metadata
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-1
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: 0.805356
- name: Recall
type: recall
value: 0.822381
- name: F1
type: f1
value: 0.813779
- name: Accuracy
type: accuracy
value: 0.969573
bert-finetuned-ner-1
Este es modelo resultado de un finetuning de google-bert/bert-base-cased sobre el conll2002 dataset. Los siguientes son los resultados sobre el conjunto de evaluación:
- Training Loss: 0.000900
- Validation Loss: 0.306902
- Precision: 0.805356
- Recall: 0.822381
- F1: 0.813779
- Accuracy: 0.969573
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- num_epochs: 8
- weight_decay: 0.001
Training results
Epoch | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|
1.0 | 0.0045 | 0.263534 | 0.787187 | 0.815947 | 0.801309 | 0.968117 |
2.0 | 0.0054 | 0.261010 | 0.776933 | 0.798713 | 0.787673 | 0.966914 |
3.0 | 0.0031 | 0.288264 | 0.787994 | 0.811351 | 0.799502 | 0.967351 |
4.0 | 0.0030 | 0.261651 | 0.799186 | 0.812040 | 0.805562 | 0.969476 |
5.0 | 0.0023 | 0.281675 | 0.792880 | 0.813649 | 0.803130 | 0.968544 |
6.0 | 0.0014 | 0.285965 | 0.790842 | 0.817555 | 0.803977 | 0.969311 |
7.0 | 0.0009 | 0.320790 | 0.795583 | 0.811121 | 0.803277 | 0.968049 |
8.0 | 0.0009 | 0.306902 | 0.805356 | 0.822381 | 0.813779 | 0.969573 |