| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - conll2002 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: roberta-base-bne-capitel-ner |
| 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: 0.8637694213015087 |
| - name: Recall |
| type: recall |
| value: 0.8814338235294118 |
| - name: F1 |
| type: f1 |
| value: 0.8725122256340272 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9780298635072827 |
| --- |
| |
| <!-- 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. --> |
|
|
| # roberta-base-bne-capitel-ner |
|
|
| Este modelo es un finetuning de [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) sobre el dataset conll2002. |
| Este modelo logra los siguientes resultados sobre el conjunto de testeo: |
| - Loss: 0.1137 |
| - Precision: 0.8638 |
| - Recall: 0.8814 |
| - F1: 0.8725 |
| - Accuracy: 0.9780 |
|
|
| ## Model description |
|
|
| |
|
|
| ## Intended uses & limitations |
|
|
| CoNLL2002 es el conjunto de datos español de la Tarea Compartida CoNLL-2002 (Tjong Kim Sang, 2002). El conjunto de datos está anotado con cuatro tipos de entidades nombradas (personas, ubicaciones, organizaciones y otras entidades diversas) formateadas en el formato estándar Beginning-Inside-Outside (BIO). El corpus consta de 8.324 sentencias de tren con 19.400 entidades nombradas, |
| 1.916 sentencias de desarrollo con 4.568 entidades nombradas y 1.518 sentencias de prueba con 3.644 entidades nombradas. |
|
|
| ## Training and evaluation data |
|
|
| El modelo fue entrenado con una GPU 3080 TI de 10 Gz a 5 épocas y con un batch-seize de 8 y evaluado con F1-score por cada una de las épocas. |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0041 | 1.0 | 1041 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 | |
| | 0.004 | 2.0 | 2082 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 | |
| | 0.0039 | 3.0 | 3123 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 | |
| | 0.003 | 4.0 | 4164 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 | |
| | 0.0032 | 5.0 | 5205 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.30.0 |
| - Pytorch 2.0.1+cu117 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
|
|
