README.md

metadata

library_name: transformers
license: apache-2.0
base_model: PlanTL-GOB-ES/bsc-bio-ehr-es
tags:
  - token-classification
  - generated_from_trainer
datasets:
  - Rodrigo1771/cantemist-fasttext-75-ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: output
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: Rodrigo1771/cantemist-fasttext-75-ner
          type: Rodrigo1771/cantemist-fasttext-75-ner
          config: CantemistNer
          split: validation
          args: CantemistNer
        metrics:
          - name: Precision
            type: precision
            value: 0.8462436745815493
          - name: Recall
            type: recall
            value: 0.8562426152028357
          - name: F1
            type: f1
            value: 0.8512137823022711
          - name: Accuracy
            type: accuracy
            value: 0.991867253328732

output

This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on the Rodrigo1771/cantemist-fasttext-75-ner dataset. It achieves the following results on the evaluation set:

• Loss: 0.0478
• Precision: 0.8462
• Recall: 0.8562
• F1: 0.8512
• Accuracy: 0.9919

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: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0571	0.9992	616	0.0266	0.7767	0.8492	0.8113	0.9906
0.018	2.0	1233	0.0304	0.8075	0.8476	0.8271	0.9914
0.0101	2.9992	1849	0.0356	0.8159	0.8468	0.8311	0.9906
0.0057	4.0	2466	0.0365	0.8239	0.8460	0.8348	0.9910
0.0027	4.9992	3082	0.0396	0.8211	0.8480	0.8343	0.9916
0.0018	6.0	3699	0.0435	0.8306	0.8633	0.8467	0.9915
0.0013	6.9992	4315	0.0478	0.8462	0.8562	0.8512	0.9919
0.0008	8.0	4932	0.0469	0.8347	0.8614	0.8478	0.9915
0.0004	8.9992	5548	0.0515	0.8414	0.8610	0.8511	0.9919
0.0002	9.9919	6160	0.0520	0.8386	0.8598	0.8491	0.9918

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1