NER-finetuning-BETO-PRO
This model is a fine-tuned version of google-bert/bert-base-cased on the conll2002 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1391
- Precision: 0.7331
- Recall: 0.7923
- F1: 0.7616
- Accuracy: 0.9655
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: Use 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1028 | 1.0 | 1041 | 0.1424 | 0.7051 | 0.7603 | 0.7317 | 0.9618 |
| 0.0678 | 2.0 | 2082 | 0.1391 | 0.7331 | 0.7923 | 0.7616 | 0.9655 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for raulgdp/NER-finetuning-BETO-PRO
Base model
google-bert/bert-base-casedDataset used to train raulgdp/NER-finetuning-BETO-PRO
Evaluation results
- Precision on conll2002validation set self-reported0.733
- Recall on conll2002validation set self-reported0.792
- F1 on conll2002validation set self-reported0.762
- Accuracy on conll2002validation set self-reported0.966