metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2002
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: roberta-base-bne-capitel-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: 0.8637694213015087
          - name: Recall
            type: recall
            value: 0.8814338235294118
          - name: F1
            type: f1
            value: 0.8725122256340272
          - name: Accuracy
            type: accuracy
            value: 0.9780298635072827

roberta-base-bne-capitel-ner

Este modelo es un finetuning de BSC-LT/roberta-base-bne-capitel-ner sobre el dataset conll2002. Este modelo logra los siguientes resultados sobre el conjunto de testeo:

Loss: 0.1137
Precision: 0.8638
Recall: 0.8814
F1: 0.8725
Accuracy: 0.9780

Model description

El modelo fue entrenado con una GPU 3080 TI de 10 Gz a 5 épocas y con un batch-seize de 8.

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0041	1.0	1041	0.1137	0.8638	0.8814	0.8725	0.9780
0.004	2.0	2082	0.1137	0.8638	0.8814	0.8725	0.9780
0.0039	3.0	3123	0.1137	0.8638	0.8814	0.8725	0.9780
0.003	4.0	4164	0.1137	0.8638	0.8814	0.8725	0.9780
0.0032	5.0	5205	0.1137	0.8638	0.8814	0.8725	0.9780

Framework versions

Transformers 4.30.0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3