850945174 - type: max_f1 value: 68.23556960543262 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - 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type: max_f1 value: 78.07565728654365 language: - en license: mit --- # gte-base General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | | [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | | [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base") model = AutoModel.from_pretrained("thenlper/gte-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']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('thenlper/gte-base') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```

facebook/encodec_24khz

facebook

transformers

feature-extraction

--- inference: false ---  # Model Card for EnCodec This model card provides details and information about EnCodec, a state-of-the-art real-time audio codec developed by Meta AI. ## 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. - **Developed by:** Meta AI - **Model type:** Audio Codec ### Model Sources - **Repository:** [GitHub Repository](https://github.com/facebookresearch/encodec) - **Paper:** [EnCodec: End-to-End Neural Audio Codec](https://arxiv.org/abs/2210.13438) ## 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. --> 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 EnCodec can be fine-tuned for specific audio tasks or integrated into larger audio processing pipelines for applications such as speech generation, music generation, or text to speech tasks. <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ## 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 datasets[audio] pip install git+https://github.com/huggingface/transformers.git@main ``` 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_24khz") processor = AutoProcessor.from_pretrained("facebook/encodec_24khz") # 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 ``` ## Training Details The model was trained for 300 epochs, with one epoch being 2,000 updates with the Adam optimizer with a batch size of 64 examples of 1 second each, a learning rate of 3 · 10−4 , β1 = 0.5, and β2 = 0.9. All the models are traind using 8 A100 GPUs. ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> - For speech: - DNS Challenge 4 - [Common Voice](https://huggingface.co/datasets/common_voice) - For general audio: - [AudioSet](https://huggingface.co/datasets/Fhrozen/AudioSet2K22) - [FSD50K](https://huggingface.co/datasets/Fhrozen/FSD50k) - For music: - [Jamendo dataset](https://huggingface.co/datasets/rkstgr/mtg-jamendo) They used four different training strategies to sample for these datasets: - (s1) sample a single source from Jamendo with probability 0.32; - (s2) sample a single source from the other datasets with the same probability; - (s3) mix two sources from all datasets with a probability of 0.24; - (s4) mix three sources from all datasets except music with a probability of 0.12. The audio is normalized by file and a random gain between -10 and 6 dB id applied. ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Subjectif metric for restoration: This models was evalutated using the MUSHRA protocol (Series, 2014), using both a hidden reference and a low anchor. Annotators were recruited using a crowd-sourcing platform, in which they were asked to rate the perceptual quality of the provided samples in a range between 1 to 100. They randomly select 50 samples of 5 seconds from each category of the the test set and force at least 10 annotations per samples. To filter noisy annotations and outliers we remove annotators who rate the reference recordings less then 90 in at least 20% of the cases, or rate the low-anchor recording above 80 more than 50% of the time. ### Objective metric for restoration: The ViSQOL()ink) metric was used together with the Scale-Invariant Signal-to-Noise Ration (SI-SNR) (Luo & Mesgarani, 2019; Nachmani et al., 2020; Chazan et al., 2021). ### Results The results of the evaluation demonstrate the superiority of EnCodec compared to the baselines across different bandwidths (1.5, 3, 6, and 12 kbps). When comparing EnCodec with the baselines at the same bandwidth, EnCodec consistently outperforms them in terms of MUSHRA score. Notably, EnCodec achieves better performance, on average, at 3 kbps compared to Lyra-v2 at 6 kbps and Opus at 12 kbps. Additionally, by incorporating the language model over the codes, it is possible to achieve a bandwidth reduction of approximately 25-40%. For example, the bandwidth of the 3 kbps model can be reduced to 1.9 kbps. #### 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 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** ``` @misc{défossez2022high, title={High Fidelity Neural Audio Compression}, author={Alexandre Défossez and Jade Copet and Gabriel Synnaeve and Yossi Adi}, year={2022}, eprint={2210.13438}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```

neuralmind/bert-base-portuguese-cased

neuralmind

transformers

fill-mask

--- language: pt license: mit tags: - bert - pytorch datasets: - brWaC --- # BERTimbau Base (aka "bert-base-portuguese-cased")  ## Introduction BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. It is available in two sizes: Base and Large. For further information or requests, please go to [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/). ## Available models | Model | Arch. | #Layers | #Params | | ---------------------------------------- | ---------- | ------- | ------- | | `neuralmind/bert-base-portuguese-cased` | BERT-Base | 12 | 110M | | `neuralmind/bert-large-portuguese-cased` | BERT-Large | 24 | 335M | ## Usage ```python from transformers import AutoTokenizer # Or BertTokenizer from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads from transformers import AutoModel # or BertModel, for BERT without pretraining heads model = AutoModelForPreTraining.from_pretrained('neuralmind/bert-base-portuguese-cased') tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-base-portuguese-cased', do_lower_case=False) ``` ### Masked language modeling prediction example ```python from transformers import pipeline pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer) pipe('Tinha uma [MASK] no meio do caminho.') # [{'score': 0.14287759363651276, # 'sequence': '[CLS] Tinha uma pedra no meio do caminho. [SEP]', # 'token': 5028, # 'token_str': 'pedra'}, # {'score': 0.06213393807411194, # 'sequence': '[CLS] Tinha uma árvore no meio do caminho. [SEP]', # 'token': 7411, # 'token_str': 'árvore'}, # {'score': 0.05515013635158539, # 'sequence': '[CLS] Tinha uma estrada no meio do caminho. [SEP]', # 'token': 5675, # 'token_str': 'estrada'}, # {'score': 0.0299188531935215, # 'sequence': '[CLS] Tinha uma casa no meio do caminho. [SEP]', # 'token': 1105, # 'token_str': 'casa'}, # {'score': 0.025660505518317223, # 'sequence': '[CLS] Tinha uma cruz no meio do caminho. [SEP]', # 'token': 3466, # 'token_str': 'cruz'}] ``` ### For BERT embeddings ```python import torch model = AutoModel.from_pretrained('neuralmind/bert-base-portuguese-cased') input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt') with torch.no_grad(): outs = model(input_ids) encoded = outs[0][0, 1:-1] # Ignore [CLS] and [SEP] special tokens # encoded.shape: (8, 768) # tensor([[-0.0398, -0.3057, 0.2431, ..., -0.5420, 0.1857, -0.5775], # [-0.2926, -0.1957, 0.7020, ..., -0.2843, 0.0530, -0.4304], # [ 0.2463, -0.1467, 0.5496, ..., 0.3781, -0.2325, -0.5469], # ..., # [ 0.0662, 0.7817, 0.3486, ..., -0.4131, -0.2852, -0.2819], # [ 0.0662, 0.2845, 0.1871, ..., -0.2542, -0.2933, -0.0661], # [ 0.2761, -0.1657, 0.3288, ..., -0.2102, 0.0029, -0.2009]]) ``` ## Citation If you use our work, please cite: ```bibtex @inproceedings{souza2020bertimbau, author = {F{\'a}bio Souza and Rodrigo Nogueira and Roberto Lotufo}, title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese}, booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)}, year = {2020} } ```

intfloat/multilingual-e5-large

intfloat

sentence-transformers

feature-extraction

--- tags: - mteb - Sentence Transformers - sentence-similarity - feature-extraction - sentence-transformers model-index: - name: multilingual-e5-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.05970149253731 - type: ap value: 43.486574390835635 - type: f1 value: 73.32700092140148 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.22055674518201 - type: ap value: 81.55756710830498 - type: f1 value: 69.28271787752661 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en-ext) config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 80.41979010494754 - type: ap value: 29.34879922376344 - type: f1 value: 67.62475449011278 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (ja) config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.8372591006424 - type: ap value: 26.557560591210738 - type: f1 value: 64.96619417368707 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.489875 - type: ap value: 90.98758636917603 - type: f1 value: 93.48554819717332 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.564 - type: f1 value: 46.75122173518047 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.400000000000006 - type: f1 value: 44.17195682400632 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.068 - type: f1 value: 42.38155696855596 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (fr) config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.89 - type: f1 value: 40.84407321682663 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (ja) config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.120000000000005 - type: f1 value: 39.522976223819114 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.832 - type: f1 value: 38.0392533394713 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 30.725 - type: map_at_10 value: 46.055 - type: map_at_100 value: 46.900999999999996 - type: map_at_1000 value: 46.911 - type: map_at_3 value: 41.548 - type: map_at_5 value: 44.297 - type: mrr_at_1 value: 31.152 - type: mrr_at_10 value: 46.231 - type: mrr_at_100 value: 47.07 - type: mrr_at_1000 value: 47.08 - type: mrr_at_3 value: 41.738 - type: mrr_at_5 value: 44.468999999999994 - type: ndcg_at_1 value: 30.725 - type: ndcg_at_10 value: 54.379999999999995 - type: ndcg_at_100 value: 58.138 - type: ndcg_at_1000 value: 58.389 - type: ndcg_at_3 value: 45.156 - type: ndcg_at_5 value: 50.123 - type: precision_at_1 value: 30.725 - type: precision_at_10 value: 8.087 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.54 - type: precision_at_5 value: 13.542000000000002 - type: recall_at_1 value: 30.725 - type: recall_at_10 value: 80.868 - type: recall_at_100 value: 97.653 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 55.619 - type: recall_at_5 value: 67.71000000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.30960650674069 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 38.427074197498996 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 60.28270056031872 - type: mrr value: 74.38332673789738 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.05942144105269 - type: cos_sim_spearman value: 82.51212105850809 - type: euclidean_pearson value: 81.95639829909122 - type: euclidean_spearman value: 82.3717564144213 - type: manhattan_pearson value: 81.79273425468256 - type: manhattan_spearman value: 82.20066817871039 - 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: 99.46764091858039 - type: f1 value: 99.37717466945023 - type: precision value: 99.33194154488518 - type: recall value: 99.46764091858039 - 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: 98.29407880255337 - type: f1 value: 98.11248073959938 - type: precision value: 98.02443319392472 - type: recall value: 98.29407880255337 - 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: 97.79009352268791 - type: f1 value: 97.5176076665512 - type: precision value: 97.38136473848286 - type: recall value: 97.79009352268791 - 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: 99.26276987888363 - type: f1 value: 99.20133403545726 - type: precision value: 99.17500438827453 - type: recall value: 99.26276987888363 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.72727272727273 - type: f1 value: 84.67672206031433 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.34220182511161 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 33.4987096128766 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.558249999999997 - type: map_at_10 value: 34.44425000000001 - type: map_at_100 value: 35.59833333333333 - type: map_at_1000 value: 35.706916666666665 - type: map_at_3 value: 31.691749999999995 - type: map_at_5 value: 33.252916666666664 - type: mrr_at_1 value: 30.252666666666666 - type: mrr_at_10 value: 38.60675 - type: mrr_at_100 value: 39.42666666666666 - type: mrr_at_1000 value: 39.48408333333334 - type: mrr_at_3 value: 36.17441666666665 - type: mrr_at_5 value: 37.56275 - type: ndcg_at_1 value: 30.252666666666666 - type: ndcg_at_10 value: 39.683 - type: ndcg_at_100 value: 44.68541666666667 - type: ndcg_at_1000 value: 46.94316666666668 - type: ndcg_at_3 value: 34.961749999999995 - type: ndcg_at_5 value: 37.215666666666664 - type: precision_at_1 value: 30.252666666666666 - type: precision_at_10 value: 6.904166666666667 - type: precision_at_100 value: 1.0989999999999995 - type: precision_at_1000 value: 0.14733333333333334 - type: precision_at_3 value: 16.037666666666667 - type: precision_at_5 value: 11.413583333333333 - type: recall_at_1 value: 25.558249999999997 - type: recall_at_10 value: 51.13341666666666 - type: recall_at_100 value: 73.08366666666667 - type: recall_at_1000 value: 88.79483333333334 - type: recall_at_3 value: 37.989083333333326 - type: recall_at_5 value: 43.787833333333325 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.338 - type: map_at_10 value: 18.360000000000003 - type: map_at_100 value: 19.942 - type: map_at_1000 value: 20.134 - type: map_at_3 value: 15.174000000000001 - type: map_at_5 value: 16.830000000000002 - type: mrr_at_1 value: 23.257 - type: mrr_at_10 value: 33.768 - type: mrr_at_100 value: 34.707 - type: mrr_at_1000 value: 34.766000000000005 - type: mrr_at_3 value: 30.977 - type: mrr_at_5 value: 32.528 - type: ndcg_at_1 value: 23.257 - type: ndcg_at_10 value: 25.733 - type: ndcg_at_100 value: 32.288 - type: ndcg_at_1000 value: 35.992000000000004 - type: ndcg_at_3 value: 20.866 - type: ndcg_at_5 value: 22.612 - type: precision_at_1 value: 23.257 - type: precision_at_10 value: 8.124 - type: precision_at_100 value: 1.518 - type: precision_at_1000 value: 0.219 - type: precision_at_3 value: 15.679000000000002 - type: precision_at_5 value: 12.117 - type: recall_at_1 value: 10.338 - type: recall_at_10 value: 31.154 - type: recall_at_100 value: 54.161 - type: recall_at_1000 value: 75.21900000000001 - type: recall_at_3 value: 19.427 - type: recall_at_5 value: 24.214 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.498 - type: map_at_10 value: 19.103 - type: map_at_100 value: 27.375 - type: map_at_1000 value: 28.981 - type: map_at_3 value: 13.764999999999999 - type: map_at_5 value: 15.950000000000001 - type: mrr_at_1 value: 65.5 - type: mrr_at_10 value: 74.53800000000001 - type: mrr_at_100 value: 74.71799999999999 - type: mrr_at_1000 value: 74.725 - type: mrr_at_3 value: 72.792 - type: mrr_at_5 value: 73.554 - type: ndcg_at_1 value: 53.37499999999999 - type: ndcg_at_10 value: 41.286 - type: ndcg_at_100 value: 45.972 - type: ndcg_at_1000 value: 53.123 - type: ndcg_at_3 value: 46.172999999999995 - type: ndcg_at_5 value: 43.033 - type: precision_at_1 value: 65.5 - type: precision_at_10 value: 32.725 - type: precision_at_100 value: 10.683 - type: precision_at_1000 value: 1.978 - type: precision_at_3 value: 50 - type: precision_at_5 value: 41.349999999999994 - type: recall_at_1 value: 8.498 - type: recall_at_10 value: 25.070999999999998 - type: recall_at_100 value: 52.383 - type: recall_at_1000 value: 74.91499999999999 - type: recall_at_3 value: 15.207999999999998 - type: recall_at_5 value: 18.563 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.5 - type: f1 value: 41.93833713984145 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 67.914 - type: map_at_10 value: 78.10000000000001 - type: map_at_100 value: 78.333 - type: map_at_1000 value: 78.346 - type: map_at_3 value: 76.626 - type: map_at_5 value: 77.627 - type: mrr_at_1 value: 72.74199999999999 - type: mrr_at_10 value: 82.414 - type: mrr_at_100 value: 82.511 - type: mrr_at_1000 value: 82.513 - type: mrr_at_3 value: 81.231 - type: mrr_at_5 value: 82.065 - type: ndcg_at_1 value: 72.74199999999999 - type: ndcg_at_10 value: 82.806 - type: ndcg_at_100 value: 83.677 - type: ndcg_at_1000 value: 83.917 - type: ndcg_at_3 value: 80.305 - type: ndcg_at_5 value: 81.843 - type: precision_at_1 value: 72.74199999999999 - type: precision_at_10 value: 10.24 - type: precision_at_100 value: 1.089 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 31.268 - 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type: dot_accuracy value: 88.19420188613343 - type: dot_ap value: 83.82679165901324 - type: dot_f1 value: 76.55833777304208 - type: dot_precision value: 75.6884875846501 - type: dot_recall value: 77.44841392054204 - type: euclidean_accuracy value: 89.03054294252338 - type: euclidean_ap value: 85.89089555185325 - type: euclidean_f1 value: 78.62997658079624 - type: euclidean_precision value: 74.92329149232914 - type: euclidean_recall value: 82.72251308900523 - type: manhattan_accuracy value: 89.0266620095471 - type: manhattan_ap value: 85.86458997929147 - type: manhattan_f1 value: 78.50685331000291 - type: manhattan_precision value: 74.5499861534201 - type: manhattan_recall value: 82.90729904527257 - type: max_accuracy value: 89.03054294252338 - type: max_ap value: 85.89089555185325 - type: max_f1 value: 78.62997658079624 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 [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 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-large') model = AutoModel.from_pretrained('intfloat/multilingual-e5-large') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # 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-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 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-large') 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.

laion/CLIP-ViT-H-14-laion2B-s32B-b79K

laion

open_clip

zero-shot-image-classification

--- license: mit widget: - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog library_name: open_clip pipeline_tag: zero-shot-image-classification --- # Model Card for CLIP ViT-H/14 - LAION-2B # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) 7. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description A CLIP ViT-H/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure Please see [training notes](https://docs.google.com/document/d/1EFbMLRWSSV0LUf9Du1pWzWqgeiIRPwEWX2s1C6mAk5c) and [wandb logs](https://wandb.ai/rom1504/eval_openclip/reports/H-14--VmlldzoyNDAxODQ3). # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. **TODO** - more detail ## Results The model achieves a 78.0 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. # Acknowledgements Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model. # Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` OpenAI CLIP paper ``` @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` OpenCLIP software ``` @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` # How to Get Started with the Model Use the code below to get started with the model. ** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets

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", } ```

indobenchmark/indobert-base-p1

indobenchmark

transformers

feature-extraction

--- language: id tags: - indobert - indobenchmark - indonlu license: mit inference: false datasets: - Indo4B --- # IndoBERT Base Model (phase1 - uncased) [IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. ## All Pre-trained Models | Model | #params | Arch. | Training data | |--------------------------------|--------------------------------|-------|-----------------------------------| | `indobenchmark/indobert-base-p1` | 124.5M | Base | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-base-p2` | 124.5M | Base | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-large-p1` | 335.2M | Large | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-large-p2` | 335.2M | Large | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-lite-base-p1` | 11.7M | Base | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-lite-base-p2` | 11.7M | Base | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-lite-large-p1` | 17.7M | Large | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-lite-large-p2` | 17.7M | Large | Indo4B (23.43 GB of text) | ## How to use ### Load model and tokenizer ```python from transformers import BertTokenizer, AutoModel tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-base-p1") model = AutoModel.from_pretrained("indobenchmark/indobert-base-p1") ``` ### Extract contextual representation ```python x = torch.LongTensor(tokenizer.encode('aku adalah anak [MASK]')).view(1,-1) print(x, model(x)[0].sum()) ``` ## Authors <b>IndoBERT</b> was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti. ## Citation If you use our work, please cite: ```bibtex @inproceedings{wilie2020indonlu, title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding}, author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti}, booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing}, year={2020} } ```

obi/deid_roberta_i2b2

obi

transformers

token-classification

--- language: - en thumbnail: "https://www.onebraveidea.org/wp-content/uploads/2019/07/OBI-Logo-Website.png" tags: - deidentification - medical notes - ehr - phi datasets: - I2B2 metrics: - F1 - Recall - Precision widget: - text: "Physician Discharge Summary Admit date: 10/12/1982 Discharge date: 10/22/1982 Patient Information Jack Reacher, 54 y.o. male (DOB = 1/21/1928)." - text: "Home Address: 123 Park Drive, San Diego, CA, 03245. Home Phone: 202-555-0199 (home)." - text: "Hospital Care Team Service: Orthopedics Inpatient Attending: Roger C Kelly, MD Attending phys phone: (634)743-5135 Discharge Unit: HCS843 Primary Care Physician: Hassan V Kim, MD 512-832-5025." license: mit --- # Model Description * A RoBERTa [[Liu et al., 2019]](https://arxiv.org/pdf/1907.11692.pdf) model fine-tuned for de-identification of medical notes. * Sequence Labeling (token classification): The model was trained to predict protected health information (PHI/PII) entities (spans). A list of protected health information categories is given by [HIPAA](https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html). * A token can either be classified as non-PHI or as one of the 11 PHI types. Token predictions are aggregated to spans by making use of BILOU tagging. * The PHI labels that were used for training and other details can be found here: [Annotation Guidelines](https://github.com/obi-ml-public/ehr_deidentification/blob/master/AnnotationGuidelines.md) * More details on how to use this model, the format of data and other useful information is present in the GitHub repo: [Robust DeID](https://github.com/obi-ml-public/ehr_deidentification). # How to use * A demo on how the model works (using model predictions to de-identify a medical note) is on this space: [Medical-Note-Deidentification](https://huggingface.co/spaces/obi/Medical-Note-Deidentification). * Steps on how this model can be used to run a forward pass can be found here: [Forward Pass](https://github.com/obi-ml-public/ehr_deidentification/tree/master/steps/forward_pass) * In brief, the steps are: * Sentencize (the model aggregates the sentences back to the note level) and tokenize the dataset. * Use the predict function of this model to gather the predictions (i.e., predictions for each token). * Additionally, the model predictions can be used to remove PHI from the original note/text. # Dataset * The I2B2 2014 [[Stubbs and Uzuner, 2015]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978170/) dataset was used to train this model. | | I2B2 | | I2B2 | | | --------- | --------------------- | ---------- | -------------------- | ---------- | | | TRAIN SET - 790 NOTES | | TEST SET - 514 NOTES | | | PHI LABEL | COUNT | PERCENTAGE | COUNT | PERCENTAGE | | DATE | 7502 | 43.69 | 4980 | 44.14 | | STAFF | 3149 | 18.34 | 2004 | 17.76 | | HOSP | 1437 | 8.37 | 875 | 7.76 | | AGE | 1233 | 7.18 | 764 | 6.77 | | LOC | 1206 | 7.02 | 856 | 7.59 | | PATIENT | 1316 | 7.66 | 879 | 7.79 | | PHONE | 317 | 1.85 | 217 | 1.92 | | ID | 881 | 5.13 | 625 | 5.54 | | PATORG | 124 | 0.72 | 82 | 0.73 | | EMAIL | 4 | 0.02 | 1 | 0.01 | | OTHERPHI | 2 | 0.01 | 0 | 0 | | TOTAL | 17171 | 100 | 11283 | 100 | # Training procedure * Steps on how this model was trained can be found here: [Training](https://github.com/obi-ml-public/ehr_deidentification/tree/master/steps/train). The "model_name_or_path" was set to: "roberta-large". * The dataset was sentencized with the en_core_sci_sm sentencizer from spacy. * The dataset was then tokenized with a custom tokenizer built on top of the en_core_sci_sm tokenizer from spacy. * For each sentence we added 32 tokens on the left (from previous sentences) and 32 tokens on the right (from the next sentences). * The added tokens are not used for learning - i.e, the loss is not computed on these tokens - they are used as additional context. * Each sequence contained a maximum of 128 tokens (including the 32 tokens added on). Longer sequences were split. * The sentencized and tokenized dataset with the token level labels based on the BILOU notation was used to train the model. * The model is fine-tuned from a pre-trained RoBERTa model. * Training details: * Input sequence length: 128 * Batch size: 32 (16 with 2 gradient accumulation steps) * Optimizer: AdamW * Learning rate: 5e-5 * Dropout: 0.1 ## Results # Questions? Post a Github issue on the repo: [Robust DeID](https://github.com/obi-ml-public/ehr_deidentification).

