---
tags:
- mteb
model-index:
- name: universal-sentence-encoder-4
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 70.67164179104478
    - type: ap
      value: 32.834763426716584
    - type: f1
      value: 64.42714654873818
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 67.73207500000001
    - type: ap
      value: 62.47524029220297
    - type: f1
      value: 67.48570902687877
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 32.62
    - type: f1
      value: 32.13548057908922
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 35.12555128655114
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-s2s
      name: MTEB ArxivClusteringS2S
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 23.456590839508902
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 64.7357059982867
    - type: cos_sim_spearman
      value: 63.37975740377988
    - type: euclidean_pearson
      value: 63.49896800825232
    - type: euclidean_spearman
      value: 63.37975740377988
    - type: manhattan_pearson
      value: 64.00838198208166
    - type: manhattan_spearman
      value: 63.31710537380123
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 74.12012987012989
    - type: f1
      value: 73.23976030012078
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 31.169576856541603
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 18.81055418061209
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 38.64000000000001
    - type: f1
      value: 35.09699868913662
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 68.43239999999999
    - type: ap
      value: 62.76719976357937
    - type: f1
      value: 68.3208799558774
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 90.11627906976744
    - type: f1
      value: 89.69218132313695
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 64.9954400364797
    - type: f1
      value: 46.61477433032086
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 67.23268325487558
    - type: f1
      value: 64.41484453213448
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 72.78749159381303
    - type: f1
      value: 71.69036260308698
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-p2p
      name: MTEB MedrxivClusteringP2P
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 28.88138682114816
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-s2s
      name: MTEB MedrxivClusteringS2S
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 23.311493283906064
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering
      name: MTEB RedditClustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 49.71559043766936
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering-p2p
      name: MTEB RedditClusteringP2P
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 57.91704617095672
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 77.38041457279672
    - type: cos_sim_spearman
      value: 69.79282361714223
    - type: euclidean_pearson
      value: 74.02315074475364
    - type: euclidean_spearman
      value: 69.79282304260158
    - type: manhattan_pearson
      value: 73.01688608657159
    - type: manhattan_spearman
      value: 68.22940563625058
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 72.58420052741107
    - type: cos_sim_spearman
      value: 67.05792005966953
    - type: euclidean_pearson
      value: 68.35629372749483
    - type: euclidean_spearman
      value: 67.05773819854602
    - type: manhattan_pearson
      value: 67.12625747442266
    - type: manhattan_spearman
      value: 65.7252617197503
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 69.55548148750847
    - type: cos_sim_spearman
      value: 71.54484814980987
    - type: euclidean_pearson
      value: 71.22072782671158
    - type: euclidean_spearman
      value: 71.54484814980987
    - type: manhattan_pearson
      value: 70.3490839338159
    - type: manhattan_spearman
      value: 70.76414952692804
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 73.74818660795891
    - type: cos_sim_spearman
      value: 70.59138342620027
    - type: euclidean_pearson
      value: 72.60887534657319
    - type: euclidean_spearman
      value: 70.5913727471932
    - type: manhattan_pearson
      value: 71.95704368086712
    - type: manhattan_spearman
      value: 69.58620240967204
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 79.6360473168554
    - type: cos_sim_spearman
      value: 80.26596000690931
    - type: euclidean_pearson
      value: 79.82176999472074
    - type: euclidean_spearman
      value: 80.26596000690931
    - type: manhattan_pearson
      value: 78.94486463380255
    - type: manhattan_spearman
      value: 79.20674341072848
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 74.84322872565855
    - type: cos_sim_spearman
      value: 75.75894131062728
    - type: euclidean_pearson
      value: 75.5333191548161
    - type: euclidean_spearman
      value: 75.75894090191032
    - type: manhattan_pearson
      value: 74.96648591478875
    - type: manhattan_spearman
      value: 75.07346800275856
  - 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: 83.79136005773537
    - type: cos_sim_spearman
      value: 84.94411992446793
    - type: euclidean_pearson
      value: 83.60843297866558
    - type: euclidean_spearman
      value: 84.94411992446793
    - type: manhattan_pearson
      value: 82.81698401022742
    - type: manhattan_spearman
      value: 84.02022263657062
  - 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: 60.02909344132073
    - type: cos_sim_spearman
      value: 60.000369508382704
    - type: euclidean_pearson
      value: 61.54466129341342
    - type: euclidean_spearman
      value: 60.000369508382704
    - type: manhattan_pearson
      value: 58.76127065249476
    - type: manhattan_spearman
      value: 58.08063159428285
