モデル概要

mini-gteはDistilBERTアーキテクチャに基づく軽量文埋め込みモデルで、主にテキスト分類、情報検索、クラスタリングなどの自然言語処理タスクに使用されます。このモデルは複数のMTEBベンチマークテストで優れた性能を示し、効率的なテキスト表現が必要なシナリオに特に適しています。

モデル特徴

軽量で効率的

DistilBERTアーキテクチャを採用し、性能を維持しながらモデルサイズを大幅に削減

マルチタスク対応

テキスト分類、情報検索、クラスタリングなど様々なタスクで良好な性能を発揮

優れたベンチマーク性能

MTEBの複数ベンチマークテストで競争力のある結果を達成

モデル能力

テキスト分類

情報検索

テキストクラスタリング

文埋め込み生成

使用事例

電子商取引

製品レビュー感情分析

Amazon製品レビューの感情傾向を分析

Amazon極性分類タスクで92.94%の精度を達成

虚偽レビュー検出

Amazonプラットフォーム上の虚偽レビューを識別

Amazon虚偽分類タスクで74.90%の精度を達成

学術研究

論文クラスタリング

arXiv論文を主題別にクラスタリング

arXiv論文クラスタリングタスクでV-measure47.25を達成

情報検索

議論検索

議論データセットから関連議論を検索

ArguAna検索タスクでNDCG@1056.61を達成

base_model: distilbert/distilbert-base-uncased library_name: sentence-transformers model-index:

