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Fine-tuning completed
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metadata
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: NeRUBioS_xlm_RoBERTa_base_Training_Testing
    results: []

NeRUBioS_xlm_RoBERTa_base_Training_Testing

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

  • Loss: 0.3585
  • Negref Precision: 0.5638
  • Negref Recall: 0.6035
  • Negref F1: 0.5830
  • Neg Precision: 0.9508
  • Neg Recall: 0.9642
  • Neg F1: 0.9575
  • Nsco Precision: 0.8692
  • Nsco Recall: 0.9047
  • Nsco F1: 0.8866
  • Unc Precision: 0.8005
  • Unc Recall: 0.8846
  • Unc F1: 0.8404
  • Usco Precision: 0.6696
  • Usco Recall: 0.7815
  • Usco F1: 0.7212
  • Precision: 0.8184
  • Recall: 0.8628
  • F1: 0.8400
  • Accuracy: 0.9482

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: 12

Training results

Training Loss Epoch Step Validation Loss Negref Precision Negref Recall Negref F1 Neg Precision Neg Recall Neg F1 Nsco Precision Nsco Recall Nsco F1 Unc Precision Unc Recall Unc F1 Usco Precision Usco Recall Usco F1 Precision Recall F1 Accuracy
0.2259 1.0 1729 0.2246 0.4076 0.4890 0.4446 0.9112 0.9515 0.9310 0.7928 0.8654 0.8275 0.7015 0.8256 0.7585 0.4629 0.6735 0.5487 0.7158 0.8122 0.7610 0.9287
0.16 2.0 3458 0.2028 0.5217 0.5301 0.5259 0.9283 0.9642 0.9459 0.8311 0.8896 0.8593 0.7812 0.8513 0.8147 0.5734 0.7532 0.6511 0.7817 0.8405 0.8100 0.9397
0.1235 3.0 5187 0.2148 0.5176 0.4963 0.5067 0.9520 0.9607 0.9563 0.8641 0.8850 0.8744 0.7684 0.8846 0.8224 0.6113 0.7481 0.6728 0.8038 0.8350 0.8191 0.9439
0.0949 4.0 6916 0.2261 0.5054 0.6211 0.5573 0.9327 0.9642 0.9482 0.8450 0.8828 0.8635 0.7976 0.8487 0.8224 0.6034 0.7352 0.6628 0.7818 0.8512 0.8150 0.9450
0.0633 5.0 8645 0.2354 0.5609 0.5947 0.5773 0.9417 0.9649 0.9532 0.8669 0.9062 0.8861 0.8062 0.8641 0.8342 0.6334 0.7506 0.6871 0.8118 0.8573 0.8340 0.9461
0.0495 6.0 10374 0.2829 0.5585 0.5962 0.5767 0.9445 0.9684 0.9563 0.8671 0.9077 0.8869 0.8116 0.8615 0.8358 0.6526 0.7532 0.6993 0.8151 0.8592 0.8366 0.9442
0.0365 7.0 12103 0.2699 0.5446 0.5830 0.5631 0.9552 0.9572 0.9562 0.8804 0.9024 0.8913 0.8080 0.8846 0.8446 0.6521 0.7661 0.7045 0.8182 0.8550 0.8362 0.9473
0.0265 8.0 13832 0.3082 0.5630 0.5580 0.5605 0.9466 0.9593 0.9529 0.8702 0.9024 0.8860 0.8038 0.8718 0.8364 0.6571 0.7635 0.7063 0.8194 0.8502 0.8345 0.9460
0.0216 9.0 15561 0.3286 0.5485 0.5977 0.5720 0.9388 0.9691 0.9537 0.8715 0.9077 0.8892 0.8085 0.8769 0.8413 0.6453 0.7763 0.7048 0.8105 0.8633 0.8361 0.9455
0.0133 10.0 17290 0.3503 0.5732 0.6094 0.5907 0.9481 0.9628 0.9554 0.8698 0.8994 0.8843 0.8137 0.8846 0.8477 0.6816 0.7815 0.7281 0.8223 0.8616 0.8415 0.9482
0.0088 11.0 19019 0.3476 0.5584 0.6182 0.5868 0.9450 0.9656 0.9552 0.8614 0.9070 0.8836 0.8080 0.8846 0.8446 0.6659 0.7789 0.7180 0.8126 0.8661 0.8385 0.9483
0.0093 12.0 20748 0.3585 0.5638 0.6035 0.5830 0.9508 0.9642 0.9575 0.8692 0.9047 0.8866 0.8005 0.8846 0.8404 0.6696 0.7815 0.7212 0.8184 0.8628 0.8400 0.9482

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2