---
license: mit
base_model: croissantllm/CroissantCool-v0.2
datasets: asi/wikitext_fr
tags:
- generated_from_trainer
- mteb
metrics:
- accuracy
model-index:
- name: final
  results:
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloProfClusteringP2P
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 55.581940197813005
    - 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: 24.224941313005694
    - 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: 36.62420987618669
    - type: mrr
      value: 37.12633988540724
  - task:
      type: Retrieval
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloprofRetrieval
      config: default
      split: test
      revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
    metrics:
    - type: map_at_1
      value: 1.3390000000000002
    - type: map_at_10
      value: 2.723
    - type: map_at_100
      value: 3.2680000000000002
    - type: map_at_1000
      value: 3.45
    - type: map_at_20
      value: 2.933
    - type: map_at_3
      value: 2.152
    - type: map_at_5
      value: 2.387
    - type: mrr_at_1
      value: 1.3390000000000002
    - type: mrr_at_10
      value: 2.723
    - type: mrr_at_100
      value: 3.2680000000000002
    - type: mrr_at_1000
      value: 3.45
    - type: mrr_at_20
      value: 2.933
    - type: mrr_at_3
      value: 2.152
    - type: mrr_at_5
      value: 2.387
    - type: ndcg_at_1
      value: 1.3390000000000002
    - type: ndcg_at_10
      value: 3.6830000000000003
    - type: ndcg_at_100
      value: 7.188999999999999
    - type: ndcg_at_1000
      value: 13.244
    - type: ndcg_at_20
      value: 4.441
    - type: ndcg_at_3
      value: 2.4299999999999997
    - type: ndcg_at_5
      value: 2.855
    - type: precision_at_1
      value: 1.3390000000000002
    - type: precision_at_10
      value: 0.687
    - type: precision_at_100
      value: 0.253
    - type: precision_at_1000
      value: 0.076
    - type: precision_at_20
      value: 0.492
    - type: precision_at_3
      value: 1.079
    - type: precision_at_5
      value: 0.855
    - type: recall_at_1
      value: 1.3390000000000002
    - type: recall_at_10
      value: 6.865
    - type: recall_at_100
      value: 25.345000000000002
    - type: recall_at_1000
      value: 75.64800000000001
    - type: recall_at_20
      value: 9.844999999999999
    - type: recall_at_3
      value: 3.238
    - type: recall_at_5
      value: 4.275
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (fr)
      config: fr
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 31.394
    - type: f1
      value: 31.16026998979532
    - type: scores_per_experiment
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  - task:
      type: Retrieval
    dataset:
      type: maastrichtlawtech/bsard
      name: MTEB BSARDRetrieval
      config: default
      split: test
      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
    metrics:
    - type: map_at_1
      value: 0.44999999999999996
    - type: map_at_10
      value: 1.051
    - type: map_at_100
      value: 1.2510000000000001
    - type: map_at_1000
      value: 1.295
    - type: map_at_20
      value: 1.114
    - type: map_at_3
      value: 0.826
    - type: map_at_5
      value: 0.938
    - type: mrr_at_1
      value: 0.44999999999999996
    - type: mrr_at_10
      value: 1.051
    - type: mrr_at_100
      value: 1.2510000000000001
    - type: mrr_at_1000
      value: 1.295
    - type: mrr_at_20
      value: 1.114
    - type: mrr_at_3
      value: 0.826
    - type: mrr_at_5
      value: 0.938
    - type: ndcg_at_1
      value: 0.44999999999999996
    - type: ndcg_at_10
      value: 1.438
    - type: ndcg_at_100
      value: 2.726
    - type: ndcg_at_1000
      value: 4.671
    - type: ndcg_at_20
      value: 1.667
    - type: ndcg_at_3
      value: 0.96
    - type: ndcg_at_5
      value: 1.154
    - type: precision_at_1
      value: 0.44999999999999996
    - type: precision_at_10
      value: 0.27
    - type: precision_at_100
      value: 0.095
    - type: precision_at_1000
      value: 0.027
    - type: precision_at_20
      value: 0.18
    - type: precision_at_3
      value: 0.44999999999999996
    - type: precision_at_5
      value: 0.36
    - type: recall_at_1
      value: 0.44999999999999996
    - type: recall_at_10
      value: 2.703
    - type: recall_at_100
      value: 9.459
    - type: recall_at_1000
