---
tags:
- mteb
model-index:
- name: universal-sentence-encoder-multilingual-large-3
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 70.80597014925372
    - type: ap
      value: 32.82048192776259
    - type: f1
      value: 64.5323001151201
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 67.04549999999999
    - type: ap
      value: 61.7344066191823
    - type: f1
      value: 66.66233213924507
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 35.85
    - type: f1
      value: 35.332188148679464
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 34.745135349238126
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-s2s
      name: MTEB ArxivClusteringS2S
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 22.620886813816306
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 80.30945408208555
    - type: cos_sim_spearman
      value: 79.13734536677386
    - type: euclidean_pearson
      value: 78.92356402711572
    - type: euclidean_spearman
      value: 79.13734536677386
    - type: manhattan_pearson
      value: 79.0536298599996
    - type: manhattan_spearman
      value: 79.15240595090333
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 74.66883116883116
    - type: f1
      value: 73.79377347715479
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 28.750702236182818
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 20.142702408387194
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 42.30500000000001
    - type: f1
      value: 38.547388314307206
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 63.690000000000005
    - type: ap
      value: 59.157513278784734
    - type: f1
      value: 63.35865572988864
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 92.48062015503875
    - type: f1
      value: 92.14919344822017
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 70.26675786593708
    - type: f1
      value: 47.72003620900994
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 69.04505716207129
    - type: f1
      value: 65.75319040584333
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 75.80363147276395
    - type: f1
      value: 74.16118757920125
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-p2p
      name: MTEB MedrxivClusteringP2P
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 31.197732425855694
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-s2s
      name: MTEB MedrxivClusteringS2S
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 25.802309075396522
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering
      name: MTEB RedditClustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 46.17008358584782
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering-p2p
      name: MTEB RedditClusteringP2P
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 56.53148530944687
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 81.9794493404352
    - type: cos_sim_spearman
      value: 76.42957100142304
    - type: euclidean_pearson
      value: 78.82942656726047
    - type: euclidean_spearman
      value: 76.4295710840889
    - type: manhattan_pearson
      value: 78.13314706410813
    - type: manhattan_spearman
      value: 74.9822593004123
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 80.7673081071098
    - type: cos_sim_spearman
      value: 74.24891322087522
    - type: euclidean_pearson
      value: 76.52411182468802
    - type: euclidean_spearman
      value: 74.24929140605082
    - type: manhattan_pearson
      value: 76.8324387036746
    - type: manhattan_spearman
      value: 74.53614579807713
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 69.28614557615222
    - type: cos_sim_spearman
      value: 71.81704450585194
    - type: euclidean_pearson
      value: 71.1658590877318
    - type: euclidean_spearman
      value: 71.81704444201455
    - type: manhattan_pearson
      value: 71.36497478266207
    - type: manhattan_spearman
      value: 72.06541804714345
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 74.87060297964618
    - type: cos_sim_spearman
      value: 71.3835314374386
    - type: euclidean_pearson
      value: 73.38159929423239
    - type: euclidean_spearman
      value: 71.38353144149953
    - type: manhattan_pearson
      value: 73.52351725668174
    - type: manhattan_spearman
      value: 71.51640478420119
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 82.0540026051573
    - type: cos_sim_spearman
      value: 82.4705078026881
    - type: euclidean_pearson
      value: 81.93203207566977
    - type: euclidean_spearman
      value: 82.47050765607385
    - type: manhattan_pearson
      value: 81.95496687772686
    - type: manhattan_spearman
      value: 82.32489988477197
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 76.11630455802185
    - type: cos_sim_spearman
      value: 77.53749233675596
    - type: euclidean_pearson
      value: 77.21678350170754
    - type: euclidean_spearman
      value: 77.53749219731857
    - type: manhattan_pearson
      value: 77.0111066160541
    - type: manhattan_spearman
      value: 77.19561900456223
  - 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: 86.04867872683484
    - type: cos_sim_spearman
      value: 86.38343806077555
    - type: euclidean_pearson
      value: 86.62923572982524
    - type: euclidean_spearman
      value: 86.38343806077555
    - type: manhattan_pearson
      value: 85.88819314699656
    - type: manhattan_spearman
      value: 85.40841620897656
  - 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: 50.81940075037091
