metadata
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: sentence_croissant_alpha_v0.3
results:
- task:
type: Clustering
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloProfClusteringP2P
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 56.72912207023513
- type: v_measures
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- task:
type: Clustering
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloProfClusteringS2S
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
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value: 37.62128894914382
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- task:
type: Reranking
dataset:
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
name: MTEB AlloprofReranking
config: default
split: test
revision: e40c8a63ce02da43200eccb5b0846fcaa888f562
metrics:
- type: map
value: 68.30621526032894
- type: mrr
value: 69.67719384817829
- task:
type: Retrieval
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloprofRetrieval
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
metrics:
- type: map_at_1
value: 32.254
- type: map_at_10
value: 43.834
- type: map_at_100
value: 44.728
- type: map_at_1000
value: 44.769999999999996
- type: map_at_20
value: 44.361
- type: map_at_3
value: 40.753
- type: map_at_5
value: 42.486000000000004
- type: mrr_at_1
value: 32.254
- type: mrr_at_10
value: 43.834
- type: mrr_at_100
value: 44.728
- type: mrr_at_1000
value: 44.769999999999996
- type: mrr_at_20
value: 44.361
- type: mrr_at_3
value: 40.753
- type: mrr_at_5
value: 42.486000000000004
- type: ndcg_at_1
value: 32.254
- type: ndcg_at_10
value: 49.845
- type: ndcg_at_100
value: 54.37800000000001
- type: ndcg_at_1000
value: 55.498000000000005
- type: ndcg_at_20
value: 51.772
- type: ndcg_at_3
value: 43.486000000000004
- type: ndcg_at_5
value: 46.594
- type: precision_at_1
value: 32.254
- type: precision_at_10
value: 6.891
- type: precision_at_100
value: 0.905
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 3.8280000000000003
- type: precision_at_3
value: 17.127
- type: precision_at_5
value: 11.779
- type: recall_at_1
value: 32.254
- type: recall_at_10
value: 68.91199999999999
- type: recall_at_100
value: 90.501
- type: recall_at_1000
value: 99.309
- type: recall_at_20
value: 76.554
- type: recall_at_3
value: 51.382000000000005
- type: recall_at_5
value: 58.894999999999996
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type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.106
- type: f1
value: 34.825583560299656
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type: Retrieval
dataset:
type: maastrichtlawtech/bsard
name: MTEB BSARDRetrieval
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
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value: 0
- type: map_at_10
value: 0.15
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value: 0.19499999999999998
- type: map_at_1000
value: 0.243
- type: map_at_20
value: 0.18
- type: map_at_3
value: 0.15
- type: map_at_5
value: 0.15
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0.15
- type: mrr_at_100
value: 0.19499999999999998
- type: mrr_at_1000
value: 0.243
- type: mrr_at_20
value: 0.18
- type: mrr_at_3
value: 0.15
- type: mrr_at_5
value: 0.15
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0.22499999999999998
- type: ndcg_at_100
value: 0.545
- type: ndcg_at_1000
value: 2.622
- type: ndcg_at_20
value: 0.338
- type: ndcg_at_3
value: 0.22499999999999998
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value: 0.22499999999999998
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0.045
- type: precision_at_100
value: 0.023
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value: 0.02
- type: precision_at_20
value: 0.045
- type: precision_at_3
value: 0.15
- type: precision_at_5
value: 0.09
- type: recall_at_1
value: 0
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value: 0.44999999999999996
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value: 2.252
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value: 20.27
- type: recall_at_20
value: 0.901
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value: 0.44999999999999996
