---
tags:
- mteb
model-index:
- name: universal-sentence-encoder-multilingual-3
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 69.83582089552239
    - type: ap
      value: 31.78315481798218
    - type: f1
      value: 63.49599891839609
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 65.24337500000001
    - type: ap
      value: 60.210780218392365
    - type: f1
      value: 64.93158500771304
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 33.95400000000001
    - type: f1
      value: 33.54319450350246
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 33.721026148085606
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-s2s
      name: MTEB ArxivClusteringS2S
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 23.989923856346987
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 80.07123979932489
    - type: cos_sim_spearman
      value: 78.19395037690312
    - type: euclidean_pearson
      value: 78.16919457797398
    - type: euclidean_spearman
      value: 78.19395037690312
    - type: manhattan_pearson
      value: 77.91224702148509
    - type: manhattan_spearman
      value: 77.37284733016378
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 72.78571428571429
    - type: f1
      value: 71.7761168332973
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 30.883580770801693
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 21.053335896002615
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 35.6
    - type: f1
      value: 32.46907363797937
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 66.32400000000001
    - type: ap
      value: 60.96534738097902
    - type: f1
      value: 66.17106527460737
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 90.40355677154584
    - type: f1
      value: 90.02197700114169
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 62.282261741906076
    - type: f1
      value: 42.66509438922704
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 66.11634162743779
    - type: f1
      value: 63.22561882532336
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 73.73570948217888
    - type: f1
      value: 72.60926811627525
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-p2p
      name: MTEB MedrxivClusteringP2P
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 28.682218411117113
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-s2s
      name: MTEB MedrxivClusteringS2S
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 24.249068423345125
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering
      name: MTEB RedditClustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 43.81857444082751
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering-p2p
      name: MTEB RedditClusteringP2P
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 58.37019068224756
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 80.40531780187507
    - type: cos_sim_spearman
      value: 74.43055388885399
    - type: euclidean_pearson
      value: 77.56731280002846
    - type: euclidean_spearman
      value: 74.43055238430316
    - type: manhattan_pearson
      value: 77.0225674793828
    - type: manhattan_spearman
      value: 73.0157763009755
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 77.89513323602442
    - type: cos_sim_spearman
      value: 72.57556877265816
    - type: euclidean_pearson
      value: 73.85821376234655
    - type: euclidean_spearman
      value: 72.57491371628555
    - type: manhattan_pearson
      value: 74.09836169887191
    - type: manhattan_spearman
      value: 72.68258315762111
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 70.35242787171663
    - type: cos_sim_spearman
      value: 72.21957543640191
    - type: euclidean_pearson
      value: 71.69509554469398
    - type: euclidean_spearman
      value: 72.21957537220672
    - type: manhattan_pearson
      value: 71.48837487133736
    - type: manhattan_spearman
      value: 72.10777865383778
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 74.16772362444075
    - type: cos_sim_spearman
      value: 69.97673740601287
    - type: euclidean_pearson
      value: 72.31559291876557
    - type: euclidean_spearman
      value: 69.97673740601287
    - type: manhattan_pearson
      value: 72.24271795357289
    - type: manhattan_spearman
      value: 69.49367729167946
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 81.79182911906513
    - type: cos_sim_spearman
      value: 82.22437412512745
    - type: euclidean_pearson
      value: 81.83097544031064
    - type: euclidean_spearman
      value: 82.22436565654309
    - type: manhattan_pearson
      value: 81.36464923871902
    - type: manhattan_spearman
      value: 81.5343779056359
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 75.43557616859144
    - type: cos_sim_spearman
      value: 76.91365659685123
    - type: euclidean_pearson
      value: 76.56513219626397
    - type: euclidean_spearman
      value: 76.91365659685123
    - type: manhattan_pearson
      value: 76.17721999284116
    - type: manhattan_spearman
      value: 76.35088101292793
  - 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: 84.94818288101212
    - type: cos_sim_spearman
      value: 85.2167560886111
    - type: euclidean_pearson
      value: 85.17622731729108
    - type: euclidean_spearman
      value: 85.2167560886111
    - type: manhattan_pearson
      value: 84.8324354792735
    - type: manhattan_spearman
      value: 84.69897393431455
  - 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: 61.591839696719234
    - type: cos_sim_spearman