      value: 26.577
    - type: recall_at_20
      value: 3.604
    - type: recall_at_3
      value: 1.351
    - type: recall_at_5
      value: 1.802
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/clustering-hal-s2s
      name: MTEB HALClusteringS2S
      config: default
      split: test
      revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
    metrics:
    - type: v_measure
      value: 10.851593075169648
    - type: v_measures
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  - task:
      type: Clustering
    dataset:
      type: mlsum
      name: MTEB MLSUMClusteringP2P (fr)
      config: fr
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 40.96147064025931
    - type: v_measures
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  - task:
      type: Clustering
    dataset:
      type: mlsum
      name: MTEB MLSUMClusteringS2S (fr)
      config: fr
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 40.71439317097922
    - 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: 72.64015032884434
    - type: f1
      value: 72.14605359682105
    - type: scores_per_experiment
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  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (fr)
      config: fr
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 54.26558095834638
    - type: f1
      value: 36.089718847466244
    - type: scores_per_experiment
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  - task:
      type: Classification
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClassification (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: accuracy
      value: 67.77251184834124
    - type: f1
      value: 64.6618177920645
    - type: scores_per_experiment
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  - task:
      type: Clustering
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClusteringP2P (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 40.457326311863824
    - 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: 22.17780386072903
    - 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: 51.523201075991935
    - type: f1
      value: 48.89402486278762
    - type: scores_per_experiment
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (fr)
      config: fr
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 56.65097511768662
    - type: f1
      value: 55.06103895004255
    - type: scores_per_experiment
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  - task:
      type: Retrieval
    dataset:
      type: jinaai/mintakaqa
      name: MTEB MintakaRetrieval (fr)
      config: fr
      split: test
      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
    metrics:
    - type: map_at_1
      value: 0.819
    - type: map_at_10
      value: 1.409
    - type: map_at_100
      value: 1.6119999999999999
    - type: map_at_1000
      value: 1.752
    - type: map_at_20
      value: 1.473
    - type: map_at_3
      value: 1.16
    - type: map_at_5
      value: 1.308
    - type: mrr_at_1
      value: 0.819
    - type: mrr_at_10
      value: 1.409
    - type: mrr_at_100
      value: 1.6119999999999999
    - type: mrr_at_1000
      value: 1.752
    - type: mrr_at_20
      value: 1.473
    - type: mrr_at_3
      value: 1.16
    - type: mrr_at_5
      value: 1.308
    - type: ndcg_at_1
      value: 0.819
    - type: ndcg_at_10
      value: 1.7999999999999998
    - type: ndcg_at_100
      value: 3.154
    - type: ndcg_at_1000
      value: 9.197
    - type: ndcg_at_20
      value: 2.036
    - type: ndcg_at_3
      value: 1.282
    - type: ndcg_at_5
      value: 1.55
    - type: precision_at_1
      value: 0.819
    - type: precision_at_10
      value: 0.307
    - type: precision_at_100
      value: 0.10300000000000001
    - type: precision_at_1000
      value: 0.063
    - type: precision_at_20
      value: 0.201
    - type: precision_at_3
      value: 0.5459999999999999
    - type: precision_at_5
      value: 0.459
    - type: recall_at_1
      value: 0.819
    - type: recall_at_10
      value: 3.071
    - type: recall_at_100
      value: 10.278
    - type: recall_at_1000
      value: 63.309000000000005
    - type: recall_at_20
      value: 4.013
    - type: recall_at_3
      value: 1.638
    - type: recall_at_5
      value: 2.2929999999999997
  - task:
      type: Classification
    dataset:
