metadata
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: sentence_croissant_alpha_v0.4
results:
- task:
type: Clustering
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloProfClusteringP2P
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 57.428864270265436
- 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:
- type: v_measure
value: 39.08542806793302
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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: 68.79422871247168
- type: mrr
value: 70.03750651095211
- task:
type: Retrieval
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloprofRetrieval
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
metrics:
- type: map_at_1
value: 31.952
- type: map_at_10
value: 43.183
- type: map_at_100
value: 44.135999999999996
- type: map_at_1000
value: 44.177
- type: map_at_20
value: 43.753
- type: map_at_3
value: 40.055
- type: map_at_5
value: 42.024
- type: mrr_at_1
value: 31.952
- type: mrr_at_10
value: 43.183
- type: mrr_at_100
value: 44.135999999999996
- type: mrr_at_1000
value: 44.177
- type: mrr_at_20
value: 43.753
- type: mrr_at_3
value: 40.055
- type: mrr_at_5
value: 42.024
- type: ndcg_at_1
value: 31.952
- type: ndcg_at_10
value: 49.053999999999995
- type: ndcg_at_100
value: 53.842
- type: ndcg_at_1000
value: 54.978
- type: ndcg_at_20
value: 51.092000000000006
- type: ndcg_at_3
value: 42.718
- type: ndcg_at_5
value: 46.28
- type: precision_at_1
value: 31.952
- type: precision_at_10
value: 6.762
- type: precision_at_100
value: 0.9039999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 3.7800000000000002
- type: precision_at_3
value: 16.811
- type: precision_at_5
value: 11.822000000000001
- type: recall_at_1
value: 31.952
- type: recall_at_10
value: 67.617
- type: recall_at_100
value: 90.371
- type: recall_at_1000
value: 99.35199999999999
- type: recall_at_20
value: 75.604
- type: recall_at_3
value: 50.432
- type: recall_at_5
value: 59.111000000000004
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 34.624
- type: f1
value: 34.43455600252049
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type: Retrieval
dataset:
type: maastrichtlawtech/bsard
name: MTEB BSARDRetrieval
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0.075
- type: map_at_100
value: 0.334
- type: map_at_1000
value: 0.424
- type: map_at_20
value: 0.13
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0.075
- type: mrr_at_100
value: 0.334
- type: mrr_at_1000
value: 0.424
- type: mrr_at_20
value: 0.13
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0.16
- type: ndcg_at_100
value: 1.939
- type: ndcg_at_1000
value: 5.1339999999999995
- type: ndcg_at_20
value: 0.378
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0.045
- type: precision_at_100
value: 0.099
- type: precision_at_1000
value: 0.037
- type: precision_at_20
value: 0.068
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0.44999999999999996
- type: recall_at_100
value: 9.91
- type: recall_at_1000
value: 36.937
- type: recall_at_20
value: 1.351
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Retrieval
dataset:
type: manu/fquad2_test
name: MTEB FQuADRetrieval
config: default
split: test
revision: 5384ce827bbc2156d46e6fcba83d75f8e6e1b4a6
metrics:
- type: map_at_1
value: 52.75
- type: map_at_10
value: 63.44499999999999
- type: map_at_100
value: 63.976
- type: map_at_1000
value: 63.99
- type: map_at_20
value: 63.846000000000004
- type: map_at_3
value: 60.458
- type: map_at_5
value: 62.021
- type: mrr_at_1
value: 52.75
- type: mrr_at_10
value: 63.44499999999999
- type: mrr_at_100
value: 63.976
- type: mrr_at_1000
value: 63.99
- type: mrr_at_20
value: 63.846000000000004
- type: mrr_at_3
value: 60.458
- type: mrr_at_5
value: 62.021
- type: ndcg_at_1
value: 52.75
- type: ndcg_at_10
value: 69.236
- type: ndcg_at_100
value: 71.541
- type: ndcg_at_1000
value: 71.819
- type: ndcg_at_20
value: 70.607
- type: ndcg_at_3
value: 62.973
- type: ndcg_at_5
value: 65.77
- type: precision_at_1
value: 52.75
- type: precision_at_10
value: 8.774999999999999
- type: precision_at_100
value: 0.98
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.65
- type: precision_at_3
value: 23.416999999999998
- type: precision_at_5
value: 15.4
- type: recall_at_1
value: 52.75
- type: recall_at_10
value: 87.75
- type: recall_at_100
value: 98
- type: recall_at_1000
value: 100
- type: recall_at_20
value: 93
- type: recall_at_3
value: 70.25
- type: recall_at_5
value: 77
- task:
type: Clustering
dataset:
type: lyon-nlp/clustering-hal-s2s
name: MTEB HALClusteringS2S
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
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- task:
type: Clustering
dataset:
type: mlsum
name: MTEB MLSUMClusteringP2P
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 42.02993966683553
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- task:
type: Clustering