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value: 0.44999999999999996
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type: Retrieval
dataset:
type: manu/fquad2_test
name: MTEB FQuADRetrieval
config: default
split: test
revision: 5384ce827bbc2156d46e6fcba83d75f8e6e1b4a6
metrics:
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value: 53
- type: map_at_10
value: 65.393
- type: map_at_100
value: 65.791
- type: map_at_1000
value: 65.79899999999999
- type: map_at_20
value: 65.644
- type: map_at_3
value: 62.74999999999999
- type: map_at_5
value: 64.075
- type: mrr_at_1
value: 53
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value: 65.393
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value: 65.791
- type: mrr_at_1000
value: 65.79899999999999
- type: mrr_at_20
value: 65.644
- type: mrr_at_3
value: 62.74999999999999
- type: mrr_at_5
value: 64.075
- type: ndcg_at_1
value: 53
- type: ndcg_at_10
value: 71.38600000000001
- type: ndcg_at_100
value: 73.275
- type: ndcg_at_1000
value: 73.42
- type: ndcg_at_20
value: 72.28099999999999
- type: ndcg_at_3
value: 65.839
- type: ndcg_at_5
value: 68.217
- type: precision_at_1
value: 53
- type: precision_at_10
value: 9.025
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.688
- type: precision_at_3
value: 24.917
- type: precision_at_5
value: 16.1
- type: recall_at_1
value: 53
- type: recall_at_10
value: 90.25
- type: recall_at_100
value: 99
- type: recall_at_1000
value: 100
- type: recall_at_20
value: 93.75
- type: recall_at_3
value: 74.75
- type: recall_at_5
value: 80.5
- task:
type: Clustering
dataset:
type: lyon-nlp/clustering-hal-s2s
name: MTEB HALClusteringS2S
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
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- task:
type: Clustering
dataset:
type: mlsum
name: MTEB MLSUMClusteringP2P
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 41.82320155017088
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- task:
type: Clustering
dataset:
type: mlsum
name: MTEB MLSUMClusteringS2S
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
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- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.52489821484498
- type: f1
value: 88.39995340840026
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 65.92546194801128
- type: f1
value: 46.53109996877417
- task:
type: Classification
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClassification (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 75.1658767772512
- type: f1
value: 71.02734472473519
- task:
type: Clustering
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClusteringP2P (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 42.63310602457368
- type: v_measures
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- task:
type: Clustering
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClusteringS2S (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
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value: 36.39726683344252
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value:
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- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fr)
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.48285137861467
- type: f1
value: 64.74690799351637
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (fr)
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.47276395427033
- type: f1
value: 71.9261164692627
- task:
type: Retrieval
dataset:
type: jinaai/mintakaqa
name: MTEB MintakaRetrieval (fr)
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: map_at_1
value: 16.134
- type: map_at_10
value: 26.040000000000003
- type: map_at_100
value: 27.233
- type: map_at_1000
value: 27.315
- type: map_at_20
value: 26.741999999999997
- type: map_at_3
value: 23.219
- type: map_at_5
value: 24.962999999999997
- type: mrr_at_1
value: 16.134
- type: mrr_at_10
value: 26.040000000000003
- type: mrr_at_100
value: 27.233
- type: mrr_at_1000
value: 27.315
- type: mrr_at_20
value: 26.741999999999997
- type: mrr_at_3
value: 23.219
- type: mrr_at_5
value: 24.962999999999997
- type: ndcg_at_1
value: 16.134