      type: allocine
      name: MTEB MovieReviewSentimentClassification
      config: default
      split: test
      revision: a4654f4896408912913a62ace89614879a549287
    metrics:
    - type: accuracy
      value: 65.771484375
    - type: ap
      value: 58.88731378768377
    - type: f1
      value: 65.63879270001055
    - type: scores_per_experiment
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  - task:
      type: PairClassification
    dataset:
      type: GEM/opusparcus
      name: MTEB OpusparcusPC (fr)
      config: fr
      split: test
      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
    metrics:
    - type: cos_sim_accuracy
      value: 99.90069513406156
    - type: cos_sim_ap
      value: 100.0
    - type: cos_sim_f1
      value: 99.95032290114257
    - type: cos_sim_precision
      value: 100.0
    - type: cos_sim_recall
      value: 99.90069513406156
    - type: dot_accuracy
      value: 99.90069513406156
    - type: dot_ap
      value: 100.0
    - type: dot_f1
      value: 99.95032290114257
    - type: dot_precision
      value: 100.0
    - type: dot_recall
      value: 99.90069513406156
    - type: euclidean_accuracy
      value: 99.90069513406156
    - type: euclidean_ap
      value: 100.0
    - type: euclidean_f1
      value: 99.95032290114257
    - type: euclidean_precision
      value: 100.0
    - type: euclidean_recall
      value: 99.90069513406156
    - type: manhattan_accuracy
      value: 99.90069513406156
    - type: manhattan_ap
      value: 100.0
    - type: manhattan_f1
      value: 99.95032290114257
    - type: manhattan_precision
      value: 100.0
    - type: manhattan_recall
      value: 99.90069513406156
    - type: max_accuracy
      value: 99.90069513406156
    - type: max_ap
      value: 100.0
    - type: max_f1
      value: 99.95032290114257
  - task:
      type: PairClassification
    dataset:
      type: paws-x
      name: MTEB PawsX (fr)
      config: fr
      split: test
      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
    metrics:
    - type: cos_sim_accuracy
      value: 59.0
    - type: cos_sim_ap
      value: 56.320346544376406
    - type: cos_sim_f1
      value: 62.74792643346557
    - type: cos_sim_precision
      value: 46.524064171122994
    - type: cos_sim_recall
      value: 96.3455149501661
    - type: dot_accuracy
      value: 55.7
    - type: dot_ap
      value: 46.71252478881971
    - type: dot_f1
      value: 62.29281767955801
    - type: dot_precision
      value: 45.25840441545409
    - type: dot_recall
      value: 99.88925802879291
    - type: euclidean_accuracy
      value: 58.699999999999996
    - type: euclidean_ap
      value: 56.46116374322776
    - type: euclidean_f1
      value: 62.71125494426466
    - type: euclidean_precision
      value: 46.43237486687966
    - type: euclidean_recall
      value: 96.56699889258029
    - type: manhattan_accuracy
      value: 58.699999999999996
    - type: manhattan_ap
      value: 56.47330912340628
    - type: manhattan_f1
      value: 62.64014466546113
    - type: manhattan_precision
      value: 46.50912996777659
    - type: manhattan_recall
      value: 95.90254706533776
    - type: max_accuracy
      value: 59.0
    - type: max_ap
      value: 56.47330912340628
    - type: max_f1
      value: 62.74792643346557
  - task:
      type: STS
    dataset:
      type: Lajavaness/SICK-fr
      name: MTEB SICKFr
      config: default
      split: test
      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
    metrics:
    - type: cos_sim_pearson
      value: 38.073122812837504
    - type: cos_sim_spearman
      value: 43.503372662239165
    - type: euclidean_pearson
      value: 46.33404452205143
    - type: euclidean_spearman
      value: 45.78244167225132
    - type: manhattan_pearson
      value: 47.44323302000745
    - type: manhattan_spearman
      value: 46.38251854201864
  - 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: 31.103039356525112
    - type: cos_sim_spearman
      value: 53.56664221111779
    - type: euclidean_pearson
      value: 36.3392983409797
    - type: euclidean_spearman
      value: 52.62686684274186
    - type: manhattan_pearson
      value: 36.85082171392844
    - type: manhattan_spearman
      value: 53.53895289560877
  - 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: 35.908744486971926
    - type: cos_sim_spearman