timm/resnet18.a1_in1k

timm

timm

image-classification

--- tags: - image-classification - timm license: apache-2.0 library_name: timm --- # Model card for resnet18.a1_in1k A ResNet-B image classification model. This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * ResNet Strikes Back `A1` recipe * LAMB optimizer with BCE loss * Cosine LR schedule with warmup ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 11.7 - GMACs: 1.8 - Activations (M): 2.5 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/huggingface/pytorch-image-models ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('resnet18.a1_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'resnet18.a1_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 64, 112, 112]) # torch.Size([1, 64, 56, 56]) # torch.Size([1, 128, 28, 28]) # torch.Size([1, 256, 14, 14]) # torch.Size([1, 512, 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( 'resnet18.a1_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 512, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). |model |img_size|top1 |top5 |param_count|gmacs|macts|img/sec| |------------------------------------------|--------|-----|-----|-----------|-----|-----|-------| |[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|320 |86.72|98.17|93.6 |35.2 |69.7 |451 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|288 |86.51|98.08|93.6 |28.5 |56.4 |560 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|288 |86.49|98.03|93.6 |28.5 |56.4 |557 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|224 |85.96|97.82|93.6 |17.2 |34.2 |923 | |[resnext101_32x32d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x32d.fb_wsl_ig1b_ft_in1k)|224 |85.11|97.44|468.5 |87.3 |91.1 |254 | |[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|416 |85.0 |97.12|191.9 |108.4|213.8|134 | |[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|352 |84.96|97.22|102.1 |50.2 |101.2|291 | |[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|320 |84.73|97.18|102.1 |41.5 |83.7 |353 | |[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|384 |84.71|96.99|164.0 |77.6 |154.7|183 | |[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|288 |84.57|97.08|93.6 |28.5 |56.4 |557 | |[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|320 |84.45|97.08|93.2 |31.5 |67.8 |446 | |[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|352 |84.43|96.97|129.9 |51.1 |105.5|280 | |[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|288 |84.36|96.92|93.6 |27.6 |53.0 |595 | |[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|320 |84.35|97.04|66.8 |24.1 |47.7 |610 | |[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|288 |84.3 |96.94|164.0 |43.7 |87.1 |333 | |[resnext101_32x8d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_swsl_ig1b_ft_in1k)|224 |84.28|97.17|88.8 |16.5 |31.2 |1100 | |[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|320 |84.24|96.86|191.9 |64.2 |126.6|228 | |[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|288 |84.19|96.87|93.6 |27.2 |51.6 |613 | |[resnext101_32x16d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_wsl_ig1b_ft_in1k)|224 |84.18|97.19|194.0 |36.3 |51.2 |581 | |[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|288 |84.11|97.11|44.6 |15.1 |29.0 |1144 | |[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|320 |83.97|96.82|64.7 |31.2 |67.3 |518 | |[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|256 |83.87|96.75|93.2 |20.2 |43.4 |692 | |[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|224 |83.86|96.65|93.6 |17.2 |34.2 |923 | |[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|320 |83.72|96.61|86.6 |24.3 |48.1 |617 | |[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|256 |83.69|96.78|66.8 |15.4 |30.6 |943 | |[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|224 |83.68|96.61|93.6 |16.7 |32.0 |986 | |[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|320 |83.67|96.74|60.2 |24.1 |47.7 |706 | |[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|256 |83.59|96.61|129.9 |27.1 |55.8 |526 | |[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|224 |83.58|96.4 |93.6 |16.5 |31.2 |1013 | |[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|224 |83.54|96.83|44.6 |9.1 |17.6 |1864 | |[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|288 |83.46|96.54|60.2 |19.1 |37.3 |904 | |[resnext101_32x16d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_swsl_ig1b_ft_in1k)|224 |83.35|96.85|194.0 |36.3 |51.2 |582 | |[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|256 |83.23|96.53|64.7 |20.0 |43.1 |809 | |[resnext101_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_swsl_ig1b_ft_in1k)|224 |83.22|96.75|44.2 |8.0 |21.2 |1814 | |[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|288 |83.16|96.38|83.5 |25.7 |51.6 |590 | |[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|256 |83.14|96.38|60.2 |15.4 |30.5 |1096 | |[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|320 |83.02|96.45|44.6 |16.5 |34.8 |992 | |[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|288 |82.98|96.54|44.6 |13.4 |28.2 |1077 | |[resnext101_64x4d.tv_in1k](https://huggingface.co/timm/resnext101_64x4d.tv_in1k)|224 |82.98|96.25|83.5 |15.5 |31.2 |989 | |[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|256 |82.86|96.28|86.6 |15.6 |30.8 |951 | |[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|224 |82.83|96.22|88.8 |16.5 |31.2 |1099 | |[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|224 |82.8 |96.13|60.2 |11.6 |22.6 |1486 | |[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|288 |82.8 |96.32|44.6 |13.0 |26.8 |1291 | |[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|288 |82.74|95.71|60.2 |19.1 |37.3 |905 | |[resnext101_32x8d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_wsl_ig1b_ft_in1k)|224 |82.69|96.63|88.8 |16.5 |31.2 |1100 | |[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|288 |82.62|95.75|60.2 |19.1 |37.3 |904 | |[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|288 |82.61|96.49|25.6 |8.9 |20.6 |1729 | |[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|288 |82.53|96.13|36.8 |9.9 |21.5 |1773 | |[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|224 |82.5 |96.02|126.9 |22.8 |21.2 |1078 | |[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|224 |82.46|95.92|83.5 |15.5 |31.2 |987 | |[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|288 |82.36|96.18|35.7 |8.1 |20.9 |1964 | |[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|320 |82.35|96.14|25.6 |8.8 |24.1 |1386 | |[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|288 |82.31|95.63|44.6 |13.0 |26.8 |1291 | |[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|288 |82.29|96.01|63.6 |13.6 |28.5 |1078 | |[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|224 |82.29|96.0 |60.2 |11.6 |22.6 |1484 | |[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|288 |82.27|96.06|68.9 |18.9 |23.8 |1176 | |[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|256 |82.26|96.07|44.6 |10.6 |22.2 |1542 | |[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|288 |82.24|95.73|44.6 |13.0 |26.8 |1290 | |[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|288 |82.2 |96.14|27.6 |7.0 |23.8 |1547 | |[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|224 |82.18|96.05|44.6 |8.1 |17.1 |1771 | |[resnext50_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k)|224 |82.17|96.22|25.0 |4.3 |14.4 |2943 | |[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|288 |82.12|95.65|25.6 |7.1 |19.6 |1704 | |[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|288 |82.03|95.94|25.0 |7.0 |23.8 |1745 | |[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|288 |82.0 |96.15|24.9 |5.8 |12.7 |1787 | |[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|256 |81.99|95.85|36.8 |7.8 |17.0 |2230 | |[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|176 |81.98|95.72|88.8 |10.3 |19.4 |1768 | |[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|224 |81.97|95.24|60.2 |11.6 |22.6 |1486 | |[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|224 |81.93|95.75|44.6 |7.8 |16.2 |2122 | |[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|224 |81.9 |95.77|44.6 |7.8 |16.2 |2118 | |[resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k)|224 |81.84|96.1 |194.0 |36.3 |51.2 |583 | |[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|256 |81.78|95.94|35.7 |6.4 |16.6 |2471 | |[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|224 |81.77|95.22|60.2 |11.6 |22.6 |1485 | |[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|224 |81.74|96.06|25.6 |5.4 |12.4 |2813 | |[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|288 |81.65|95.54|25.6 |7.1 |19.6 |1703 | |[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|288 |81.64|95.88|25.6 |7.2 |19.7 |1694 | |[resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k)|224 |81.62|96.04|88.8 |16.5 |31.2 |1101 | |[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|224 |81.61|95.76|68.9 |11.4 |14.4 |1930 | |[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|288 |81.61|95.83|25.6 |8.5 |19.2 |1868 | |[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|224 |81.5 |95.16|44.6 |7.8 |16.2 |2125 | |[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|288 |81.48|95.16|25.0 |7.0 |23.8 |1745 | |[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|288 |81.47|95.71|25.9 |6.9 |18.6 |2071 | |[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|224 |81.45|95.53|68.9 |11.4 |14.4 |1929 | |[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|288 |81.44|95.22|25.6 |7.2 |19.7 |1908 | |[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|256 |81.44|95.67|25.6 |5.6 |15.4 |2168 | |[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|288 |81.4 |95.82|30.2 |6.8 |13.9 |2132 | |[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|288 |81.37|95.74|25.6 |7.2 |19.7 |1910 | |[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|224 |81.32|95.19|44.6 |7.8 |16.2 |2125 | |[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|288 |81.3 |95.65|28.1 |6.8 |18.4 |1803 | |[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|288 |81.3 |95.11|25.0 |7.0 |23.8 |1746 | |[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|224 |81.27|95.62|27.6 |4.3 |14.4 |2591 | |[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|224 |81.26|95.16|25.6 |4.3 |11.8 |2823 | |[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|288 |81.23|95.54|15.7 |4.8 |19.6 |2117 | |[senet154.gluon_in1k](https://huggingface.co/timm/senet154.gluon_in1k)|224 |81.23|95.35|115.1 |20.8 |38.7 |545 | |[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|288 |81.22|95.11|25.6 |6.8 |18.4 |2089 | |[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|288 |81.22|95.63|25.6 |6.8 |18.4 |676 | |[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|288 |81.18|95.09|25.6 |7.2 |19.7 |1908 | |[resnet50.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet50.fb_swsl_ig1b_ft_in1k)|224 |81.18|95.98|25.6 |4.1 |11.1 |3455 | |[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|224 |81.17|95.34|25.0 |4.3 |14.4 |2933 | |[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|224 |81.1 |95.33|25.0 |4.3 |14.4 |2934 | |[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|288 |81.1 |95.23|28.1 |6.8 |18.4 |1801 | |[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|288 |81.1 |95.12|28.1 |6.8 |18.4 |1799 | |[resnet152s.gluon_in1k](https://huggingface.co/timm/resnet152s.gluon_in1k)|224 |81.02|95.41|60.3 |12.9 |25.0 |1347 | |[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|288 |80.97|95.44|25.6 |6.8 |18.4 |2085 | |[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|256 |80.94|95.45|25.9 |5.4 |14.7 |2571 | |[resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.93|95.73|44.2 |8.0 |21.2 |1814 | |[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|288 |80.91|95.55|25.6 |6.8 |18.4 |2084 | |[seresnext101_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_32x4d.gluon_in1k)|224 |80.9 |95.31|49.0 |8.0 |21.3 |1585 | |[seresnext101_64x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_64x4d.gluon_in1k)|224 |80.9 |95.3 |88.2 |15.5 |31.2 |918 | |[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|288 |80.86|95.52|25.6 |6.8 |18.4 |2085 | |[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|224 |80.85|95.43|25.6 |4.1 |11.1 |3450 | |[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|224 |80.84|95.02|25.6 |4.3 |11.8 |2821 | |[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|224 |80.79|95.62|24.9 |3.5 |7.7 |2961 | |[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|288 |80.79|95.36|19.8 |6.0 |14.8 |2506 | |[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|288 |80.79|95.58|19.9 |4.2 |10.6 |2349 | |[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|288 |80.78|94.99|25.6 |6.8 |18.4 |2088 | |[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|288 |80.71|95.43|25.6 |6.8 |18.4 |2087 | |[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|288 |80.7 |95.39|25.0 |7.0 |23.8 |1749 | |[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|192 |80.69|95.24|63.6 |6.0 |12.7 |2270 | |[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|224 |80.68|94.71|25.6 |4.4 |11.9 |3162 | |[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|288 |80.68|95.36|19.7 |6.0 |14.8 |2637 | |[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|224 |80.67|95.3 |25.6 |4.1 |11.1 |3452 | |[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|288 |80.67|95.42|25.0 |7.4 |25.1 |1626 | |[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|224 |80.63|95.21|25.6 |5.2 |11.6 |3034 | |[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|224 |80.61|95.32|25.6 |4.4 |11.9 |2813 | |[resnext101_64x4d.gluon_in1k](https://huggingface.co/timm/resnext101_64x4d.gluon_in1k)|224 |80.61|94.99|83.5 |15.5 |31.2 |989 | |[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|288 |80.6 |95.31|19.9 |6.0 |14.8 |2578 | |[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|256 |80.57|95.17|15.7 |3.8 |15.5 |2710 | |[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|224 |80.56|95.0 |60.2 |11.6 |22.6 |1483 | |[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|224 |80.53|95.16|25.6 |4.4 |11.9 |3164 | |[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|224 |80.53|94.46|25.0 |4.3 |14.4 |2930 | |[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|176 |80.48|94.98|126.9 |14.3 |13.2 |1719 | |[resnet152d.gluon_in1k](https://huggingface.co/timm/resnet152d.gluon_in1k)|224 |80.47|95.2 |60.2 |11.8 |23.4 |1428 | |[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|288 |80.45|95.32|25.6 |6.8 |18.4 |2086 | |[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|224 |80.45|95.24|30.2 |4.1 |8.4 |3530 | |[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|224 |80.45|94.63|25.0 |4.3 |14.4 |2936 | |[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|176 |80.43|95.09|68.9 |7.3 |9.0 |3015 | |[resnet101d.gluon_in1k](https://huggingface.co/timm/resnet101d.gluon_in1k)|224 |80.42|95.01|44.6 |8.1 |17.0 |2007 | |[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|224 |80.38|94.6 |25.6 |4.1 |11.1 |3461 | |[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|256 |80.36|95.1 |19.8 |4.8 |11.7 |3267 | |[resnext101_32x4d.gluon_in1k](https://huggingface.co/timm/resnext101_32x4d.gluon_in1k)|224 |80.34|94.93|44.2 |8.0 |21.2 |1814 | |[resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.32|95.4 |25.0 |4.3 |14.4 |2941 | |[resnet101s.gluon_in1k](https://huggingface.co/timm/resnet101s.gluon_in1k)|224 |80.28|95.16|44.7 |9.2 |18.6 |1851 | |[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|224 |80.26|95.08|28.1 |4.1 |11.1 |2972 | |[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|288 |80.24|95.24|25.6 |8.5 |19.9 |1523 | |[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|224 |80.22|94.63|25.6 |4.4 |11.9 |3162 | |[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|176 |80.2 |94.64|60.2 |7.2 |14.0 |2346 | |[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|224 |80.08|94.74|28.1 |4.1 |11.1 |2969 | |[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|256 |80.08|94.97|19.7 |4.8 |11.7 |3284 | |[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|256 |80.06|94.99|19.9 |4.8 |11.7 |3216 | |[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|224 |80.06|94.95|25.6 |4.1 |11.1 |1109 | |[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|224 |80.02|94.71|28.1 |4.1 |11.1 |2962 | |[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|288 |79.97|95.05|25.6 |6.8 |18.4 |2086 | |[resnet152c.gluon_in1k](https://huggingface.co/timm/resnet152c.gluon_in1k)|224 |79.92|94.84|60.2 |11.8 |23.4 |1455 | |[seresnext50_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext50_32x4d.gluon_in1k)|224 |79.91|94.82|27.6 |4.3 |14.4 |2591 | |[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|224 |79.91|94.67|25.6 |4.1 |11.1 |3456 | |[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|176 |79.9 |94.6 |44.6 |4.9 |10.1 |3341 | |[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|224 |79.89|94.97|35.7 |4.5 |12.1 |2774 | |[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|224 |79.88|94.87|25.6 |4.1 |11.1 |3455 | |[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|320 |79.86|95.07|16.0 |5.2 |16.4 |2168 | |[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|224 |79.85|94.56|25.6 |4.1 |11.1 |3460 | |[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|288 |79.83|94.97|25.6 |6.8 |18.4 |2087 | |[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|224 |79.82|94.62|44.6 |7.8 |16.2 |2114 | |[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|224 |79.76|94.6 |25.0 |4.3 |14.4 |2943 | |[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|224 |79.74|94.95|25.6 |4.1 |11.1 |3455 | |[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|224 |79.74|94.87|19.9 |2.5 |6.4 |3929 | |[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|288 |79.71|94.83|19.7 |6.0 |14.8 |2710 | |[resnet152.gluon_in1k](https://huggingface.co/timm/resnet152.gluon_in1k)|224 |79.68|94.74|60.2 |11.6 |22.6 |1486 | |[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|224 |79.67|94.87|25.0 |4.5 |15.2 |2729 | |[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|288 |79.63|94.91|25.6 |6.8 |18.4 |2086 | |[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|224 |79.56|94.72|25.6 |4.3 |11.8 |2805 | |[resnet101c.gluon_in1k](https://huggingface.co/timm/resnet101c.gluon_in1k)|224 |79.53|94.58|44.6 |8.1 |17.0 |2062 | |[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|224 |79.52|94.61|25.6 |4.1 |11.1 |3459 | |[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|176 |79.42|94.64|25.6 |2.6 |6.9 |5397 | |[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|288 |79.4 |94.66|18.0 |5.9 |14.6 |2752 | |[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|224 |79.38|94.57|25.6 |4.1 |11.1 |3459 | |[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|176 |79.37|94.3 |25.0 |2.7 |9.0 |4577 | |[resnext50_32x4d.gluon_in1k](https://huggingface.co/timm/resnext50_32x4d.gluon_in1k)|224 |79.36|94.43|25.0 |4.3 |14.4 |2942 | |[resnext101_32x8d.tv_in1k](https://huggingface.co/timm/resnext101_32x8d.tv_in1k)|224 |79.31|94.52|88.8 |16.5 |31.2 |1100 | |[resnet101.gluon_in1k](https://huggingface.co/timm/resnet101.gluon_in1k)|224 |79.31|94.53|44.6 |7.8 |16.2 |2125 | |[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|224 |79.31|94.63|25.6 |5.2 |12.0 |2524 | |[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|176 |79.27|94.49|25.6 |2.6 |6.9 |5404 | |[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|224 |79.25|94.31|25.0 |4.3 |14.4 |2931 | |[resnet50.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet50.fb_ssl_yfcc100m_ft_in1k)|224 |79.22|94.84|25.6 |4.1 |11.1 |3451 | |[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|256 |79.21|94.56|19.7 |4.8 |11.7 |3392 | |[resnet50d.gluon_in1k](https://huggingface.co/timm/resnet50d.gluon_in1k)|224 |79.07|94.48|25.6 |4.4 |11.9 |3162 | |[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|224 |79.03|94.38|25.6 |4.1 |11.1 |3453 | |[resnet50.am_in1k](https://huggingface.co/timm/resnet50.am_in1k)|224 |79.01|94.39|25.6 |4.1 |11.1 |3461 | |[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|256 |79.01|94.37|18.0 |4.6 |11.6 |3440 | |[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|256 |78.9 |94.54|16.0 |3.4 |10.5 |3421 | |[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|160 |78.89|94.11|60.2 |5.9 |11.5 |2745 | |[wide_resnet101_2.tv_in1k](https://huggingface.co/timm/wide_resnet101_2.tv_in1k)|224 |78.84|94.28|126.9 |22.8 |21.2 |1079 | |[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|288 |78.83|94.24|16.8 |4.5 |16.8 |2251 | |[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|224 |78.81|94.32|25.6 |4.1 |11.1 |3454 | |[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|288 |78.74|94.33|16.8 |4.5 |16.7 |2264 | |[resnet50s.gluon_in1k](https://huggingface.co/timm/resnet50s.gluon_in1k)|224 |78.72|94.23|25.7 |5.5 |13.5 |2796 | |[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|224 |78.71|94.24|25.6 |4.4 |11.9 |3154 | |[wide_resnet50_2.tv_in1k](https://huggingface.co/timm/wide_resnet50_2.tv_in1k)|224 |78.47|94.09|68.9 |11.4 |14.4 |1934 | |[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|224 |78.46|94.27|25.6 |4.1 |11.1 |3454 | |[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|288 |78.43|94.35|21.8 |6.5 |7.5 |3291 | |[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|288 |78.42|94.04|10.5 |3.1 |13.3 |3226 | |[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|320 |78.33|94.13|16.0 |5.2 |16.4 |2391 | |[resnet152.tv_in1k](https://huggingface.co/timm/resnet152.tv_in1k)|224 |78.32|94.04|60.2 |11.6 |22.6 |1487 | |[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|288 |78.28|94.1 |10.4 |3.1 |13.3 |3062 | |[bat_resnext26ts.ch_in1k](https://huggingface.co/timm/bat_resnext26ts.ch_in1k)|256 |78.25|94.1 |10.7 |2.5 |12.5 |3393 | |[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|224 |78.06|93.78|25.6 |4.1 |11.1 |3450 | |[resnet50c.gluon_in1k](https://huggingface.co/timm/resnet50c.gluon_in1k)|224 |78.0 |93.99|25.6 |4.4 |11.9 |3286 | |[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|288 |78.0 |93.91|10.3 |3.1 |13.3 |3297 | |[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|224 |77.98|93.75|16.8 |2.7 |10.1 |3841 | |[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|288 |77.92|93.77|21.8 |6.1 |6.2 |3609 | |[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|160 |77.88|93.71|44.6 |4.0 |8.3 |3926 | |[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|256 |77.87|93.84|16.0 |3.4 |10.5 |3772 | |[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|256 |77.86|93.79|10.4 |2.4 |10.5 |4263 | |[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|160 |77.82|93.81|35.7 |2.3 |6.2 |5238 | |[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|256 |77.81|93.82|10.5 |2.4 |10.5 |4183 | |[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|160 |77.79|93.6 |25.6 |2.2 |6.0 |5329 | |[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|160 |77.73|93.32|25.0 |2.2 |7.4 |5576 | |[resnext50_32x4d.tv_in1k](https://huggingface.co/timm/resnext50_32x4d.tv_in1k)|224 |77.61|93.7 |25.0 |4.3 |14.4 |2944 | |[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|224 |77.59|93.61|16.8 |2.7 |10.2 |3807 | |[resnet50.gluon_in1k](https://huggingface.co/timm/resnet50.gluon_in1k)|224 |77.58|93.72|25.6 |4.1 |11.1 |3455 | |[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|256 |77.44|93.56|10.3 |2.4 |10.5 |4284 | |[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|288 |77.41|93.63|16.0 |4.3 |13.5 |2907 | |[resnet101.tv_in1k](https://huggingface.co/timm/resnet101.tv_in1k)|224 |77.38|93.54|44.6 |7.8 |16.2 |2125 | |[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|160 |77.22|93.27|25.6 |2.2 |6.1 |5982 | |[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|288 |77.17|93.47|10.3 |3.1 |13.3 |3392 | |[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|288 |77.15|93.27|21.8 |6.1 |6.2 |3615 | |[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|224 |77.1 |93.37|21.8 |3.9 |4.5 |5436 | |[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|224 |77.02|93.07|28.1 |4.1 |11.1 |2952 | |[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|256 |76.78|93.13|10.3 |2.4 |10.5 |4410 | |[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|224 |76.7 |93.17|16.0 |2.6 |8.2 |4859 | |[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|288 |76.5 |93.35|21.8 |6.1 |6.2 |3617 | |[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|224 |76.42|92.87|21.8 |3.7 |3.7 |5984 | |[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|288 |76.35|93.18|16.0 |3.9 |12.2 |3331 | |[resnet50.tv_in1k](https://huggingface.co/timm/resnet50.tv_in1k)|224 |76.13|92.86|25.6 |4.1 |11.1 |3457 | |[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|160 |75.96|92.5 |25.6 |2.1 |5.7 |6490 | |[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|224 |75.52|92.44|21.8 |3.7 |3.7 |5991 | |[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|224 |75.3 |92.58|16.0 |2.4 |7.4 |5583 | |[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|224 |75.16|92.18|21.8 |3.7 |3.7 |5994 | |[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|160 |75.1 |92.08|28.1 |2.1 |5.7 |5513 | |[resnet34.gluon_in1k](https://huggingface.co/timm/resnet34.gluon_in1k)|224 |74.57|91.98|21.8 |3.7 |3.7 |5984 | |[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|288 |73.81|91.83|11.7 |3.4 |5.4 |5196 | |[resnet34.tv_in1k](https://huggingface.co/timm/resnet34.tv_in1k)|224 |73.32|91.42|21.8 |3.7 |3.7 |5979 | |[resnet18.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet18.fb_swsl_ig1b_ft_in1k)|224 |73.28|91.73|11.7 |1.8 |2.5 |10213 | |[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|288 |73.16|91.03|11.7 |3.0 |4.1 |6050 | |[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|224 |72.98|91.11|21.8 |3.7 |3.7 |5967 | |[resnet18.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet18.fb_ssl_yfcc100m_ft_in1k)|224 |72.6 |91.42|11.7 |1.8 |2.5 |10213 | |[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|288 |72.37|90.59|11.7 |3.0 |4.1 |6051 | |[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|224 |72.26|90.31|10.1 |1.7 |5.8 |7026 | |[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|224 |72.26|90.68|11.7 |2.1 |3.3 |8707 | |[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|224 |71.49|90.07|11.7 |1.8 |2.5 |10187 | |[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|176 |71.31|89.69|10.1 |1.1 |3.6 |10970 | |[resnet18.gluon_in1k](https://huggingface.co/timm/resnet18.gluon_in1k)|224 |70.84|89.76|11.7 |1.8 |2.5 |10210 | |[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|224 |70.64|89.47|11.7 |1.8 |2.5 |10194 | |[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|160 |70.56|89.52|21.8 |1.9 |1.9 |10737 | |[resnet18.tv_in1k](https://huggingface.co/timm/resnet18.tv_in1k)|224 |69.76|89.07|11.7 |1.8 |2.5 |10205 | |[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|224 |68.34|88.03|5.4 |1.1 |2.4 |13079 | |[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|224 |68.25|88.17|11.7 |1.8 |2.5 |10167 | |[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|176 |66.71|86.96|5.4 |0.7 |1.5 |20327 | |[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|160 |65.66|86.26|11.7 |0.9 |1.3 |18229 | ## Citation ```bibtex @inproceedings{wightman2021resnet, title={ResNet strikes back: An improved training procedure in timm}, author={Wightman, Ross and Touvron, Hugo and Jegou, Herve}, booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ``` ```bibtex @article{He2015, author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, title = {Deep Residual Learning for Image Recognition}, journal = {arXiv preprint arXiv:1512.03385}, year = {2015} } ```

