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JA
Dunzhang Stella En 400M V5
D
Dunzhang Stella En 400M V5
Marqoによって開発
Stella 400Mは中規模の英語テキスト処理モデルで、分類と情報検索タスクに特化しています。
ダウンロード数 17.20k
リリース時間 : 9/25/2024
モデル概要
このモデルは主にテキスト分類と情報検索タスクに使用され、複数の標準データセットで優れた性能を発揮します。
モデル特徴
高性能分類
Amazon製品レビュー分類タスクで97.19%の精度を達成
マルチタスク能力
分類や情報検索など、様々なテキスト処理タスクをサポート
中規模設計
400Mパラメータのバランス設計で、性能と効率を両立
モデル能力
テキスト分類
感情分析
情報検索
テキスト類似度計算
使用事例
電子商取引
製品レビュー分類
Amazon製品レビューの感情傾向を自動分類
Amazon極性分類タスクで97.19%の精度を達成
レビューマルチ分類
Amazonレビューを複数星評価で分類
Amazonレビューマルチ分類タスクで59.53%の精度を達成
情報検索
論点検索
ArguAnaデータセットで論点マッチング検索を実施
64.24の主要スコアを達成
model-index:
- name: stella_en_400M_v5
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy value: 92.35820895522387
- type: ap value: 70.81322736988783
- type: ap_weighted value: 70.81322736988783
- type: f1 value: 88.9505466159595
- type: f1_weighted value: 92.68630932872613
- type: main_score value: 92.35820895522387 task: type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy value: 97.1945
- type: ap value: 96.08192192244094
- type: ap_weighted value: 96.08192192244094
- type: f1 value: 97.1936887167346
- type: f1_weighted value: 97.1936887167346
- type: main_score value: 97.1945 task: type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy value: 59.528000000000006
- type: f1 value: 59.21016819840188
- type: f1_weighted value: 59.21016819840188
- type: main_score value: 59.528000000000006 task: type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
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- type: map_at_100 value: 56.833999999999996
- type: map_at_1000 value: 56.835
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- type: map_at_3 value: 52.181
- type: map_at_5 value: 54.628
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- type: mrr_at_100 value: 57.17548614361504
- type: mrr_at_1000 value: 57.176650626377466
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- type: ndcg_at_3 value: 55.938
- type: ndcg_at_5 value: 60.370000000000005
- type: precision_at_1 value: 40.398
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- type: recall_at_1 value: 40.398
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- type: recall_at_1000 value: 99.644
- type: recall_at_20 value: 96.72800000000001
- type: recall_at_3 value: 66.78500000000001
- type: recall_at_5 value: 77.596 task: type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: main_score value: 55.1564333205451
- type: v_measure value: 55.1564333205451
- type: v_measure_std value: 14.696883012214512 task: type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: main_score value: 49.823698316694795
- type: v_measure value: 49.823698316694795
- type: v_measure_std value: 14.951660654298186 task: type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
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- type: map value: 66.15294503553424
- type: mrr value: 78.53438420612935
- type: nAUC_map_diff1 value: 12.569697092717997
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- type: nAUC_mrr_max value: 34.96072022474653
- type: nAUC_mrr_std value: 21.649217605241045 task: type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
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- type: cosine_spearman value: 83.29587385660628
- type: euclidean_pearson value: 84.58764190565167
- type: euclidean_spearman value: 83.30069324352772
- type: main_score value: 83.29587385660628
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- type: manhattan_spearman value: 83.87480271054358
- type: pearson value: 85.7824546319275
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- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
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- type: f1 value: 89.26182507266391
- type: f1_weighted value: 89.26182507266391
- type: main_score value: 89.30194805194806 task: type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
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- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
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- type: v_measure value: 45.80539715556144
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- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: mteb/cqadupstack
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- dataset:
config: default
name: MTEB Touche2020
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
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- type: ndcg_at_1000 value: 53.556000000000004
- type: ndcg_at_20 value: 30.808000000000003
- type: ndcg_at_3 value: 35.831
- type: ndcg_at_5 value: 32.845
- type: precision_at_1 value: 40.816
- type: precision_at_10 value: 27.143
- type: precision_at_100 value: 8.449
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- type: precision_at_3 value: 35.374
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- type: recall_at_1 value: 2.9739999999999998
- type: recall_at_10 value: 19.39
- type: recall_at_100 value: 51.636
- type: recall_at_1000 value: 86.99900000000001
- type: recall_at_20 value: 26.478
- type: recall_at_3 value: 7.703
- type: recall_at_5 value: 11.42 task: type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy value: 86.9384765625
