Add tokie .tkz tokenizer for Cohere/Cohere-embed-multilingual-v3.0

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.tkz filter=lfs diff=lfs merge=lfs -text
+tokie-banner.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,1108 @@
+---
+tags:
+- tokie
+- mteb
+model-index:
+- name: embed-multilingual-v3.0
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 77.85074626865672
+    - type: ap
+      value: 41.53151744002314
+    - type: f1
+      value: 71.94656880817726
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 95.600375
+    - type: ap
+      value: 93.57882128753579
+    - type: f1
+      value: 95.59945484944305
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 49.794
+    - type: f1
+      value: 48.740439663130985
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 55.105000000000004
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 48.15653426568874
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 40.78876256237919
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.12873500780318
+    - type: mrr
+      value: 75.87037769863255
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.01183720167818
+    - type: cos_sim_spearman
+      value: 85.00916590717613
+    - type: euclidean_pearson
+      value: 84.072733561361
+    - type: euclidean_spearman
+      value: 85.00916590717613
+    - type: manhattan_pearson
+      value: 83.89233507343208
+    - type: manhattan_spearman
+      value: 84.87482549674115
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 86.09415584415584
+    - type: f1
+      value: 86.05173549773973
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 40.49773000165541
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 36.909633073998876
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 49.481
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 47.449999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 59.227
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 37.729
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 29.673
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 44.278
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 43.218
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 40.63741666666667
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 33.341
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 29.093999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 40.801
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 40.114
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 33.243
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 29.958000000000002
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 41.004000000000005
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 48.150000000000006
+    - type: f1
+      value: 43.69803436468346
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 88.532
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 44.105
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 70.612
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 93.9672
+    - type: ap
+      value: 90.72947025321227
+    - type: f1
+      value: 93.96271599852622
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 43.447
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 94.92476060191517
+    - type: f1
+      value: 94.69383758972194
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 78.8873689010488
+    - type: f1
+      value: 62.537485052253885
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 74.51244115669132
+    - type: f1
+      value: 72.40074466830153
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 79.00470746469401
+    - type: f1
+      value: 79.03758200183096
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 36.183215937303736
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 33.443759055792135
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.58713095176127
+    - type: mrr
+      value: 33.7326038566206
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 36.417
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 63.415
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 88.924
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 58.10997801688676
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 65.02444843766075
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 19.339000000000002
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.61540076033945
+    - type: cos_sim_spearman
+      value: 82.1820253476181
+    - type: euclidean_pearson
+      value: 83.73901215845989
+    - type: euclidean_spearman
+      value: 82.182021064594
+    - type: manhattan_pearson
+      value: 83.76685139192031
+    - type: manhattan_spearman
+      value: 82.14074705306663
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.62241109228789
+    - type: cos_sim_spearman
+      value: 77.62042143066208
+    - type: euclidean_pearson
+      value: 82.77237785274072
+    - type: euclidean_spearman
+      value: 77.62042142290566
+    - type: manhattan_pearson
+      value: 82.70945589621266
+    - type: manhattan_spearman
+      value: 77.57245632826351
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.8307075352031
+    - type: cos_sim_spearman
+      value: 85.15620774806095
+    - type: euclidean_pearson
+      value: 84.21956724564915
+    - type: euclidean_spearman
+      value: 85.15620774806095
+    - type: manhattan_pearson
+      value: 84.0677597021641
+    - type: manhattan_spearman
+      value: 85.02572172855729
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.33749463516592
