metadata

library_name: transformers
license: cc-by-4.0
base_model: NazaGara/NER-fine-tuned-BETO
tags:
  - generated_from_trainer
datasets:
  - conll2002
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: NER-finetuning-BETO-actualizado
    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: 0.8385333636984742
          - name: Recall
            type: recall
            value: 0.8460477941176471
          - name: F1
            type: f1
            value: 0.8422738190552442
          - name: Accuracy
            type: accuracy
            value: 0.9687422514257377

NER-finetuning-BETO-actualizado

This model is a fine-tuned version of NazaGara/NER-fine-tuned-BETO on the conll2002 dataset. It achieves the following results on the evaluation set:

Loss: 0.2642
Precision: 0.8385
Recall: 0.8460
F1: 0.8423
Accuracy: 0.9687

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: 5e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0778	1.0	2081	0.1603	0.8164	0.8277	0.8220	0.9676
0.0608	2.0	4162	0.1756	0.8114	0.8136	0.8125	0.9640
0.0362	3.0	6243	0.1986	0.8236	0.8226	0.8231	0.9650
0.0277	4.0	8324	0.1904	0.8092	0.8359	0.8223	0.9665
0.0217	5.0	10405	0.1988	0.8137	0.8389	0.8261	0.9658
0.0146	6.0	12486	0.2298	0.8470	0.8536	0.8503	0.9684
0.0091	7.0	14567	0.2509	0.8315	0.8412	0.8363	0.9665
0.0058	8.0	16648	0.2375	0.8274	0.8435	0.8354	0.9688
0.0033	9.0	18729	0.2489	0.8349	0.8421	0.8385	0.9683
0.0023	10.0	20810	0.2642	0.8385	0.8460	0.8423	0.9687

Framework versions

Transformers 4.51.2
Pytorch 2.6.0+cu126
Datasets 3.5.0
Tokenizers 0.21.1