---
tags:
- mteb
model-index:
- name: universal-sentence-encoder-large-5
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 76.19402985074628
    - type: ap
      value: 39.249966888759666
    - type: f1
      value: 70.17510532980124
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 69.6285
    - type: ap
      value: 63.97317997322299
    - type: f1
      value: 69.48624121982243
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 35.534
    - type: f1
      value: 34.974303844745194
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 34.718110225806626
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-s2s
      name: MTEB ArxivClusteringS2S
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 25.267234486849127
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 69.65040443392367
    - type: cos_sim_spearman
      value: 69.35579718635816
    - type: euclidean_pearson
      value: 68.74078260783044
    - type: euclidean_spearman
      value: 69.35579718635816
    - type: manhattan_pearson
      value: 68.97023207188357
    - type: manhattan_spearman
      value: 69.2063961917937
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 78.12987012987013
    - type: f1
      value: 77.40193921057201
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 28.39184796722482
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 20.5151608432177
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 45.48
    - type: f1
      value: 41.2632839288363
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 64.0552
    - type: ap
      value: 59.25851636836455
    - type: f1
      value: 63.90501571634165
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 92.94117647058823
    - type: f1
      value: 92.7110107115347
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 74.43456452348381
    - type: f1
      value: 52.53178214778298
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 71.68796234028245
    - type: f1
      value: 68.47828954699564
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 77.20242098184264
    - type: f1
      value: 76.27977367157321
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-p2p
      name: MTEB MedrxivClusteringP2P
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 30.266855488757034
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-s2s
      name: MTEB MedrxivClusteringS2S
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 24.580327378539057
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering
      name: MTEB RedditClustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 56.928616405043684
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering-p2p
      name: MTEB RedditClusteringP2P
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 58.94536303256525
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 82.43899708996477
    - type: cos_sim_spearman
      value: 76.84011555220044
    - type: euclidean_pearson
      value: 79.6116260676631
    - type: euclidean_spearman
      value: 76.84012073472658
    - type: manhattan_pearson
      value: 78.49980966442152
    - type: manhattan_spearman
      value: 75.49233078465171
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 79.8291506264289
    - type: cos_sim_spearman
      value: 72.49093632759003
    - type: euclidean_pearson
      value: 75.42130137819414
    - type: euclidean_spearman
      value: 72.49048089395136
    - type: manhattan_pearson
      value: 74.17957476459091
    - type: manhattan_spearman
      value: 71.6143674273714
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 70.91903439531401
    - type: cos_sim_spearman
      value: 73.65106317244273
    - type: euclidean_pearson
      value: 73.22383725261588
    - type: euclidean_spearman
      value: 73.65106317244273
    - type: manhattan_pearson
      value: 72.98314057093636
    - type: manhattan_spearman
      value: 73.52101907069579
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 75.19632733755482
    - type: cos_sim_spearman
      value: 71.88328402076041
    - type: euclidean_pearson
      value: 74.02395011081532
    - type: euclidean_spearman
      value: 71.88328903479953
    - type: manhattan_pearson
      value: 73.52941749980135
    - type: manhattan_spearman
      value: 71.32905921324534
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 82.42736501667461
    - type: cos_sim_spearman
      value: 82.89997148218205
    - type: euclidean_pearson
      value: 82.3189209945513
    - type: euclidean_spearman
      value: 82.89997089267106
    - type: manhattan_pearson
      value: 81.78597437071429
    - type: manhattan_spearman
      value: 82.21582873302081
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 78.44968010602165
    - type: cos_sim_spearman
      value: 79.82626284236876
    - type: euclidean_pearson
      value: 79.4157474030238
    - type: euclidean_spearman
      value: 79.82626269881543
    - type: manhattan_pearson
      value: 79.13275737559012
    - type: manhattan_spearman
      value: 79.4847570398719
  - 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: 84.51882547098218
    - type: cos_sim_spearman
      value: 85.19309361840223
    - type: euclidean_pearson
      value: 84.78417242196153
    - type: euclidean_spearman
      value: 85.19307726106497
    - type: manhattan_pearson
      value: 84.09108278425708
    - type: manhattan_spearman
      value: 84.13590986630149
  - 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: 44.814384769251085
    - type: cos_sim_spearman
