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--- |
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tags: |
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- sparse sparsity quantized onnx embeddings int8 |
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- mteb |
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model-index: |
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- name: gte-small-quant |
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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: |
|
- type: accuracy |
|
value: 72.88059701492537 |
|
- type: ap |
|
value: 35.74239003564444 |
|
- type: f1 |
|
value: 66.98065758287116 |
|
- task: |
|
type: Classification |
|
dataset: |
|
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: |
|
- type: accuracy |
|
value: 91.031575 |
|
- type: ap |
|
value: 87.60741691468986 |
|
- type: f1 |
|
value: 91.00983458583187 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
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: |
|
- type: accuracy |
|
value: 46.943999999999996 |
|
- type: f1 |
|
value: 46.33280307575562 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
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: |
|
- type: map |
|
value: 60.75683986813218 |
|
- type: mrr |
|
value: 73.51624675724399 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
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name: MTEB BIOSSES |
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config: default |
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split: test |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
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metrics: |
|
- type: cos_sim_pearson |
|
value: 89.07092347634877 |
|
- type: cos_sim_spearman |
|
value: 87.80621759170344 |
|
- type: euclidean_pearson |
|
value: 87.29751551472525 |
|
- type: euclidean_spearman |
|
value: 87.5634409755362 |
|
- type: manhattan_pearson |
|
value: 87.56100206227441 |
|
- type: manhattan_spearman |
|
value: 87.45982415672536 |
|
- task: |
|
type: Classification |
|
dataset: |
|
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: |
|
- type: accuracy |
|
value: 83.46753246753246 |
|
- type: f1 |
|
value: 83.39526091362032 |
|
- task: |
|
type: Classification |
|
dataset: |
|
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: |
|
- type: accuracy |
|
value: 45.800000000000004 |
|
- type: f1 |
|
value: 40.76055487612189 |
|
- task: |
|
type: Classification |
|
dataset: |
|
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: |
|
- type: accuracy |
|
value: 85.0096 |
|
- type: ap |
|
value: 79.91059611360778 |
|
- type: f1 |
|
value: 84.9738791599706 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
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: |
|
- type: accuracy |
|
value: 92.51025991792065 |
|
- type: f1 |
|
value: 92.2852224639839 |
|
- task: |
|
type: Classification |
|
dataset: |
|
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: |
|
- type: accuracy |
|
value: 69.61924304605563 |
|
- type: f1 |
|
value: 51.832892524807505 |
|
- task: |
|
type: Classification |
|
dataset: |
|
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: |
|
- type: accuracy |
|
value: 70.2320107599193 |
|
- type: f1 |
|
value: 68.03367707473218 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
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name: MTEB MassiveScenarioClassification (en) |
|
config: en |
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split: test |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 75.28581035642232 |
|
- type: f1 |
|
value: 75.43554941058956 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
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config: default |
|
split: test |
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revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
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metrics: |
|
- type: cos_sim_pearson |
|
value: 83.58628262329275 |
|
- type: cos_sim_spearman |
|
value: 77.30534089053104 |
|
- type: euclidean_pearson |
|
value: 80.86400799226335 |
|
- type: euclidean_spearman |
|
value: 77.26947744139412 |
|
- type: manhattan_pearson |
|
value: 80.79442484789072 |
|
- type: manhattan_spearman |
|
value: 77.18043722794019 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
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name: MTEB STS12 |
|
config: default |
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split: test |
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revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.77293561742106 |
|
- type: cos_sim_spearman |
|
value: 73.98616407095425 |
|
- type: euclidean_pearson |
|
value: 78.7096804108132 |
|
- type: euclidean_spearman |
|
value: 73.52379687387366 |
|
- type: manhattan_pearson |
|
value: 78.80694876432868 |
|
- type: manhattan_spearman |
|
value: 73.64907838788528 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
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name: MTEB STS13 |
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config: default |
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split: test |
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
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metrics: |