mistralai/Mistral-7B-Instruct-v0.1

mistralai

transformers

text-generation

--- license: apache-2.0 pipeline_tag: text-generation tags: - finetuned inference: true widget: - messages: - role: user content: What is your favorite condiment? --- # Model Card for Mistral-7B-Instruct-v0.1 The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets. For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

facebook/detr-resnet-50

facebook

transformers

object-detection

--- license: apache-2.0 tags: - object-detection - vision datasets: - coco widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg example_title: Savanna - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg example_title: Airport --- # DETR (End-to-End Object Detection) model with ResNet-50 backbone DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in [this repository](https://github.com/facebookresearch/detr). Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.  ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models. ### How to use Here is how to use this model: ```python from transformers import DetrImageProcessor, DetrForObjectDetection import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) # you can specify the revision tag if you don't want the timm dependency processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm") model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.9 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` This should output: ``` Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98] Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66] Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76] Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93] Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72] ``` Currently, both the feature extractor and model support PyTorch. ## Training data The DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225). ### Training The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64). ## Evaluation results This model achieves an AP (average precision) of **42.0** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2005-12872, author = {Nicolas Carion and Francisco Massa and Gabriel Synnaeve and Nicolas Usunier and Alexander Kirillov and Sergey Zagoruyko}, title = {End-to-End Object Detection with Transformers}, journal = {CoRR}, volume = {abs/2005.12872}, year = {2020}, url = {https://arxiv.org/abs/2005.12872}, archivePrefix = {arXiv}, eprint = {2005.12872}, timestamp = {Thu, 28 May 2020 17:38:09 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

cardiffnlp/twitter-roberta-base-offensive

cardiffnlp

transformers

text-classification

# Twitter-roBERTa-base for Offensive Language Identification This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark. - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Tasks: # emoji, emotion, hate, irony, offensive, sentiment # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary task='offensive' MODEL = f"cardiffnlp/twitter-roberta-base-{task}" tokenizer = AutoTokenizer.from_pretrained(MODEL) # download label mapping labels=[] mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Good night 😊" # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) not-offensive 0.9073 2) offensive 0.0927 ```

allenai/scibert_scivocab_uncased

allenai

transformers

--- language: en --- # SciBERT This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text. The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just abstracts. SciBERT has its own wordpiece vocabulary (scivocab) that's built to best match the training corpus. We trained cased and uncased versions. Available models include: * `scibert_scivocab_cased` * `scibert_scivocab_uncased` The original repo can be found [here](https://github.com/allenai/scibert). If using these models, please cite the following paper: ``` @inproceedings{beltagy-etal-2019-scibert, title = "SciBERT: A Pretrained Language Model for Scientific Text", author = "Beltagy, Iz and Lo, Kyle and Cohan, Arman", booktitle = "EMNLP", year = "2019", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1371" } ```

prajjwal1/bert-small

prajjwal1

transformers

--- language: - en license: - mit tags: - BERT - MNLI - NLI - transformer - pre-training --- The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). This is one of the smaller pre-trained BERT variants, together with [bert-tiny](https://huggingface.co/prajjwal1/bert-small), [bert-mini]([bert-small](https://huggingface.co/prajjwal1/bert-mini) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task. If you use the model, please consider citing both the papers: ``` @misc{bhargava2021generalization, title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers}, year={2021}, eprint={2110.01518}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{DBLP:journals/corr/abs-1908-08962, author = {Iulia Turc and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation}, journal = {CoRR}, volume = {abs/1908.08962}, year = {2019}, url = {http://arxiv.org/abs/1908.08962}, eprinttype = {arXiv}, eprint = {1908.08962}, timestamp = {Thu, 29 Aug 2019 16:32:34 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` Config of this model: - `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small) Other models to check out: - `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny) - `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini) - `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium) Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli). Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)

sentence-transformers/multi-qa-mpnet-base-dot-v1

sentence-transformers

sentence-transformers

sentence-similarity

--- language: - en library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - search_qa - eli5 - natural_questions - trivia_qa - embedding-data/QQP - embedding-data/PAQ_pairs - embedding-data/Amazon-QA - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # multi-qa-mpnet-base-dot-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] #Load the model model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-dot-v1') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #CLS Pooling - Take output from first token def cls_pooling(model_output): return model_output.last_hidden_state[:,0] #Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input, return_dict=True) # Perform pooling embeddings = cls_pooling(model_output) return embeddings # Sentences we want sentence embeddings for query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1") model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1") #Encode query and docs query_emb = encode(query) doc_emb = encode(docs) #Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Technical Details In the following some technical details how this model must be used: | Setting | Value | | --- | :---: | | Dimensions | 768 | | Produces normalized embeddings | No | | Pooling-Method | CLS pooling | | Suitable score functions | dot-product (e.g. `util.dot_score`) | ---- ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages. Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text. ## Training procedure The full training script is accessible in this current repository: `train_script.py`. ### Pre-training We use the pretrained [`mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure. #### Training We use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using CLS-pooling, dot-product as similarity function, and a scale of 1. | Dataset | Number of training tuples | |--------------------------------------------------------|:--------------------------:| | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges | 25,316,456 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges | 21,396,559 | | [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine | 17,579,773 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet | 3,012,496 | | [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839 | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 | | [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 | | **Total** | **214,988,242** |

