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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 |
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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: 68.82089552238806 |
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- type: ap |
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value: 31.260622493912688 |
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- type: f1 |
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value: 62.701989024087304 |
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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: 77.11532499999998 |
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- type: ap |
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value: 71.29001033390622 |
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- type: f1 |
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value: 77.0225646895571 |
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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: 40.93600000000001 |
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- type: f1 |
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value: 39.24591989399245 |
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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: 35.237007515497126 |
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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: 31.08692637060412 |
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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: 55.312310786737015 |
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- type: mrr |
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value: 69.50842017324011 |
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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: 69.56168831168831 |
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- type: f1 |
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value: 68.14675364705445 |
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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: 30.20098791829512 |
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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: 27.38014535599197 |
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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: 46.224999999999994 |
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- type: f1 |
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value: 39.319662595355354 |
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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: 62.17159999999999 |
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- type: ap |
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value: 58.35784294974692 |
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- type: f1 |
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value: 61.8942294000012 |
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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: 86.68946648426811 |
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- type: f1 |
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value: 86.26529827823835 |
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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: 49.69676242590059 |
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- type: f1 |
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value: 33.74537894406717 |
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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: 59.028244788164095 |
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- type: f1 |
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value: 55.31452888309622 |
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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: 66.58708809683928 |
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- type: f1 |
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value: 65.90050839709882 |
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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: 27.16644221915073 |
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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: 27.5164150501441 |
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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: 45.61660066180842 |
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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: 47.86938629331837 |
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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: 99.7980198019802 |
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- type: cos_sim_ap |
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value: 94.25805747549842 |
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- type: cos_sim_f1 |
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value: 89.56262425447315 |
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- type: cos_sim_precision |
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value: 89.03162055335969 |
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- type: cos_sim_recall |
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value: 90.10000000000001 |
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- type: dot_accuracy |
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value: 99.7980198019802 |
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- type: dot_ap |
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value: 94.25806137565444 |
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- type: dot_f1 |
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value: 89.56262425447315 |
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- type: dot_precision |
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value: 89.03162055335969 |
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- type: dot_recall |
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value: 90.10000000000001 |
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- type: euclidean_accuracy |
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value: 99.7980198019802 |
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- type: euclidean_ap |
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value: 94.25805747549843 |
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- type: euclidean_f1 |
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value: 89.56262425447315 |
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- type: euclidean_precision |
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value: 89.03162055335969 |
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- type: euclidean_recall |
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value: 90.10000000000001 |
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- type: manhattan_accuracy |
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value: 99.7980198019802 |
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- type: manhattan_ap |
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value: 94.35547438808531 |
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- type: manhattan_f1 |
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value: 89.78574987543598 |
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- type: manhattan_precision |
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value: 89.47368421052632 |
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- type: manhattan_recall |
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value: 90.10000000000001 |
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- type: max_accuracy |
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value: 99.7980198019802 |
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- type: max_ap |
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value: 94.35547438808531 |
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- type: max_f1 |
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value: 89.78574987543598 |
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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: 52.619948149973 |
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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: 30.050148689318583 |
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- 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: 66.1018 |
|
- type: ap |
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value: 12.152100246603089 |
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- type: f1 |
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value: 50.78295258419767 |
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- 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: |
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- type: accuracy |
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value: 60.77532541029994 |
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- type: f1 |
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value: 60.7949438635894 |
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- task: |
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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 |
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value: 40.793779391259136 |
|
- 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 |
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value: 83.10186564940096 |
