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roberta-es-clinical-trials-ner-fd-text_cl

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: lcampillos/roberta-es-clinical-trials-ner
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - precision
  - f1
model-index:
  - name: roberta-es-clinical-trials-ner-fd-text_cl
    results: []

roberta-es-clinical-trials-ner-fd-text_cl

This model is a fine-tuned version of lcampillos/roberta-es-clinical-trials-ner on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0955
  • Accuracy: 0.8961
  • Recall: 0.9353
  • Precision: 0.8926
  • F1: 0.9134

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy Recall Precision F1
No log 1.0 231 0.2599 0.9107 0.9273 0.9209 0.9241
No log 2.0 462 0.4050 0.9008 0.9482 0.8897 0.9180
0.1696 3.0 693 0.5082 0.8944 0.9412 0.8857 0.9126
0.1696 4.0 924 0.7025 0.8821 0.9323 0.8748 0.9026
0.0363 5.0 1155 0.6880 0.9026 0.9432 0.8959 0.9190
0.0363 6.0 1386 0.6909 0.9096 0.9263 0.9199 0.9231
0.0051 7.0 1617 0.8435 0.8938 0.9462 0.8813 0.9126
0.0051 8.0 1848 0.9259 0.8891 0.9452 0.8755 0.9090
0.0085 9.0 2079 0.7661 0.9043 0.9253 0.9126 0.9189
0.0085 10.0 2310 0.8466 0.8915 0.9452 0.8787 0.9107
0.0063 11.0 2541 0.8288 0.9043 0.9183 0.9183 0.9183
0.0063 12.0 2772 0.9942 0.8827 0.9472 0.8653 0.9044
0.009 13.0 3003 0.5731 0.9294 0.9223 0.9556 0.9387
0.009 14.0 3234 0.7689 0.9084 0.9402 0.9068 0.9232
0.009 15.0 3465 1.2144 0.8687 0.9532 0.8432 0.8948
0.0017 16.0 3696 0.9313 0.8956 0.9283 0.8970 0.9124
0.0017 17.0 3927 0.8994 0.9049 0.9213 0.9167 0.9190
0.001 18.0 4158 0.9995 0.8956 0.9323 0.8940 0.9127
0.001 19.0 4389 1.0237 0.8932 0.9333 0.8898 0.9110
0.0007 20.0 4620 1.0355 0.8938 0.9373 0.8877 0.9118
0.0007 21.0 4851 1.0372 0.8944 0.9343 0.8908 0.9120
0.0006 22.0 5082 1.0451 0.8944 0.9343 0.8908 0.9120
0.0006 23.0 5313 1.0461 0.8944 0.9343 0.8908 0.9120
0.0004 24.0 5544 1.0582 0.8932 0.9343 0.8891 0.9111
0.0004 25.0 5775 1.0680 0.8944 0.9373 0.8886 0.9123
0.0008 26.0 6006 1.0828 0.8921 0.9343 0.8874 0.9102
0.0008 27.0 6237 1.0875 0.8932 0.9442 0.8819 0.9120
0.0008 28.0 6468 1.0394 0.8961 0.9223 0.9025 0.9123
0.0022 29.0 6699 1.0938 0.8961 0.9353 0.8926 0.9134
0.0022 30.0 6930 1.0955 0.8961 0.9353 0.8926 0.9134

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.0
  • Tokenizers 0.21.1