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--- |
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license: mit |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- gte |
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- mteb |
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model-index: |
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- name: gte-micro-v4 |
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results: |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_counterfactual |
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name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
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metrics: |
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- type: accuracy |
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value: 71.83582089552239 |
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- type: ap |
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value: 34.436093320979126 |
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- type: f1 |
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value: 65.82844954638102 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_polarity |
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name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
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metrics: |
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- type: accuracy |
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value: 80.03957500000001 |
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- type: ap |
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value: 74.4510899901909 |
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- type: f1 |
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value: 79.98034714963279 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_reviews_multi |
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name: MTEB AmazonReviewsClassification (en) |
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config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
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- type: accuracy |
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value: 39.754 |
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- type: f1 |
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value: 39.423135672769796 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/arxiv-clustering-p2p |
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name: MTEB ArxivClusteringP2P |
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config: default |
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split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
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metrics: |
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- type: v_measure |
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value: 42.85928858083004 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/arxiv-clustering-s2s |
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name: MTEB ArxivClusteringS2S |
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config: default |
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split: test |
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
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metrics: |
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- type: v_measure |
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value: 32.475201371814784 |
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- task: |
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type: Reranking |
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dataset: |
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type: mteb/askubuntudupquestions-reranking |
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name: MTEB AskUbuntuDupQuestions |
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config: default |
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split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
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metrics: |
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- type: map |
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value: 58.01141755339977 |
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- type: mrr |
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value: 71.70821791320407 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/banking77 |
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name: MTEB Banking77Classification |
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config: default |
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split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
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metrics: |
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- type: accuracy |
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value: 80.9220779220779 |
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- type: f1 |
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value: 80.86851039874094 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/biorxiv-clustering-p2p |
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name: MTEB BiorxivClusteringP2P |
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config: default |
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split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
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metrics: |
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- type: v_measure |
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value: 36.82555236565894 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/biorxiv-clustering-s2s |
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name: MTEB BiorxivClusteringS2S |
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config: default |
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split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
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metrics: |
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- type: v_measure |
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value: 29.243444611175995 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/emotion |
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name: MTEB EmotionClassification |
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config: default |
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split: test |
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revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
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metrics: |
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- type: accuracy |
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value: 44.87500000000001 |
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- type: f1 |
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value: 39.78455417008123 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/imdb |
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name: MTEB ImdbClassification |
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config: default |
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split: test |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
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metrics: |
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- type: accuracy |
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value: 71.9568 |
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- type: ap |
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value: 65.91179027501194 |
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- type: f1 |
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value: 71.85575290323182 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/mtop_domain |
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name: MTEB MTOPDomainClassification (en) |
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config: en |
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split: test |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
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metrics: |
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- type: accuracy |
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value: 90.87323301413589 |
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- type: f1 |
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value: 90.45433994230181 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/mtop_intent |
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name: MTEB MTOPIntentClassification (en) |
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config: en |
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split: test |
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
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metrics: |
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- type: accuracy |
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value: 68.53169174646602 |
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- type: f1 |