- type: ap value: 31.737513704141552
- type: ap_weighted value: 31.737513704141552
- type: f1 value: 71.5490757306975
- type: f1_weighted value: 89.14632533489856
- type: main_score value: 86.9384765625 task: type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy value: 73.57668364459535
- type: f1 value: 73.90467103648074
- type: f1_weighted value: 73.42158415034704
- type: main_score value: 73.57668364459535 task: type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: main_score value: 58.574148097494685
- type: v_measure value: 58.574148097494685
- type: v_measure_std value: 0.9443161637490822 task: type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cosine_accuracy value: 88.1385229778864
- type: cosine_accuracy_threshold value: 83.86307954788208
- type: cosine_ap value: 80.17965893449055
- type: cosine_f1 value: 73.0614300100705
- type: cosine_f1_threshold value: 80.7942807674408
- type: cosine_precision value: 69.8603755416466
- type: cosine_recall value: 76.56992084432717
- type: dot_accuracy value: 88.2100494724921
- type: dot_accuracy_threshold value: 83.84793996810913
- type: dot_ap value: 80.18603932881858
- type: dot_f1 value: 73.07643714466204
- type: dot_f1_threshold value: 80.87586164474487
- type: dot_precision value: 70.10909090909091
- type: dot_recall value: 76.3060686015831
- type: euclidean_accuracy value: 88.1385229778864
- type: euclidean_accuracy_threshold value: 56.77661895751953
- type: euclidean_ap value: 80.1784070881624
- type: euclidean_f1 value: 73.04830369529574
- type: euclidean_f1_threshold value: 61.91838979721069
- type: euclidean_precision value: 69.96859144720948
- type: euclidean_recall value: 76.41160949868075
- type: main_score value: 80.18603932881858
- type: manhattan_accuracy value: 88.0431543184121
- type: manhattan_accuracy_threshold value: 3755.6137084960938
- type: manhattan_ap value: 79.98270453664578
- type: manhattan_f1 value: 72.68242015061023
- type: manhattan_f1_threshold value: 3892.494583129883
- type: manhattan_precision value: 71.54907975460122
- type: manhattan_recall value: 73.85224274406332
- type: max_ap value: 80.18603932881858
- type: max_f1 value: 73.07643714466204
- type: max_precision value: 71.54907975460122
- type: max_recall value: 76.56992084432717
- type: similarity_accuracy value: 88.1385229778864
- type: similarity_accuracy_threshold value: 83.86307954788208
- type: similarity_ap value: 80.17965893449055
- type: similarity_f1 value: 73.0614300100705
- type: similarity_f1_threshold value: 80.7942807674408
- type: similarity_precision value: 69.8603755416466
- type: similarity_recall value: 76.56992084432717 task: type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cosine_accuracy value: 89.7892653393876
- type: cosine_accuracy_threshold value: 79.69566583633423
- type: cosine_ap value: 87.4579867302024
- type: cosine_f1 value: 79.91620843152658
- type: cosine_f1_threshold value: 78.53609323501587
- type: cosine_precision value: 77.7155329210622
- type: cosine_recall value: 82.24514936864799
- type: dot_accuracy value: 89.78732487289945
- type: dot_accuracy_threshold value: 80.05315661430359
- type: dot_ap value: 87.44916182456272
- type: dot_f1 value: 79.90419878751591
- type: dot_f1_threshold value: 78.57890725135803
- type: dot_precision value: 77.73409057812728
- type: dot_recall value: 82.19895287958116
- type: euclidean_accuracy value: 89.78538440641131
- type: euclidean_accuracy_threshold value: 62.29925751686096
- type: euclidean_ap value: 87.45904868911386
- type: euclidean_f1 value: 79.93127404474657
- type: euclidean_f1_threshold value: 65.61101078987122
- type: euclidean_precision value: 77.62060210373595
- type: euclidean_recall value: 82.38373883584848
- type: main_score value: 87.46554314325058
- type: manhattan_accuracy value: 89.76597974152986
- type: manhattan_accuracy_threshold value: 3988.5299682617188
- type: manhattan_ap value: 87.46554314325058
- type: manhattan_f1 value: 79.97181740645973
- type: manhattan_f1_threshold value: 4235.905838012695
- type: manhattan_precision value: 77.13713427283783
- type: manhattan_recall value: 83.02279026793964
- type: max_ap value: 87.46554314325058
- type: max_f1 value: 79.97181740645973
- type: max_precision value: 77.73409057812728
- type: max_recall value: 83.02279026793964
- type: similarity_accuracy value: 89.7892653393876
- type: similarity_accuracy_threshold value: 79.69566583633423
- type: similarity_ap value: 87.4579867302024
- type: similarity_f1 value: 79.91620843152658
- type: similarity_f1_threshold value: 78.53609323501587
- type: similarity_precision value: 77.7155329210622
- type: similarity_recall value: 82.24514936864799 task: type: PairClassification tags:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- mteb
- sentence-transformers
- transformers
- sentence-similarity license: mit
Marqo Stella v2
This model is similar to the original Dunzhang stella 400m model, with a fused matryoshka layer. The hierarchical structuring from a Matryoshka Layer reduces the computational overhead for generating embeddings, while leaving relevance metrics unchanged.