name: prdev/mini-gte results:
- dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics:
  - type: accuracy value: 74.8955
  - type: f1 value: 68.84209999999999
  - type: f1_weighted value: 77.1819
  - type: ap value: 37.731500000000004
  - type: ap_weighted value: 37.731500000000004
  - type: main_score value: 74.8955 task: type: Classification
- dataset: config: default name: MTEB AmazonPolarityClassification (default) revision: e2d317d38cd51312af73b3d32a06d1a08b442046 split: test type: mteb/amazon_polarity metrics:
  - type: accuracy value: 92.9424
  - type: f1 value: 92.9268
  - type: f1_weighted value: 92.9268
  - type: ap value: 89.2255
  - type: ap_weighted value: 89.2255
  - type: main_score value: 92.9424 task: type: Classification
- dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics:
  - type: accuracy value: 53.09199999999999
  - type: f1 value: 52.735299999999995
  - type: f1_weighted value: 52.735299999999995
  - type: main_score value: 53.09199999999999 task: type: Classification
- dataset: config: default name: MTEB ArguAna (default) revision: c22ab2a51041ffd869aaddef7af8d8215647e41a split: test type: mteb/arguana metrics:
  - type: ndcg_at_1 value: 31.791999999999998
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  - type: ndcg_at_5 value: 51.842999999999996
  - type: ndcg_at_10 value: 56.614
  - type: ndcg_at_20 value: 59.211999999999996
  - type: ndcg_at_100 value: 60.148999999999994
  - type: ndcg_at_1000 value: 60.231
  - type: map_at_1 value: 31.791999999999998
  - type: map_at_3 value: 43.35
  - type: map_at_5 value: 45.928000000000004
  - type: map_at_10 value: 47.929
  - type: map_at_20 value: 48.674
  - type: map_at_100 value: 48.825
  - type: map_at_1000 value: 48.827999999999996
  - type: recall_at_1 value: 31.791999999999998
  - type: recall_at_3 value: 58.392999999999994
  - type: recall_at_5 value: 69.63000000000001
  - type: recall_at_10 value: 84.211
  - type: recall_at_20 value: 94.23899999999999
  - type: recall_at_100 value: 99.004
  - type: recall_at_1000 value: 99.644
  - type: precision_at_1 value: 31.791999999999998
  - type: precision_at_3 value: 19.464000000000002
  - type: precision_at_5 value: 13.926
  - type: precision_at_10 value: 8.421
  - type: precision_at_20 value: 4.712000000000001
  - type: precision_at_100 value: 0.9900000000000001
  - type: precision_at_1000 value: 0.1
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- dataset: config: default name: MTEB ArxivClusteringP2P (default) revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d split: test type: mteb/arxiv-clustering-p2p metrics:
  - type: v_measure value: 47.2524
  - type: v_measure_std value: 13.7772
  - type: main_score value: 47.2524 task: type: Clustering
- dataset: config: default name: MTEB ArxivClusteringS2S (default) revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 split: test type: mteb/arxiv-clustering-s2s metrics:
  - type: v_measure value: 40.7262
  - type: v_measure_std value: 14.125499999999999
  - type: main_score value: 40.7262 task: type: Clustering
- dataset: config: default name: MTEB AskUbuntuDupQuestions (default) revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 split: test type: mteb/askubuntudupquestions-reranking metrics:
  - type: map value: 61.57319999999999
  - type: mrr value: 74.6714
  - type: nAUC_map_max value: 21.8916
  - type: nAUC_map_std value: 17.9941
  - type: nAUC_map_diff1 value: 1.5548
  - type: nAUC_mrr_max value: 34.139399999999995
  - type: nAUC_mrr_std value: 18.133499999999998
  - type: nAUC_mrr_diff1 value: 13.3597
  - type: main_score value: 61.57319999999999 task: type: Reranking
- dataset: config: default name: MTEB BIOSSES (default) revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics:
  - type: pearson value: 86.7849
  - type: spearman value: 84.7302
  - type: cosine_pearson value: 86.7849
  - type: cosine_spearman value: 84.7302
  - type: manhattan_pearson value: 84.48179999999999
  - type: manhattan_spearman value: 84.0507
  - type: euclidean_pearson value: 84.8613
  - type: euclidean_spearman value: 84.6266
  - type: main_score value: 84.7302 task: type: STS
- dataset: config: default name: MTEB Banking77Classification (default) revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 split: test type: mteb/banking77 metrics:
  - type: accuracy value: 85.7175
  - type: f1 value: 85.6781
  - type: f1_weighted value: 85.6781
  - type: main_score value: 85.7175 task: type: Classification
- dataset: config: default name: MTEB BiorxivClusteringP2P (default) revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 split: test type: mteb/biorxiv-clustering-p2p metrics:
  - type: v_measure value: 40.0588
  - type: v_measure_std value: 0.8872
  - type: main_score value: 40.0588 task: type: Clustering
- dataset: config: default name: MTEB BiorxivClusteringS2S (default) revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 split: test type: mteb/biorxiv-clustering-s2s metrics:
  - type: v_measure value: 36.382799999999996
  - type: v_measure_std value: 1.167
  - type: main_score value: 36.382799999999996 task: type: Clustering
- dataset: config: default name: MTEB CQADupstackAndroidRetrieval (default) revision: f46a197baaae43b4f621051089b82a364682dfeb split: test type: mteb/cqadupstack-android metrics:
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- dataset: config: default name: MTEB SICK-R (default) revision: 20a6d6f312dd54037fe07a32d58e5e168867909d split: test type: mteb/sickr-sts metrics:
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  - type: nauc_mrr_at_10_max value: -39.0726
  - type: nauc_mrr_at_10_std value: -6.830799999999999
  - type: nauc_mrr_at_10_diff1 value: 5.1214
  - type: nauc_mrr_at_20_max value: -38.6519
  - type: nauc_mrr_at_20_std value: -8.6379
  - type: nauc_mrr_at_20_diff1 value: 6.436699999999999
  - type: nauc_mrr_at_100_max value: -38.065599999999996
  - type: nauc_mrr_at_100_std value: -8.444
  - type: nauc_mrr_at_100_diff1 value: 6.2007
  - type: nauc_mrr_at_1000_max value: -38.065599999999996
  - type: nauc_mrr_at_1000_std value: -8.444
  - type: nauc_mrr_at_1000_diff1 value: 6.2007
  - type: main_score value: 23.86 task: type: Retrieval
- dataset: config: default name: MTEB ToxicConversationsClassification (default) revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de split: test type: mteb/toxic_conversations_50k metrics:
  - type: accuracy value: 78.21289999999999
