---
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:
    - type: v_measure
      value: 37.62128894914382
    - type: v_measures
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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
  - task:
      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
  - task:
      type: Retrieval
    dataset:
      type: maastrichtlawtech/bsard
      name: MTEB BSARDRetrieval
      config: default
      split: test
      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
    metrics:
    - type: map_at_1
      value: 0.0
    - type: map_at_10
      value: 0.15
    - type: map_at_100
      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: 0.0
    - 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: 0.0
    - 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
    - type: ndcg_at_5
      value: 0.22499999999999998
    - type: precision_at_1
      value: 0.0
    - type: precision_at_10
      value: 0.045
    - type: precision_at_100
      value: 0.023
    - type: precision_at_1000
      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: 0.0
    - type: recall_at_10
      value: 0.44999999999999996
    - type: recall_at_100
      value: 2.252
    - type: recall_at_1000
      value: 20.27
    - type: recall_at_20
      value: 0.901
    - type: recall_at_3
      value: 0.44999999999999996
    - type: recall_at_5
      value: 0.44999999999999996
  - task:
      type: Retrieval
    dataset:
      type: manu/fquad2_test
      name: MTEB FQuADRetrieval
      config: default
      split: test
      revision: 5384ce827bbc2156d46e6fcba83d75f8e6e1b4a6
    metrics:
    - type: map_at_1
      value: 53.0
    - 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: 53.0
    - type: mrr_at_10
      value: 65.393
    - type: mrr_at_100
      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: 53.0
    - 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: 53.0
    - 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: 53.0
    - type: recall_at_10
      value: 90.25
    - type: recall_at_100
      value: 99.0
    - type: recall_at_1000
      value: 100.0
    - 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
    metrics:
    - type: v_measure
      value: 25.756762769106768
    - type: v_measures
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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
    - type: v_measures
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  - task:
      type: Clustering
    dataset:
      type: mlsum
      name: MTEB MLSUMClusteringS2S
      config: fr
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 41.83097630331037
    - type: v_measures
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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:
    - type: v_measure
      value: 36.39726683344252
    - 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: 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: 100.0
    - 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: 1.0
    - 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: 95.0
    - type: recall_at_100
      value: 100.0
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_20
      value: 97.0
    - type: recall_at_3
      value: 82.0
    - type: recall_at_5
      value: 92.0
  - 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](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 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})
)
```

## Citing & Authors

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