+    - type: cos_sim_spearman
+      value: 80.01967438481185
+    - type: euclidean_pearson
+      value: 82.16884494022196
+    - type: euclidean_spearman
+      value: 80.01967218194336
+    - type: manhattan_pearson
+      value: 81.94431512413773
+    - type: manhattan_spearman
+      value: 79.81636247503731
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.2070761097028
+    - type: cos_sim_spearman
+      value: 88.92297656560552
+    - type: euclidean_pearson
+      value: 87.95961374550303
+    - type: euclidean_spearman
+      value: 88.92298798854765
+    - type: manhattan_pearson
+      value: 87.85515971478168
+    - type: manhattan_spearman
+      value: 88.8100644762342
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.48103354546488
+    - type: cos_sim_spearman
+      value: 86.91850928862898
+    - type: euclidean_pearson
+      value: 86.06766986527145
+    - type: euclidean_spearman
+      value: 86.91850928862898
+    - type: manhattan_pearson
+      value: 86.02705585360717
+    - type: manhattan_spearman
+      value: 86.86666545434721
+  - 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: 90.30267248880148
+    - type: cos_sim_spearman
+      value: 90.08752166657892
+    - type: euclidean_pearson
+      value: 90.4697525265135
+    - type: euclidean_spearman
+      value: 90.08752166657892
+    - type: manhattan_pearson
+      value: 90.57174978064741
+    - type: manhattan_spearman
+      value: 90.212834942229
+  - 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: 67.10616236380835
+    - type: cos_sim_spearman
+      value: 66.81483164137016
+    - type: euclidean_pearson
+      value: 68.48505128040803
+    - type: euclidean_spearman
+      value: 66.81483164137016
+    - type: manhattan_pearson
+      value: 68.46133268524885
+    - type: manhattan_spearman
+      value: 66.83684227990202
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.12768629069949
+    - type: cos_sim_spearman
+      value: 88.78683817318573
+    - type: euclidean_pearson
+      value: 88.47603251297261
+    - type: euclidean_spearman
+      value: 88.78683817318573
+    - type: manhattan_pearson
+      value: 88.46483630890225
+    - type: manhattan_spearman
+      value: 88.76593424921617
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 84.30886658431281
+    - type: mrr
+      value: 95.5964251797585
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 70.04599999999999
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.87524752475248
+    - type: cos_sim_ap
+      value: 96.79160651306724
+    - type: cos_sim_f1
+      value: 93.57798165137615
+    - type: cos_sim_precision
+      value: 95.42619542619542
+    - type: cos_sim_recall
+      value: 91.8
+    - type: dot_accuracy
+      value: 99.87524752475248
+    - type: dot_ap
+      value: 96.79160651306724
+    - type: dot_f1
+      value: 93.57798165137615
+    - type: dot_precision
+      value: 95.42619542619542
+    - type: dot_recall
+      value: 91.8
+    - type: euclidean_accuracy
+      value: 99.87524752475248
+    - type: euclidean_ap
+      value: 96.79160651306724
+    - type: euclidean_f1
+      value: 93.57798165137615
+    - type: euclidean_precision
+      value: 95.42619542619542
+    - type: euclidean_recall
+      value: 91.8
+    - type: manhattan_accuracy
+      value: 99.87326732673267
+    - type: manhattan_ap
+      value: 96.7574606340297
+    - type: manhattan_f1
+      value: 93.45603271983639
+    - type: manhattan_precision
+      value: 95.60669456066945
+    - type: manhattan_recall
+      value: 91.4
+    - type: max_accuracy
+      value: 99.87524752475248
+    - type: max_ap
+      value: 96.79160651306724
+    - type: max_f1
+      value: 93.57798165137615
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 68.12288811917144
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 35.22267280169542
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 52.39780995606098
+    - type: mrr
+      value: 53.26826563958916
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.15118979569649
+    - type: cos_sim_spearman
+      value: 30.99428921914572
+    - type: dot_pearson
+      value: 31.151189338601924
+    - type: dot_spearman
+      value: 30.99428921914572
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 83.372
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: ndcg_at_10
+      value: 32.698
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 71.1998
+    - type: ap
+      value: 14.646205259325157
+    - type: f1
+      value: 54.96172518137252
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 62.176004527447645
+    - type: f1
+      value: 62.48549068096645
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 50.13767789739772
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.38016331882935
+    - type: cos_sim_ap
+      value: 75.1635976260804
+    - type: cos_sim_f1
+      value: 69.29936305732484
+    - type: cos_sim_precision
+      value: 66.99507389162561
+    - type: cos_sim_recall
+      value: 71.76781002638522
+    - type: dot_accuracy
+      value: 86.38016331882935
+    - type: dot_ap
+      value: 75.16359359202374
+    - type: dot_f1
+      value: 69.29936305732484
+    - type: dot_precision
+      value: 66.99507389162561
+    - type: dot_recall
+      value: 71.76781002638522
+    - type: euclidean_accuracy
+      value: 86.38016331882935
+    - type: euclidean_ap
+      value: 75.16360246558416
+    - type: euclidean_f1
+      value: 69.29936305732484
