| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-bert/bert-base-multilingual-cased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - conll2002 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: NER-finetuning-Bert-base |
| 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: 0.8351917930419268 |
| - name: Recall |
| type: recall |
| value: 0.8605238970588235 |
| - name: F1 |
| type: f1 |
| value: 0.8476686283386147 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9714759394660772 |
| --- |
| |
| <!-- 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-Bert-base |
|
|
| This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the conll2002 dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1521 |
| - Precision: 0.8352 |
| - Recall: 0.8605 |
| - F1: 0.8477 |
| - Accuracy: 0.9715 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0855 | 1.0 | 1041 | 0.1281 | 0.8272 | 0.8371 | 0.8321 | 0.9688 | |
| | 0.0536 | 2.0 | 2082 | 0.1357 | 0.8134 | 0.8465 | 0.8296 | 0.9686 | |
| | 0.0333 | 3.0 | 3123 | 0.1227 | 0.8593 | 0.8713 | 0.8653 | 0.9740 | |
| | 0.0221 | 4.0 | 4164 | 0.1482 | 0.8474 | 0.8564 | 0.8519 | 0.9710 | |
| | 0.0163 | 5.0 | 5205 | 0.1521 | 0.8352 | 0.8605 | 0.8477 | 0.9715 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.51.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.5.1 |
| - Tokenizers 0.21.1 |
|
|