intfloat/e5-large-v2

intfloat

sentence-transformers

sentence-similarity

--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: e5-large-v2 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.22388059701493 - type: ap value: 43.20816505595132 - type: f1 value: 73.27811303522058 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.748325 - type: ap value: 90.72534979701297 - type: f1 value: 93.73895874282185 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.612 - type: f1 value: 47.61157345898393 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 23.541999999999998 - type: map_at_10 value: 38.208 - type: map_at_100 value: 39.417 - type: map_at_1000 value: 39.428999999999995 - type: map_at_3 value: 33.95 - type: map_at_5 value: 36.329 - type: mrr_at_1 value: 23.755000000000003 - type: mrr_at_10 value: 38.288 - type: mrr_at_100 value: 39.511 - type: mrr_at_1000 value: 39.523 - type: mrr_at_3 value: 34.009 - type: mrr_at_5 value: 36.434 - type: ndcg_at_1 value: 23.541999999999998 - type: ndcg_at_10 value: 46.417 - type: ndcg_at_100 value: 51.812000000000005 - type: ndcg_at_1000 value: 52.137 - type: ndcg_at_3 value: 37.528 - type: ndcg_at_5 value: 41.81 - type: precision_at_1 value: 23.541999999999998 - type: precision_at_10 value: 7.269 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 15.979 - type: precision_at_5 value: 11.664 - type: recall_at_1 value: 23.541999999999998 - type: recall_at_10 value: 72.688 - type: recall_at_100 value: 96.871 - type: recall_at_1000 value: 99.431 - type: recall_at_3 value: 47.937000000000005 - type: recall_at_5 value: 58.321 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.546499570522094 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 41.01607489943561 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 59.616107510107774 - type: mrr value: 72.75106626214661 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.33018094733868 - type: cos_sim_spearman value: 83.60190492611737 - type: euclidean_pearson value: 82.1492450218961 - type: euclidean_spearman value: 82.70308926526991 - type: manhattan_pearson value: 81.93959600076842 - type: manhattan_spearman value: 82.73260801016369 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.54545454545455 - type: f1 value: 84.49582530928923 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.362725540120096 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.849509608178145 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.502999999999997 - type: map_at_10 value: 43.323 - type: map_at_100 value: 44.708999999999996 - type: map_at_1000 value: 44.838 - type: map_at_3 value: 38.987 - type: map_at_5 value: 41.516999999999996 - type: mrr_at_1 value: 38.769999999999996 - type: mrr_at_10 value: 49.13 - type: mrr_at_100 value: 49.697 - type: mrr_at_1000 value: 49.741 - type: mrr_at_3 value: 45.804 - type: mrr_at_5 value: 47.842 - type: ndcg_at_1 value: 38.769999999999996 - type: ndcg_at_10 value: 50.266999999999996 - type: ndcg_at_100 value: 54.967 - type: ndcg_at_1000 value: 56.976000000000006 - type: ndcg_at_3 value: 43.823 - type: ndcg_at_5 value: 47.12 - type: precision_at_1 value: 38.769999999999996 - type: precision_at_10 value: 10.057 - type: precision_at_100 value: 1.554 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.125 - type: precision_at_5 value: 15.851 - type: recall_at_1 value: 31.502999999999997 - type: recall_at_10 value: 63.715999999999994 - type: recall_at_100 value: 83.61800000000001 - type: recall_at_1000 value: 96.63199999999999 - type: recall_at_3 value: 45.403 - type: recall_at_5 value: 54.481 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.833000000000002 - type: map_at_10 value: 37.330999999999996 - type: map_at_100 value: 38.580999999999996 - type: map_at_1000 value: 38.708 - type: map_at_3 value: 34.713 - type: map_at_5 value: 36.104 - type: mrr_at_1 value: 35.223 - type: mrr_at_10 value: 43.419000000000004 - type: mrr_at_100 value: 44.198 - type: mrr_at_1000 value: 44.249 - type: mrr_at_3 value: 41.614000000000004 - type: mrr_at_5 value: 42.553000000000004 - type: ndcg_at_1 value: 35.223 - type: ndcg_at_10 value: 42.687999999999995 - type: ndcg_at_100 value: 47.447 - type: ndcg_at_1000 value: 49.701 - type: ndcg_at_3 value: 39.162 - type: ndcg_at_5 value: 40.557 - type: precision_at_1 value: 35.223 - type: precision_at_10 value: 7.962 - type: precision_at_100 value: 1.304 - type: precision_at_1000 value: 0.18 - type: precision_at_3 value: 19.023 - type: precision_at_5 value: 13.184999999999999 - type: recall_at_1 value: 27.833000000000002 - type: recall_at_10 value: 51.881 - type: recall_at_100 value: 72.04 - type: recall_at_1000 value: 86.644 - type: recall_at_3 value: 40.778 - type: recall_at_5 value: 45.176 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.175 - type: map_at_10 value: 51.174 - type: map_at_100 value: 52.26499999999999 - type: map_at_1000 value: 52.315999999999995 - type: map_at_3 value: 47.897 - type: map_at_5 value: 49.703 - type: mrr_at_1 value: 43.448 - type: mrr_at_10 value: 54.505 - type: mrr_at_100 value: 55.216 - type: mrr_at_1000 value: 55.242000000000004 - type: mrr_at_3 value: 51.98500000000001 - type: mrr_at_5 value: 53.434000000000005 - type: ndcg_at_1 value: 43.448 - type: ndcg_at_10 value: 57.282 - type: ndcg_at_100 value: 61.537 - type: ndcg_at_1000 value: 62.546 - type: ndcg_at_3 value: 51.73799999999999 - type: ndcg_at_5 value: 54.324 - type: precision_at_1 value: 43.448 - type: precision_at_10 value: 9.292 - type: precision_at_100 value: 1.233 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 23.218 - type: precision_at_5 value: 15.887 - type: recall_at_1 value: 38.175 - type: recall_at_10 value: 72.00999999999999 - type: recall_at_100 value: 90.155 - type: recall_at_1000 value: 97.257 - type: recall_at_3 value: 57.133 - type: recall_at_5 value: 63.424 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.405 - type: map_at_10 value: 30.043 - type: map_at_100 value: 31.191000000000003 - type: map_at_1000 value: 31.275 - type: map_at_3 value: 27.034000000000002 - type: map_at_5 value: 28.688000000000002 - type: mrr_at_1 value: 24.068 - type: mrr_at_10 value: 31.993 - type: mrr_at_100 value: 32.992 - type: mrr_at_1000 value: 33.050000000000004 - type: mrr_at_3 value: 28.964000000000002 - type: mrr_at_5 value: 30.653000000000002 - type: ndcg_at_1 value: 24.068 - type: ndcg_at_10 value: 35.198 - type: ndcg_at_100 value: 40.709 - type: ndcg_at_1000 value: 42.855 - type: ndcg_at_3 value: 29.139 - type: ndcg_at_5 value: 32.045 - type: precision_at_1 value: 24.068 - type: precision_at_10 value: 5.65 - type: precision_at_100 value: 0.885 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 12.279 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 22.405 - type: recall_at_10 value: 49.391 - type: recall_at_100 value: 74.53699999999999 - type: recall_at_1000 value: 90.605 - type: recall_at_3 value: 33.126 - type: recall_at_5 value: 40.073 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.309999999999999 - type: map_at_10 value: 20.688000000000002 - type: map_at_100 value: 22.022 - type: map_at_1000 value: 22.152 - type: map_at_3 value: 17.954 - type: map_at_5 value: 19.439 - type: mrr_at_1 value: 16.294 - type: mrr_at_10 value: 24.479 - type: mrr_at_100 value: 25.515 - type: mrr_at_1000 value: 25.593 - type: mrr_at_3 value: 21.642 - type: mrr_at_5 value: 23.189999999999998 - type: ndcg_at_1 value: 16.294 - type: ndcg_at_10 value: 25.833000000000002 - type: ndcg_at_100 value: 32.074999999999996 - type: ndcg_at_1000 value: 35.083 - type: ndcg_at_3 value: 20.493 - type: ndcg_at_5 value: 22.949 - type: precision_at_1 value: 16.294 - type: precision_at_10 value: 5.112 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 9.908999999999999 - type: precision_at_5 value: 7.587000000000001 - type: recall_at_1 value: 13.309999999999999 - type: recall_at_10 value: 37.851 - type: recall_at_100 value: 64.835 - type: recall_at_1000 value: 86.334 - type: recall_at_3 value: 23.493 - type: recall_at_5 value: 29.528 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.857999999999997 - type: map_at_10 value: 35.503 - type: map_at_100 value: 36.957 - type: map_at_1000 value: 37.065 - type: map_at_3 value: 32.275999999999996 - type: map_at_5 value: 34.119 - type: mrr_at_1 value: 31.954 - type: mrr_at_10 value: 40.851 - type: mrr_at_100 value: 41.863 - type: mrr_at_1000 value: 41.900999999999996 - type: mrr_at_3 value: 38.129999999999995 - type: mrr_at_5 value: 39.737 - type: ndcg_at_1 value: 31.954 - type: ndcg_at_10 value: 41.343999999999994 - type: ndcg_at_100 value: 47.397 - type: ndcg_at_1000 value: 49.501 - type: ndcg_at_3 value: 36.047000000000004 - type: ndcg_at_5 value: 38.639 - type: precision_at_1 value: 31.954 - type: precision_at_10 value: 7.68 - type: precision_at_100 value: 1.247 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 17.132 - type: precision_at_5 value: 12.589 - type: recall_at_1 value: 25.857999999999997 - type: recall_at_10 value: 53.43599999999999 - type: recall_at_100 value: 78.82400000000001 - type: recall_at_1000 value: 92.78999999999999 - type: recall_at_3 value: 38.655 - type: recall_at_5 value: 45.216 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.709 - type: map_at_10 value: 34.318 - type: map_at_100 value: 35.657 - type: map_at_1000 value: 35.783 - type: map_at_3 value: 31.326999999999998 - type: map_at_5 value: 33.021 - type: mrr_at_1 value: 30.137000000000004 - type: mrr_at_10 value: 39.093 - type: mrr_at_100 value: 39.992 - type: mrr_at_1000 value: 40.056999999999995 - type: mrr_at_3 value: 36.606 - type: mrr_at_5 value: 37.861 - type: ndcg_at_1 value: 30.137000000000004 - type: ndcg_at_10 value: 39.974 - type: ndcg_at_100 value: 45.647999999999996 - type: ndcg_at_1000 value: 48.259 - type: ndcg_at_3 value: 35.028 - type: ndcg_at_5 value: 37.175999999999995 - type: precision_at_1 value: 30.137000000000004 - type: precision_at_10 value: 7.363 - type: precision_at_100 value: 1.184 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 16.857 - type: precision_at_5 value: 11.963 - type: recall_at_1 value: 24.709 - type: recall_at_10 value: 52.087 - type: recall_at_100 value: 76.125 - type: recall_at_1000 value: 93.82300000000001 - type: recall_at_3 value: 38.149 - type: recall_at_5 value: 43.984 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.40791666666667 - type: map_at_10 value: 32.458083333333335 - type: map_at_100 value: 33.691916666666664 - type: map_at_1000 value: 33.81191666666666 - type: map_at_3 value: 29.51625 - type: map_at_5 value: 31.168083333333335 - type: mrr_at_1 value: 27.96591666666666 - type: mrr_at_10 value: 36.528583333333344 - type: mrr_at_100 value: 37.404 - type: mrr_at_1000 value: 37.464333333333336 - type: mrr_at_3 value: 33.92883333333333 - type: mrr_at_5 value: 35.41933333333333 - type: ndcg_at_1 value: 27.96591666666666 - type: ndcg_at_10 value: 37.89141666666666 - type: ndcg_at_100 value: 43.23066666666666 - type: ndcg_at_1000 value: 45.63258333333333 - type: ndcg_at_3 value: 32.811249999999994 - type: ndcg_at_5 value: 35.22566666666667 - type: precision_at_1 value: 27.96591666666666 - type: precision_at_10 value: 6.834083333333332 - type: precision_at_100 value: 1.12225 - type: precision_at_1000 value: 0.15241666666666667 - type: precision_at_3 value: 15.264333333333335 - type: precision_at_5 value: 11.039416666666666 - type: recall_at_1 value: 23.40791666666667 - type: recall_at_10 value: 49.927083333333336 - type: recall_at_100 value: 73.44641666666668 - type: recall_at_1000 value: 90.19950000000001 - type: recall_at_3 value: 35.88341666666667 - type: recall_at_5 value: 42.061249999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.592000000000002 - type: map_at_10 value: 26.895999999999997 - type: map_at_100 value: 27.921000000000003 - type: map_at_1000 value: 28.02 - type: map_at_3 value: 24.883 - type: map_at_5 value: 25.812 - type: mrr_at_1 value: 22.698999999999998 - type: mrr_at_10 value: 29.520999999999997 - type: mrr_at_100 value: 30.458000000000002 - type: mrr_at_1000 value: 30.526999999999997 - type: mrr_at_3 value: 27.633000000000003 - type: mrr_at_5 value: 28.483999999999998 - type: ndcg_at_1 value: 22.698999999999998 - type: ndcg_at_10 value: 31.061 - type: ndcg_at_100 value: 36.398 - type: ndcg_at_1000 value: 38.89 - type: ndcg_at_3 value: 27.149 - type: ndcg_at_5 value: 28.627000000000002 - type: precision_at_1 value: 22.698999999999998 - type: precision_at_10 value: 5.106999999999999 - type: precision_at_100 value: 0.857 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 11.963 - type: precision_at_5 value: 8.221 - type: recall_at_1 value: 19.592000000000002 - type: recall_at_10 value: 41.329 - type: recall_at_100 value: 66.094 - type: recall_at_1000 value: 84.511 - type: recall_at_3 value: 30.61 - type: recall_at_5 value: 34.213 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 14.71 - type: map_at_10 value: 20.965 - type: map_at_100 value: 21.994 - type: map_at_1000 value: 22.133 - type: map_at_3 value: 18.741 - type: map_at_5 value: 19.951 - type: mrr_at_1 value: 18.307000000000002 - type: mrr_at_10 value: 24.66 - type: mrr_at_100 value: 25.540000000000003 - type: mrr_at_1000 value: 25.629 - type: mrr_at_3 value: 22.511 - type: mrr_at_5 value: 23.72 - type: ndcg_at_1 value: 18.307000000000002 - type: ndcg_at_10 value: 25.153 - type: ndcg_at_100 value: 30.229 - type: ndcg_at_1000 value: 33.623 - type: ndcg_at_3 value: 21.203 - type: ndcg_at_5 value: 23.006999999999998 - type: precision_at_1 value: 18.307000000000002 - type: precision_at_10 value: 4.725 - type: precision_at_100 value: 0.8659999999999999 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 10.14 - type: precision_at_5 value: 7.481 - type: recall_at_1 value: 14.71 - type: recall_at_10 value: 34.087 - type: recall_at_100 value: 57.147999999999996 - type: recall_at_1000 value: 81.777 - type: recall_at_3 value: 22.996 - type: recall_at_5 value: 27.73 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.472 - type: map_at_10 value: 32.699 - type: map_at_100 value: 33.867000000000004 - type: map_at_1000 value: 33.967000000000006 - type: map_at_3 value: 29.718 - type: map_at_5 value: 31.345 - type: mrr_at_1 value: 28.265 - type: mrr_at_10 value: 36.945 - type: mrr_at_100 value: 37.794 - type: mrr_at_1000 value: 37.857 - type: mrr_at_3 value: 34.266000000000005 - type: mrr_at_5 value: 35.768 - type: ndcg_at_1 value: 28.265 - type: ndcg_at_10 value: 38.35 - type: ndcg_at_100 value: 43.739 - type: ndcg_at_1000 value: 46.087 - type: ndcg_at_3 value: 33.004 - type: ndcg_at_5 value: 35.411 - type: precision_at_1 value: 28.265 - type: precision_at_10 value: 6.715999999999999 - type: precision_at_100 value: 1.059 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 15.299 - type: precision_at_5 value: 10.951 - type: recall_at_1 value: 23.472 - type: recall_at_10 value: 51.413 - type: recall_at_100 value: 75.17 - type: recall_at_1000 value: 91.577 - type: recall_at_3 value: 36.651 - type: recall_at_5 value: 42.814 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.666 - type: map_at_10 value: 32.963 - type: map_at_100 value: 34.544999999999995 - type: map_at_1000 value: 34.792 - type: map_at_3 value: 29.74 - type: map_at_5 value: 31.5 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 38.013000000000005 - type: mrr_at_100 value: 38.997 - type: mrr_at_1000 value: 39.055 - type: mrr_at_3 value: 34.947 - type: mrr_at_5 value: 36.815 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 39.361000000000004 - type: ndcg_at_100 value: 45.186 - type: ndcg_at_1000 value: 47.867 - type: ndcg_at_3 value: 33.797 - type: ndcg_at_5 value: 36.456 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.532 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 15.876000000000001 - type: precision_at_5 value: 11.779 - type: recall_at_1 value: 23.666 - type: recall_at_10 value: 51.858000000000004 - type: recall_at_100 value: 77.805 - type: recall_at_1000 value: 94.504 - type: recall_at_3 value: 36.207 - type: recall_at_5 value: 43.094 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.662 - type: map_at_10 value: 23.594 - type: map_at_100 value: 24.593999999999998 - type: map_at_1000 value: 24.694 - type: map_at_3 value: 20.925 - type: map_at_5 value: 22.817999999999998 - type: mrr_at_1 value: 17.375 - type: mrr_at_10 value: 25.734 - type: mrr_at_100 value: 26.586 - type: mrr_at_1000 value: 26.671 - type: mrr_at_3 value: 23.044 - type: mrr_at_5 value: 24.975 - type: ndcg_at_1 value: 17.375 - type: ndcg_at_10 value: 28.186 - type: ndcg_at_100 value: 33.436 - type: ndcg_at_1000 value: 36.203 - type: ndcg_at_3 value: 23.152 - type: ndcg_at_5 value: 26.397 - type: precision_at_1 value: 17.375 - type: precision_at_10 value: 4.677 - type: precision_at_100 value: 0.786 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 10.351 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 15.662 - type: recall_at_10 value: 40.066 - type: recall_at_100 value: 65.006 - type: recall_at_1000 value: 85.94000000000001 - type: recall_at_3 value: 27.400000000000002 - type: recall_at_5 value: 35.002 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 8.853 - type: map_at_10 value: 15.568000000000001 - type: map_at_100 value: 17.383000000000003 - type: map_at_1000 value: 17.584 - type: map_at_3 value: 12.561 - type: map_at_5 value: 14.056 - type: mrr_at_1 value: 18.958 - type: mrr_at_10 value: 28.288000000000004 - type: mrr_at_100 value: 29.432000000000002 - type: mrr_at_1000 value: 29.498 - type: mrr_at_3 value: 25.049 - type: mrr_at_5 value: 26.857 - type: ndcg_at_1 value: 18.958 - type: ndcg_at_10 value: 22.21 - type: ndcg_at_100 value: 29.596 - type: ndcg_at_1000 value: 33.583 - type: ndcg_at_3 value: 16.994999999999997 - type: ndcg_at_5 value: 18.95 - type: precision_at_1 value: 18.958 - type: precision_at_10 value: 7.192 - type: precision_at_100 value: 1.5 - type: precision_at_1000 value: 0.22399999999999998 - type: precision_at_3 value: 12.573 - type: precision_at_5 value: 10.202 - type: recall_at_1 value: 8.853 - type: recall_at_10 value: 28.087 - type: recall_at_100 value: 53.701 - type: recall_at_1000 value: 76.29899999999999 - type: recall_at_3 value: 15.913 - type: recall_at_5 value: 20.658 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.077 - type: map_at_10 value: 20.788999999999998 - type: map_at_100 value: 30.429000000000002 - type: map_at_1000 value: 32.143 - type: map_at_3 value: 14.692 - type: map_at_5 value: 17.139 - type: mrr_at_1 value: 70.75 - type: mrr_at_10 value: 78.036 - type: mrr_at_100 value: 78.401 - type: mrr_at_1000 value: 78.404 - type: mrr_at_3 value: 76.75 - type: mrr_at_5 value: 77.47500000000001 - type: ndcg_at_1 value: 58.12500000000001 - type: ndcg_at_10 value: 44.015 - type: ndcg_at_100 value: 49.247 - type: ndcg_at_1000 value: 56.211999999999996 - type: ndcg_at_3 value: 49.151 - type: ndcg_at_5 value: 46.195 - type: precision_at_1 value: 70.75 - type: precision_at_10 value: 35.5 - type: precision_at_100 value: 11.355 - type: precision_at_1000 value: 2.1950000000000003 - type: precision_at_3 value: 53.083000000000006 - type: precision_at_5 value: 44.800000000000004 - type: recall_at_1 value: 9.077 - type: recall_at_10 value: 26.259 - type: recall_at_100 value: 56.547000000000004 - type: recall_at_1000 value: 78.551 - type: recall_at_3 value: 16.162000000000003 - type: recall_at_5 value: 19.753999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 49.44500000000001 - type: f1 value: 44.67067691783401 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 68.182 - type: map_at_10 value: 78.223 - type: map_at_100 value: 78.498 - type: map_at_1000 value: 78.512 - type: map_at_3 value: 76.71 - type: map_at_5 value: 77.725 - type: mrr_at_1 value: 73.177 - type: mrr_at_10 value: 82.513 - type: mrr_at_100 value: 82.633 - type: mrr_at_1000 value: 82.635 - type: mrr_at_3 value: 81.376 - type: mrr_at_5 value: 82.182 - type: ndcg_at_1 value: 73.177 - type: ndcg_at_10 value: 82.829 - type: ndcg_at_100 value: 83.84 - type: ndcg_at_1000 value: 84.07900000000001 - type: ndcg_at_3 value: 80.303 - type: ndcg_at_5 value: 81.846 - type: precision_at_1 value: 73.177 - type: precision_at_10 value: 10.241999999999999 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 31.247999999999998 - type: precision_at_5 value: 19.697 - type: recall_at_1 value: 68.182 - type: recall_at_10 value: 92.657 - type: recall_at_100 value: 96.709 - type: recall_at_1000 value: 98.184 - type: recall_at_3 value: 85.9 - type: recall_at_5 value: 89.755 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 21.108 - type: map_at_10 value: 33.342 - type: map_at_100 value: 35.281 - type: map_at_1000 value: 35.478 - type: map_at_3 value: 29.067 - type: map_at_5 value: 31.563000000000002 - type: mrr_at_1 value: 41.667 - type: mrr_at_10 value: 49.913000000000004 - type: mrr_at_100 value: 50.724000000000004 - type: mrr_at_1000 value: 50.766 - type: mrr_at_3 value: 47.504999999999995 - type: mrr_at_5 value: 49.033 - type: ndcg_at_1 value: 41.667 - type: ndcg_at_10 value: 41.144 - type: ndcg_at_100 value: 48.326 - type: ndcg_at_1000 value: 51.486 - type: ndcg_at_3 value: 37.486999999999995 - type: ndcg_at_5 value: 38.78 - type: precision_at_1 value: 41.667 - type: precision_at_10 value: 11.358 - type: precision_at_100 value: 1.873 - type: precision_at_1000 value: 0.244 - type: precision_at_3 value: 25 - type: precision_at_5 value: 18.519 - type: recall_at_1 value: 21.108 - type: recall_at_10 value: 47.249 - type: recall_at_100 value: 74.52 - type: recall_at_1000 value: 93.31 - type: recall_at_3 value: 33.271 - type: recall_at_5 value: 39.723000000000006 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.317 - type: map_at_10 value: 64.861 - type: map_at_100 value: 65.697 - type: map_at_1000 value: 65.755 - type: map_at_3 value: 61.258 - type: map_at_5 value: 63.590999999999994 - type: mrr_at_1 value: 80.635 - type: mrr_at_10 value: 86.528 - type: mrr_at_100 value: 86.66199999999999 - type: mrr_at_1000 value: 86.666 - type: mrr_at_3 value: 85.744 - type: mrr_at_5 value: 86.24300000000001 - type: ndcg_at_1 value: 80.635 - type: ndcg_at_10 value: 73.13199999999999 - type: ndcg_at_100 value: 75.927 - type: ndcg_at_1000 value: 76.976 - type: ndcg_at_3 value: 68.241 - type: ndcg_at_5 value: 71.071 - type: precision_at_1 value: 80.635 - type: precision_at_10 value: 15.326 - type: precision_at_100 value: 1.7500000000000002 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 43.961 - type: precision_at_5 value: 28.599999999999998 - type: recall_at_1 value: 40.317 - type: recall_at_10 value: 76.631 - type: recall_at_100 value: 87.495 - type: recall_at_1000 value: 94.362 - type: recall_at_3 value: 65.94200000000001 - type: recall_at_5 value: 71.499 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.686 - type: ap value: 87.5577120393173 - type: f1 value: 91.6629447355139 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 23.702 - type: map_at_10 value: 36.414 - type: map_at_100 value: 37.561 - type: map_at_1000 value: 37.605 - type: map_at_3 value: 32.456 - type: map_at_5 value: 34.827000000000005 - type: mrr_at_1 value: 24.355 - type: mrr_at_10 value: 37.01 - type: mrr_at_100 value: 38.085 - type: mrr_at_1000 value: 38.123000000000005 - type: mrr_at_3 value: 33.117999999999995 - type: mrr_at_5 value: 35.452 - type: ndcg_at_1 value: 24.384 - type: ndcg_at_10 value: 43.456 - type: ndcg_at_100 value: 48.892 - type: ndcg_at_1000 value: 49.964 - type: ndcg_at_3 value: 35.475 - type: ndcg_at_5 value: 39.711 - type: precision_at_1 value: 24.384 - type: precision_at_10 value: 6.7940000000000005 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 15.052999999999999 - type: precision_at_5 value: 11.189 - type: recall_at_1 value: 23.702 - type: recall_at_10 value: 65.057 - type: recall_at_100 value: 90.021 - type: recall_at_1000 value: 98.142 - type: recall_at_3 value: 43.551 - type: recall_at_5 value: 53.738 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.62380300957591 - type: f1 value: 94.49871222100734 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 77.14090287277702 - type: f1 value: 60.32101258220515 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.84330867518494 - type: f1 value: 71.92248688515255 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.10692669804976 - type: f1 value: 77.9904839122866 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.822988923078444 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.38394880253403 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.82504612539082 - type: mrr value: 32.84462298174977 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.029 - type: map_at_10 value: 14.088999999999999 - type: map_at_100 value: 17.601 - type: map_at_1000 value: 19.144 - type: map_at_3 value: 10.156 - type: map_at_5 value: 11.892 - type: mrr_at_1 value: 46.44 - type: mrr_at_10 value: 56.596999999999994 - type: mrr_at_100 value: 57.11000000000001 - type: mrr_at_1000 value: 57.14 - type: mrr_at_3 value: 54.334 - type: mrr_at_5 value: 55.774 - type: ndcg_at_1 value: 44.891999999999996 - type: ndcg_at_10 value: 37.134 - type: ndcg_at_100 value: 33.652 - type: ndcg_at_1000 value: 42.548 - type: ndcg_at_3 value: 41.851 - type: ndcg_at_5 value: 39.842 - type: precision_at_1 value: 46.44 - type: precision_at_10 value: 27.647 - type: precision_at_100 value: 8.309999999999999 - type: precision_at_1000 value: 2.146 - type: precision_at_3 value: 39.422000000000004 - type: precision_at_5 value: 34.675 - type: recall_at_1 value: 6.029 - type: recall_at_10 value: 18.907 - type: recall_at_100 value: 33.76 - type: recall_at_1000 value: 65.14999999999999 - type: recall_at_3 value: 11.584999999999999 - type: recall_at_5 value: 14.626 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 39.373000000000005 - type: map_at_10 value: 55.836 - type: map_at_100 value: 56.611999999999995 - type: map_at_1000 value: 56.63 - type: map_at_3 value: 51.747 - type: map_at_5 value: 54.337999999999994 - type: mrr_at_1 value: 44.147999999999996 - type: mrr_at_10 value: 58.42699999999999 - type: mrr_at_100 value: 58.902 - type: mrr_at_1000 value: 58.914 - type: mrr_at_3 value: 55.156000000000006 - type: mrr_at_5 value: 57.291000000000004 - type: ndcg_at_1 value: 44.119 - type: ndcg_at_10 value: 63.444 - type: ndcg_at_100 value: 66.40599999999999 - type: ndcg_at_1000 value: 66.822 - type: ndcg_at_3 value: 55.962 - type: ndcg_at_5 value: 60.228 - type: precision_at_1 value: 44.119 - type: precision_at_10 value: 10.006 - type: precision_at_100 value: 1.17 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 25.135 - type: precision_at_5 value: 17.59 - type: recall_at_1 value: 39.373000000000005 - type: recall_at_10 value: 83.78999999999999 - type: recall_at_100 value: 96.246 - type: recall_at_1000 value: 99.324 - type: recall_at_3 value: 64.71900000000001 - type: recall_at_5 value: 74.508 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 69.199 - type: map_at_10 value: 82.892 - type: map_at_100 value: 83.578 - type: map_at_1000 value: 83.598 - type: map_at_3 value: 79.948 - type: map_at_5 value: 81.779 - type: mrr_at_1 value: 79.67 - type: mrr_at_10 value: 86.115 - type: mrr_at_100 value: 86.249 - type: mrr_at_1000 value: 86.251 - type: mrr_at_3 value: 85.08200000000001 - type: mrr_at_5 value: 85.783 - type: ndcg_at_1 value: 79.67 - type: ndcg_at_10 value: 86.839 - type: ndcg_at_100 value: 88.252 - type: ndcg_at_1000 value: 88.401 - type: ndcg_at_3 value: 83.86200000000001 - type: ndcg_at_5 value: 85.473 - type: precision_at_1 value: 79.67 - type: precision_at_10 value: 13.19 - type: precision_at_100 value: 1.521 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 36.677 - type: precision_at_5 value: 24.118000000000002 - type: recall_at_1 value: 69.199 - type: recall_at_10 value: 94.321 - type: recall_at_100 value: 99.20400000000001 - type: recall_at_1000 value: 99.947 - type: recall_at_3 value: 85.787 - type: recall_at_5 value: 90.365 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.82810046856353 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.38132611783628 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.127000000000001 - type: map_at_10 value: 12.235 - type: map_at_100 value: 14.417 - type: map_at_1000 value: 14.75 - type: map_at_3 value: 8.906 - type: map_at_5 value: 10.591000000000001 - type: mrr_at_1 value: 25.2 - type: mrr_at_10 value: 35.879 - type: mrr_at_100 value: 36.935 - type: mrr_at_1000 value: 36.997 - type: mrr_at_3 value: 32.783 - type: mrr_at_5 value: 34.367999999999995 - 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task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.67641885599572 - type: cos_sim_spearman value: 80.46450725650428 - type: euclidean_pearson value: 81.61645042715865 - type: euclidean_spearman value: 80.61418394236874 - type: manhattan_pearson value: 81.55712034928871 - type: manhattan_spearman value: 80.57905670523951 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.86650310886782 - type: cos_sim_spearman value: 89.76081629222328 - type: euclidean_pearson value: 89.1530747029954 - type: euclidean_spearman value: 89.80990657280248 - type: manhattan_pearson value: 89.10640563278132 - type: manhattan_spearman value: 89.76282108434047 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - 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type: euclidean_pearson value: 64.6918197393619 - type: euclidean_spearman value: 63.440182631197764 - type: manhattan_pearson value: 64.55692904121835 - type: manhattan_spearman value: 63.424877742756266 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.37793104662344 - type: cos_sim_spearman value: 87.7357802629067 - type: euclidean_pearson value: 87.4286301545109 - type: euclidean_spearman value: 87.78452920777421 - type: manhattan_pearson value: 87.42445169331255 - type: manhattan_spearman value: 87.78537677249598 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 84.31465405081792 - type: mrr value: 95.7173781193389 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - 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type: recall_at_1000 value: 100 - type: recall_at_3 value: 72.628 - type: recall_at_5 value: 78.094 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.8029702970297 - type: cos_sim_ap value: 94.9210324173411 - type: cos_sim_f1 value: 89.8521162672106 - type: cos_sim_precision value: 91.67533818938605 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.69504950495049 - type: dot_ap value: 90.4919719146181 - type: dot_f1 value: 84.72289156626506 - type: dot_precision value: 81.76744186046511 - type: dot_recall value: 87.9 - type: euclidean_accuracy value: 99.79702970297029 - type: euclidean_ap value: 94.87827463795753 - type: euclidean_f1 value: 89.55680081507896 - type: euclidean_precision value: 91.27725856697819 - type: euclidean_recall value: 87.9 - type: manhattan_accuracy value: 99.7990099009901 - type: manhattan_ap value: 94.87587025149682 - type: manhattan_f1 value: 89.76298537569339 - type: manhattan_precision value: 90.53916581892166 - type: manhattan_recall value: 89 - type: max_accuracy value: 99.8029702970297 - type: max_ap value: 94.9210324173411 - type: max_f1 value: 89.8521162672106 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.92385753948724 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.671756975431144 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.677928036739004 - type: mrr value: 51.56413133435193 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.523589340819683 - type: cos_sim_spearman value: 30.187407518823235 - type: dot_pearson value: 29.039713969699015 - type: dot_spearman value: 29.114740651155508 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.211 - type: map_at_10 value: 1.6199999999999999 - type: map_at_100 value: 8.658000000000001 - type: map_at_1000 value: 21.538 - type: map_at_3 value: 0.575 - type: map_at_5 value: 0.919 - type: mrr_at_1 value: 78 - type: mrr_at_10 value: 86.18599999999999 - type: mrr_at_100 value: 86.18599999999999 - type: mrr_at_1000 value: 86.18599999999999 - type: mrr_at_3 value: 85 - type: mrr_at_5 value: 85.9 - type: ndcg_at_1 value: 74 - type: ndcg_at_10 value: 66.542 - type: ndcg_at_100 value: 50.163999999999994 - type: ndcg_at_1000 value: 45.696999999999996 - type: ndcg_at_3 value: 71.531 - type: ndcg_at_5 value: 70.45 - type: precision_at_1 value: 78 - type: precision_at_10 value: 69.39999999999999 - type: precision_at_100 value: 51.06 - type: precision_at_1000 value: 20.022000000000002 - type: precision_at_3 value: 76 - type: precision_at_5 value: 74.8 - type: recall_at_1 value: 0.211 - type: recall_at_10 value: 1.813 - type: recall_at_100 value: 12.098 - type: recall_at_1000 value: 42.618 - type: recall_at_3 value: 0.603 - type: recall_at_5 value: 0.987 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.2079999999999997 - type: map_at_10 value: 7.777000000000001 - type: map_at_100 value: 12.825000000000001 - type: map_at_1000 value: 14.196 - type: map_at_3 value: 4.285 - type: map_at_5 value: 6.177 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 42.635 - type: mrr_at_100 value: 43.955 - type: mrr_at_1000 value: 43.955 - type: mrr_at_3 value: 38.435 - type: mrr_at_5 value: 41.088 - type: ndcg_at_1 value: 28.571 - type: ndcg_at_10 value: 20.666999999999998 - type: ndcg_at_100 value: 31.840000000000003 - type: ndcg_at_1000 value: 43.191 - type: ndcg_at_3 value: 23.45 - type: ndcg_at_5 value: 22.994 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 17.959 - type: precision_at_100 value: 6.755 - type: precision_at_1000 value: 1.4200000000000002 - type: precision_at_3 value: 23.810000000000002 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 2.2079999999999997 - type: recall_at_10 value: 13.144 - type: recall_at_100 value: 42.491 - type: recall_at_1000 value: 77.04299999999999 - type: recall_at_3 value: 5.3469999999999995 - type: recall_at_5 value: 9.139 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - 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type: cos_sim_recall value: 74.1688654353562 - type: dot_accuracy value: 84.80061989628658 - type: dot_ap value: 70.7952548895177 - type: dot_f1 value: 65.44780728844965 - type: dot_precision value: 61.53310104529617 - type: dot_recall value: 69.89445910290237 - type: euclidean_accuracy value: 86.94641473445789 - type: euclidean_ap value: 76.80774009393652 - type: euclidean_f1 value: 70.30522503879979 - type: euclidean_precision value: 68.94977168949772 - type: euclidean_recall value: 71.71503957783642 - type: manhattan_accuracy value: 86.8629671574179 - type: manhattan_ap value: 76.76518632600317 - type: manhattan_f1 value: 70.16056518946692 - type: manhattan_precision value: 68.360450563204 - type: manhattan_recall value: 72.0580474934037 - type: max_accuracy value: 86.94641473445789 - type: max_ap value: 76.91572747061197 - type: max_f1 value: 70.30522503879979 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.10428066907285 - type: cos_sim_ap value: 86.25114759921435 - type: cos_sim_f1 value: 78.37857884586856 - type: cos_sim_precision value: 75.60818546078993 - type: cos_sim_recall value: 81.35971666153372 - type: dot_accuracy value: 87.41995575736406 - type: dot_ap value: 81.51838010086782 - type: dot_f1 value: 74.77398015435503 - type: dot_precision value: 71.53002390662354 - type: dot_recall value: 78.32614721281182 - type: euclidean_accuracy value: 89.12368533395428 - type: euclidean_ap value: 86.33456799874504 - type: euclidean_f1 value: 78.45496750232127 - type: euclidean_precision value: 75.78388462366364 - type: euclidean_recall value: 81.32121958731136 - type: manhattan_accuracy value: 89.10622113556099 - type: manhattan_ap value: 86.31215061745333 - type: manhattan_f1 value: 78.40684906011539 - type: manhattan_precision value: 75.89536643366722 - type: manhattan_recall value: 81.09023714197721 - type: max_accuracy value: 89.12368533395428 - type: max_ap value: 86.33456799874504 - type: max_f1 value: 78.45496750232127 language: - en license: mit --- # E5-large-v2 [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 24 layers and the embedding size is 1024. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ". # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-large-v2') model = AutoModel.from_pretrained('intfloat/e5-large-v2') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Training Details Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). ## Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/e5-large-v2') input_texts = [ 'query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ``` ## Limitations This model only works for English texts. Long texts will be truncated to at most 512 tokens.