      value: 44.20832000787315
    - type: euclidean_pearson
      value: 44.37808372773975
    - type: euclidean_spearman
      value: 46.10319258119541
    - type: manhattan_pearson
      value: 45.55526037587735
    - type: manhattan_spearman
      value: 46.736062997919625
  - 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: 29.78422374922367
    - type: cos_sim_spearman
      value: 30.5368810546113
    - type: dot_pearson
      value: 20.865163411951603
    - type: dot_spearman
      value: 20.197932825061066
  - 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: 58.08333333333333
    - type: mrr
      value: 58.08333333333333
  - 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: 11.0
    - type: map_at_10
      value: 19.556
    - type: map_at_100
      value: 21.740000000000002
    - type: map_at_1000
      value: 21.740000000000002
    - type: map_at_20
      value: 20.623
    - type: map_at_3
      value: 16.667
    - type: map_at_5
      value: 17.967
    - type: mrr_at_1
      value: 11.0
    - type: mrr_at_10
      value: 19.556
    - type: mrr_at_100
      value: 21.740000000000002
    - type: mrr_at_1000
      value: 21.740000000000002
    - type: mrr_at_20
      value: 20.623
    - type: mrr_at_3
      value: 16.667
    - type: mrr_at_5
      value: 17.967
    - type: ndcg_at_1
      value: 11.0
    - type: ndcg_at_10
      value: 25.252999999999997
    - type: ndcg_at_100
      value: 36.85
    - type: ndcg_at_1000
      value: 36.85
    - type: ndcg_at_20
      value: 29.076
    - type: ndcg_at_3
      value: 18.786
    - type: ndcg_at_5
      value: 21.194
    - type: precision_at_1
      value: 11.0
    - type: precision_at_10
      value: 4.3999999999999995
    - type: precision_at_100
      value: 1.0
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_20
      value: 2.9499999999999997
    - type: precision_at_3
      value: 8.333
    - type: precision_at_5
      value: 6.2
    - type: recall_at_1
      value: 11.0
    - type: recall_at_10
      value: 44.0
    - type: recall_at_100
      value: 100.0
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_20
      value: 59.0
    - type: recall_at_3
      value: 25.0
    - type: recall_at_5
      value: 31.0
  - task:
      type: Retrieval
    dataset:
      type: jinaai/xpqa
      name: MTEB XPQARetrieval (fr)
      config: fr
      split: test
      revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
    metrics:
    - type: map_at_1
      value: 9.028
    - type: map_at_10
      value: 13.422999999999998
    - type: map_at_100
      value: 14.48
    - type: map_at_1000
      value: 14.693999999999999
    - type: map_at_20
      value: 13.963999999999999
    - type: map_at_3
      value: 11.888
    - type: map_at_5
      value: 12.654000000000002
    - type: mrr_at_1
      value: 16.422
    - type: mrr_at_10
      value: 20.678
    - type: mrr_at_100
      value: 21.534
    - type: mrr_at_1000
      value: 21.648999999999997
    - type: mrr_at_20
      value: 21.127000000000002
    - type: mrr_at_3
      value: 19.292
    - type: mrr_at_5
      value: 20.093
    - type: ndcg_at_1
      value: 16.422
    - type: ndcg_at_10
      value: 16.999
    - type: ndcg_at_100
      value: 22.039
    - type: ndcg_at_1000
      value: 27.791
    - type: ndcg_at_20
      value: 18.698
    - type: ndcg_at_3
      value: 15.1
    - type: ndcg_at_5
      value: 15.468000000000002
    - type: precision_at_1
      value: 16.422
    - type: precision_at_10
      value: 4.393
    - type: precision_at_100
      value: 0.893
    - type: precision_at_1000
      value: 0.174
    - type: precision_at_20
      value: 2.757
    - type: precision_at_3
      value: 9.657
    - type: precision_at_5
      value: 6.9430000000000005
    - type: recall_at_1
      value: 9.028
    - type: recall_at_10
      value: 20.258000000000003
    - type: recall_at_100
      value: 40.485
    - type: recall_at_1000
      value: 80.545
    - type: recall_at_20
      value: 25.834000000000003
    - type: recall_at_3
      value: 14.069999999999999
    - type: recall_at_5
      value: 16.611
language:
- fr
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# llm2vec-croissant-mntp

This model is a fine-tuned version of [croissantllm/CroissantCool-v0.2](https://huggingface.co/croissantllm/CroissantCool-v0.2) on [asi/wikitext_fr](asi/wikitext_fr).