  - type: f1 value: 60.9322
  - type: f1_weighted value: 82.69539999999999
  - type: ap value: 19.0474
  - type: ap_weighted value: 19.0474
  - type: main_score value: 78.21289999999999 task: type: Classification
- dataset: config: default name: MTEB TweetSentimentExtractionClassification (default) revision: d604517c81ca91fe16a244d1248fc021f9ecee7a split: test type: mteb/tweet_sentiment_extraction metrics:
  - type: accuracy value: 61.3865
  - type: f1 value: 61.8066
  - type: f1_weighted value: 60.887
  - type: main_score value: 61.3865 task: type: Classification
- dataset: config: default name: MTEB TwentyNewsgroupsClustering (default) revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics:
  - type: v_measure value: 49.3006
  - type: v_measure_std value: 0.8814000000000001
  - type: main_score value: 49.3006 task: type: Clustering
- dataset: config: default name: MTEB TwitterSemEval2015 (default) revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 split: test type: mteb/twittersemeval2015-pairclassification metrics:
  - type: similarity_accuracy value: 85.96289999999999
  - type: similarity_accuracy_threshold value: 81.7629
  - type: similarity_f1 value: 67.1044
  - type: similarity_f1_threshold value: 77.98479999999999
  - type: similarity_precision value: 64.3497
  - type: similarity_recall value: 70.10549999999999
  - type: similarity_ap value: 73.406
  - type: cosine_accuracy value: 85.96289999999999
  - type: cosine_accuracy_threshold value: 81.7629
  - type: cosine_f1 value: 67.1044
  - type: cosine_f1_threshold value: 77.98479999999999
  - type: cosine_precision value: 64.3497
  - type: cosine_recall value: 70.10549999999999
  - type: cosine_ap value: 73.406
  - type: manhattan_accuracy value: 85.8735
  - type: manhattan_accuracy_threshold value: 22606.6437
  - type: manhattan_f1 value: 67.173
  - type: manhattan_f1_threshold value: 24147.3145
  - type: manhattan_precision value: 65.5857
  - type: manhattan_recall value: 68.8391
  - type: manhattan_ap value: 73.4081
  - type: euclidean_accuracy value: 85.94500000000001
  - type: euclidean_accuracy_threshold value: 1019.4165999999999
  - type: euclidean_f1 value: 67.0857
  - type: euclidean_f1_threshold value: 1125.1016
  - type: euclidean_precision value: 64.07300000000001
  - type: euclidean_recall value: 70.3958
  - type: euclidean_ap value: 73.3824
  - type: dot_accuracy value: 85.3013
  - type: dot_accuracy_threshold value: 23726.3916
  - type: dot_f1 value: 65.8888
  - type: dot_f1_threshold value: 22265.913399999998
  - type: dot_precision value: 62.2128
  - type: dot_recall value: 70.0264
  - type: dot_ap value: 71.5363
  - type: max_accuracy value: 85.96289999999999
  - type: max_f1 value: 67.173
  - type: max_precision value: 65.5857
  - type: max_recall value: 70.3958
  - type: max_ap value: 73.4081
  - type: main_score value: 73.4081 task: type: PairClassification
- dataset: config: default name: MTEB TwitterURLCorpus (default) revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics:
  - type: similarity_accuracy value: 88.82100000000001
  - type: similarity_accuracy_threshold value: 75.9351
  - type: similarity_f1 value: 77.7423
  - type: similarity_f1_threshold value: 72.788
  - type: similarity_precision value: 74.2279
  - type: similarity_recall value: 81.6061
  - type: similarity_ap value: 85.4324
  - type: cosine_accuracy value: 88.82100000000001
  - type: cosine_accuracy_threshold value: 75.9351
  - type: cosine_f1 value: 77.7423
  - type: cosine_f1_threshold value: 72.788
  - type: cosine_precision value: 74.2279
  - type: cosine_recall value: 81.6061
  - type: cosine_ap value: 85.4324
  - type: manhattan_accuracy value: 88.786
  - type: manhattan_accuracy_threshold value: 25561.4807
  - type: manhattan_f1 value: 77.5953
  - type: manhattan_f1_threshold value: 26504.0619
  - type: manhattan_precision value: 76.1432
  - type: manhattan_recall value: 79.1038
  - type: manhattan_ap value: 85.3477
  - type: euclidean_accuracy value: 88.76859999999999
  - type: euclidean_accuracy_threshold value: 1181.4543
  - type: euclidean_f1 value: 77.645
  - type: euclidean_f1_threshold value: 1224.4793
  - type: euclidean_precision value: 75.5925
  - type: euclidean_recall value: 79.8121
  - type: euclidean_ap value: 85.3781
  - type: dot_accuracy value: 88.5707
  - type: dot_accuracy_threshold value: 21387.7899
  - type: dot_f1 value: 77.4888
  - type: dot_f1_threshold value: 20875.6653
  - type: dot_precision value: 75.58009999999999
  - type: dot_recall value: 79.4965
  - type: dot_ap value: 84.88550000000001
  - type: max_accuracy value: 88.82100000000001
  - type: max_f1 value: 77.7423
  - type: max_precision value: 76.1432
  - type: max_recall value: 81.6061
  - type: max_ap value: 85.4324
  - type: main_score value: 85.4324 task: type: PairClassification pipeline_tag: sentence-similarity tags:
sentence-transformers
sentence-similarity
feature-extraction
mteb license: apache-2.0 language:
en

Mini-GTE

QTACK Logo

Overview

This is the first model developed by QTACK and serves as a proof of concept for our distillation approach! Built upon a distillbert-based architecture, Mini-GTE is distilled from GTE and designed for efficiency without sacrificing accuracy at only 66M parameters. As a standalone sentence transformer, it ranks 2nd on the MTEB classic leaderboard in the <100M parameter category and 63rd overall which makes it a strong choice for real-time query encoding, semantic search, and similarity tasks.

Model Details

Model Type: Sentence Transformer
Base model: distilbert/distilbert-base-uncased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Usage

Optimized for quick inference
Great at quickly generating high quality encodings
Easy to plug and play since it is distilled from GTE
We want to see how you’re using our model so we’ll give you a free coffee/$10 gift card if you get on call with us and show us what you’ve built!

Getting Started

Installation

Mini-GTE is built on the Sentence Transformers framework. To install the required packages, run:

pip install -U sentence-transformers

Quick Start

Here's a quick example to get you started:

from sentence_transformers import SentenceTransformer

# Download directly from Hugging Face
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape) # Expected: [3, 768]

# Compute the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape) # Expected: [3, 3]