| --- |
| base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english |
| tags: |
| - generated_from_trainer |
| datasets: |
| - conll2002 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: xml-roberta-large-finetuned-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: 0.880600409370025 |
| - name: Recall |
| type: recall |
| value: 0.8897058823529411 |
| - name: F1 |
| type: f1 |
| value: 0.8851297291118985 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9806463992982264 |
| --- |
| |
| <!-- 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. --> |
|
|
| # xml-roberta-large-finetuned-ner |
|
|
| Este es modelo resultado de un finetuning de |
| [FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english) sobre el conll2002 dataset. |
| Los siguientes son los resultados sobre el conjunto de evaluación: |
| - Loss: 0.1364 |
| - Precision: 0.8806 |
| - Recall: 0.8897 |
| - F1: 0.8851 |
| - Accuracy: 0.9806 |
|
|
| ## Model description |
|
|
| Este es el modelo más grande de roberta [FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english)- |
| Este modelo fue ajustado usando el framework Kaggle [https://www.kaggle.com/settings]. Para realizar el preentrenamiento del modelo se tuvo que crear un directorio temporal en Kaggle |
| con el fin de almacenar de manera temoporal el modelo que pesa alrededor de 35 Gz. |
|
|
| ## 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: 4 |
| - 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0743 | 1.0 | 2081 | 0.1131 | 0.8385 | 0.8587 | 0.8485 | 0.9771 | |
| | 0.049 | 2.0 | 4162 | 0.1429 | 0.8492 | 0.8564 | 0.8528 | 0.9756 | |
| | 0.031 | 3.0 | 6243 | 0.1298 | 0.8758 | 0.8817 | 0.8787 | 0.9800 | |
| | 0.0185 | 4.0 | 8324 | 0.1279 | 0.8827 | 0.8890 | 0.8859 | 0.9808 | |
| | 0.0125 | 5.0 | 10405 | 0.1364 | 0.8806 | 0.8897 | 0.8851 | 0.9806 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.41.1 |
| - Pytorch 2.1.2 |
| - Datasets 2.19.1 |
| - Tokenizers 0.19.1 |
|
|