      value: 61.90385583991066
    - type: euclidean_pearson
      value: 63.49437829898753
    - type: euclidean_spearman
      value: 61.90385583991066
    - type: manhattan_pearson
      value: 60.78565195357755
    - type: manhattan_spearman
      value: 59.802983782193905
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 81.37748727839765
    - type: cos_sim_spearman
      value: 80.28214972273051
    - type: euclidean_pearson
      value: 81.0274577938496
    - type: euclidean_spearman
      value: 80.28214972273051
    - type: manhattan_pearson
      value: 80.18978223617367
    - type: manhattan_spearman
      value: 79.17781398461948
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.62475247524752
    - type: cos_sim_ap
      value: 87.05032305596566
    - type: cos_sim_f1
      value: 80.25987006496752
    - type: cos_sim_precision
      value: 80.21978021978022
    - type: cos_sim_recall
      value: 80.30000000000001
    - type: dot_accuracy
      value: 99.62475247524752
    - type: dot_ap
      value: 87.05032305596568
    - type: dot_f1
      value: 80.25987006496752
    - type: dot_precision
      value: 80.21978021978022
    - type: dot_recall
      value: 80.30000000000001
    - type: euclidean_accuracy
      value: 99.62475247524752
    - type: euclidean_ap
      value: 87.05031186021066
    - type: euclidean_f1
      value: 80.25987006496752
    - type: euclidean_precision
      value: 80.21978021978022
    - type: euclidean_recall
      value: 80.30000000000001
    - type: manhattan_accuracy
      value: 99.64554455445544
    - type: manhattan_ap
      value: 87.773065204317
    - type: manhattan_f1
      value: 81.32094943240455
    - type: manhattan_precision
      value: 84.00852878464818
    - type: manhattan_recall
      value: 78.8
    - type: max_accuracy
      value: 99.64554455445544
    - type: max_ap
      value: 87.773065204317
    - type: max_f1
      value: 81.32094943240455
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering
      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 47.82768409586564
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 33.010238746581386
  - task:
      type: Summarization
    dataset:
      type: mteb/summeval
      name: MTEB SummEval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.93460869020161
    - type: cos_sim_spearman
      value: 30.793100826598852
    - type: dot_pearson
      value: 30.934611387819803
    - type: dot_spearman
      value: 30.793100826598852
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 67.56439999999999
    - type: ap
      value: 12.622864890343005
    - type: f1
      value: 51.866745839430514
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 57.2354272778721
    - type: f1
      value: 57.42332031933637
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 37.46769271159515
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 81.53424330929249
    - type: cos_sim_ap
      value: 57.033163698414114
    - type: cos_sim_f1
      value: 54.54983148772268
    - type: cos_sim_precision
      value: 50.154935812306334
    - type: cos_sim_recall
      value: 59.788918205804755
    - type: dot_accuracy
      value: 81.53424330929249
    - type: dot_ap
      value: 57.03315710343203
    - type: dot_f1
      value: 54.54983148772268
    - type: dot_precision
      value: 50.154935812306334
    - type: dot_recall
      value: 59.788918205804755
    - type: euclidean_accuracy
      value: 81.53424330929249
    - type: euclidean_ap
      value: 57.033158170117446
    - type: euclidean_f1
      value: 54.54983148772268
    - type: euclidean_precision
      value: 50.154935812306334
    - type: euclidean_recall
      value: 59.788918205804755
    - type: manhattan_accuracy
      value: 81.29582166060678
    - type: manhattan_ap
      value: 55.74973597316332
    - type: manhattan_f1
      value: 53.15203955500617
    - type: manhattan_precision
      value: 50.0
    - type: manhattan_recall
      value: 56.72823218997362
    - type: max_accuracy
      value: 81.53424330929249
    - type: max_ap
      value: 57.033163698414114
    - type: max_f1
      value: 54.54983148772268
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 87.27053983777701
    - type: cos_sim_ap
      value: 82.20632443836952
    - type: cos_sim_f1
      value: 74.4764795144158
    - type: cos_sim_precision
      value: 73.40711935387377
    - type: cos_sim_recall
      value: 75.57745611333539
    - type: dot_accuracy
      value: 87.27053983777701
    - type: dot_ap
      value: 82.20632570091198
    - type: dot_f1
      value: 74.4764795144158
    - type: dot_precision
      value: 73.40711935387377
    - type: dot_recall
      value: 75.57745611333539
    - type: euclidean_accuracy
      value: 87.27053983777701
    - type: euclidean_ap
      value: 82.20632379487282
    - type: euclidean_f1
      value: 74.4764795144158
    - type: euclidean_precision
      value: 73.40711935387377
    - type: euclidean_recall
      value: 75.57745611333539
    - type: manhattan_accuracy
      value: 87.18321884581053
    - type: manhattan_ap
      value: 81.95324384723243
    - type: manhattan_f1
      value: 74.28318388132404
    - type: manhattan_precision
      value: 71.32733328608887
    - type: manhattan_recall
      value: 77.49461040960887
    - type: max_accuracy
      value: 87.27053983777701
    - type: max_ap
      value: 82.20632570091198
    - type: max_f1
      value: 74.4764795144158
---
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()
```