metadata
base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
xml-roberta-large-finetuned-ner
Los siguientes son los resultados sobre el conjunto de evaluación:
- 'eval_loss': 0.0929097980260849,
- 'eval_precision': 0.8704318936877077,
- 'eval_recall': 0.8833942118572633,
- 'eval_f1': 0.8768651513038628,
- 'eval_accuracy': 0.982701988941157,
Model description
Este es el modelo más grande de roberta 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.
The following hyperparameters were used during training:
- eval_strategy="epoch",
- save_strategy="epoch",
- learning_rate=2e-5, # (Aprendizaje se esta cambiando)
- per_device_train_batch_size=16,
- per_device_eval_batch_size=16,
- num_train_epochs=5,
- weight_decay=0.1,
- max_grad_norm=1.0,
- adam_epsilon=1e-5,
- fp16=True,
- save_total_limit=2,
- load_best_model_at_end=True,
- push_to_hub=True,
- metric_for_best_model="f1",
- seed=42,
| Metric | Value |
|---|---|
| eval_loss | 0.12918254733085632 |
| eval_precision | 0.8674463937621832 |
| eval_recall | 0.8752458555774094 |
| eval_f1 | 0.8713286713286713 |
| eval_accuracy | 0.9813980358174466 |
| eval_runtime | 3.6357 |
| eval_samples_per_second | 417.526 |
| eval_steps_per_second | 26.13 |
| epoch | 5.0 |
| Label | Precision | Recall | F1 | Number |
|---|---|---|---|---|
| LOC | 0.8867924528301887 | 0.8238007380073801 | 0.8541367766618843 | 1084 |
| MISC | 0.7349726775956285 | 0.7911764705882353 | 0.7620396600566574 | 340 |
| ORG | 0.8400272294077604 | 0.8814285714285715 | 0.8602300453119553 | 1400 |
| PER | 0.9599465954606141 | 0.9782312925170068 | 0.9690026954177898 | 735 |