sshleifer/tiny-gpt2

sshleifer

transformers

text-generation

Entry not found

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

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/distilbert-base-nli-stsb-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-stsb-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-stsb-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/distilbert-base-nli-stsb-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-stsb-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", } ```

microsoft/mdeberta-v3-base

microsoft

transformers

fill-mask

--- language: - multilingual - en - ar - bg - de - el - es - fr - hi - ru - sw - th - tr - ur - vi - zh tags: - deberta - deberta-v3 - mdeberta - 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. mDeBERTa is multilingual version of DeBERTa which use the same structure as DeBERTa and was trained with CC100 multilingual data. The mDeBERTa V3 base model comes with 12 layers and a hidden size of 768. It has 86M backbone parameters with a vocabulary containing 250K tokens which introduces 190M parameters in the Embedding layer. This model was trained using the 2.5T CC100 data as XLM-R. #### Fine-tuning on NLU tasks We present the dev results on XNLI with zero-shot cross-lingual transfer setting, i.e. training with English data only, test on other languages. | Model |avg | en | fr| es | de | el | bg | ru |tr |ar |vi | th | zh | hi | sw | ur | |--------------| ----|----|----|---- |-- |-- |-- | -- |-- |-- |-- | -- | -- | -- | -- | -- | | XLM-R-base |76.2 |85.8|79.7|80.7 |78.7 |77.5 |79.6 |78.1 |74.2 |73.8 |76.5 |74.6 |76.7| 72.4| 66.5| 68.3| | mDeBERTa-base|**79.8**+/-0.2|**88.2**|**82.6**|**84.4** |**82.7** |**82.3** |**82.4** |**80.8** |**79.5** |**78.5** |**78.1** |**76.4** |**79.5**| **75.9**| **73.9**| **72.4**| #### Fine-tuning with HF transformers ```bash #!/bin/bash cd transformers/examples/pytorch/text-classification/ pip install datasets output_dir="ds_results" num_gpus=8 batch_size=4 python -m torch.distributed.launch --nproc_per_node=${num_gpus} \ run_xnli.py \ --model_name_or_path microsoft/mdeberta-v3-base \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --train_language en \ --language en \ --evaluation_strategy steps \ --max_seq_length 256 \ --warmup_steps 3000 \ --per_device_train_batch_size ${batch_size} \ --learning_rate 2e-5 \ --num_train_epochs 6 \ --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} } ```

LTP/small

LTP

transformers

   | Language | version | | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [Python](python/interface/README.md) | [](https://pypi.org/project/ltp) [](https://pypi.org/project/ltp-core) [](https://pypi.org/project/ltp-extension) | | [Rust](rust/ltp/README.md) | [](https://crates.io/crates/ltp) | # LTP 4 LTP（Language Technology Platform） 提供了一系列中文自然语言处理工具，用户可以使用这些工具对于中文文本进行分词、词性标注、句法分析等等工作。 ## 引用 如果您在工作中使用了 LTP，您可以引用这篇论文 ```bibtex @article{che2020n, title={N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models}, author={Che, Wanxiang and Feng, Yunlong and Qin, Libo and Liu, Ting}, journal={arXiv preprint arXiv:2009.11616}, year={2020} } ``` **参考书：** 由哈工大社会计算与信息检索研究中心（HIT-SCIR）的多位学者共同编著的《[自然语言处理：基于预训练模型的方法](https://item.jd.com/13344628.html) 》（作者：车万翔、郭江、崔一鸣；主审：刘挺）一书现已正式出版，该书重点介绍了新的基于预训练模型的自然语言处理技术，包括基础知识、预训练词向量和预训练模型三大部分，可供广大LTP用户学习参考。 ### 更新说明 - 4.2.0 - \[结构性变化\] 将 LTP 拆分成 2 个部分，维护和训练更方便，结构更清晰 - \[Legacy 模型\] 针对广大用户对于**推理速度**的需求，使用 Rust 重写了基于感知机的算法，准确率与 LTP3 版本相当，速度则是 LTP v3 的 **3.55** 倍，开启多线程更可获得 **17.17** 倍的速度提升，但目前仅支持分词、词性、命名实体三大任务 - \[深度学习模型\] 即基于 PyTorch 实现的深度学习模型，支持全部的6大任务（分词/词性/命名实体/语义角色/依存句法/语义依存） - \[其他改进\] 改进了模型训练方法 - \[共同\] 提供了训练脚本和训练样例，使得用户能够更方便地使用私有的数据，自行训练个性化的模型 - \[深度学习模型\] 采用 hydra 对训练过程进行配置，方便广大用户修改模型训练参数以及对 LTP 进行扩展（比如使用其他包中的 Module） - \[其他变化\] 分词、依存句法分析 (Eisner) 和 语义依存分析 (Eisner) 任务的解码算法使用 Rust 实现，速度更快 - \[新特性\] 模型上传至 [Huggingface Hub](https://huggingface.co/LTP)，支持自动下载，下载速度更快，并且支持用户自行上传自己训练的模型供LTP进行推理使用 - \[破坏性变更\] 改用 Pipeline API 进行推理，方便后续进行更深入的性能优化（如SDP和SDPG很大一部分是重叠的，重用可以加快推理速度），使用说明参见[Github快速使用部分](https://github.com/hit-scir/ltp) - 4.1.0 - 提供了自定义分词等功能 - 修复了一些bug - 4.0.0 - 基于Pytorch 开发，原生 Python 接口 - 可根据需要自由选择不同速度和指标的模型 - 分词、词性、命名实体、依存句法、语义角色、语义依存6大任务 ## 快速使用 ### [Python](python/interface/README.md) ```bash pip install -U ltp ltp-core ltp-extension -i https://pypi.org/simple # 安装 ltp ``` **注：** 如果遇到任何错误，请尝试使用上述命令重新安装 ltp，如果依然报错，请在 Github issues 中反馈。 ```python import torch from ltp import LTP ltp = LTP("LTP/small") # 默认加载 Small 模型 # 将模型移动到 GPU 上 if torch.cuda.is_available(): # ltp.cuda() ltp.to("cuda") output = ltp.pipeline(["他叫汤姆去拿外衣。"], tasks=["cws", "pos", "ner", "srl", "dep", "sdp"]) # 使用字典格式作为返回结果 print(output.cws) # print(output[0]) / print(output['cws']) # 也可以使用下标访问 print(output.pos) print(output.sdp) # 使用感知机算法实现的分词、词性和命名实体识别，速度比较快，但是精度略低 ltp = LTP("LTP/legacy") # cws, pos, ner = ltp.pipeline(["他叫汤姆去拿外衣。"], tasks=["cws", "ner"]).to_tuple() # error: NER 需要 词性标注任务的结果 cws, pos, ner = ltp.pipeline(["他叫汤姆去拿外衣。"], tasks=["cws", "pos", "ner"]).to_tuple() # to tuple 可以自动转换为元组格式 # 使用元组格式作为返回结果 print(cws, pos, ner) ``` **[详细说明](python/interface/docs/quickstart.rst)** ### [Rust](rust/ltp/README.md) ```rust use std::fs::File; use itertools::multizip; use ltp::{CWSModel, POSModel, NERModel, ModelSerde, Format, Codec}; fn main() -> Result<(), Box<dyn std::error::Error>> { let file = File::open("data/legacy-models/cws_model.bin")?; let cws: CWSModel = ModelSerde::load(file, Format::AVRO(Codec::Deflate))?; let file = File::open("data/legacy-models/pos_model.bin")?; let pos: POSModel = ModelSerde::load(file, Format::AVRO(Codec::Deflate))?; let file = File::open("data/legacy-models/ner_model.bin")?; let ner: NERModel = ModelSerde::load(file, Format::AVRO(Codec::Deflate))?; let words = cws.predict("他叫汤姆去拿外衣。")?; let pos = pos.predict(&words)?; let ner = ner.predict((&words, &pos))?; for (w, p, n) in multizip((words, pos, ner)) { println!("{}/{}/{}", w, p, n); } Ok(()) } ``` ## 模型性能以及下载地址 | 深度学习模型 | 分词 | 词性 | 命名实体 | 语义角色 | 依存句法 | 语义依存 | 速度(句/S) | | :---------------------------------------: | :---: | :---: | :---: | :---: | :---: | :---: | :-----: | | [Base](https://huggingface.co/LTP/base) | 98.7 | 98.5 | 95.4 | 80.6 | 89.5 | 75.2 | 39.12 | | [Base1](https://huggingface.co/LTP/base1) | 99.22 | 98.73 | 96.39 | 79.28 | 89.57 | 76.57 | --.-- | | [Base2](https://huggingface.co/LTP/base2) | 99.18 | 98.69 | 95.97 | 79.49 | 90.19 | 76.62 | --.-- | | [Small](https://huggingface.co/LTP/small) | 98.4 | 98.2 | 94.3 | 78.4 | 88.3 | 74.7 | 43.13 | | [Tiny](https://huggingface.co/LTP/tiny) | 96.8 | 97.1 | 91.6 | 70.9 | 83.8 | 70.1 | 53.22 | | 感知机算法 | 分词 | 词性 | 命名实体 | 速度(句/s) | 备注 | | :-----------------------------------------: | :---: | :---: | :---: | :------: | :------------------------: | | [Legacy](https://huggingface.co/LTP/legacy) | 97.93 | 98.41 | 94.28 | 21581.48 | [性能详情](rust/ltp/README.md) | **注：感知机算法速度为开启16线程速度** ## 构建 Wheel 包 ```shell script make bdist ``` ## 其他语言绑定 **感知机算法** - [Rust](rust/ltp) - [C/C++](rust/ltp-cffi) **深度学习算法** - [Rust](https://github.com/HIT-SCIR/libltp/tree/master/ltp-rs) - [C++](https://github.com/HIT-SCIR/libltp/tree/master/ltp-cpp) - [Java](https://github.com/HIT-SCIR/libltp/tree/master/ltp-java) ## 作者信息 - 冯云龙 \<\<[ylfeng@ir.hit.edu.cn](mailto:ylfeng@ir.hit.edu.cn)>> ## 开源协议 1. 语言技术平台面向国内外大学、中科院各研究所以及个人研究者免费开放源代码，但如上述机构和个人将该平台用于商业目的（如企业合作项目等）则需要付费。 2. 除上述机构以外的企事业单位，如申请使用该平台，需付费。 3. 凡涉及付费问题，请发邮件到 car@ir.hit.edu.cn 洽商。 4. 如果您在 LTP 基础上发表论文或取得科研成果，请您在发表论文和申报成果时声明“使用了哈工大社会计算与信息检索研究中心研制的语言技术平台（LTP）”. 同时，发信给car@ir.hit.edu.cn，说明发表论文或申报成果的题目、出处等。

cardiffnlp/twitter-xlm-roberta-base-sentiment

cardiffnlp

transformers

text-classification

--- language: multilingual widget: - text: "🤗" - text: "T'estimo! ❤️" - text: "I love you!" - text: "I hate you 🤮" - text: "Mahal kita!" - text: "사랑해!" - text: "난 너가 싫어" - text: "😍😍😍" --- # twitter-XLM-roBERTa-base for Sentiment Analysis This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details). - Paper: [XLM-T: A Multilingual Language Model Toolkit for Twitter](https://arxiv.org/abs/2104.12250). - Git Repo: [XLM-T official repository](https://github.com/cardiffnlp/xlm-t). This model has been integrated into the [TweetNLP library](https://github.com/cardiffnlp/tweetnlp). ## Example Pipeline ```python from transformers import pipeline model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment" sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path) sentiment_task("T'estimo!") ``` ``` [{'label': 'Positive', 'score': 0.6600581407546997}] ``` ## Full classification example ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer, AutoConfig import numpy as np from scipy.special import softmax # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) MODEL = f"cardiffnlp/twitter-xlm-roberta-base-sentiment" tokenizer = AutoTokenizer.from_pretrained(MODEL) config = AutoConfig.from_pretrained(MODEL) # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Good night 😊" # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) # Print labels and scores ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = config.id2label[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) Positive 0.7673 2) Neutral 0.2015 3) Negative 0.0313 ``` ### Reference ``` @inproceedings{barbieri-etal-2022-xlm, title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond", author = "Barbieri, Francesco and Espinosa Anke, Luis and Camacho-Collados, Jose", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.27", pages = "258--266" } ```

colbert-ir/colbertv2.0

colbert-ir

transformers

--- license: mit language: - en tags: - ColBERT --- <p align="center"> <img align="center" src="https://github.com/stanford-futuredata/ColBERT/blob/main/docs/images/colbertofficial.png?raw=true" width="430px" /> </p> <p align="left"> # ColBERT (v2) ### ColBERT is a _fast_ and _accurate_ retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanford-futuredata/ColBERT/blob/main/docs/intro2new.ipynb) <p align="center"> <img align="center" src="https://github.com/stanford-futuredata/ColBERT/blob/main/docs/images/ColBERT-Framework-MaxSim-W370px.png?raw=true" /> </p> <p align="center"> <b>Figure 1:</b> ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. </p> As Figure 1 illustrates, ColBERT relies on fine-grained **contextual late interaction**: it encodes each passage into a **matrix** of token-level embeddings (shown above in blue). Then at search time, it embeds every query into another matrix (shown in green) and efficiently finds passages that contextually match the query using scalable vector-similarity (`MaxSim`) operators. These rich interactions allow ColBERT to surpass the quality of _single-vector_ representation models, while scaling efficiently to large corpora. You can read more in our papers: * [**ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT**](https://arxiv.org/abs/2004.12832) (SIGIR'20). * [**Relevance-guided Supervision for OpenQA with ColBERT**](https://arxiv.org/abs/2007.00814) (TACL'21). * [**Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval**](https://arxiv.org/abs/2101.00436) (NeurIPS'21). * [**ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction**](https://arxiv.org/abs/2112.01488) (NAACL'22). * [**PLAID: An Efficient Engine for Late Interaction Retrieval**](https://arxiv.org/abs/2205.09707) (CIKM'22). ---- ## 🚨 **Announcements** * (1/29/23) We have merged a new index updater feature and support for additional Hugging Face models! These are in beta so please give us feedback as you try them out. * (1/24/23) If you're looking for the **DSP** framework for composing ColBERTv2 and LLMs, it's at: https://github.com/stanfordnlp/dsp ---- ## ColBERTv1 The ColBERTv1 code from the SIGIR'20 paper is in the [`colbertv1` branch](https://github.com/stanford-futuredata/ColBERT/tree/colbertv1). See [here](#branches) for more information on other branches. ## Installation ColBERT requires Python 3.7+ and Pytorch 1.9+ and uses the [Hugging Face Transformers](https://github.com/huggingface/transformers) library. We strongly recommend creating a conda environment using the commands below. (If you don't have conda, follow the official [conda installation guide](https://docs.anaconda.com/anaconda/install/linux/#installation).) We have also included a new environment file specifically for CPU-only environments (`conda_env_cpu.yml`), but note that if you are testing CPU execution on a machine that includes GPUs you might need to specify `CUDA_VISIBLE_DEVICES=""` as part of your command. Note that a GPU is required for training and indexing. ``` conda env create -f conda_env[_cpu].yml conda activate colbert ``` If you face any problems, please [open a new issue](https://github.com/stanford-futuredata/ColBERT/issues) and we'll help you promptly! ## Overview Using ColBERT on a dataset typically involves the following steps. **Step 0: Preprocess your collection.** At its simplest, ColBERT works with tab-separated (TSV) files: a file (e.g., `collection.tsv`) will contain all passages and another (e.g., `queries.tsv`) will contain a set of queries for searching the collection. **Step 1: Download the [pre-trained ColBERTv2 checkpoint](https://downloads.cs.stanford.edu/nlp/data/colbert/colbertv2/colbertv2.0.tar.gz).** This checkpoint has been trained on the MS MARCO Passage Ranking task. You can also _optionally_ [train your own ColBERT model](#training). **Step 2: Index your collection.** Once you have a trained ColBERT model, you need to [index your collection](#indexing) to permit fast retrieval. This step encodes all passages into matrices, stores them on disk, and builds data structures for efficient search. **Step 3: Search the collection with your queries.** Given the model and index, you can [issue queries over the collection](#retrieval) to retrieve the top-k passages for each query. Below, we illustrate these steps via an example run on the MS MARCO Passage Ranking task. ## API Usage Notebook **NEW**: We have an experimental notebook on [Google Colab](https://colab.research.google.com/github/stanford-futuredata/ColBERT/blob/main/docs/intro2new.ipynb) that you can use with free GPUs. Indexing 10,000 on the free Colab T4 GPU takes six minutes. This Jupyter notebook **[docs/intro.ipynb notebook](docs/intro.ipynb)** illustrates using the key features of ColBERT with the new Python API. It includes how to download the ColBERTv2 model checkpoint trained on MS MARCO Passage Ranking and how to download our new LoTTE benchmark. ## Data This repository works directly with a simple **tab-separated file** format to store queries, passages, and top-k ranked lists. * Queries: each line is `qid \t query text`. * Collection: each line is `pid \t passage text`. * Top-k Ranking: each line is `qid \t pid \t rank`. This works directly with the data format of the [MS MARCO Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) dataset. You will need the training triples (`triples.train.small.tar.gz`), the official top-1000 ranked lists for the dev set queries (`top1000.dev`), and the dev set relevant passages (`qrels.dev.small.tsv`). For indexing the full collection, you will also need the list of passages (`collection.tar.gz`). ## Indexing For fast retrieval, indexing precomputes the ColBERT representations of passages. Example usage: ```python from colbert.infra import Run, RunConfig, ColBERTConfig from colbert import Indexer if __name__=='__main__': with Run().context(RunConfig(nranks=1, experiment="msmarco")): config = ColBERTConfig( nbits=2, root="/path/to/experiments", ) indexer = Indexer(checkpoint="/path/to/checkpoint", config=config) indexer.index(name="msmarco.nbits=2", collection="/path/to/MSMARCO/collection.tsv") ``` ## Retrieval We typically recommend that you use ColBERT for **end-to-end** retrieval, where it directly finds its top-k passages from the full collection: ```python from colbert.data import Queries from colbert.infra import Run, RunConfig, ColBERTConfig from colbert import Searcher if __name__=='__main__': with Run().context(RunConfig(nranks=1, experiment="msmarco")): config = ColBERTConfig( root="/path/to/experiments", ) searcher = Searcher(index="msmarco.nbits=2", config=config) queries = Queries("/path/to/MSMARCO/queries.dev.small.tsv") ranking = searcher.search_all(queries, k=100) ranking.save("msmarco.nbits=2.ranking.tsv") ``` You can optionally specify the `ncells`, `centroid_score_threshold`, and `ndocs` search hyperparameters to trade off between speed and result quality. Defaults for different values of `k` are listed in colbert/searcher.py. We can evaluate the MSMARCO rankings using the following command: ``` python -m utility.evaluate.msmarco_passages --ranking "/path/to/msmarco.nbits=2.ranking.tsv" --qrels "/path/to/MSMARCO/qrels.dev.small.tsv" ``` ## Training We provide a [pre-trained model checkpoint](https://downloads.cs.stanford.edu/nlp/data/colbert/colbertv2/colbertv2.0.tar.gz), but we also detail how to train from scratch here. Note that this example demonstrates the ColBERTv1 style of training, but the provided checkpoint was trained with ColBERTv2. Training requires a JSONL triples file with a `[qid, pid+, pid-]` list per line. The query IDs and passage IDs correspond to the specified `queries.tsv` and `collection.tsv` files respectively. Example usage (training on 4 GPUs): ```python from colbert.infra import Run, RunConfig, ColBERTConfig from colbert import Trainer if __name__=='__main__': with Run().context(RunConfig(nranks=4, experiment="msmarco")): config = ColBERTConfig( bsize=32, root="/path/to/experiments", ) trainer = Trainer( triples="/path/to/MSMARCO/triples.train.small.tsv", queries="/path/to/MSMARCO/queries.train.small.tsv", collection="/path/to/MSMARCO/collection.tsv", config=config, ) checkpoint_path = trainer.train() print(f"Saved checkpoint to {checkpoint_path}...") ``` ## Running a lightweight ColBERTv2 server We provide a script to run a lightweight server which serves k (upto 100) results in ranked order for a given search query, in JSON format. This script can be used to power DSP programs. To run the server, update the environment variables `INDEX_ROOT` and `INDEX_NAME` in the `.env` file to point to the appropriate ColBERT index. The run the following command: ``` python server.py ``` A sample query: ``` http://localhost:8893/api/search?query=Who won the 2022 FIFA world cup&k=25 ``` ## Branches ### Supported branches * [`main`](https://github.com/stanford-futuredata/ColBERT/tree/main): Stable branch with ColBERTv2 + PLAID. * [`colbertv1`](https://github.com/stanford-futuredata/ColBERT/tree/colbertv1): Legacy branch for ColBERTv1. ### Deprecated branches * [`new_api`](https://github.com/stanford-futuredata/ColBERT/tree/new_api): Base ColBERTv2 implementation. * [`cpu_inference`](https://github.com/stanford-futuredata/ColBERT/tree/cpu_inference): ColBERTv2 implementation with CPU search support. * [`fast_search`](https://github.com/stanford-futuredata/ColBERT/tree/fast_search): ColBERTv2 implementation with PLAID. * [`binarization`](https://github.com/stanford-futuredata/ColBERT/tree/binarization): ColBERT with a baseline binarization-based compression strategy (as opposed to ColBERTv2's residual compression, which we found to be more robust). ## Acknowledgments ColBERT logo designed by Chuyi Zhang.

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.

google/gemma-2b

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 2B base version of the Gemma model. You can also visit the model card of the [7B base model](https://huggingface.co/google/gemma-7b), [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-2b-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 the model You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-2b`. In that repository, we provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA * A script to perform SFT using FSDP on TPU devices * A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") 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-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", 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-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", 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-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", 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-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", 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/ml-pathways). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: | Benchmark | Metric | 2B Params | 7B Params | | ------------------------------ | ------------- | ----------- | --------- | | [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 | | [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 | | [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 | | [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 49.7 | 51.8 | | [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 | | [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 | | [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 | | [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 | | [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 | | [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 | | [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 | | [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 12.5 | 23 | | [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 | | [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 | | [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 | | [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 | | [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 | | [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 | | ------------------------------ | ------------- | ----------- | --------- | | **Average** | | **45.0** | **56.9** | ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. **Update**: These numbers reflect the new numbers from the updated v1.1 IT models. For the original v1 numbers, please consult the technical report's appendix for the results. | Benchmark | Metric | Gemma v1.1 IT 2B | Gemma v1.1 IT 7B | | ------------------------------ | ------------- | ----------- | --------- | | [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) | | 31.81 | 44.84 | | [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.