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- type: cos_sim_ap |
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value: 63.85437966517539 |
|
- type: cos_sim_f1 |
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value: 60.5209914011128 |
|
- type: cos_sim_precision |
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value: 58.11073336571151 |
|
- type: cos_sim_recall |
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value: 63.13984168865435 |
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- type: dot_accuracy |
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value: 83.10186564940096 |
|
- type: dot_ap |
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value: 63.85440662982004 |
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- type: dot_f1 |
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value: 60.5209914011128 |
|
- type: dot_precision |
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value: 58.11073336571151 |
|
- type: dot_recall |
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value: 63.13984168865435 |
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- type: euclidean_accuracy |
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value: 83.10186564940096 |
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- type: euclidean_ap |
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value: 63.85438236123812 |
|
- type: euclidean_f1 |
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value: 60.5209914011128 |
|
- type: euclidean_precision |
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value: 58.11073336571151 |
|
- type: euclidean_recall |
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value: 63.13984168865435 |
|
- type: manhattan_accuracy |
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value: 82.95881266018954 |
|
- type: manhattan_ap |
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value: 63.548796919332496 |
|
- type: manhattan_f1 |
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value: 60.2080461210678 |
|
- type: manhattan_precision |
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value: 57.340654094055864 |
|
- type: manhattan_recall |
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value: 63.377308707124016 |
|
- type: max_accuracy |
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value: 83.10186564940096 |
|
- type: max_ap |
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value: 63.85440662982004 |
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- type: max_f1 |
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value: 60.5209914011128 |
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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: 87.93417937672217 |
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- type: cos_sim_ap |
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value: 84.07115019218789 |
|
- type: cos_sim_f1 |
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value: 75.7513225528083 |
|
- type: cos_sim_precision |
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value: 73.8748627881449 |
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- type: cos_sim_recall |
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value: 77.72559285494303 |
|
- type: dot_accuracy |
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value: 87.93417937672217 |
|
- type: dot_ap |
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value: 84.0711576640934 |
|
- type: dot_f1 |
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value: 75.7513225528083 |
|
- type: dot_precision |
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value: 73.8748627881449 |
|
- type: dot_recall |
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value: 77.72559285494303 |
|
- type: euclidean_accuracy |
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value: 87.93417937672217 |
|
- type: euclidean_ap |
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value: 84.07114662252135 |
|
- type: euclidean_f1 |
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value: 75.7513225528083 |
|
- type: euclidean_precision |
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value: 73.8748627881449 |
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- type: euclidean_recall |
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value: 77.72559285494303 |
|
- type: manhattan_accuracy |
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value: 87.90507237940001 |
|
- type: manhattan_ap |
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value: 84.00643428398385 |
|
- type: manhattan_f1 |
|
value: 75.80849007508735 |
|
- type: manhattan_precision |
|
value: 73.28589909443726 |
|
- type: manhattan_recall |
|
value: 78.51093316907914 |
|
- type: max_accuracy |
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value: 87.93417937672217 |
|
- type: max_ap |
|
value: 84.0711576640934 |
|
- type: max_f1 |
|
value: 75.80849007508735 |
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--- |
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# gte-micro |
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|
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This is a distill of [gte-small](https://huggingface.co/thenlper/gte-small). |
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|
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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 (same as [gte-small](https://huggingface.co/thenlper/gte-small)) |
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|
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Use in [semantic-autocomplete](https://github.com/Mihaiii/semantic-autocomplete) |
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OR |
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in code |
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|
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```python |
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import torch.nn.functional as F |
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from torch import Tensor |
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from transformers import AutoTokenizer, AutoModel |
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|
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def average_pool(last_hidden_states: Tensor, |
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attention_mask: Tensor) -> Tensor: |
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last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
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return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
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|
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input_texts = [ |
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"what is the capital of China?", |
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"how to implement quick sort in python?", |
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"Beijing", |
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"sorting algorithms" |
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] |
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|
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tokenizer = AutoTokenizer.from_pretrained("Mihaiii/gte-micro") |
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model = AutoModel.from_pretrained("Mihaiii/gte-micro") |
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|
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# Tokenize the input texts |
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batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
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|
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outputs = model(**batch_dict) |
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embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
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|
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# (Optionally) normalize embeddings |
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embeddings = F.normalize(embeddings, p=2, dim=1) |
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scores = (embeddings[:1] @ embeddings[1:].T) * 100 |
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print(scores.tolist()) |
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``` |
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|
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Use with sentence-transformers: |
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```python |
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from sentence_transformers import SentenceTransformer |
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from sentence_transformers.util import cos_sim |
|
|
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sentences = ['That is a happy person', 'That is a very happy person'] |
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|
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model = SentenceTransformer('Mihaiii/gte-micro') |
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embeddings = model.encode(sentences) |
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print(cos_sim(embeddings[0], embeddings[1])) |
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``` |
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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. |
