| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-bert/bert-base-uncased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - conll2002 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: NER-finetuning-BETO-PRO |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: conll2002 |
| type: conll2002 |
| config: es |
| split: validation |
| args: es |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.7017726798748697 |
| - name: Recall |
| type: recall |
| value: 0.7732077205882353 |
| - name: F1 |
| type: f1 |
| value: 0.7357603585875151 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9536327652922068 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # NER-finetuning-BETO-PRO |
|
|
| This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the conll2002 dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1981 |
| - Precision: 0.7018 |
| - Recall: 0.7732 |
| - F1: 0.7358 |
| - Accuracy: 0.9536 |
|
|
| ## 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 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: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.1941 | 1.0 | 1041 | 0.1965 | 0.6201 | 0.6836 | 0.6503 | 0.9422 | |
| | 0.1276 | 2.0 | 2082 | 0.1843 | 0.6666 | 0.7387 | 0.7008 | 0.9487 | |
| | 0.0885 | 3.0 | 3123 | 0.1760 | 0.7056 | 0.7601 | 0.7319 | 0.9538 | |
| | 0.0623 | 4.0 | 4164 | 0.1856 | 0.6982 | 0.7670 | 0.7310 | 0.9532 | |
| | 0.0485 | 5.0 | 5205 | 0.1981 | 0.7018 | 0.7732 | 0.7358 | 0.9536 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.50.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.5.0 |
| - Tokenizers 0.21.1 |
|
|