- type: ndcg_at_10
value: 31.255
- type: ndcg_at_100
value: 37.462
- type: ndcg_at_1000
value: 39.85
- type: ndcg_at_20
value: 33.853
- type: ndcg_at_3
value: 25.513
- type: ndcg_at_5
value: 28.653000000000002
- type: precision_at_1
value: 16.134
- type: precision_at_10
value: 4.779
- type: precision_at_100
value: 0.777
- type: precision_at_1000
value: 0.097
- type: precision_at_20
value: 2.907
- type: precision_at_3
value: 10.715
- type: precision_at_5
value: 7.951999999999999
- type: recall_at_1
value: 16.134
- type: recall_at_10
value: 47.789
- type: recall_at_100
value: 77.682
- type: recall_at_1000
value: 96.929
- type: recall_at_20
value: 58.148999999999994
- type: recall_at_3
value: 32.146
- type: recall_at_5
value: 39.762
- task:
type: PairClassification
dataset:
type: GEM/opusparcus
name: MTEB OpusparcusPC (fr)
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 84.40054495912807
- type: cos_sim_ap
value: 93.71617772707746
- type: cos_sim_f1
value: 89.00624099855978
- type: cos_sim_precision
value: 86.15241635687732
- type: cos_sim_recall
value: 92.05561072492551
- type: dot_accuracy
value: 82.35694822888283
- type: dot_ap
value: 92.22992449042768
- type: dot_f1
value: 87.84786641929499
- type: dot_precision
value: 82.4194952132289
- type: dot_recall
value: 94.04170804369414
- type: euclidean_accuracy
value: 82.90190735694823
- type: euclidean_ap
value: 93.27345126956494
- type: euclidean_f1
value: 87.82608695652175
- type: euclidean_precision
value: 85.51269990592662
- type: euclidean_recall
value: 90.26812313803376
- type: manhattan_accuracy
value: 82.9700272479564
- type: manhattan_ap
value: 93.34994137379041
- type: manhattan_f1
value: 87.776708373436
- type: manhattan_precision
value: 85.15406162464986
- type: manhattan_recall
value: 90.56603773584906
- type: max_accuracy
value: 84.40054495912807
- type: max_ap
value: 93.71617772707746
- type: max_f1
value: 89.00624099855978
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (fr)
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 63.849999999999994
- type: cos_sim_ap
value: 65.42493744372587
- type: cos_sim_f1
value: 63.87434554973822
- type: cos_sim_precision
value: 52.69978401727862
- type: cos_sim_recall
value: 81.06312292358804
- type: dot_accuracy
value: 55.35
- type: dot_ap
value: 48.9364958676423
- type: dot_f1
value: 62.491349480968864
- type: dot_precision
value: 45.44539506794162
- type: dot_recall
value: 100
- type: euclidean_accuracy
value: 64.4
- type: euclidean_ap
value: 65.8622099022063
- type: euclidean_f1
value: 63.762044407205686
- type: euclidean_precision
value: 51.280323450134766
- type: euclidean_recall
value: 84.27464008859357
- type: manhattan_accuracy
value: 64.5
- type: manhattan_ap
value: 65.89565256625798
- type: manhattan_f1
value: 63.75364128173118
- type: manhattan_precision
value: 51.06666666666667
- type: manhattan_recall
value: 84.82834994462901
- type: max_accuracy
value: 64.5
- type: max_ap
value: 65.89565256625798
- type: max_f1
value: 63.87434554973822
- task:
type: STS
dataset:
type: Lajavaness/SICK-fr
name: MTEB SICKFr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_pearson
value: 79.55191450239424
- type: cos_sim_spearman
value: 71.89209513298714
- type: euclidean_pearson
value: 74.18063891200164
- type: euclidean_spearman
value: 69.61463203410928
- type: manhattan_pearson
value: 74.26272426503743
- type: manhattan_spearman
value: 69.64261630235363
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 79.18370529169849
- type: cos_sim_spearman
value: 80.80074537316342
- type: euclidean_pearson
value: 72.62308682855334
- type: euclidean_spearman
value: 75.64665618431559
- type: manhattan_pearson
value: 72.75806349827452
- type: manhattan_spearman
value: 75.34151992740627
- task:
type: STS
dataset:
type: PhilipMay/stsb_multi_mt
name: MTEB STSBenchmarkMultilingualSTS (fr)
config: fr
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson
value: 81.29611168887834
- type: cos_sim_spearman
value: 80.23434765396613
- type: euclidean_pearson
value: 77.85740285296822
- type: euclidean_spearman
value: 78.42089083386267
- type: manhattan_pearson
value: 77.85850984492824
