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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-test |
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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: 71.43283582089552 |
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- type: ap |
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value: 33.56235301308992 |
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- type: f1 |
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value: 65.18510976313922 |
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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: 77.72055 |
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- type: ap |
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value: 72.30281215701287 |
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- type: f1 |
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value: 77.62429097469116 |
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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: 38.956 |
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- type: f1 |
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value: 38.59075995638611 |
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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: 41.14317775707504 |
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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: 31.79440862639374 |
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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: 80.40259740259741 |
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- type: f1 |
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value: 80.33885811790022 |
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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: 44.54 |
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- type: f1 |
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value: 39.40201192446353 |
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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: 70.5904 |
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- type: ap |
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value: 64.61751544665012 |
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- type: f1 |
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value: 70.47776028292148 |
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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: 90.49703602371181 |
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- type: f1 |
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value: 90.05253119123799 |
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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: 67.52393980848153 |
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- type: f1 |
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value: 49.95609666042009 |
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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: 68.4969737726967 |
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- type: f1 |
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value: 66.32116772424203 |
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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: 73.54741089441829 |
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- type: f1 |
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value: 73.47537036064044 |
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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: 66.6912 |
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- type: ap |
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value: 12.157396278930436 |
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- type: f1 |
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value: 51.00574525406295 |
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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: 59.29258630447085 |
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- type: f1 |
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value: 59.6485358241374 |
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--- |
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--- |
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# gte-micro-v2 |
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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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<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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## 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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```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-v2') |
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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-v2') |
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model = AutoModel.from_pretrained('Mihaiii/gte-micro-v2') |
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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. |