    - type: cos_sim_spearman
      value: 52.853775517979265
    - type: euclidean_pearson
      value: 53.19987444831206
    - type: euclidean_spearman
      value: 52.853775517979265
    - type: manhattan_pearson
      value: 53.10152120352485
    - type: manhattan_spearman
      value: 52.882886362489124
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 82.15848984078094
    - type: cos_sim_spearman
      value: 81.24223670044107
    - type: euclidean_pearson
      value: 81.80955840510725
    - type: euclidean_spearman
      value: 81.24224792494685
    - type: manhattan_pearson
      value: 81.20700319509191
    - type: manhattan_spearman
      value: 80.56078137874846
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.71089108910891
    - type: cos_sim_ap
      value: 90.8870929231928
    - type: cos_sim_f1
      value: 85.3719420868697
    - type: cos_sim_precision
      value: 85.24426719840478
    - type: cos_sim_recall
      value: 85.5
    - type: dot_accuracy
      value: 99.71089108910891
    - type: dot_ap
      value: 90.88709292319278
    - type: dot_f1
      value: 85.3719420868697
    - type: dot_precision
      value: 85.24426719840478
    - type: dot_recall
      value: 85.5
    - type: euclidean_accuracy
      value: 99.71089108910891
    - type: euclidean_ap
      value: 90.8870929231928
    - type: euclidean_f1
      value: 85.3719420868697
    - type: euclidean_precision
      value: 85.24426719840478
    - type: euclidean_recall
      value: 85.5
    - type: manhattan_accuracy
      value: 99.72871287128713
    - type: manhattan_ap
      value: 91.50016707647607
    - type: manhattan_f1
      value: 86.21700879765396
    - type: manhattan_precision
      value: 84.32122370936902
    - type: manhattan_recall
      value: 88.2
    - type: max_accuracy
      value: 99.72871287128713
    - type: max_ap
      value: 91.50016707647607
    - type: max_f1
      value: 86.21700879765396
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering
      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 49.339384566987555
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 33.39729645390336
  - task:
      type: Summarization
    dataset:
      type: mteb/summeval
      name: MTEB SummEval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.459235703560942
    - type: cos_sim_spearman
      value: 29.710719599360587
    - type: dot_pearson
      value: 30.459236115198866
    - type: dot_spearman
      value: 29.714606257782066
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 68.223
    - type: ap
      value: 13.10327282975004
    - type: f1
      value: 52.52588280152648
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 59.18788907753254
    - type: f1
      value: 59.47679105840768
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 36.93253191095803
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 83.37009000417238
    - type: cos_sim_ap
      value: 63.75973129735431
    - type: cos_sim_f1
      value: 59.62504595025121
    - type: cos_sim_precision
      value: 55.66231983527798
    - type: cos_sim_recall
      value: 64.1952506596306
    - type: dot_accuracy
      value: 83.37009000417238
    - type: dot_ap
      value: 63.759728820348414
    - type: dot_f1
      value: 59.62504595025121
    - type: dot_precision
      value: 55.66231983527798
    - type: dot_recall
      value: 64.1952506596306
    - type: euclidean_accuracy
      value: 83.37009000417238
    - type: euclidean_ap
      value: 63.75972622477462
    - type: euclidean_f1
      value: 59.62504595025121
    - type: euclidean_precision
      value: 55.66231983527798
    - type: euclidean_recall
      value: 64.1952506596306
    - type: manhattan_accuracy
      value: 83.28068188591524
    - type: manhattan_ap
      value: 63.109413220673375
    - type: manhattan_f1
      value: 59.085923217550274
    - type: manhattan_precision
      value: 54.903737259343146
    - type: manhattan_recall
      value: 63.95778364116095
    - type: max_accuracy
      value: 83.37009000417238
    - type: max_ap
      value: 63.75973129735431
    - type: max_f1
      value: 59.62504595025121
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.34167733923235
    - type: cos_sim_ap
      value: 84.20066403502292
    - type: cos_sim_f1
      value: 76.64693381906498
    - type: cos_sim_precision
      value: 75.56869200838072
    - type: cos_sim_recall
      value: 77.75639051432091
    - type: dot_accuracy
      value: 88.34167733923235
    - type: dot_ap
      value: 84.20066476075668
    - type: dot_f1
      value: 76.64693381906498
    - type: dot_precision
      value: 75.56869200838072
    - type: dot_recall
      value: 77.75639051432091
    - type: euclidean_accuracy
      value: 88.34167733923235
    - type: euclidean_ap
      value: 84.20066533105057
    - type: euclidean_f1
      value: 76.64693381906498
    - type: euclidean_precision
      value: 75.56869200838072
    - type: euclidean_recall
      value: 77.75639051432091
    - type: manhattan_accuracy
      value: 88.32809407381535
    - type: manhattan_ap
      value: 84.17666758732113
    - type: manhattan_f1
      value: 76.6911654417279
    - type: manhattan_precision
      value: 74.75146198830409
    - type: manhattan_recall
      value: 78.73421619956883
    - type: max_accuracy
      value: 88.34167733923235
    - type: max_ap
      value: 84.20066533105057
    - type: max_f1
      value: 76.6911654417279
---

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()
```