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