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- type: cos_sim_pearson |
|
value: 82.12995363427328 |
|
- type: cos_sim_spearman |
|
value: 84.23345798311749 |
|
- type: euclidean_pearson |
|
value: 83.94003648503143 |
|
- type: euclidean_spearman |
|
value: 84.74522675669463 |
|
- type: manhattan_pearson |
|
value: 83.82868963165394 |
|
- type: manhattan_spearman |
|
value: 84.61059125620956 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.88504872832357 |
|
- type: cos_sim_spearman |
|
value: 80.09345991196561 |
|
- type: euclidean_pearson |
|
value: 81.99899431994811 |
|
- type: euclidean_spearman |
|
value: 80.25520445997002 |
|
- type: manhattan_pearson |
|
value: 81.9635758954928 |
|
- type: manhattan_spearman |
|
value: 80.24335353637277 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
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split: test |
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revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.55052353126385 |
|
- type: cos_sim_spearman |
|
value: 88.1950992730786 |
|
- type: euclidean_pearson |
|
value: 87.83472249083056 |
|
- type: euclidean_spearman |
|
value: 88.43301043636015 |
|
- type: manhattan_pearson |
|
value: 87.75102815516877 |
|
- type: manhattan_spearman |
|
value: 88.34719608377306 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.58832350766542 |
|
- type: cos_sim_spearman |
|
value: 83.60857270697358 |
|
- type: euclidean_pearson |
|
value: 82.9059299279255 |
|
- type: euclidean_spearman |
|
value: 83.87380773329784 |
|
- type: manhattan_pearson |
|
value: 82.76009241925925 |
|
- type: manhattan_spearman |
|
value: 83.72876466499108 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-en) |
|
config: en-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.96440735880392 |
|
- type: cos_sim_spearman |
|
value: 87.79655666183349 |
|
- type: euclidean_pearson |
|
value: 88.47129589774806 |
|
- type: euclidean_spearman |
|
value: 87.95235258398374 |
|
- type: manhattan_pearson |
|
value: 88.37144209103296 |
|
- type: manhattan_spearman |
|
value: 87.81869790317533 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 66.66468384683428 |
|
- type: cos_sim_spearman |
|
value: 66.84275911821702 |
|
- type: euclidean_pearson |
|
value: 67.73972664535547 |
|
- type: euclidean_spearman |
|
value: 66.57863145583491 |
|
- type: manhattan_pearson |
|
value: 67.91309920462287 |
|
- type: manhattan_spearman |
|
value: 66.67487869242575 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.07668437020894 |
|
- type: cos_sim_spearman |
|
value: 85.13186558138277 |
|
- type: euclidean_pearson |
|
value: 85.28607166042313 |
|
- type: euclidean_spearman |
|
value: 85.25082312265897 |
|
- type: manhattan_pearson |
|
value: 85.0870328315141 |
|
- type: manhattan_spearman |
|
value: 85.10612962221282 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 84.33835340608282 |
|
- type: mrr |
|
value: 95.54063220729888 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.81386138613861 |
|
- type: cos_sim_ap |
|
value: 95.49398397880566 |
|
- type: cos_sim_f1 |
|
value: 90.5050505050505 |
|
- type: cos_sim_precision |
|
value: 91.42857142857143 |
|
- type: cos_sim_recall |
|
value: 89.60000000000001 |
|
- type: dot_accuracy |
|
value: 99.75742574257426 |
|
- type: dot_ap |
|
value: 93.40675781804289 |
|
- type: dot_f1 |
|
value: 87.45519713261648 |
|
- type: dot_precision |
|
value: 89.61175236096537 |
|
- type: dot_recall |
|
value: 85.39999999999999 |
|
- type: euclidean_accuracy |
|
value: 99.81485148514851 |
|
- type: euclidean_ap |
|
value: 95.39724876386569 |
|
- type: euclidean_f1 |
|
value: 90.5793450881612 |
|
- type: euclidean_precision |
|
value: 91.26903553299492 |
|
- type: euclidean_recall |
|
value: 89.9 |
|
- type: manhattan_accuracy |
|
value: 99.81485148514851 |
|
- type: manhattan_ap |
|
value: 95.46515830873487 |
|
- type: manhattan_f1 |
|
value: 90.56974459724951 |
|
- type: manhattan_precision |
|
value: 88.996138996139 |
|
- type: manhattan_recall |
|
value: 92.2 |
|
- type: max_accuracy |
|
value: 99.81485148514851 |
|
- type: max_ap |
|
value: 95.49398397880566 |
|
- type: max_f1 |
|
value: 90.5793450881612 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 51.68384236354744 |
|
- type: mrr |
|
value: 52.52933749257278 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 69.7972 |
|
- type: ap |
|
value: 13.790209566654962 |
|
- type: f1 |
|
value: 53.73625700975159 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 57.81550650820599 |
|
- type: f1 |
|
value: 58.22494506904567 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 84.30589497526375 |
|
- type: cos_sim_ap |
|
value: 68.60854966172107 |
|
- type: cos_sim_f1 |
|
value: 65.06926244852113 |
|
- type: cos_sim_precision |
|
value: 61.733364906464594 |
|
- type: cos_sim_recall |
|
value: 68.7862796833773 |
|
- type: dot_accuracy |
|
value: 81.63557250998392 |
|
- type: dot_ap |
|
value: 58.80135920860792 |
|
- type: dot_f1 |
|
value: 57.39889705882353 |
|
- type: dot_precision |
|
value: 50.834350834350836 |
|
- type: dot_recall |
|
value: 65.91029023746702 |
|
- type: euclidean_accuracy |
|
value: 84.37742146986946 |
|
- type: euclidean_ap |
|
value: 68.88494996210581 |
|
- type: euclidean_f1 |
|
value: 65.23647001462702 |
|
- type: euclidean_precision |
|