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value: 50.49367676485481 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_massive_intent |
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name: MTEB MassiveIntentClassification (en) |
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config: en |
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split: test |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
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metrics: |
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- type: accuracy |
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value: 69.11230665770007 |
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- type: f1 |
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value: 66.9035022957204 |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_massive_scenario |
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name: MTEB MassiveScenarioClassification (en) |
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config: en |
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split: test |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
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metrics: |
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- type: accuracy |
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value: 74.15601882985877 |
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- type: f1 |
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value: 74.059011768806 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/medrxiv-clustering-p2p |
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name: MTEB MedrxivClusteringP2P |
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config: default |
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split: test |
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
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metrics: |
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- type: v_measure |
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value: 32.551619758274406 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/medrxiv-clustering-s2s |
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name: MTEB MedrxivClusteringS2S |
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config: default |
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split: test |
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
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metrics: |
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- type: v_measure |
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value: 30.80210958999942 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/reddit-clustering |
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name: MTEB RedditClustering |
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config: default |
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split: test |
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revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
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metrics: |
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- type: v_measure |
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value: 48.27542501963987 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/reddit-clustering-p2p |
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name: MTEB RedditClusteringP2P |
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config: default |
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split: test |
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revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 |
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metrics: |
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- type: v_measure |
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value: 53.55942763860501 |
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- task: |
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type: PairClassification |
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dataset: |
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type: mteb/sprintduplicatequestions-pairclassification |
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name: MTEB SprintDuplicateQuestions |
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config: default |
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split: test |
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revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
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metrics: |
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- type: cos_sim_accuracy |
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value: 99.82673267326733 |
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- type: cos_sim_ap |
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value: 95.53621808930455 |
|
- type: cos_sim_f1 |
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value: 91.19275289380975 |
|
- type: cos_sim_precision |
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value: 91.7933130699088 |
|
- type: cos_sim_recall |
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value: 90.60000000000001 |
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- type: dot_accuracy |
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value: 99.75445544554455 |
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- type: dot_ap |
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value: 92.76410342229411 |
|
- type: dot_f1 |
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value: 87.50612444879961 |
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- type: dot_precision |
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value: 85.78290105667628 |
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- type: dot_recall |
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value: 89.3 |
|
- type: euclidean_accuracy |
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value: 99.82673267326733 |
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- type: euclidean_ap |
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value: 95.46124795179632 |
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- type: euclidean_f1 |
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value: 91.01181304571135 |
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- type: euclidean_precision |
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value: 93.55860612460401 |
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- type: euclidean_recall |
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value: 88.6 |
|
- type: manhattan_accuracy |
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value: 99.82871287128712 |
|
- type: manhattan_ap |
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value: 95.51436288466519 |
|
- type: manhattan_f1 |
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value: 91.11891620672353 |
|
- type: manhattan_precision |
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value: 91.44008056394763 |
|
- type: manhattan_recall |
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value: 90.8 |
|
- type: max_accuracy |
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value: 99.82871287128712 |
|
- type: max_ap |
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value: 95.53621808930455 |
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- type: max_f1 |
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value: 91.19275289380975 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/stackexchange-clustering |
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name: MTEB StackExchangeClustering |
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config: default |
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split: test |
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revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
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metrics: |
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- type: v_measure |
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value: 55.0721745308552 |
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- task: |
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type: Clustering |
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dataset: |
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type: mteb/stackexchange-clustering-p2p |
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name: MTEB StackExchangeClusteringP2P |
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config: default |
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split: test |
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revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
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metrics: |
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- type: v_measure |
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value: 31.91639764792279 |
|
- task: |
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type: Classification |
