metadata

library_name: transformers
license: cc-by-4.0
base_model: NazaGara/NER-fine-tuned-BETO
tags:
  - generated_from_trainer
datasets:
  - biobert_json
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: NER-finetuning-BETO-CM-V1
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: biobert_json
          type: biobert_json
          config: Biobert_json
          split: validation
          args: Biobert_json
        metrics:
          - type: precision
            value: 0.949653802801782
            name: Precision
          - type: recall
            value: 0.9613670941099761
            name: Recall
          - type: f1
            value: 0.9554745511003105
            name: F1
          - type: accuracy
            value: 0.976855614973262
            name: Accuracy
pipeline_tag: token-classification

NER-finetuning-BETO-CM-V1

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

Loss: 0.1236
Precision: 0.9497
Recall: 0.9614
F1: 0.9555
Accuracy: 0.9769

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: 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
0.3411	1.0	612	0.1137	0.9437	0.9474	0.9456	0.9707
0.1072	2.0	1224	0.1090	0.9304	0.9685	0.9491	0.9727
0.0757	3.0	1836	0.1024	0.9450	0.9692	0.9569	0.9768
0.0589	4.0	2448	0.1050	0.9492	0.9666	0.9578	0.9774
0.0419	5.0	3060	0.1054	0.9498	0.9621	0.9559	0.9771
0.0365	6.0	3672	0.1124	0.9460	0.9583	0.9521	0.9753
0.0299	7.0	4284	0.1119	0.9495	0.9632	0.9563	0.9774
0.0282	8.0	4896	0.1187	0.9482	0.9625	0.9553	0.9771
0.0221	9.0	5508	0.1203	0.9496	0.9608	0.9551	0.9768
0.0192	10.0	6120	0.1236	0.9497	0.9614	0.9555	0.9769

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3