README.md

---
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
  - task:
      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:
    - type: v_measure
      value: 25.766659053012397
    - type: v_measures
      value:
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  - task:
      type: Clustering
    dataset:
      type: mlsum
      name: MTEB MLSUMClusteringP2P
      config: fr
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
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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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    - type: v_measures
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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: 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](https://www.SBERT.net) model: It maps sentences & paragraphs to a 2048 dimensional dense vector space and can be used for tasks like clustering or semantic search.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
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

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})


## 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})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->