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dataset: |
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type: mteb/toxic_conversations_50k |
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name: MTEB ToxicConversationsClassification |
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config: default |
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split: test |
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revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
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metrics: |
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- type: accuracy |
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value: 66.0402 |
|
- type: ap |
|
value: 12.106715125588833 |
|
- type: f1 |
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value: 50.67443088623853 |
|
- task: |
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type: Classification |
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dataset: |
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type: mteb/tweet_sentiment_extraction |
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name: MTEB TweetSentimentExtractionClassification |
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config: default |
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split: test |
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revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
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metrics: |
|
- type: accuracy |
|
value: 59.42840973401245 |
|
- type: f1 |
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value: 59.813350770208665 |
|
- task: |
|
type: Clustering |
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dataset: |
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type: mteb/twentynewsgroups-clustering |
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name: MTEB TwentyNewsgroupsClustering |
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config: default |
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split: test |
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revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
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metrics: |
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- type: v_measure |
|
value: 41.37273187829312 |
|
- task: |
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type: PairClassification |
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dataset: |
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type: mteb/twittersemeval2015-pairclassification |
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name: MTEB TwitterSemEval2015 |
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config: default |
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split: test |
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revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
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metrics: |
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- type: cos_sim_accuracy |
|
value: 84.10919711509806 |
|
- type: cos_sim_ap |
|
value: 67.55255054010537 |
|
- type: cos_sim_f1 |
|
value: 64.22774378823823 |
|
- type: cos_sim_precision |
|
value: 60.9623133443944 |
|
- type: cos_sim_recall |
|
value: 67.86279683377309 |
|
- type: dot_accuracy |
|
value: 80.62228050306967 |
|
- type: dot_ap |
|
value: 54.81480289413879 |
|
- type: dot_f1 |
|
value: 54.22550997534184 |
|
- type: dot_precision |
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value: 47.13561964146532 |
|
- type: dot_recall |
|
value: 63.82585751978892 |
|
- type: euclidean_accuracy |
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value: 84.04363116170948 |
|
- type: euclidean_ap |
|
value: 67.77652401372912 |
|
- type: euclidean_f1 |
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value: 64.46694460988684 |
|
- type: euclidean_precision |
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value: 58.762214983713356 |
|
- type: euclidean_recall |
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value: 71.39841688654354 |
|
- type: manhattan_accuracy |
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value: 83.94230196101806 |
|
- type: manhattan_ap |
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value: 67.419155052755 |
|
- type: manhattan_f1 |
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value: 64.15049692380501 |
|
- type: manhattan_precision |
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value: 58.151008151008156 |
|
- type: manhattan_recall |
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value: 71.53034300791556 |
|
- type: max_accuracy |
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value: 84.10919711509806 |
|
- type: max_ap |
|
value: 67.77652401372912 |
|
- type: max_f1 |
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value: 64.46694460988684 |
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- task: |
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type: PairClassification |
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dataset: |
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type: mteb/twitterurlcorpus-pairclassification |
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name: MTEB TwitterURLCorpus |
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config: default |
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split: test |
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revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
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metrics: |
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- type: cos_sim_accuracy |
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value: 88.25823728024217 |
|
- type: cos_sim_ap |
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value: 84.67785320317506 |
|
- type: cos_sim_f1 |
|
value: 76.67701296330108 |
|
- type: cos_sim_precision |
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value: 72.92491491282907 |
|
- type: cos_sim_recall |
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value: 80.83615645210965 |
|
- type: dot_accuracy |
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value: 84.63344588038964 |
|
- type: dot_ap |
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value: 75.25182203961072 |
|
- type: dot_f1 |
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value: 70.35217601881962 |
|
- type: dot_precision |
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value: 63.87737152908657 |
|
- type: dot_recall |
|
value: 78.28765013858947 |
|
- type: euclidean_accuracy |
|
value: 88.2504754142896 |
|
- type: euclidean_ap |
|
value: 84.68882859374924 |
|
- type: euclidean_f1 |
|
value: 76.69534508021188 |
|
- type: euclidean_precision |
|
value: 74.89177489177489 |
|
- type: euclidean_recall |
|
value: 78.58792731752386 |
|
- type: manhattan_accuracy |
|
value: 88.26211821321846 |
|
- type: manhattan_ap |
|
value: 84.60061548046698 |
|
- type: manhattan_f1 |
|
value: 76.63928519959647 |
|
- type: manhattan_precision |
|
value: 72.02058504875406 |
|
- type: manhattan_recall |
|
value: 81.89097628580228 |
|
- type: max_accuracy |
|
value: 88.26211821321846 |
|
- type: max_ap |
|
value: 84.68882859374924 |
|
- type: max_f1 |
|
value: 76.69534508021188 |
|
--- |
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# gte-micro-v4 |
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This is a distill of [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny). |
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## Intended purpose |
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|
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<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span> |
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|
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## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny)) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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|
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('Mihaiii/gte-micro-v4') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny)) |
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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. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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#Mean Pooling - Take attention mask into account for correct averaging |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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# Sentences we want sentence embeddings for |
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sentences = ['This is an example sentence', 'Each sentence is converted'] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('Mihaiii/gte-micro-v4') |
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model = AutoModel.from_pretrained('Mihaiii/gte-micro-v4') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, mean pooling. |
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small)) |
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This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. |
