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---
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
---
<!-- 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. -->
# bert-finetuned-ner-1
Este es modelo resultado de un finetuning de
[google-bert/bert-base-cased](https://huggingface.co/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 |
|