It achieves the following results on the evaluation set:
- Loss: 1.8867
- Accuracy: 0.6078

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.0884 | 100  | 4.7866          | 0.1990   |
| No log        | 0.1768 | 200  | 4.0496          | 0.3309   |
| No log        | 0.2653 | 300  | 3.6525          | 0.3779   |
| No log        | 0.3537 | 400  | 3.2410          | 0.4258   |
| 3.9116        | 0.4421 | 500  | 3.6305          | 0.3912   |
| 3.9116        | 0.5305 | 600  | 3.1770          | 0.4406   |
| 3.9116        | 0.6189 | 700  | 2.4478          | 0.5199   |
| 3.9116        | 0.7073 | 800  | 2.2383          | 0.5508   |
| 3.9116        | 0.7958 | 900  | 2.1547          | 0.5635   |
| 2.4568        | 0.8842 | 1000 | 2.0868          | 0.5759   |
| 2.4568        | 0.9726 | 1100 | 2.0399          | 0.5820   |
| 2.4568        | 1.0610 | 1200 | 2.0102          | 0.5873   |
| 2.4568        | 1.1494 | 1300 | 1.9805          | 0.5897   |
| 2.4568        | 1.2378 | 1400 | 1.9590          | 0.5955   |
| 1.9305        | 1.3263 | 1500 | 1.9381          | 0.5982   |
| 1.9305        | 1.4147 | 1600 | 1.9249          | 0.5995   |
| 1.9305        | 1.5031 | 1700 | 1.9223          | 0.6017   |
| 1.9305        | 1.5915 | 1800 | 1.9091          | 0.6037   |
| 1.9305        | 1.6799 | 1900 | 1.9038          | 0.6042   |
| 1.8511        | 1.7683 | 2000 | 1.8982          | 0.6045   |
| 1.8511        | 1.8568 | 2100 | 1.8924          | 0.6060   |
| 1.8511        | 1.9452 | 2200 | 1.8844          | 0.6072   |
| 1.8511        | 2.0336 | 2300 | 1.8873          | 0.6087   |
| 1.8511        | 2.1220 | 2400 | 1.8889          | 0.6068   |
| 1.8197        | 2.2104 | 2500 | 1.8848          | 0.6080   |
| 1.8197        | 2.2989 | 2600 | 1.8736          | 0.6091   |
| 1.8197        | 2.3873 | 2700 | 1.8858          | 0.6072   |
| 1.8197        | 2.4757 | 2800 | 1.8814          | 0.6088   |
| 1.8197        | 2.5641 | 2900 | 1.8649          | 0.6103   |
| 1.8116        | 2.6525 | 3000 | 1.8647          | 0.6091   |
| 1.8116        | 2.7409 | 3100 | 1.8755          | 0.6101   |
| 1.8116        | 2.8294 | 3200 | 1.8755          | 0.6099   |
| 1.8116        | 2.9178 | 3300 | 1.8867          | 0.6078   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.0.1+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1