- type: manhattan_spearman
value: 78.42578976788568
- task:
type: Summarization
dataset:
type: lyon-nlp/summarization-summeval-fr-p2p
name: MTEB SummEvalFr
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
- type: cos_sim_pearson
value: 31.143644380271375
- type: cos_sim_spearman
value: 32.45645175142292
- type: dot_pearson
value: 28.46685825407204
- type: dot_spearman
value: 29.164040487051512
- task:
type: Reranking
dataset:
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
name: MTEB SyntecReranking
config: default
split: test
revision: b205c5084a0934ce8af14338bf03feb19499c84d
metrics:
- type: map
value: 82.65
- type: mrr
value: 82.65
- task:
type: Retrieval
dataset:
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
name: MTEB SyntecRetrieval
config: default
split: test
revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
metrics:
- type: map_at_1
value: 56.99999999999999
- type: map_at_10
value: 70.6
- type: map_at_100
value: 70.814
- type: map_at_1000
value: 70.814
- type: map_at_20
value: 70.733
- type: map_at_3
value: 67.833
- type: map_at_5
value: 70.18299999999999
- type: mrr_at_1
value: 56.99999999999999
- type: mrr_at_10
value: 70.6
- type: mrr_at_100
value: 70.814
- type: mrr_at_1000
value: 70.814
- type: mrr_at_20
value: 70.733
- type: mrr_at_3
value: 67.833
- type: mrr_at_5
value: 70.18299999999999
- type: ndcg_at_1
value: 56.99999999999999
- type: ndcg_at_10
value: 76.626
- type: ndcg_at_100
value: 77.69500000000001
- type: ndcg_at_1000
value: 77.69500000000001
- type: ndcg_at_20
value: 77.12400000000001
- type: ndcg_at_3
value: 71.464
- type: ndcg_at_5
value: 75.639
- type: precision_at_1
value: 56.99999999999999
- type: precision_at_10
value: 9.5
- type: precision_at_100
value: 1
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.8500000000000005
- type: precision_at_3
value: 27.333000000000002
- type: precision_at_5
value: 18.4
- type: recall_at_1
value: 56.99999999999999
- type: recall_at_10
value: 95
- type: recall_at_100
value: 100
- type: recall_at_1000
value: 100
- type: recall_at_20
value: 97
- type: recall_at_3
value: 82
- type: recall_at_5
value: 92
- task:
type: Retrieval
dataset:
type: jinaai/xpqa
name: MTEB XPQARetrieval (fr)
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
- type: map_at_1
value: 39.217
- type: map_at_10
value: 60.171
- type: map_at_100
value: 61.736999999999995
- type: map_at_1000
value: 61.787000000000006
- type: map_at_20
value: 61.211000000000006
- type: map_at_3
value: 53.43
- type: map_at_5
value: 57.638
- type: mrr_at_1
value: 62.617
- type: mrr_at_10
value: 69.32300000000001
- type: mrr_at_100
value: 69.95400000000001
- type: mrr_at_1000
value: 69.968
- type: mrr_at_20
value: 69.77799999999999
- type: mrr_at_3
value: 67.423
- type: mrr_at_5
value: 68.445
- type: ndcg_at_1
value: 62.617
- type: ndcg_at_10
value: 66.55499999999999
- type: ndcg_at_100
value: 71.521
- type: ndcg_at_1000
value: 72.32300000000001
- type: ndcg_at_20
value: 69.131
- type: ndcg_at_3
value: 60.88099999999999
- type: ndcg_at_5
value: 62.648
- type: precision_at_1
value: 62.617
- type: precision_at_10
value: 15.540999999999999
- type: precision_at_100
value: 1.9529999999999998
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_20
value: 8.658000000000001
- type: precision_at_3
value: 36.805
- type: precision_at_5
value: 26.622
- type: recall_at_1
value: 39.217
- type: recall_at_10
value: 75.547
- type: recall_at_100
value: 94.226
- type: recall_at_1000
value: 99.433
- type: recall_at_20
value: 83.883
- type: recall_at_3
value: 57.867999999999995
- type: recall_at_5
value: 66.08800000000001
{MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 2048 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Training
The model was trained with the parameters:
DataLoader:
torch.utils.data.dataloader.DataLoader of length 1401 with parameters:
{'batch_size': 256, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss:
__main__.MultipleNegativesRankingLoss_with_logging
Parameters of the fit()-Method:
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: LlamaModel
(1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)