metadata
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
- 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.
# !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()