+    - type: euclidean_precision
+      value: 66.99507389162561
+    - type: euclidean_recall
+      value: 71.76781002638522
+    - type: manhattan_accuracy
+      value: 86.27883411813792
+    - type: manhattan_ap
+      value: 75.02872038741897
+    - type: manhattan_f1
+      value: 69.29256284011403
+    - type: manhattan_precision
+      value: 68.07535641547861
+    - type: manhattan_recall
+      value: 70.55408970976254
+    - type: max_accuracy
+      value: 86.38016331882935
+    - type: max_ap
+      value: 75.16360246558416
+    - type: max_f1
+      value: 69.29936305732484
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.39729110878255
+    - type: cos_sim_ap
+      value: 86.48560260020555
+    - type: cos_sim_f1
+      value: 79.35060602690982
+    - type: cos_sim_precision
+      value: 76.50632549496105
+    - type: cos_sim_recall
+      value: 82.41453649522637
+    - type: dot_accuracy
+      value: 89.39729110878255
+    - type: dot_ap
+      value: 86.48559829915334
+    - type: dot_f1
+      value: 79.35060602690982
+    - type: dot_precision
+      value: 76.50632549496105
+    - type: dot_recall
+      value: 82.41453649522637
+    - type: euclidean_accuracy
+      value: 89.39729110878255
+    - type: euclidean_ap
+      value: 86.48559993122497
+    - type: euclidean_f1
+      value: 79.35060602690982
+    - type: euclidean_precision
+      value: 76.50632549496105
+    - type: euclidean_recall
+      value: 82.41453649522637
+    - type: manhattan_accuracy
+      value: 89.36042224550782
+    - type: manhattan_ap
+      value: 86.47238558562499
+    - type: manhattan_f1
+      value: 79.24500641378047
+    - type: manhattan_precision
+      value: 75.61726236273344
+    - type: manhattan_recall
+      value: 83.23837388358484
+    - type: max_accuracy
+      value: 89.39729110878255
+    - type: max_ap
+      value: 86.48560260020555
+    - type: max_f1
+      value: 79.35060602690982
+---
+<p align="center">
+  <img src="tokie-banner.png" alt="tokie banner">
+</p>
+
+
+
+# Cohere embed-multilingual-v3.0
+
+This repository contains the tokenizer for the Cohere `embed-multilingual-v3.0` model. See our blogpost [Cohere Embed V3](https://txt.cohere.com/introducing-embed-v3/) for more details on this model.
+
+You can use the embedding model either via the Cohere API, AWS SageMaker or in your private deployments.
+
+## Usage Cohere API
+
+The following code snippet shows the usage of the Cohere API. Install the cohere SDK via:
+```
+pip install -U cohere
+```
+
+Get your free API key on: www.cohere.com
+
+
+```python
+# This snippet shows and example how to use the Cohere Embed V3 models for semantic search.
+# Make sure to have the Cohere SDK in at least v4.30 install: pip install -U cohere 
+# Get your API key from: www.cohere.com
+import cohere
+import numpy as np
+
+cohere_key = "{YOUR_COHERE_API_KEY}"   #Get your API key from www.cohere.com
+co = cohere.Client(cohere_key)
+
+docs = ["The capital of France is Paris",
+        "PyTorch is a machine learning framework based on the Torch library.",
+        "The average cat lifespan is between 13-17 years"]
+
+
+#Encode your documents with input type 'search_document'
+doc_emb = co.embed(docs, input_type="search_document", model="embed-multilingual-v3.0").embeddings
+doc_emb = np.asarray(doc_emb)
+
+
+#Encode your query with input type 'search_query'
+query = "What is Pytorch"
+query_emb = co.embed([query], input_type="search_query", model="embed-multilingual-v3.0").embeddings
+query_emb = np.asarray(query_emb)
+query_emb.shape
+
+#Compute the dot product between query embedding and document embedding
+scores = np.dot(query_emb, doc_emb.T)[0]
+
+#Find the highest scores
+max_idx = np.argsort(-scores)
+
+print(f"Query: {query}")
+for idx in max_idx:
+  print(f"Score: {scores[idx]:.2f}")
+  print(docs[idx])
+  print("--------")
+```
+
+## Usage AWS SageMaker
+The embedding model can be privately deployed in your AWS Cloud using our [AWS SageMaker marketplace offering](https://aws.amazon.com/marketplace/pp/prodview-z6huxszcqc25i). It runs privately in your VPC, with latencies as low as 5ms for query encoding.
+
+## Usage AWS Bedrock
+Soon the model will also be available via AWS Bedrock. Stay tuned
+
+## Private Deployment
+You want to run the model on your own hardware? [Contact Sales](https://cohere.com/contact-sales) to learn more.
+
+## Supported Languages
+This model was trained on nearly 1B English training pairs and nearly 0.5B Non-English training pairs from 100+ languages. 
+
+Evaluation results can be found in the [Embed V3.0 Benchmark Results spreadsheet](https://docs.google.com/spreadsheets/d/1w7gnHWMDBdEUrmHgSfDnGHJgVQE5aOiXCCwO3uNH_mI/edit?usp=sharing).

config.json

@@ -0,0 +1,4 @@
+{
+	"n_positions": 512,
+	"hidden_dim": 1024
+}

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
+size 5069051

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62c24cdc13d4c9952d63718d6c9fa4c287974249e16b7ade6d5a85e7bbb75626
+size 17082660

tokenizer.tkz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6f5a2d0efd3128e2ed3b9a76e9dafe8d45d2f40d3c6c9dddbf76470a231c2c0
+size 20616294

tokenizer_config.json

@@ -0,0 +1,21 @@
+{
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 512,
+  "name_or_path": "../sbert_models/cohere-embed-multilingual-v3.0-not-rotated/",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "special_tokens_map_file": "../sbert_models/cohere-embed-multilingual-v3.0-not-rotated/special_tokens_map.json",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}