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 78.72737325889034
    - type: cos_sim_spearman
      value: 77.08420739421672
    - type: euclidean_pearson
      value: 77.85606384422326
    - type: euclidean_spearman
      value: 77.08420739421672
    - type: manhattan_pearson
      value: 76.63643674764234
    - type: manhattan_spearman
      value: 75.68141928725497
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.61980198019802
    - type: cos_sim_ap
      value: 86.36827719686161
    - type: cos_sim_f1
      value: 80.35625927758537
    - type: cos_sim_precision
      value: 79.52987267384917
    - type: cos_sim_recall
      value: 81.2
    - type: dot_accuracy
      value: 99.61980198019802
    - type: dot_ap
      value: 86.36827719686161
    - type: dot_f1
      value: 80.35625927758537
    - type: dot_precision
      value: 79.52987267384917
    - type: dot_recall
      value: 81.2
    - type: euclidean_accuracy
      value: 99.61980198019802
    - type: euclidean_ap
      value: 86.3682771543572
    - type: euclidean_f1
      value: 80.35625927758537
    - type: euclidean_precision
      value: 79.52987267384917
    - type: euclidean_recall
      value: 81.2
    - type: manhattan_accuracy
      value: 99.63564356435643
    - type: manhattan_ap
      value: 87.12233265654545
    - type: manhattan_f1
      value: 80.78920041536864
    - type: manhattan_precision
      value: 84.01727861771057
    - type: manhattan_recall
      value: 77.8
    - type: max_accuracy
      value: 99.63564356435643
    - type: max_ap
      value: 87.12233265654545
    - type: max_f1
      value: 80.78920041536864
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering
      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 54.64344160961463
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 31.57666192425415
  - task:
      type: Summarization
    dataset:
      type: mteb/summeval
      name: MTEB SummEval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 29.77545231337259
    - type: cos_sim_spearman
      value: 29.420072483158698
    - type: dot_pearson
      value: 29.775453888622426
    - type: dot_spearman
      value: 29.420072483158698
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 68.98920000000001
    - type: ap
      value: 12.803310864930856
    - type: f1
      value: 52.67881359079218
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 57.00622524052065
    - type: f1
      value: 57.2232294145327
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 38.23134611732841
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 82.25546879656673
    - type: cos_sim_ap
      value: 60.64429023120581
    - type: cos_sim_f1
      value: 57.75789091788608
    - type: cos_sim_precision
      value: 53.31547220361688
    - type: cos_sim_recall
      value: 63.007915567282325
    - type: dot_accuracy
      value: 82.25546879656673
    - type: dot_ap
      value: 60.64428864700383
    - type: dot_f1
      value: 57.75789091788608
    - type: dot_precision
      value: 53.31547220361688
    - type: dot_recall
      value: 63.007915567282325
    - type: euclidean_accuracy
      value: 82.25546879656673
    - type: euclidean_ap
      value: 60.64429357965402
    - type: euclidean_f1
      value: 57.75789091788608
    - type: euclidean_precision
      value: 53.31547220361688
    - type: euclidean_recall
      value: 63.007915567282325
    - type: manhattan_accuracy
      value: 82.14221851344102
    - type: manhattan_ap
      value: 59.542389876094134
    - type: manhattan_f1
      value: 56.935892792466504
    - type: manhattan_precision
      value: 52.48163810371689
    - type: manhattan_recall
      value: 62.21635883905014
    - type: max_accuracy
      value: 82.25546879656673
    - type: max_ap
      value: 60.64429357965402
    - type: max_f1
      value: 57.75789091788608
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 86.99305312997244
    - type: cos_sim_ap
      value: 81.6822772881566
    - type: cos_sim_f1
      value: 73.70381406436233
    - type: cos_sim_precision
      value: 71.38528138528139
    - type: cos_sim_recall
      value: 76.17801047120419
    - type: dot_accuracy
      value: 86.99305312997244
    - type: dot_ap
      value: 81.6822783032573
    - type: dot_f1
      value: 73.70381406436233
    - type: dot_precision
      value: 71.38528138528139
    - type: dot_recall
      value: 76.17801047120419
    - type: euclidean_accuracy
      value: 86.99305312997244
    - type: euclidean_ap
      value: 81.68228020882522
    - type: euclidean_f1
      value: 73.70381406436233
    - type: euclidean_precision
      value: 71.38528138528139
    - type: euclidean_recall
      value: 76.17801047120419
    - type: manhattan_accuracy
      value: 86.84557767687352
    - type: manhattan_ap
      value: 81.3912803407899
    - type: manhattan_f1
      value: 73.48302793631723
    - type: manhattan_precision
      value: 71.71650542362944
    - type: manhattan_recall
      value: 75.33877425315676
    - type: max_accuracy
      value: 86.99305312997244
    - type: max_ap
      value: 81.68228020882522
    - type: max_f1
      value: 73.70381406436233
---

This is a part of the [MTEB test](https://huggingface.co/spaces/mteb/leaderboard).

```
# !pip install tensorflow_text 

import tensorflow_hub as hub
from tensorflow_text import SentencepieceTokenizer
import tensorflow as tf

embedder=hub.load("https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3")

class USE():
    def encode(self, sentences, batch_size=32, **kwargs):
        embeddings = []
        for i in range(0, len(sentences), batch_size):
            batch_sentences = sentences[i:i+batch_size]
            batch_embeddings = embedder(batch_sentences)
            embeddings.extend(batch_embeddings)
        return embeddings


model = USE()
```