dataset:
type: mlsum
name: MTEB MLSUMClusteringS2S
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 41.82620802291007
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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: 86.96836830566866
- type: f1
value: 86.90127164146239
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 62.58690886313811
- type: f1
value: 42.18625313836482
- task:
type: Classification
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClassification (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 74.28909952606635
- type: f1
value: 69.99196191661413
- task:
type: Clustering
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClusteringP2P (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 49.679569712615525
- 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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- 0.07594121916826525
- 1
- 0.06369976992079955
- 0.15103131617237134
- 0.5547022737610682
- 0.07594121916826525
- 1
- 0.06369976992079955
- 0.15103131617237134
- 0.5547022737610682
- 0.07594121916826525
- 1
- 0.06369976992079955
- 0.15103131617237134
- 0.5547022737610682
- 0.07594121916826525
- 1
- 0.06369976992079955
- 0.15103131617237134
- 0.5547022737610682
- 0.07594121916826525
- 1
- 0.06369976992079955
- 0.15103131617237134
- 0.5547022737610682
- 0.07594121916826525
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fr)
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.67249495628784
- type: f1
value: 64.41193358780023
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (fr)
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.61398789509079
- type: f1
value: 71.87797304491475
- task:
type: Retrieval
dataset:
type: jinaai/mintakaqa
name: MTEB MintakaRetrieval (fr)
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: map_at_1
value: 16.339000000000002
- type: map_at_10
value: 25.808999999999997
- type: map_at_100
value: 27.023999999999997
- type: map_at_1000
value: 27.1
- type: map_at_20
value: 26.484999999999996
- type: map_at_3
value: 22.891000000000002
- type: map_at_5
value: 24.568
- type: mrr_at_1
value: 16.339000000000002
- type: mrr_at_10
value: 25.808999999999997
- type: mrr_at_100
value: 27.023999999999997
- type: mrr_at_1000
value: 27.1
- type: mrr_at_20
value: 26.484999999999996
- type: mrr_at_3
value: 22.891000000000002
- type: mrr_at_5
value: 24.568
- type: ndcg_at_1
value: 16.339000000000002
- type: ndcg_at_10
value: 31.057000000000002
- type: ndcg_at_100
value: 37.458999999999996
- type: ndcg_at_1000
value: 39.72
- type: ndcg_at_20
value: 33.548
- type: ndcg_at_3
value: 25.016
- type: ndcg_at_5
value: 28.028
- type: precision_at_1
value: 16.339000000000002
- type: precision_at_10
value: 4.787
- type: precision_at_100
value: 0.7889999999999999
- type: precision_at_1000
value: 0.097
- type: precision_at_20
value: 2.889
- type: precision_at_3
value: 10.388
- type: precision_at_5
value: 7.6899999999999995
- type: recall_at_1
value: 16.339000000000002
- type: recall_at_10
value: 47.871
- type: recall_at_100
value: 78.86999999999999
- type: recall_at_1000
value: 97.174
- type: recall_at_20
value: 57.781000000000006
- type: recall_at_3
value: 31.163
- type: recall_at_5
value: 38.452
- task:
type: PairClassification
dataset:
type: GEM/opusparcus
name: MTEB OpusparcusPC (fr)
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 83.10626702997274
- type: cos_sim_ap
value: 93.37313589264696
- type: cos_sim_f1
value: 88.13397129186603
- type: cos_sim_precision
value: 85.0415512465374
- type: cos_sim_recall
value: 91.45978152929494
- type: dot_accuracy
value: 80.44959128065395
- type: dot_ap
value: 90.98443280536087
- type: dot_f1
value: 86.37002341920376
- type: dot_precision
value: 81.73758865248227
- type: dot_recall
value: 91.55908639523336
- type: euclidean_accuracy
value: 82.62942779291554
- type: euclidean_ap
value: 93.12711849212847
- type: euclidean_f1
value: 87.64044943820224
- type: euclidean_precision
value: 82.9052258635961
- type: euclidean_recall
value: 92.9493545183714
- type: manhattan_accuracy
value: 82.49318801089919
- type: manhattan_ap
value: 93.17735641898214
- type: manhattan_f1
value: 87.5884851344974
- type: manhattan_precision
value: 83.45323741007195
- type: manhattan_recall
value: 92.15491559086395
- type: max_accuracy
value: 83.10626702997274
- type: max_ap
value: 93.37313589264696
- type: max_f1
value: 88.13397129186603
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (fr)
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 65.4
- type: cos_sim_ap
value: 66.91762455306835
- type: cos_sim_f1
value: 64.69093988145639
- type: cos_sim_precision
value: 52.36463331048663
- type: cos_sim_recall
value: 84.60686600221484
- type: dot_accuracy
value: 56.39999999999999
- type: dot_ap
value: 50.65829702535556
- type: dot_f1
value: 62.491349480968864
- type: dot_precision
