NER-finetuning-BETO
Este es el modelo de BETO para NER NazaGara/NER-fine-tuned-BETO on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2140
- Precision: 0.8424
- Recall: 0.8545
- F1: 0.8484
- Accuracy: 0.9691
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0482 | 1.0 | 1041 | 0.1522 | 0.8309 | 0.8481 | 0.8394 | 0.9687 |
| 0.0302 | 2.0 | 2082 | 0.1661 | 0.8293 | 0.8527 | 0.8408 | 0.9696 |
| 0.0164 | 3.0 | 3123 | 0.1691 | 0.8403 | 0.8536 | 0.8469 | 0.9696 |
| 0.011 | 4.0 | 4164 | 0.2026 | 0.8427 | 0.8516 | 0.8471 | 0.9693 |
| 0.0073 | 5.0 | 5205 | 0.2140 | 0.8424 | 0.8545 | 0.8484 | 0.9691 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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NazaGara/NER-fine-tuned-BETO