      value: 48.43949857027059
    - type: euclidean_pearson
      value: 47.479132435178855
    - type: euclidean_spearman
      value: 48.43949857027059
    - type: manhattan_pearson
      value: 47.16203934707649
    - type: manhattan_spearman
      value: 48.289920897667095
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 81.25646447054616
    - type: cos_sim_spearman
      value: 79.93231051166357
    - type: euclidean_pearson
      value: 80.65225742476945
    - type: euclidean_spearman
      value: 79.93231051166357
    - type: manhattan_pearson
      value: 79.84341819764376
    - type: manhattan_spearman
      value: 79.07650150491334
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.5910891089109
    - type: cos_sim_ap
      value: 84.37184771930944
    - type: cos_sim_f1
      value: 78.78787878787878
    - type: cos_sim_precision
      value: 80.99260823653644
    - type: cos_sim_recall
      value: 76.7
    - type: dot_accuracy
      value: 99.5910891089109
    - type: dot_ap
      value: 84.37184771930944
    - type: dot_f1
      value: 78.78787878787878
    - type: dot_precision
      value: 80.99260823653644
    - type: dot_recall
      value: 76.7
    - type: euclidean_accuracy
      value: 99.5910891089109
    - type: euclidean_ap
      value: 84.37185436709098
    - type: euclidean_f1
      value: 78.78787878787878
    - type: euclidean_precision
      value: 80.99260823653644
    - type: euclidean_recall
      value: 76.7
    - type: manhattan_accuracy
      value: 99.6108910891089
    - type: manhattan_ap
      value: 85.13355467581354
    - type: manhattan_f1
      value: 80.2788844621514
    - type: manhattan_precision
      value: 79.96031746031747
    - type: manhattan_recall
      value: 80.60000000000001
    - type: max_accuracy
      value: 99.6108910891089
    - type: max_ap
      value: 85.13355467581354
    - type: max_f1
      value: 80.2788844621514
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering
      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 60.8469558550317
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 33.14392913702168
  - task:
      type: Summarization
    dataset:
      type: mteb/summeval
      name: MTEB SummEval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 29.566148619704457
    - type: cos_sim_spearman
      value: 29.01201818902588
    - type: dot_pearson
      value: 29.566149876183374
    - type: dot_spearman
      value: 29.014046950422795
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 70.17420000000001
    - type: ap
      value: 13.49623412034604
    - type: f1
      value: 53.7079366494688
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 59.309564233163556
    - type: f1
      value: 59.33623172630094
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 42.42960819361032
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.04500208618943
    - type: cos_sim_ap
      value: 70.12785509302904
    - type: cos_sim_f1
      value: 65.36573392243496
    - type: cos_sim_precision
      value: 61.10601193207894
    - type: cos_sim_recall
      value: 70.26385224274406
    - type: dot_accuracy
      value: 85.04500208618943
    - type: dot_ap
      value: 70.12785837450095
    - type: dot_f1
      value: 65.36573392243496
    - type: dot_precision
      value: 61.10601193207894
    - type: dot_recall
      value: 70.26385224274406
    - type: euclidean_accuracy
      value: 85.04500208618943
    - type: euclidean_ap
      value: 70.1278575285826
    - type: euclidean_f1
      value: 65.36573392243496
    - type: euclidean_precision
      value: 61.10601193207894
    - type: euclidean_recall
      value: 70.26385224274406
    - type: manhattan_accuracy
      value: 85.03308100375514
    - type: manhattan_ap
      value: 69.67192372362932
    - type: manhattan_f1
      value: 64.95726495726495
    - type: manhattan_precision
      value: 61.218771888862946
    - type: manhattan_recall
      value: 69.1820580474934
    - type: max_accuracy
      value: 85.04500208618943
    - type: max_ap
      value: 70.12785837450095
    - type: max_f1
      value: 65.36573392243496
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.18644002018085
    - type: cos_sim_ap
      value: 84.09120337117118
    - type: cos_sim_f1
      value: 76.33478718604302
    - type: cos_sim_precision
      value: 74.59582598471486
    - type: cos_sim_recall
      value: 78.15676008623345
    - type: dot_accuracy
      value: 88.18644002018085
    - type: dot_ap
      value: 84.09120289232122
    - type: dot_f1
      value: 76.33478718604302
    - type: dot_precision
      value: 74.59582598471486
    - type: dot_recall
      value: 78.15676008623345
    - type: euclidean_accuracy
      value: 88.18644002018085
    - type: euclidean_ap
      value: 84.091202102378
    - type: euclidean_f1
      value: 76.33478718604302
    - type: euclidean_precision
      value: 74.59582598471486
    - type: euclidean_recall
      value: 78.15676008623345
    - type: manhattan_accuracy
      value: 88.19032095315714
    - type: manhattan_ap
      value: 84.0865561436236
    - type: manhattan_f1
      value: 76.16665422235496
    - type: manhattan_precision
      value: 73.93100449340484
    - type: manhattan_recall
      value: 78.54173082845703
    - type: max_accuracy
      value: 88.19032095315714
    - type: max_ap
      value: 84.09120337117118
    - type: max_f1
      value: 76.33478718604302
---
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()
```