BAAI/bge-m3

BAAI

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity license: mit --- For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding # BGE-M3 ([paper](https://arxiv.org/pdf/2402.03216.pdf), [code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3)) In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity. - Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval. - Multi-Linguality: It can support more than 100 working languages. - Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens. **Some suggestions for retrieval pipeline in RAG** We recommend to use the following pipeline: hybrid retrieval + re-ranking. - Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. A classic example: using both embedding retrieval and the BM25 algorithm. Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval. This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings. To use hybrid retrieval, you can refer to [Vespa](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb ) and [Milvus](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py). - As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model. Utilizing the re-ranking model (e.g., [bge-reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker), [bge-reranker-v2](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker)) after retrieval can further filter the selected text. ## News: - 2024/3/20: **Thanks Milvus team!** Now you can use hybrid retrieval of bge-m3 in Milvus: [pymilvus/examples /hello_hybrid_sparse_dense.py](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py). - 2024/3/8: **Thanks for the [experimental results](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) from @[Yannael](https://huggingface.co/Yannael). In this benchmark, BGE-M3 achieves top performance in both English and other languages, surpassing models such as OpenAI.** - 2024/3/2: Release unified fine-tuning [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune) and [data](https://huggingface.co/datasets/Shitao/bge-m3-data) - 2024/2/6: We release the [MLDR](https://huggingface.co/datasets/Shitao/MLDR) (a long document retrieval dataset covering 13 languages) and [evaluation pipeline](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR). - 2024/2/1: **Thanks for the excellent tool from Vespa.** You can easily use multiple modes of BGE-M3 following this [notebook](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb) ## Specs - Model | Model Name | Dimension | Sequence Length | Introduction | |:----:|:---:|:---:|:---:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | 1024 | 8192 | multilingual; unified fine-tuning (dense, sparse, and colbert) from bge-m3-unsupervised| | [BAAI/bge-m3-unsupervised](https://huggingface.co/BAAI/bge-m3-unsupervised) | 1024 | 8192 | multilingual; contrastive learning from bge-m3-retromae | | [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) | -- | 8192 | multilingual; extend the max_length of [xlm-roberta](https://huggingface.co/FacebookAI/xlm-roberta-large) to 8192 and further pretrained via [retromae](https://github.com/staoxiao/RetroMAE)| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | English model | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | English model | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | English model | - Data | Dataset | Introduction | |:----------------------------------------------------------:|:-------------------------------------------------:| | [MLDR](https://huggingface.co/datasets/Shitao/MLDR) | Docuemtn Retrieval Dataset, covering 13 languages | | [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) | Fine-tuning data used by bge-m3 | ## FAQ **1. Introduction for different retrieval methods** - Dense retrieval: map the text into a single embedding, e.g., [DPR](https://arxiv.org/abs/2004.04906), [BGE-v1.5](https://github.com/FlagOpen/FlagEmbedding) - Sparse retrieval (lexical matching): a vector of size equal to the vocabulary, with the majority of positions set to zero, calculating a weight only for tokens present in the text. e.g., BM25, [unicoil](https://arxiv.org/pdf/2106.14807.pdf), and [splade](https://arxiv.org/abs/2107.05720) - Multi-vector retrieval: use multiple vectors to represent a text, e.g., [ColBERT](https://arxiv.org/abs/2004.12832). **2. How to use BGE-M3 in other projects?** For embedding retrieval, you can employ the BGE-M3 model using the same approach as BGE. The only difference is that the BGE-M3 model no longer requires adding instructions to the queries. For hybrid retrieval, you can use [Vespa](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb ) and [Milvus](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py). **3. How to fine-tune bge-M3 model?** You can follow the common in this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to fine-tune the dense embedding. If you want to fine-tune all embedding function of m3 (dense, sparse and colbert), you can refer to the [unified_fine-tuning example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune) ## Usage Install: ``` git clone https://github.com/FlagOpen/FlagEmbedding.git cd FlagEmbedding pip install -e . ``` or: ``` pip install -U FlagEmbedding ``` ### Generate Embedding for text - Dense Embedding ```python from FlagEmbedding import BGEM3FlagModel model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation sentences_1 = ["What is BGE M3?", "Defination of BM25"] sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"] embeddings_1 = model.encode(sentences_1, batch_size=12, max_length=8192, # If you don't need such a long length, you can set a smaller value to speed up the encoding process. )['dense_vecs'] embeddings_2 = model.encode(sentences_2)['dense_vecs'] similarity = embeddings_1 @ embeddings_2.T print(similarity) # [[0.6265, 0.3477], [0.3499, 0.678 ]] ``` You also can use sentence-transformers and huggingface transformers to generate dense embeddings. Refer to [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding#usage) for details. - Sparse Embedding (Lexical Weight) ```python from FlagEmbedding import BGEM3FlagModel model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation sentences_1 = ["What is BGE M3?", "Defination of BM25"] sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"] output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=False) output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=False) # you can see the weight for each token: print(model.convert_id_to_token(output_1['lexical_weights'])) # [{'What': 0.08356, 'is': 0.0814, 'B': 0.1296, 'GE': 0.252, 'M': 0.1702, '3': 0.2695, '?': 0.04092}, # {'De': 0.05005, 'fin': 0.1368, 'ation': 0.04498, 'of': 0.0633, 'BM': 0.2515, '25': 0.3335}] # compute the scores via lexical mathcing lexical_scores = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][0]) print(lexical_scores) # 0.19554901123046875 print(model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_1['lexical_weights'][1])) # 0.0 ``` - Multi-Vector (ColBERT) ```python from FlagEmbedding import BGEM3FlagModel model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) sentences_1 = ["What is BGE M3?", "Defination of BM25"] sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"] output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True) output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=True) print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][0])) print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][1])) # 0.7797 # 0.4620 ``` ### Compute score for text pairs Input a list of text pairs, you can get the scores computed by different methods. ```python from FlagEmbedding import BGEM3FlagModel model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) sentences_1 = ["What is BGE M3?", "Defination of BM25"] sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"] sentence_pairs = [[i,j] for i in sentences_1 for j in sentences_2] print(model.compute_score(sentence_pairs, max_passage_length=128, # a smaller max length leads to a lower latency weights_for_different_modes=[0.4, 0.2, 0.4])) # weights_for_different_modes(w) is used to do weighted sum: w[0]*dense_score + w[1]*sparse_score + w[2]*colbert_score # { # 'colbert': [0.7796499729156494, 0.4621465802192688, 0.4523794651031494, 0.7898575067520142], # 'sparse': [0.195556640625, 0.00879669189453125, 0.0, 0.1802978515625], # 'dense': [0.6259765625, 0.347412109375, 0.349853515625, 0.67822265625], # 'sparse+dense': [0.482503205537796, 0.23454029858112335, 0.2332356721162796, 0.5122477412223816], # 'colbert+sparse+dense': [0.6013619303703308, 0.3255828022956848, 0.32089319825172424, 0.6232916116714478] # } ``` ## Evaluation We provide the evaluation script for [MKQA](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MKQA) and [MLDR](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR) ### Benchmarks from the open-source community  The BGE-M3 model emerged as the top performer on this benchmark (OAI is short for OpenAI). For more details, please refer to the [article](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) and [Github Repo](https://github.com/Yannael/multilingual-embeddings) ### Our results - Multilingual (Miracl dataset)  - Cross-lingual (MKQA dataset)  - Long Document Retrieval - MLDR:  Please note that [MLDR](https://huggingface.co/datasets/Shitao/MLDR) is a document retrieval dataset we constructed via LLM, covering 13 languages, including test set, validation set, and training set. We utilized the training set from MLDR to enhance the model's long document retrieval capabilities. Therefore, comparing baselines with `Dense w.o.long`(fine-tuning without long document dataset) is more equitable. Additionally, this long document retrieval dataset will be open-sourced to address the current lack of open-source multilingual long text retrieval datasets. We believe that this data will be helpful for the open-source community in training document retrieval models. - NarritiveQA:  - Comparison with BM25 We utilized Pyserini to implement BM25, and the test results can be reproduced by this [script](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR#bm25-baseline). We tested BM25 using two different tokenizers: one using Lucene Analyzer and the other using the same tokenizer as M3 (i.e., the tokenizer of xlm-roberta). The results indicate that BM25 remains a competitive baseline, especially in long document retrieval.  ## Training - Self-knowledge Distillation: combining multiple outputs from different retrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival) - Efficient Batching: Improve the efficiency when fine-tuning on long text. The small-batch strategy is simple but effective, which also can used to fine-tune large embedding model. - MCLS: A simple method to improve the performance on long text without fine-tuning. If you have no enough resource to fine-tuning model with long text, the method is useful. Refer to our [report](https://arxiv.org/pdf/2402.03216.pdf) for more details. ## Acknowledgement Thanks to the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc. Thanks to the open-sourced libraries like [Tevatron](https://github.com/texttron/tevatron), [Pyserini](https://github.com/castorini/pyserini). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge-m3, title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu}, year={2024}, eprint={2402.03216}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

sentence-transformers/paraphrase-multilingual-mpnet-base-v2

sentence-transformers

sentence-transformers

sentence-similarity

--- language: - multilingual - ar - bg - ca - cs - da - de - el - en - es - et - fa - fi - fr - gl - gu - he - hi - hr - hu - hy - id - it - ja - ka - ko - ku - lt - lv - mk - mn - mr - ms - my - nb - nl - pl - pt - ro - ru - sk - sl - sq - sr - sv - th - tr - uk - ur - vi license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers language_bcp47: - fr-ca - pt-br - zh-cn - zh-tw pipeline_tag: sentence-similarity --- # sentence-transformers/paraphrase-multilingual-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-multilingual-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-multilingual-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-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, average 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-multilingual-mpnet-base-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

h94/IP-Adapter-FaceID

h94

diffusers

text-to-image

--- tags: - text-to-image - stable-diffusion language: - en library_name: diffusers --- # IP-Adapter-FaceID Model Card <div align="center"> [**Project Page**](https://ip-adapter.github.io) **|** [**Paper (ArXiv)**](https://arxiv.org/abs/2308.06721) **|** [**Code**](https://github.com/tencent-ailab/IP-Adapter) </div> --- ## Introduction An experimental version of IP-Adapter-FaceID: we use face ID embedding from a face recognition model instead of CLIP image embedding, additionally, we use LoRA to improve ID consistency. IP-Adapter-FaceID can generate various style images conditioned on a face with only text prompts.  **Update 2023/12/27**: IP-Adapter-FaceID-Plus: face ID embedding (for face ID) + CLIP image embedding (for face structure) <div align="center">  </div> **Update 2023/12/28**: IP-Adapter-FaceID-PlusV2: face ID embedding (for face ID) + controllable CLIP image embedding (for face structure) You can adjust the weight of the face structure to get different generation! <div align="center">  </div> **Update 2024/01/04**: IP-Adapter-FaceID-SDXL: An experimental SDXL version of IP-Adapter-FaceID <div align="center">  </div> **Update 2024/01/17**: IP-Adapter-FaceID-PlusV2-SDXL: An experimental SDXL version of IP-Adapter-FaceID-PlusV2 **Update 2024/01/19**: IP-Adapter-FaceID-Portrait: same with IP-Adapter-FaceID but for portrait generation (no lora! no controlnet!). Specifically, it accepts multiple facial images to enhance similarity (the default is 5). <div align="center">  </div> ## Usage ### IP-Adapter-FaceID Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding: ```python import cv2 from insightface.app import FaceAnalysis import torch app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) image = cv2.imread("person.jpg") faces = app.get(image) faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) ``` Then, you can generate images conditioned on the face embeddings: ```python import torch from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL from PIL import Image from ip_adapter.ip_adapter_faceid import IPAdapterFaceID base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" vae_model_path = "stabilityai/sd-vae-ft-mse" ip_ckpt = "ip-adapter-faceid_sd15.bin" device = "cuda" noise_scheduler = DDIMScheduler( num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, steps_offset=1, ) vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16) pipe = StableDiffusionPipeline.from_pretrained( base_model_path, torch_dtype=torch.float16, scheduler=noise_scheduler, vae=vae, feature_extractor=None, safety_checker=None ) # load ip-adapter ip_model = IPAdapterFaceID(pipe, ip_ckpt, device) # generate image prompt = "photo of a woman in red dress in a garden" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" images = ip_model.generate( prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, num_samples=4, width=512, height=768, num_inference_steps=30, seed=2023 ) ``` you can also use a normal IP-Adapter and a normal LoRA to load model: ```python import torch from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL from PIL import Image from ip_adapter.ip_adapter_faceid_separate import IPAdapterFaceID base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" vae_model_path = "stabilityai/sd-vae-ft-mse" ip_ckpt = "ip-adapter-faceid_sd15.bin" lora_ckpt = "ip-adapter-faceid_sd15_lora.safetensors" device = "cuda" noise_scheduler = DDIMScheduler( num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, steps_offset=1, ) vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16) pipe = StableDiffusionPipeline.from_pretrained( base_model_path, torch_dtype=torch.float16, scheduler=noise_scheduler, vae=vae, feature_extractor=None, safety_checker=None ) # load lora and fuse pipe.load_lora_weights(lora_ckpt) pipe.fuse_lora() # load ip-adapter ip_model = IPAdapterFaceID(pipe, ip_ckpt, device) # generate image prompt = "photo of a woman in red dress in a garden" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" images = ip_model.generate( prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, num_samples=4, width=512, height=768, num_inference_steps=30, seed=2023 ) ``` ### IP-Adapter-FaceID-SDXL Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding: ```python import cv2 from insightface.app import FaceAnalysis import torch app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) image = cv2.imread("person.jpg") faces = app.get(image) faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) ``` Then, you can generate images conditioned on the face embeddings: ```python import torch from diffusers import StableDiffusionXLPipeline, DDIMScheduler from PIL import Image from ip_adapter.ip_adapter_faceid import IPAdapterFaceIDXL base_model_path = "SG161222/RealVisXL_V3.0" ip_ckpt = "ip-adapter-faceid_sdxl.bin" device = "cuda" noise_scheduler = DDIMScheduler( num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, steps_offset=1, ) pipe = StableDiffusionXLPipeline.from_pretrained( base_model_path, torch_dtype=torch.float16, scheduler=noise_scheduler, add_watermarker=False, ) # load ip-adapter ip_model = IPAdapterFaceIDXL(pipe, ip_ckpt, device) # generate image prompt = "A closeup shot of a beautiful Asian teenage girl in a white dress wearing small silver earrings in the garden, under the soft morning light" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" images = ip_model.generate( prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, num_samples=2, width=1024, height=1024, num_inference_steps=30, guidance_scale=7.5, seed=2023 ) ``` ### IP-Adapter-FaceID-Plus Firstly, you should use [insightface](https://github.com/deepinsight/insightface) to extract face ID embedding and face image: ```python import cv2 from insightface.app import FaceAnalysis from insightface.utils import face_align import torch app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) image = cv2.imread("person.jpg") faces = app.get(image) faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0) face_image = face_align.norm_crop(image, landmark=faces[0].kps, image_size=224) # you can also segment the face ``` Then, you can generate images conditioned on the face embeddings: ```python import torch from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL from PIL import Image from ip_adapter.ip_adapter_faceid import IPAdapterFaceIDPlus v2 = False base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" vae_model_path = "stabilityai/sd-vae-ft-mse" image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K" ip_ckpt = "ip-adapter-faceid-plus_sd15.bin" if not v2 else "ip-adapter-faceid-plusv2_sd15.bin" device = "cuda" noise_scheduler = DDIMScheduler( num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, steps_offset=1, ) vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16) pipe = StableDiffusionPipeline.from_pretrained( base_model_path, torch_dtype=torch.float16, scheduler=noise_scheduler, vae=vae, feature_extractor=None, safety_checker=None ) # load ip-adapter ip_model = IPAdapterFaceIDPlus(pipe, image_encoder_path, ip_ckpt, device) # generate image prompt = "photo of a woman in red dress in a garden" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" images = ip_model.generate( prompt=prompt, negative_prompt=negative_prompt, face_image=face_image, faceid_embeds=faceid_embeds, shortcut=v2, s_scale=1.0, num_samples=4, width=512, height=768, num_inference_steps=30, seed=2023 ) ``` ### IP-Adapter-FaceID-Portrait ```python import cv2 from insightface.app import FaceAnalysis import torch app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) images = ["1.jpg", "2.jpg", "3.jpg", "4.jpg", "5.jpg"] faceid_embeds = [] for image in images: image = cv2.imread("person.jpg") faces = app.get(image) faceid_embeds.append(torch.from_numpy(faces[0].normed_embedding).unsqueeze(0).unsqueeze(0)) faceid_embeds = torch.cat(faceid_embeds, dim=1) ``` ```python import torch from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL from PIL import Image from ip_adapter.ip_adapter_faceid_separate import IPAdapterFaceID base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE" vae_model_path = "stabilityai/sd-vae-ft-mse" ip_ckpt = "ip-adapter-faceid-portrait_sd15.bin" device = "cuda" noise_scheduler = DDIMScheduler( num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, steps_offset=1, ) vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16) pipe = StableDiffusionPipeline.from_pretrained( base_model_path, torch_dtype=torch.float16, scheduler=noise_scheduler, vae=vae, feature_extractor=None, safety_checker=None ) # load ip-adapter ip_model = IPAdapterFaceID(pipe, ip_ckpt, device, num_tokens=16, n_cond=5) # generate image prompt = "photo of a woman in red dress in a garden" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality, blurry" images = ip_model.generate( prompt=prompt, negative_prompt=negative_prompt, faceid_embeds=faceid_embeds, num_samples=4, width=512, height=512, num_inference_steps=30, seed=2023 ) ``` ## Limitations and Bias - The models do not achieve perfect photorealism and ID consistency. - The generalization of the models is limited due to limitations of the training data, base model and face recognition model. ## Non-commercial use **AS InsightFace pretrained models are available for non-commercial research purposes, IP-Adapter-FaceID models are released exclusively for research purposes and is not intended for commercial use.**

pyannote/speaker-diarization-3.0

pyannote

pyannote-audio

automatic-speech-recognition

--- tags: - pyannote - pyannote-audio - pyannote-audio-pipeline - audio - voice - speech - speaker - speaker-diarization - speaker-change-detection - voice-activity-detection - overlapped-speech-detection - automatic-speech-recognition license: mit extra_gated_prompt: "The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers improve it further. Though this pipeline uses MIT license and will always remain open-source, we will occasionnally email you about premium pipelines and paid services around pyannote." extra_gated_fields: Company/university: text Website: text --- Using this open-source pipeline in production? Make the most of it thanks to our [consulting services](https://herve.niderb.fr/consulting.html). # 🎹 Speaker diarization 3.0 This pipeline has been trained by Séverin Baroudi with [pyannote.audio](https://github.com/pyannote/pyannote-audio) `3.0.0` using a combination of the training sets of AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse. It ingests mono audio sampled at 16kHz and outputs speaker diarization as an [`Annotation`](http://pyannote.github.io/pyannote-core/structure.html#annotation) instance: * stereo or multi-channel audio files are automatically downmixed to mono by averaging the channels. * audio files sampled at a different rate are resampled to 16kHz automatically upon loading. ## Requirements 1. Install [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) `3.0` with `pip install pyannote.audio` 2. Accept [`pyannote/segmentation-3.0`](https://hf.co/pyannote/segmentation-3.0) user conditions 3. Accept [`pyannote/speaker-diarization-3.0`](https://hf.co/pyannote-speaker-diarization-3.0) user conditions 4. Create access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens). ## Usage ```python # instantiate the pipeline from pyannote.audio import Pipeline pipeline = Pipeline.from_pretrained( "pyannote/speaker-diarization-3.0", use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE") # run the pipeline on an audio file diarization = pipeline("audio.wav") # dump the diarization output to disk using RTTM format with open("audio.rttm", "w") as rttm: diarization.write_rttm(rttm) ``` ### Processing on GPU `pyannote.audio` pipelines run on CPU by default. You can send them to GPU with the following lines: ```python import torch pipeline.to(torch.device("cuda")) ``` Real-time factor is around 2.5% using one Nvidia Tesla V100 SXM2 GPU (for the neural inference part) and one Intel Cascade Lake 6248 CPU (for the clustering part). In other words, it takes approximately 1.5 minutes to process a one hour conversation. ### Processing from memory Pre-loading audio files in memory may result in faster processing: ```python waveform, sample_rate = torchaudio.load("audio.wav") diarization = pipeline({"waveform": waveform, "sample_rate": sample_rate}) ``` ### Monitoring progress Hooks are available to monitor the progress of the pipeline: ```python from pyannote.audio.pipelines.utils.hook import ProgressHook with ProgressHook() as hook: diarization = pipeline("audio.wav", hook=hook) ``` ### Controlling the number of speakers In case the number of speakers is known in advance, one can use the `num_speakers` option: ```python diarization = pipeline("audio.wav", num_speakers=2) ``` One can also provide lower and/or upper bounds on the number of speakers using `min_speakers` and `max_speakers` options: ```python diarization = pipeline("audio.wav", min_speakers=2, max_speakers=5) ``` ## Benchmark This pipeline has been benchmarked on a large collection of datasets. Processing is fully automatic: * no manual voice activity detection (as is sometimes the case in the literature) * no manual number of speakers (though it is possible to provide it to the pipeline) * no fine-tuning of the internal models nor tuning of the pipeline hyper-parameters to each dataset ... with the least forgiving diarization error rate (DER) setup (named *"Full"* in [this paper](https://doi.org/10.1016/j.csl.2021.101254)): * no forgiveness collar * evaluation of overlapped speech | Benchmark | [DER%](. "Diarization error rate") | [FA%](. "False alarm rate") | [Miss%](. "Missed detection rate") | [Conf%](. "Speaker confusion rate") | Expected output | File-level evaluation | | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | --------------------------- | ---------------------------------- | ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | | [AISHELL-4](http://www.openslr.org/111/) | 12.3 | 3.8 | 4.4 | 4.1 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AISHELL.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AISHELL.SpeakerDiarization.Benchmark.test.eval) | | [AliMeeting (*channel 1*)](https://www.openslr.org/119/) | 24.3 | 4.4 | 10.0 | 9.9 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AliMeeting.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AliMeeting.SpeakerDiarization.Benchmark.test.eval) | | [AMI (*headset mix,*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*)](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 19.0 | 3.6 | 9.5 | 5.9 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AMI.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AMI.SpeakerDiarization.Benchmark.test.eval) | | [AMI (*array1, channel 1,*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words)*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 22.2 | 3.8 | 11.2 | 7.3 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AMI-SDM.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AMI-SDM.SpeakerDiarization.Benchmark.test.eval) | | [AVA-AVD](https://arxiv.org/abs/2111.14448) | 49.1 | 10.8 | 15.7| 22.5 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AVA-AVD.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AVA-AVD.SpeakerDiarization.Benchmark.test.eval) | | [DIHARD 3 (*Full*)](https://arxiv.org/abs/2012.01477) | 21.7 | 6.2 | 8.1 | 7.3 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/DIHARD.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/DIHARD.SpeakerDiarization.Benchmark.test.eval) | | [MSDWild](https://x-lance.github.io/MSDWILD/) | 24.6 | 5.8 | 8.0 | 10.7 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/MSDWILD.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/MSDWILD.SpeakerDiarization.Benchmark.test.eval) | | [REPERE (*phase 2*)](https://islrn.org/resources/360-758-359-485-0/) | 7.8 | 1.8 | 2.6 | 3.5 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/REPERE.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/REPERE.SpeakerDiarization.Benchmark.test.eval) | | [VoxConverse (*v0.3*)](https://github.com/joonson/voxconverse) | 11.3 | 4.1 | 3.4 | 3.8 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/VoxConverse.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/VoxConverse.SpeakerDiarization.Benchmark.test.eval) | ## Citations ```bibtex @inproceedings{Plaquet23, author={Alexis Plaquet and Hervé Bredin}, title={{Powerset multi-class cross entropy loss for neural speaker diarization}}, year=2023, booktitle={Proc. INTERSPEECH 2023}, } ``` ```bibtex @inproceedings{Bredin23, author={Hervé Bredin}, title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}}, year=2023, booktitle={Proc. INTERSPEECH 2023}, } ```

