metadata

library_name: transformers
license: apache-2.0
base_model: BSC-LT/roberta-base-bne-capitel-ner
tags:
  - generated_from_trainer
datasets:
  - conll2002
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-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: 0.8599099099099099
          - name: Recall
            type: recall
            value: 0.8772977941176471
          - name: F1
            type: f1
            value: 0.8685168334849864
          - name: Accuracy
            type: accuracy
            value: 0.978701639744725

bert-finetuned-ner

Este es un moelo afinado sobre el modelo BSC-LT/roberta-base-bne-capitel-ner sobre conll2002 dataset. Se lora un excelente rendimiento porque el modelo original fue preentrenado con textos en español logrando los siguientes resultados:

Loss: 0.0950
Precision: 0.8599
Recall: 0.8773
F1: 0.8685
Accuracy: 0.9787

Model description

More information needed

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1045	1.0	521	0.0932	0.8593	0.8704	0.8648	0.9764
0.0343	2.0	1042	0.0870	0.8616	0.8757	0.8686	0.9781
0.019	3.0	1563	0.0950	0.8599	0.8773	0.8685	0.9787

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 2.20.0
Tokenizers 0.20.0