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CNEC2_0_Supertypes_xlm-roberta-large

Token Classification
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CNEC2_0_Supertypes_xlm-roberta-large

This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the cnec dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2155
  • Precision: 0.8565
  • Recall: 0.8971
  • F1: 0.8763
  • Accuracy: 0.9709

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: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.4393 1.0 900 0.1671 0.7756 0.8195 0.7969 0.9590
0.1716 2.0 1800 0.1409 0.8155 0.8583 0.8364 0.9662
0.1326 3.0 2700 0.1288 0.8203 0.8748 0.8467 0.9687
0.1027 4.0 3600 0.1408 0.8290 0.8732 0.8505 0.9683
0.0891 5.0 4500 0.1447 0.8485 0.9000 0.8735 0.9725
0.0715 6.0 5400 0.1393 0.8561 0.8868 0.8712 0.9713
0.0644 7.0 6300 0.1586 0.8517 0.8918 0.8713 0.9702
0.0535 8.0 7200 0.1526 0.8481 0.8810 0.8643 0.9696
0.0492 9.0 8100 0.1795 0.8529 0.8984 0.8751 0.9702
0.0391 10.0 9000 0.1903 0.8536 0.8938 0.8733 0.9693
0.0323 11.0 9900 0.1885 0.8615 0.9046 0.8825 0.9724
0.0274 12.0 10800 0.2099 0.8585 0.9025 0.8800 0.9696
0.0237 13.0 11700 0.1944 0.8624 0.9009 0.8812 0.9720
0.0245 14.0 12600 0.2129 0.8618 0.8967 0.8789 0.9711
0.0206 15.0 13500 0.2155 0.8565 0.8971 0.8763 0.9709

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0
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Evaluation results

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