pysentimiento/robertuito-sentiment-analysis

pysentimiento

pysentimiento

--- language: - es library_name: pysentimiento tags: - twitter - sentiment-analysis --- # Sentiment Analysis in Spanish ## robertuito-sentiment-analysis Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/) Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is [RoBERTuito](https://github.com/pysentimiento/robertuito), a RoBERTa model trained in Spanish tweets. Uses `POS`, `NEG`, `NEU` labels. ## Usage Use it directly with [pysentimiento](https://github.com/pysentimiento/pysentimiento) ```python from pysentimiento import create_analyzer analyzer = create_analyzer(task="sentiment", lang="es") analyzer.predict("Qué gran jugador es Messi") # returns AnalyzerOutput(output=POS, probas={POS: 0.998, NEG: 0.002, NEU: 0.000}) ``` ## Results Results for the four tasks evaluated in `pysentimiento`. Results are expressed as Macro F1 scores | model | emotion | hate_speech | irony | sentiment | |:--------------|:--------------|:--------------|:--------------|:--------------| | robertuito | 0.560 ± 0.010 | 0.759 ± 0.007 | 0.739 ± 0.005 | 0.705 ± 0.003 | | roberta | 0.527 ± 0.015 | 0.741 ± 0.012 | 0.721 ± 0.008 | 0.670 ± 0.006 | | bertin | 0.524 ± 0.007 | 0.738 ± 0.007 | 0.713 ± 0.012 | 0.666 ± 0.005 | | beto_uncased | 0.532 ± 0.012 | 0.727 ± 0.016 | 0.701 ± 0.007 | 0.651 ± 0.006 | | beto_cased | 0.516 ± 0.012 | 0.724 ± 0.012 | 0.705 ± 0.009 | 0.662 ± 0.005 | | mbert_uncased | 0.493 ± 0.010 | 0.718 ± 0.011 | 0.681 ± 0.010 | 0.617 ± 0.003 | | biGRU | 0.264 ± 0.007 | 0.592 ± 0.018 | 0.631 ± 0.011 | 0.585 ± 0.011 | Note that for Hate Speech, these are the results for Semeval 2019, Task 5 Subtask B ## Citation If you use this model in your research, please cite pysentimiento and RoBERTuito papers: ```latex @article{perez2021pysentimiento, title={pysentimiento: a python toolkit for opinion mining and social NLP tasks}, author={P{\'e}rez, Juan Manuel and Rajngewerc, Mariela and Giudici, Juan Carlos and Furman, Dami{\'a}n A and Luque, Franco and Alemany, Laura Alonso and Mart{\'\i}nez, Mar{\'\i}a Vanina}, journal={arXiv preprint arXiv:2106.09462}, year={2021} } @inproceedings{perez-etal-2022-robertuito, title = "{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish", author = "P{\'e}rez, Juan Manuel and Furman, Dami{\'a}n Ariel and Alonso Alemany, Laura and Luque, Franco M.", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.785", pages = "7235--7243", abstract = "Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.", } @inproceedings{garcia2020overview, title={Overview of TASS 2020: Introducing emotion detection}, author={Garc{\'\i}a-Vega, Manuel and D{\'\i}az-Galiano, MC and Garc{\'\i}a-Cumbreras, MA and Del Arco, FMP and Montejo-R{\'a}ez, A and Jim{\'e}nez-Zafra, SM and Mart{\'\i}nez C{\'a}mara, E and Aguilar, CA and Cabezudo, MAS and Chiruzzo, L and others}, booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020) Co-Located with 36th Conference of the Spanish Society for Natural Language Processing (SEPLN 2020), M{\'a}laga, Spain}, pages={163--170}, year={2020} } ```

amunchet/rorshark-vit-base

amunchet

transformers

image-classification

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - vision - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: rorshark-vit-base results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9922928709055877 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rorshark-vit-base This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0393 - Accuracy: 0.9923 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0597 | 1.0 | 368 | 0.0546 | 0.9865 | | 0.2009 | 2.0 | 736 | 0.0531 | 0.9865 | | 0.0114 | 3.0 | 1104 | 0.0418 | 0.9904 | | 0.0998 | 4.0 | 1472 | 0.0425 | 0.9904 | | 0.1244 | 5.0 | 1840 | 0.0393 | 0.9923 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

Systran/faster-whisper-base

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

lmsys/vicuna-7b-v1.5

lmsys

transformers

text-generation

--- inference: false license: llama2 --- # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT. - **Developed by:** [LMSYS](https://lmsys.org/) - **Model type:** An auto-regressive language model based on the transformer architecture - **License:** Llama 2 Community License Agreement - **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288) ### Model Sources - **Repository:** https://github.com/lm-sys/FastChat - **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/ - **Paper:** https://arxiv.org/abs/2306.05685 - **Demo:** https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model - Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights - APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api ## Training Details Vicuna v1.5 is fine-tuned from Llama 2 with supervised instruction fine-tuning. The training data is around 125K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation  Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)

kredor/punctuate-all

kredor

transformers

token-classification

--- license: mit datasets: - wmt/europarl metrics: - f1 - recall - precision --- This is based on [Oliver Guhr's work](https://huggingface.co/oliverguhr/fullstop-punctuation-multilang-large). The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian. ----- report ----- precision recall f1-score support 0 0.99 0.99 0.99 73317475 . 0.94 0.95 0.95 4484845 , 0.86 0.86 0.86 6100650 ? 0.88 0.85 0.86 136479 - 0.60 0.29 0.39 233630 : 0.71 0.49 0.58 152424 accuracy 0.98 84425503 macro avg 0.83 0.74 0.77 84425503 weighted avg 0.98 0.98 0.98 84425503 ----- confusion matrix ----- t/p 0 . , ? - : 0 1.0 0.0 0.0 0.0 0.0 0.0 . 0.0 1.0 0.0 0.0 0.0 0.0 , 0.1 0.0 0.9 0.0 0.0 0.0 ? 0.0 0.1 0.0 0.8 0.0 0.0 - 0.1 0.1 0.5 0.0 0.3 0.0 : 0.0 0.3 0.1 0.0 0.0 0.5

ggml-org/models

ggml-org

Note: this repo will be removed soon - do not use

microsoft/deberta-large-mnli

microsoft

transformers

text-classification

--- language: en tags: - deberta-v1 - deberta-mnli tasks: mnli thumbnail: https://huggingface.co/front/thumbnails/microsoft.png license: mit widget: - text: "[CLS] I love you. [SEP] I like you. [SEP]" --- ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates. This is the DeBERTa large model fine-tuned with MNLI task. #### Fine-tuning on NLU tasks We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks. | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B | |---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------| | | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S | | BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- | | RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- | | XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- | | [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)¹ | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 | | [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)¹ | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7| | [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)¹|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9| |**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)^1,2**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** | -------- #### Notes. - ¹ Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks. - ² To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp** ```bash cd transformers/examples/text-classification/ export TASK_NAME=mrpc python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\ --task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \\ --learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16 ``` ### Citation If you find DeBERTa useful for your work, please cite the following paper: ``` 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} } ```

mistralai/Mixtral-8x7B-Instruct-v0.1

mistralai

transformers

text-generation

--- license: apache-2.0 language: - fr - it - de - es - en inference: parameters: temperature: 0.5 widget: - messages: - role: user content: What is your favorite condiment? --- # 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. In the Transformers library, one can use [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating) which make sure the right format is applied. ## 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, device_map="auto") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") 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, device_map="auto") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, 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, device_map="auto") text = "Hello my name is" messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, 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, device_map="auto") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, 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.

google/t5-v1_1-xxl

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. 

facebook/bart-base

facebook

transformers

feature-extraction

--- license: apache-2.0 language: en --- # BART (base-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-base') model = BartModel.from_pretrained('facebook/bart-base') 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} } ```

openai-community/gpt2-xl

openai-community

transformers

text-generation

--- language: en license: mit --- # GPT-2 XL ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) ## Model Details **Model Description:** GPT-2 XL is the **1.5B parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - **Developed by:** OpenAI, see [associated research paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [GitHub repo](https://github.com/openai/gpt-2) for model developers. - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** [Modified MIT License](https://github.com/openai/gpt-2/blob/master/LICENSE) - **Related Models:** [GPT-2](https://huggingface.co/gpt2), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-Large](https://huggingface.co/gpt2-large) - **Resources for more information:** - [Research Paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) - [OpenAI Blog Post](https://openai.com/blog/better-language-models/) - [GitHub Repo](https://github.com/openai/gpt-2) - [OpenAI Model Card for GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md) - [OpenAI GPT-2 1.5B Release Blog Post](https://openai.com/blog/gpt-2-1-5b-release/) - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large ## How to Get Started with the Model Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model='gpt2-xl') set_seed(42) generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-xl') model = GPT2Model.from_pretrained('gpt2-xl') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-xl') model = TFGPT2Model.from_pretrained('gpt2-xl') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Uses #### Direct Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > The primary intended users of these models are AI researchers and practitioners. > > We primarily imagine these language models will be used by researchers to better understand the behaviors, capabilities, biases, and constraints of large-scale generative language models. #### Downstream Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Here are some secondary use cases we believe are likely: > > - Writing assistance: Grammar assistance, autocompletion (for normal prose or code) > - Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art. > - Entertainment: Creation of games, chat bots, and amusing generations. #### Misuse and Out-of-scope Use In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote: > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.** #### Biases Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example: ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model='gpt2-xl') set_seed(42) generator("The man worked as a", max_length=10, num_return_sequences=5) set_seed(42) generator("The woman worked as a", max_length=10, num_return_sequences=5) ``` This bias will also affect all fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. #### Risks and Limitations When they released the 1.5B parameter model, OpenAI wrote in a [blog post](https://openai.com/blog/gpt-2-1-5b-release/): > GPT-2 can be fine-tuned for misuse. Our partners at the Middlebury Institute of International Studies’ Center on Terrorism, Extremism, and Counterterrorism (CTEC) found that extremist groups can use GPT-2 for misuse, specifically by fine-tuning GPT-2 models on four ideological positions: white supremacy, Marxism, jihadist Islamism, and anarchism. CTEC demonstrated that it’s possible to create models that can generate synthetic propaganda for these ideologies. They also show that, despite having low detection accuracy on synthetic outputs, ML-based detection methods can give experts reasonable suspicion that an actor is generating synthetic text. The blog post further discusses the risks, limitations, and biases of the model. ## Training #### Training Data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). #### Training Procedure The model is pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. ## Evaluation The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). #### Testing Data, Factors and Metrics The model authors write in the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) that: > Since our model operates on a byte level and does not require lossy pre-processing or tokenization, we can evaluate it on any language model benchmark. Results on language modeling datasets are commonly reported in a quantity which is a scaled or ex- ponentiated version of the average negative log probability per canonical prediction unit - usually a character, a byte, or a word. We evaluate the same quantity by computing the log-probability of a dataset according to a WebText LM and dividing by the number of canonical units. For many of these datasets, WebText LMs would be tested significantly out- of-distribution, having to predict aggressively standardized text, tokenization artifacts such as disconnected punctuation and contractions, shuffled sentences, and even the string <UNK> which is extremely rare in WebText - occurring only 26 times in 40 billion bytes. We report our main results...using invertible de-tokenizers which remove as many of these tokenization / pre-processing artifacts as possible. Since these de-tokenizers are invertible, we can still calculate the log probability of a dataset and they can be thought of as a simple form of domain adaptation. #### Results The model achieves the following results without any fine-tuning (zero-shot): | Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW | |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| | (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) | | | 8.63 | 63.24 | 93.30 | 89.05 | 18.34 | 35.76 | 0.93 | 0.98 | 17.48 | 42.16 | ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware type and hours used are based on information provided by one of the model authors on [Reddit](https://bit.ly/2Tw1x4L). - **Hardware Type:** 32 TPUv3 chips - **Hours used:** 168 - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown ## Technical Specifications See the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) for details on the modeling architecture, objective, and training details. ## Citation Information ```bibtex @article{radford2019language, title={Language models are unsupervised multitask learners}, author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others}, journal={OpenAI blog}, volume={1}, number={8}, pages={9}, year={2019} } ``` ## Model Card Authors This model card was written by the Hugging Face team.

nlpaueb/legal-bert-base-uncased

nlpaueb

transformers

fill-mask

--- language: en pipeline_tag: fill-mask license: cc-by-sa-4.0 thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png tags: - legal widget: - text: "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police." --- # LEGAL-BERT: The Muppets straight out of Law School <img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/> LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domain variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks. A light-weight model (33% the size of BERT-BASE) pre-trained from scratch on legal data with competitive performance is also available. <br/><br/> --- I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". In Findings of Empirical Methods in Natural Language Processing (EMNLP 2020) (Short Papers), to be held online, 2020. (https://aclanthology.org/2020.findings-emnlp.261) --- ## Pre-training corpora The pre-training corpora of LEGAL-BERT include: * 116,062 documents of EU legislation, publicly available from EURLEX (http://eur-lex.europa.eu), the repository of EU Law running under the EU Publication Office. * 61,826 documents of UK legislation, publicly available from the UK legislation portal (http://www.legislation.gov.uk). * 19,867 cases from the European Court of Justice (ECJ), also available from EURLEX. * 12,554 cases from HUDOC, the repository of the European Court of Human Rights (ECHR) (http://hudoc.echr.coe.int/eng). * 164,141 cases from various courts across the USA, hosted in the Case Law Access Project portal (https://case.law). * 76,366 US contracts from EDGAR, the database of US Securities and Exchange Commission (SECOM) (https://www.sec.gov/edgar.shtml). ## Pre-training details * We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert). * We released a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! * Part of LEGAL-BERT is a light-weight model pre-trained from scratch on legal data, which achieves comparable performance to larger models, while being much more efficient (approximately 4 times faster) with a smaller environmental footprint. ## Models list | Model name | Model Path | Training corpora | | ------------------- | ------------------------------------ | ------------------- | | CONTRACTS-BERT-BASE | `nlpaueb/bert-base-uncased-contracts` | US contracts | | EURLEX-BERT-BASE | `nlpaueb/bert-base-uncased-eurlex` | EU legislation | | ECHR-BERT-BASE | `nlpaueb/bert-base-uncased-echr` | ECHR cases | | LEGAL-BERT-BASE * | `nlpaueb/legal-bert-base-uncased` | All | | LEGAL-BERT-SMALL | `nlpaueb/legal-bert-small-uncased` | All | \* LEGAL-BERT-BASE is the model referred to as LEGAL-BERT-SC in Chalkidis et al. (2020); a model trained from scratch in the legal corpora mentioned below using a newly created vocabulary by a sentence-piece tokenizer trained on the very same corpora. \*\* As many of you expressed interest in the LEGAL-BERT-FP models (those relying on the original BERT-BASE checkpoint), they have been released in Archive.org (https://archive.org/details/legal_bert_fp), as these models are secondary and possibly only interesting for those who aim to dig deeper in the open questions of Chalkidis et al. (2020). ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/legal-bert-base-uncased") model = AutoModel.from_pretrained("nlpaueb/legal-bert-base-uncased") ``` ## Use LEGAL-BERT variants as Language Models | Corpus | Model | Masked token | Predictions | | --------------------------------- | ---------------------------------- | ------------ | ------------ | | | **BERT-BASE-UNCASED** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('new', '0.09'), ('current', '0.04'), ('proposed', '0.03'), ('marketing', '0.03'), ('joint', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.32'), ('rape', '0.22'), ('abuse', '0.14'), ('death', '0.04'), ('violence', '0.03') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('farm', '0.25'), ('livestock', '0.08'), ('draft', '0.06'), ('domestic', '0.05'), ('wild', '0.05') | | **CONTRACTS-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('death', '0.39'), ('imprisonment', '0.07'), ('contempt', '0.05'), ('being', '0.03'), ('crime', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | (('domestic', '0.18'), ('laboratory', '0.07'), ('household', '0.06'), ('personal', '0.06'), ('the', '0.04') | | **EURLEX-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('supply', '0.11'), ('cooperation', '0.08'), ('service', '0.07'), ('licence', '0.07'), ('distribution', '0.05') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.66'), ('death', '0.07'), ('imprisonment', '0.07'), ('murder', '0.04'), ('rape', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('live', '0.43'), ('pet', '0.28'), ('certain', '0.05'), ('fur', '0.03'), ('the', '0.02') | | **ECHR-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('second', '0.24'), ('latter', '0.10'), ('draft', '0.05'), ('bilateral', '0.05'), ('arbitration', '0.04') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.99'), ('death', '0.01'), ('inhuman', '0.00'), ('beating', '0.00'), ('rape', '0.00') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('pet', '0.17'), ('all', '0.12'), ('slaughtered', '0.10'), ('domestic', '0.07'), ('individual', '0.05') | | **LEGAL-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('settlement', '0.26'), ('letter', '0.23'), ('dealer', '0.04'), ('master', '0.02'), ('supplemental', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '1.00'), ('detention', '0.00'), ('arrest', '0.00'), ('rape', '0.00'), ('death', '0.00') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('live', '0.67'), ('beef', '0.17'), ('farm', '0.03'), ('pet', '0.02'), ('dairy', '0.01') | | **LEGAL-BERT-SMALL** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('license', '0.09'), ('transition', '0.08'), ('settlement', '0.04'), ('consent', '0.03'), ('letter', '0.03') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.59'), ('pain', '0.05'), ('ptsd', '0.05'), ('death', '0.02'), ('tuberculosis', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('all', '0.08'), ('live', '0.07'), ('certain', '0.07'), ('the', '0.07'), ('farm', '0.05') ## Evaluation on downstream tasks Consider the experiments in the article "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2020, (https://aclanthology.org/2020.findings-emnlp.261) ## Author - Publication ``` @inproceedings{chalkidis-etal-2020-legal, title = "{LEGAL}-{BERT}: The Muppets straight out of Law School", author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Aletras, Nikolaos and Androutsopoulos, Ion", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", doi = "10.18653/v1/2020.findings-emnlp.261", pages = "2898--2904" } ``` ## About Us [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. [Ilias Chalkidis](https://iliaschalkidis.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) | Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |

microsoft/resnet-50

microsoft

transformers

image-classification

--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k --- # ResNet-50 v1.5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models. This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).  ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, ResNetForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50") inputs = processor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet). ### BibTeX entry and citation info ```bibtex @inproceedings{he2016deep, title={Deep residual learning for image recognition}, author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, year={2016} } ```

Helsinki-NLP/opus-mt-es-en

Helsinki-NLP

transformers

translation

--- language: - es - en tags: - translation license: apache-2.0 --- ### spa-eng * source group: Spanish * target group: English * OPUS readme: [spa-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-eng/README.md) * model: transformer * source language(s): spa * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-08-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.zip) * test set translations: [opus-2020-08-18.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.test.txt) * test set scores: [opus-2020-08-18.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-spaeng.spa.eng | 30.6 | 0.570 | | news-test2008-spaeng.spa.eng | 27.9 | 0.553 | | newstest2009-spaeng.spa.eng | 30.4 | 0.572 | | newstest2010-spaeng.spa.eng | 36.1 | 0.614 | | newstest2011-spaeng.spa.eng | 34.2 | 0.599 | | newstest2012-spaeng.spa.eng | 37.9 | 0.624 | | newstest2013-spaeng.spa.eng | 35.3 | 0.609 | | Tatoeba-test.spa.eng | 59.6 | 0.739 | ### System Info: - hf_name: spa-eng - source_languages: spa - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'en'] - src_constituents: {'spa'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-eng/opus-2020-08-18.test.txt - src_alpha3: spa - tgt_alpha3: eng - short_pair: es-en - chrF2_score: 0.7390000000000001 - bleu: 59.6 - brevity_penalty: 0.9740000000000001 - ref_len: 79376.0 - src_name: Spanish - tgt_name: English - train_date: 2020-08-18 00:00:00 - src_alpha2: es - tgt_alpha2: en - prefer_old: False - long_pair: spa-eng - helsinki_git_sha: d2f0910c89026c34a44e331e785dec1e0faa7b82 - transformers_git_sha: f7af09b4524b784d67ae8526f0e2fcc6f5ed0de9 - port_machine: brutasse - port_time: 2020-08-24-18:20

Salesforce/blip-vqa-capfilt-large

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 - large architecture (with ViT large 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-capfilt-large") model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large") 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-capfilt-large") model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large").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-capfilt-large") model = BlipForQuestionAnswering.from_pretrained("ybelkada/blip-vqa-capfilt-large", 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} } ```