value: 45.44539506794162
- type: dot_recall
value: 100
- type: euclidean_accuracy
value: 65.05
- type: euclidean_ap
value: 67.15324108025274
- type: euclidean_f1
value: 64.36681222707423
- type: euclidean_precision
value: 53.13626532083634
- type: euclidean_recall
value: 81.61683277962348
- type: manhattan_accuracy
value: 65
- type: manhattan_ap
value: 67.15521762543842
- type: manhattan_f1
value: 64.35773783428323
- type: manhattan_precision
value: 53.26560232220609
- type: manhattan_recall
value: 81.28460686600222
- type: max_accuracy
value: 65.4
- type: max_ap
value: 67.15521762543842
- type: max_f1
value: 64.69093988145639
- task:
type: STS
dataset:
type: Lajavaness/SICK-fr
name: MTEB SICKFr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_pearson
value: 80.13453477364664
- type: cos_sim_spearman
value: 71.59592251091182
- type: euclidean_pearson
value: 74.6813217331865
- type: euclidean_spearman
value: 69.83675848670511
- type: manhattan_pearson
value: 74.80033516228748
- type: manhattan_spearman
value: 69.91910080397358
- 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: 80.66905713032178
- type: cos_sim_spearman
value: 82.3452553922562
- type: euclidean_pearson
value: 74.6277040883804
- type: euclidean_spearman
value: 77.78371755530127
- type: manhattan_pearson
value: 74.81879938632161
- type: manhattan_spearman
value: 77.58712341518728
- 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: 80.4059890704687
- type: cos_sim_spearman
value: 79.21950962470463
- type: euclidean_pearson
value: 77.07401988705624
- type: euclidean_spearman
value: 77.77234330224117
- type: manhattan_pearson
value: 77.08866405845029
- type: manhattan_spearman
value: 77.8144806404841
- 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: 30.36666619115537
- type: cos_sim_spearman
value: 30.22116851214898
- type: dot_pearson
value: 28.017505248518955
- type: dot_spearman
value: 29.11540877445319
- 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: 84.06666666666666
- type: mrr
value: 84.06666666666666
- 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: 71.035
- type: map_at_100
value: 71.257
- type: map_at_1000
value: 71.257
- type: map_at_20
value: 71.125
- type: map_at_3
value: 68.667
- type: map_at_5
value: 70.267
- type: mrr_at_1
value: 56.99999999999999
- type: mrr_at_10
value: 71.035
- type: mrr_at_100
value: 71.257
- type: mrr_at_1000
value: 71.257
- type: mrr_at_20
value: 71.125
- type: mrr_at_3
value: 68.667
- type: mrr_at_5
value: 70.267
- type: ndcg_at_1
value: 56.99999999999999
- type: ndcg_at_10
value: 76.957
- type: ndcg_at_100
value: 78.037
- type: ndcg_at_1000
value: 78.037
- type: ndcg_at_20
value: 77.236
- type: ndcg_at_3
value: 72.357
- type: ndcg_at_5
value: 75.24
- 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: 4.8
- type: precision_at_3
value: 27.667
- type: precision_at_5
value: 18
- 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: 96
- type: recall_at_3
value: 83
- type: recall_at_5
value: 90
- task:
type: Retrieval
dataset:
type: jinaai/xpqa
name: MTEB XPQARetrieval (fr)
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
- type: map_at_1
value: 38.68
- type: map_at_10
value: 58.388
- type: map_at_100
value: 59.99100000000001
- type: map_at_1000
value: 60.045
- type: map_at_20
value: 59.439
- type: map_at_3
value: 52.467
- type: map_at_5
value: 55.832
- type: mrr_at_1
value: 61.282000000000004
- type: mrr_at_10
value: 68.00500000000001
- type: mrr_at_100
value: 68.659
- type: mrr_at_1000
value: 68.675
- type: mrr_at_20
value: 68.459
- type: mrr_at_3
value: 66.444
- type: mrr_at_5
value: 67.192
- type: ndcg_at_1
value: 61.282000000000004
- type: ndcg_at_10
value: 64.802
- type: ndcg_at_100
value: 70.104
- type: ndcg_at_1000
value: 71.006
- type: ndcg_at_20
value: 67.41
- type: ndcg_at_3
value: 59.75
- type: ndcg_at_5
value: 60.842
- type: precision_at_1
value: 61.282000000000004
- type: precision_at_10
value: 14.940000000000001
- type: precision_at_100
value: 1.936
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_20
value: 8.405
- type: precision_at_3
value: 35.870000000000005
- type: precision_at_5
value: 25.207
- type: recall_at_1
value: 38.68
- type: recall_at_10
value: 73.485
- type: recall_at_100
value: 93.669
- type: recall_at_1000
value: 99.506
- type: recall_at_20
value: 81.733
- type: recall_at_3
value: 57.352000000000004
- type: recall_at_5
value: 63.912
license: mit
datasets:
- manu/embedding_data_v2_100k
language:
- fr
- en
{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 199 with parameters:
{'batch_size': 512, 'sampler': 'transformers.trainer_pt_utils.LengthGroupedSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss:
__main__.MultipleNegativesRankingLoss_with_logging
Parameters of the fit()-Method:
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupCosine",
"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})
)