value: 60.62528318985048 |
|
- type: euclidean_recall |
|
value: 70.60686015831135 |
|
- type: manhattan_accuracy |
|
value: 84.21648685700661 |
|
- type: manhattan_ap |
|
value: 68.54917405273397 |
|
- type: manhattan_f1 |
|
value: 64.97045701193778 |
|
- type: manhattan_precision |
|
value: 59.826782145236514 |
|
- type: manhattan_recall |
|
value: 71.08179419525065 |
|
- type: max_accuracy |
|
value: 84.37742146986946 |
|
- type: max_ap |
|
value: 68.88494996210581 |
|
- type: max_f1 |
|
value: 65.23647001462702 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.60752124810804 |
|
- type: cos_sim_ap |
|
value: 85.16030341274225 |
|
- type: cos_sim_f1 |
|
value: 77.50186985789081 |
|
- type: cos_sim_precision |
|
value: 75.34904013961605 |
|
- type: cos_sim_recall |
|
value: 79.781336618417 |
|
- type: dot_accuracy |
|
value: 86.00147475453099 |
|
- type: dot_ap |
|
value: 79.24446611557556 |
|
- type: dot_f1 |
|
value: 72.34317740892433 |
|
- type: dot_precision |
|
value: 67.81624680048498 |
|
- type: dot_recall |
|
value: 77.51770865414228 |
|
- type: euclidean_accuracy |
|
value: 88.7026041060271 |
|
- type: euclidean_ap |
|
value: 85.30879801684605 |
|
- type: euclidean_f1 |
|
value: 77.60992108229988 |
|
- type: euclidean_precision |
|
value: 75.80384671854354 |
|
- type: euclidean_recall |
|
value: 79.50415768401602 |
|
- type: manhattan_accuracy |
|
value: 88.75305623471883 |
|
- type: manhattan_ap |
|
value: 85.24656615741652 |
|
- type: manhattan_f1 |
|
value: 77.5542141739325 |
|
- type: manhattan_precision |
|
value: 75.14079422382672 |
|
- type: manhattan_recall |
|
value: 80.12781028641824 |
|
- type: max_accuracy |
|
value: 88.75305623471883 |
|
- type: max_ap |
|
value: 85.30879801684605 |
|
- type: max_f1 |
|
value: 77.60992108229988 |
|
license: mit |
|
language: |
|
- en |
|
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# gte-small-quant |
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This is the quantized (INT8) ONNX variant of the [gte-small](https://huggingface.co/thenlper/gte-small) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization. |
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Current list of sparse and quantized gte ONNX models: |
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| Links | Sparsification Method | |
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| [zeroshot/gte-large-sparse](https://huggingface.co/zeroshot/gte-large-sparse) | Quantization (INT8) & 50% Pruning | |
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| [zeroshot/gte-large-quant](https://huggingface.co/zeroshot/gte-large-quant) | Quantization (INT8) | |
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| [zeroshot/gte-base-sparse](https://huggingface.co/zeroshot/gte-base-sparse) | Quantization (INT8) & 50% Pruning | |
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| [zeroshot/gte-base-quant](https://huggingface.co/zeroshot/gte-base-quant) | Quantization (INT8) | |
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| [zeroshot/gte-small-sparse](https://huggingface.co/zeroshot/gte-small-sparse) | Quantization (INT8) & 50% Pruning | |
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| [zeroshot/gte-small-quant](https://huggingface.co/zeroshot/gte-small-quant) | Quantization (INT8) | |
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```bash |
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pip install -U deepsparse-nightly[sentence_transformers] |
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``` |
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```python |
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from deepsparse.sentence_transformers import SentenceTransformer |
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model = SentenceTransformer('zeroshot/gte-small-quant', export=False) |
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# Our sentences we like to encode |
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sentences = ['This framework generates embeddings for each input sentence', |
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'Sentences are passed as a list of string.', |
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'The quick brown fox jumps over the lazy dog.'] |
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# Sentences are encoded by calling model.encode() |
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embeddings = model.encode(sentences) |
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# Print the embeddings |
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for sentence, embedding in zip(sentences, embeddings): |
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print("Sentence:", sentence) |
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print("Embedding:", embedding.shape) |
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print("") |
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``` |
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For further details regarding DeepSparse & Sentence Transformers integration, refer to the [DeepSparse README](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers). |
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For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). |
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