Falconsai/nsfw_image_detection

Falconsai

transformers

image-classification

--- license: apache-2.0 pipeline_tag: image-classification --- # Model Card: Fine-Tuned Vision Transformer (ViT) for NSFW Image Classification ## Model Description The **Fine-Tuned Vision Transformer (ViT)** is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks. This specific model, named "google/vit-base-patch16-224-in21k," is pre-trained on a substantial collection of images in a supervised manner, leveraging the ImageNet-21k dataset. The images in the pre-training dataset are resized to a resolution of 224x224 pixels, making it suitable for a wide range of image recognition tasks. During the training phase, meticulous attention was given to hyperparameter settings to ensure optimal model performance. The model was fine-tuned with a judiciously chosen batch size of 16. This choice not only balanced computational efficiency but also allowed for the model to effectively process and learn from a diverse array of images. To facilitate this fine-tuning process, a learning rate of 5e-5 was employed. The learning rate serves as a critical tuning parameter that dictates the magnitude of adjustments made to the model's parameters during training. In this case, a learning rate of 5e-5 was selected to strike a harmonious balance between rapid convergence and steady optimization, resulting in a model that not only learns swiftly but also steadily refines its capabilities throughout the training process. This training phase was executed using a proprietary dataset containing an extensive collection of 80,000 images, each characterized by a substantial degree of variability. The dataset was thoughtfully curated to include two distinct classes, namely "normal" and "nsfw." This diversity allowed the model to grasp nuanced visual patterns, equipping it with the competence to accurately differentiate between safe and explicit content. The overarching objective of this meticulous training process was to impart the model with a deep understanding of visual cues, ensuring its robustness and competence in tackling the specific task of NSFW image classification. The result is a model that stands ready to contribute significantly to content safety and moderation, all while maintaining the highest standards of accuracy and reliability. ## Intended Uses & Limitations ### Intended Uses - **NSFW Image Classification**: The primary intended use of this model is for the classification of NSFW (Not Safe for Work) images. It has been fine-tuned for this purpose, making it suitable for filtering explicit or inappropriate content in various applications. ### How to use Here is how to use this model to classifiy an image based on 1 of 2 classes (normal,nsfw): ```markdown # Use a pipeline as a high-level helper from PIL import Image from transformers import pipeline img = Image.open("<path_to_image_file>") classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection") classifier(img) ``` <hr> ``` markdown # Load model directly import torch from PIL import Image from transformers import AutoModelForImageClassification, ViTImageProcessor img = Image.open("<path_to_image_file>") model = AutoModelForImageClassification.from_pretrained("Falconsai/nsfw_image_detection") processor = ViTImageProcessor.from_pretrained('Falconsai/nsfw_image_detection') with torch.no_grad(): inputs = processor(images=img, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits predicted_label = logits.argmax(-1).item() model.config.id2label[predicted_label] ``` <hr> ### Limitations - **Specialized Task Fine-Tuning**: While the model is adept at NSFW image classification, its performance may vary when applied to other tasks. - Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results. ## Training Data The model's training data includes a proprietary dataset comprising approximately 80,000 images. This dataset encompasses a significant amount of variability and consists of two distinct classes: "normal" and "nsfw." The training process on this data aimed to equip the model with the ability to distinguish between safe and explicit content effectively. ### Training Stats ``` markdown - 'eval_loss': 0.07463177293539047, - 'eval_accuracy': 0.980375, - 'eval_runtime': 304.9846, - 'eval_samples_per_second': 52.462, - 'eval_steps_per_second': 3.279 ``` <hr> **Note:** It's essential to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real-world applications, particularly those involving potentially sensitive content. For more details on model fine-tuning and usage, please refer to the model's documentation and the model hub. ## References - [Hugging Face Model Hub](https://huggingface.co/models) - [Vision Transformer (ViT) Paper](https://arxiv.org/abs/2010.11929) - [ImageNet-21k Dataset](http://www.image-net.org/) **Disclaimer:** The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.

cross-encoder/ms-marco-MiniLM-L-6-v2

cross-encoder

transformers

text-classification

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

textattack/bert-base-uncased-yelp-polarity

textattack

transformers

text-classification

## TextAttack Model Card This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 5e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9699473684210527, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).

stabilityai/sdxl-turbo

stabilityai

diffusers

text-to-image

--- pipeline_tag: text-to-image inference: false license: other license_name: sai-nc-community license_link: https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.TXT --- # SDXL-Turbo Model Card <!-- Provide a quick summary of what the model is/does. -->  SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. A real-time demo is available here: http://clipdrop.co/stable-diffusion-turbo Please note: For commercial use, please refer to https://stability.ai/membership. ## Model Details ### Model Description SDXL-Turbo is a distilled version of [SDXL 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), trained for real-time synthesis. SDXL-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the [technical report](https://stability.ai/research/adversarial-diffusion-distillation)), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high image quality. This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. - **Developed by:** Stability AI - **Funded by:** Stability AI - **Model type:** Generative text-to-image model - **Finetuned from model:** [SDXL 1.0 Base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) ### Model Sources For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference). - **Repository:** https://github.com/Stability-AI/generative-models - **Paper:** https://stability.ai/research/adversarial-diffusion-distillation - **Demo:** http://clipdrop.co/stable-diffusion-turbo ## Evaluation   The charts above evaluate user preference for SDXL-Turbo over other single- and multi-step models. SDXL-Turbo evaluated at a single step is preferred by human voters in terms of image quality and prompt following over LCM-XL evaluated at four (or fewer) steps. In addition, we see that using four steps for SDXL-Turbo further improves performance. For details on the user study, we refer to the [research paper](https://stability.ai/research/adversarial-diffusion-distillation). ## Uses ### Direct Use The model is intended for both non-commercial and commercial usage. You can use this model for non-commercial or research purposes under this [license](https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.TXT). Possible research areas and tasks include - Research on generative models. - Research on real-time applications of generative models. - Research on the impact of real-time generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. For commercial use, please refer to https://stability.ai/membership. Excluded uses are described below. ### Diffusers ``` pip install diffusers transformers accelerate --upgrade ``` - **Text-to-image**: SDXL-Turbo does not make use of `guidance_scale` or `negative_prompt`, we disable it with `guidance_scale=0.0`. Preferably, the model generates images of size 512x512 but higher image sizes work as well. A **single step** is enough to generate high quality images. ```py from diffusers import AutoPipelineForText2Image import torch pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16") pipe.to("cuda") prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe." image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0] ``` - **Image-to-image**: When using SDXL-Turbo for image-to-image generation, make sure that `num_inference_steps` * `strength` is larger or equal to 1. The image-to-image pipeline will run for `int(num_inference_steps * strength)` steps, *e.g.* 0.5 * 2.0 = 1 step in our example below. ```py from diffusers import AutoPipelineForImage2Image from diffusers.utils import load_image import torch pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16") pipe.to("cuda") init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png").resize((512, 512)) prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k" image = pipe(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0] ``` ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy). ## Limitations and Bias ### Limitations - The generated images are of a fixed resolution (512x512 pix), and the model does not achieve perfect photorealism. - The model cannot render legible text. - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy. ### Recommendations The model is intended for both non-commercial and commercial usage. ## How to Get Started with the Model Check out https://github.com/Stability-AI/generative-models

j-hartmann/emotion-english-distilroberta-base

j-hartmann

transformers

text-classification

--- language: "en" tags: - distilroberta - sentiment - emotion - twitter - reddit widget: - text: "Oh wow. I didn't know that." - text: "This movie always makes me cry.." - text: "Oh Happy Day" --- # Emotion English DistilRoBERTa-base # Description ℹ With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class: 1) anger 🤬 2) disgust 🤢 3) fear 😨 4) joy 😀 5) neutral 😐 6) sadness 😭 7) surprise 😲 The model is a fine-tuned checkpoint of [DistilRoBERTa-base](https://huggingface.co/distilroberta-base). For a 'non-distilled' emotion model, please refer to the model card of the [RoBERTa-large](https://huggingface.co/j-hartmann/emotion-english-roberta-large) version. # Application 🚀 a) Run emotion model with 3 lines of code on single text example using Hugging Face's pipeline command on Google Colab: [](https://colab.research.google.com/github/j-hartmann/emotion-english-distilroberta-base/blob/main/simple_emotion_pipeline.ipynb) ```python from transformers import pipeline classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True) classifier("I love this!") ``` ```python Output: [[{'label': 'anger', 'score': 0.004419783595949411}, {'label': 'disgust', 'score': 0.0016119900392368436}, {'label': 'fear', 'score': 0.0004138521908316761}, {'label': 'joy', 'score': 0.9771687984466553}, {'label': 'neutral', 'score': 0.005764586851000786}, {'label': 'sadness', 'score': 0.002092392183840275}, {'label': 'surprise', 'score': 0.008528684265911579}]] ``` b) Run emotion model on multiple examples and full datasets (e.g., .csv files) on Google Colab: [](https://colab.research.google.com/github/j-hartmann/emotion-english-distilroberta-base/blob/main/emotion_prediction_example.ipynb) # Contact 💻 Please reach out to [jochen.hartmann@tum.de](mailto:jochen.hartmann@tum.de) if you have any questions or feedback. Thanks to Samuel Domdey and [chrsiebert](https://huggingface.co/siebert) for their support in making this model available. # Reference ✅ For attribution, please cite the following reference if you use this model. A working paper will be available soon. ``` Jochen Hartmann, "Emotion English DistilRoBERTa-base". https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/, 2022. ``` BibTex citation: ``` @misc{hartmann2022emotionenglish, author={Hartmann, Jochen}, title={Emotion English DistilRoBERTa-base}, year={2022}, howpublished = {\url{https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/}}, } ``` # Appendix 📚 Please find an overview of the datasets used for training below. All datasets contain English text. The table summarizes which emotions are available in each of the datasets. The datasets represent a diverse collection of text types. Specifically, they contain emotion labels for texts from Twitter, Reddit, student self-reports, and utterances from TV dialogues. As MELD (Multimodal EmotionLines Dataset) extends the popular EmotionLines dataset, EmotionLines itself is not included here. |Name|anger|disgust|fear|joy|neutral|sadness|surprise| |---|---|---|---|---|---|---|---| |Crowdflower (2016)|Yes|-|-|Yes|Yes|Yes|Yes| |Emotion Dataset, Elvis et al. (2018)|Yes|-|Yes|Yes|-|Yes|Yes| |GoEmotions, Demszky et al. (2020)|Yes|Yes|Yes|Yes|Yes|Yes|Yes| |ISEAR, Vikash (2018)|Yes|Yes|Yes|Yes|-|Yes|-| |MELD, Poria et al. (2019)|Yes|Yes|Yes|Yes|Yes|Yes|Yes| |SemEval-2018, EI-reg, Mohammad et al. (2018) |Yes|-|Yes|Yes|-|Yes|-| The model is trained on a balanced subset from the datasets listed above (2,811 observations per emotion, i.e., nearly 20k observations in total). 80% of this balanced subset is used for training and 20% for evaluation. The evaluation accuracy is 66% (vs. the random-chance baseline of 1/7 = 14%). # Scientific Applications 📖 Below you can find a list of papers using "Emotion English DistilRoBERTa-base". If you would like your paper to be added to the list, please send me an email. - Butt, S., Sharma, S., Sharma, R., Sidorov, G., & Gelbukh, A. (2022). What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses. Computers in Human Behavior, 107345. - Kuang, Z., Zong, S., Zhang, J., Chen, J., & Liu, H. (2022). Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings. arXiv preprint arXiv:2210.00434. - Rozado, D., Hughes, R., & Halberstadt, J. (2022). Longitudinal analysis of sentiment and emotion in news media headlines using automated labelling with Transformer language models. Plos one, 17(10), e0276367.

ai-forever/ruRoberta-large

ai-forever

transformers

fill-mask

--- language: - ru tags: - PyTorch - Transformers thumbnail: "https://github.com/sberbank-ai/model-zoo" --- # ruRoberta-large The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931). The model is pretrained by the [SberDevices](https://sberdevices.ru/) team. * Task: `mask filling` * Type: `encoder` * Tokenizer: `BBPE` * Dict size: `50 257` * Num Parameters: `355 M` * Training Data Volume `250 GB` # Authors + NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam): + Dmitry Zmitrovich # Cite us ``` @misc{zmitrovich2023family, title={A Family of Pretrained Transformer Language Models for Russian}, author={Dmitry Zmitrovich and Alexander Abramov and Andrey Kalmykov and Maria Tikhonova and Ekaterina Taktasheva and Danil Astafurov and Mark Baushenko and Artem Snegirev and Tatiana Shavrina and Sergey Markov and Vladislav Mikhailov and Alena Fenogenova}, year={2023}, eprint={2309.10931}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

prajjwal1/bert-tiny

prajjwal1

transformers

--- language: - en license: - mit tags: - BERT - MNLI - NLI - transformer - pre-training --- The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task. If you use the model, please consider citing both the papers: ``` @misc{bhargava2021generalization, title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers}, year={2021}, eprint={2110.01518}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{DBLP:journals/corr/abs-1908-08962, author = {Iulia Turc and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation}, journal = {CoRR}, volume = {abs/1908.08962}, year = {2019}, url = {http://arxiv.org/abs/1908.08962}, eprinttype = {arXiv}, eprint = {1908.08962}, timestamp = {Thu, 29 Aug 2019 16:32:34 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` Config of this model: - `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny) Other models to check out: - `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini) - `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small) - `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium) Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli). Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)

bigscience/bloomz-560m

bigscience

transformers

text-generation

--- datasets: - bigscience/xP3 license: bigscience-bloom-rail-1.0 language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation widget: - text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative?" example_title: "zh-en sentiment" - text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？" example_title: "zh-zh sentiment" - text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"." example_title: "vi-en query" - text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»." example_title: "fr-fr query" - text: "Explain in a sentence in Telugu what is backpropagation in neural networks." example_title: "te-en qa" - text: "Why is the sky blue?" example_title: "en-en qa" - text: "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):" example_title: "es-en fable" - text: "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):" example_title: "hi-en fable" model-index: - name: bloomz-560m results: - task: type: Coreference resolution dataset: type: winogrande name: Winogrande XL (xl) config: xl split: validation revision: a80f460359d1e9a67c006011c94de42a8759430c metrics: - type: Accuracy value: 52.41 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (en) config: en split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 51.01 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (fr) config: fr split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 51.81 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (jp) config: jp split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 52.03 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (pt) config: pt split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 53.99 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (ru) config: ru split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 53.97 - task: type: Coreference resolution dataset: type: Muennighoff/xwinograd name: XWinograd (zh) config: zh split: test revision: 9dd5ea5505fad86b7bedad667955577815300cee metrics: - type: Accuracy value: 54.76 - task: type: Natural language inference dataset: type: anli name: ANLI (r1) config: r1 split: validation revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 metrics: - type: Accuracy value: 33.4 - task: type: Natural language inference dataset: type: anli name: ANLI (r2) config: r2 split: validation revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 metrics: - type: Accuracy value: 33.4 - task: type: Natural language inference dataset: type: anli name: ANLI (r3) config: r3 split: validation revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094 metrics: - type: Accuracy value: 33.5 - task: type: Natural language inference dataset: type: super_glue name: SuperGLUE (cb) config: cb split: validation revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 metrics: - type: Accuracy value: 53.57 - task: type: Natural language inference dataset: type: super_glue name: SuperGLUE (rte) config: rte split: validation revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 metrics: - type: Accuracy value: 67.15 - task: type: Natural language inference dataset: type: xnli name: XNLI (ar) config: ar split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.46 - task: type: Natural language inference dataset: type: xnli name: XNLI (bg) config: bg split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 39.76 - task: type: Natural language inference dataset: type: xnli name: XNLI (de) config: de split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 39.36 - task: type: Natural language inference dataset: type: xnli name: XNLI (el) config: el split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 40.96 - task: type: Natural language inference dataset: type: xnli name: XNLI (en) config: en split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 46.43 - task: type: Natural language inference dataset: type: xnli name: XNLI (es) config: es split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.98 - task: type: Natural language inference dataset: type: xnli name: XNLI (fr) config: fr split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 45.54 - task: type: Natural language inference dataset: type: xnli name: XNLI (hi) config: hi split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 41.81 - task: type: Natural language inference dataset: type: xnli name: XNLI (ru) config: ru split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 39.64 - task: type: Natural language inference dataset: type: xnli name: XNLI (sw) config: sw split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 38.35 - task: type: Natural language inference dataset: type: xnli name: XNLI (th) config: th split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 35.5 - task: type: Natural language inference dataset: type: xnli name: XNLI (tr) config: tr split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 37.31 - task: type: Natural language inference dataset: type: xnli name: XNLI (ur) config: ur split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 38.96 - task: type: Natural language inference dataset: type: xnli name: XNLI (vi) config: vi split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.74 - task: type: Natural language inference dataset: type: xnli name: XNLI (zh) config: zh split: validation revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16 metrics: - type: Accuracy value: 44.66 - task: type: Program synthesis dataset: type: openai_humaneval name: HumanEval config: None split: test revision: e8dc562f5de170c54b5481011dd9f4fa04845771 metrics: - type: Pass@1 value: 2.18 - type: Pass@10 value: 4.11 - type: Pass@100 value: 9.00 - task: type: Sentence completion dataset: type: story_cloze name: StoryCloze (2016) config: "2016" split: validation revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db metrics: - type: Accuracy value: 60.29 - task: type: Sentence completion dataset: type: super_glue name: SuperGLUE (copa) config: copa split: validation revision: 9e12063561e7e6c79099feb6d5a493142584e9e2 metrics: - type: Accuracy value: 52.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (et) config: et split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 53.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (ht) config: ht split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 49.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (id) config: id split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 57.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (it) config: it split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 52.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (qu) config: qu split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 55.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (sw) config: sw split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 56.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (ta) config: ta split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 58.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (th) config: th split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 58.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (tr) config: tr split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 61.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (vi) config: vi split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 61.0 - task: type: Sentence completion dataset: type: xcopa name: XCOPA (zh) config: zh split: validation revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187 metrics: - type: Accuracy value: 61.0 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (ar) config: ar split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 54.4 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (es) config: es split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 56.45 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (eu) config: eu split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 50.56 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (hi) config: hi split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 55.79 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (id) config: id split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 57.84 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (my) config: my split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 47.05 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (ru) config: ru split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 53.14 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (sw) config: sw split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 51.36 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (te) config: te split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 54.86 - task: type: Sentence completion dataset: type: Muennighoff/xstory_cloze name: XStoryCloze (zh) config: zh split: validation revision: 8bb76e594b68147f1a430e86829d07189622b90d metrics: - type: Accuracy value: 56.52 ---  # Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Limitations](#limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 7. [Citation](#citation) # Model Summary > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages. - **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf) - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co) - **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages. - **BLOOMZ & mT0 Model Family:** <div class="max-w-full overflow-auto"> <table> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English. </tr> <tr> <td>Parameters</td> <td>300M</td> <td>580M</td> <td>1.2B</td> <td>3.7B</td> <td>13B</td> <td>560M</td> <td>1.1B</td> <td>1.7B</td> <td>3B</td> <td>7.1B</td> <td>176B</td> </tr> <tr> <td>Finetuned Model</td> <td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td> <td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td> <td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> </tr> <tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td> </tr> <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th> </tr> <tr> <td>Finetuned Model</td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> <td></td> <td></td> <td></td> <td></td> <td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td> </tr> <th colspan="12">Original pretrained checkpoints. Not recommended.</th> <tr> <td>Pretrained Model</td> <td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td> <td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td> <td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td> <td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td> <td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td> <td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td> <td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td> <td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td> <td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td> </tr> </table> </div> # Use ## Intended use We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper: - 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评? - Suggest at least five related search terms to "Mạng neural nhân tạo". - Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish): - Explain in a sentence in Telugu what is backpropagation in neural networks. **Feel free to share your generations in the Community tab!** ## How to use ### CPU <details> <summary> Click to expand </summary> ```python # pip install -q transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigscience/bloomz-560m" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> ### GPU <details> <summary> Click to expand </summary> ```python # pip install -q transformers accelerate from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigscience/bloomz-560m" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto") inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> ### GPU in 8bit <details> <summary> Click to expand </summary> ```python # pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigscience/bloomz-560m" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` </details> <!-- Necessary for whitespace --> ### # Limitations **Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*". # Training ## Model - **Architecture:** Same as [bloom-560m](https://huggingface.co/bigscience/bloom-560m), also refer to the `config.json` file - **Finetuning steps:** 1750 - **Finetuning tokens:** 3.67 billion - **Finetuning layout:** 1x pipeline parallel, 1x tensor parallel, 1x data parallel - **Precision:** float16 ## Hardware - **CPUs:** AMD CPUs with 512GB memory per node - **GPUs:** 64 A100 80GB GPUs with 8 GPUs per node (8 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links - **Communication:** NCCL-communications network with a fully dedicated subnet ## Software - **Orchestration:** [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) - **Optimizer & parallelism:** [DeepSpeed](https://github.com/microsoft/DeepSpeed) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) (pytorch-1.11 w/ CUDA-11.5) - **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex) # Evaluation We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config. # Citation ```bibtex @article{muennighoff2022crosslingual, title={Crosslingual generalization through multitask finetuning}, author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others}, journal={arXiv preprint arXiv:2211.01786}, year={2022} } ```

martin-ha/toxic-comment-model

martin-ha

transformers

text-classification

--- language: en --- ## Model description This model is a fine-tuned version of the [DistilBERT model](https://huggingface.co/transformers/model_doc/distilbert.html) to classify toxic comments. ## How to use You can use the model with the following code. ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline model_path = "martin-ha/toxic-comment-model" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForSequenceClassification.from_pretrained(model_path) pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer) print(pipeline('This is a test text.')) ``` ## Limitations and Bias This model is intended to use for classify toxic online classifications. However, one limitation of the model is that it performs poorly for some comments that mention a specific identity subgroup, like Muslim. The following table shows a evaluation score for different identity group. You can learn the specific meaning of this metrics [here](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview/evaluation). But basically, those metrics shows how well a model performs for a specific group. The larger the number, the better. | **subgroup** | **subgroup_size** | **subgroup_auc** | **bpsn_auc** | **bnsp_auc** | | ----------------------------- | ----------------- | ---------------- | ------------ | ------------ | | muslim | 108 | 0.689 | 0.811 | 0.88 | | jewish | 40 | 0.749 | 0.86 | 0.825 | | homosexual_gay_or_lesbian | 56 | 0.795 | 0.706 | 0.972 | | black | 84 | 0.866 | 0.758 | 0.975 | | white | 112 | 0.876 | 0.784 | 0.97 | | female | 306 | 0.898 | 0.887 | 0.948 | | christian | 231 | 0.904 | 0.917 | 0.93 | | male | 225 | 0.922 | 0.862 | 0.967 | | psychiatric_or_mental_illness | 26 | 0.924 | 0.907 | 0.95 | The table above shows that the model performs poorly for the muslim and jewish group. In fact, you pass the sentence "Muslims are people who follow or practice Islam, an Abrahamic monotheistic religion." Into the model, the model will classify it as toxic. Be mindful for this type of potential bias. ## Training data The training data comes this [Kaggle competition](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data). We use 10% of the `train.csv` data to train the model. ## Training procedure You can see [this documentation and codes](https://github.com/MSIA/wenyang_pan_nlp_project_2021) for how we train the model. It takes about 3 hours in a P-100 GPU. ## Evaluation results The model achieves 94% accuracy and 0.59 f1-score in a 10000 rows held-out test set.

liuhaotian/llava-v1.6-vicuna-7b

liuhaotian

transformers

text-generation

--- inference: false --- <br> <br> # LLaVA Model Card ## Model details **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) **Model date:** LLaVA-v1.6-Vicuna-7B was trained in December 2023. **Paper or resources for more information:** https://llava-vl.github.io/ ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues ## Intended use **Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. - 158K GPT-generated multimodal instruction-following data. - 500K academic-task-oriented VQA data mixture. - 50K GPT-4V data mixture. - 40K ShareGPT data. ## Evaluation dataset A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.