Transformers
import os
import torch
from transformers import AutoModel, AutoTokenizer, AutoConfig
from sklearn.preprocessing import normalize
query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "
queries = [
"What are some ways to reduce stress?",
"What are the benefits of drinking green tea?",
]
queries = [query_prompt + query for query in queries]
# docs do not need any prompts
docs = [
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
# The path of your model after cloning it
model_dir = "Marqo/dunzhang-stella_en_400M_v5"
model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
with torch.no_grad():
input_data = tokenizer(queries, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
query_vectors = normalize(query_vectors.cpu().numpy())
# Embed the documents
with torch.no_grad():
input_data = tokenizer(docs, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
docs_vectors = normalize(docs_vectors.cpu().numpy())
print(query_vectors.shape, docs_vectors.shape)
# (2, 1024) (2, 1024)
similarities = query_vectors @ docs_vectors.T
print(similarities)
# [[0.8397531 0.29900077]
# [0.32818374 0.80954516]]
Distilbert Base Uncased Finetuned Sst 2 English
Apache-2.0
DistilBERT - base - uncasedをベースに、SST - 2感情分析データセットで微調整されたテキスト分類モデル。正解率91.3%
テキスト分類 英語
D
distilbert
5.2M
746
Xlm Roberta Base Language Detection
MIT
XLM-RoBERTaベースの多言語検出モデル、20言語のテキスト分類をサポート
テキスト分類
Transformers 複数言語対応
Transformers 複数言語対応X
papluca
2.7M
333
Roberta Hate Speech Dynabench R4 Target
このモデルは動的データセット生成を通じてオンライン憎悪検出を改善し、検出効果を高めるために最悪ケースから学習することに焦点を当てています。
テキスト分類 Transformers 英語
Transformers 英語R
facebook
2.0M
80
Bert Base Multilingual Uncased Sentiment
MIT
bert-base-multilingual-uncasedを微調整した多言語感情分析モデルで、6言語の商品レビューの感情分析に対応しています。
テキスト分類 複数言語対応
B
nlptown
1.8M
371
Emotion English Distilroberta Base
DistilRoBERTa-baseをファインチューニングした英語テキストの感情分類モデルで、エクマンの6基本感情と中立カテゴリを予測可能。
テキスト分類 Transformers 英語
Transformers 英語E
j-hartmann
1.1M
402
Robertuito Sentiment Analysis
RoBERTuitoベースのスペイン語ツイート感情分析モデル、POS(ポジティブ)/NEG(ネガティブ)/NEU(ニュートラル)の3分類に対応
テキスト分類 スペイン語
R
pysentimiento
1.0M
88
Finbert Tone
FinBERTは金融通信テキストを元に事前学習されたBERTモデルで、金融自然言語処理分野に特化しています。finbert-toneはその微調整バージョンで、金融感情分析タスクに使用されます。
テキスト分類 Transformers 英語
Transformers 英語F
yiyanghkust
998.46k
178
Roberta Base Go Emotions
MIT
RoBERTa-baseに基づく多ラベル感情分類モデルで、go_emotionsデータセットで訓練され、28種類の感情ラベル識別をサポートします。
テキスト分類 Transformers 英語
Transformers 英語R
SamLowe
848.12k
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