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/symptemist-fasttext-8-ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: output
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: Rodrigo1771/symptemist-fasttext-8-ner
          type: Rodrigo1771/symptemist-fasttext-8-ner
          config: SympTEMIST NER
          split: validation
          args: SympTEMIST NER
        metrics:
          - name: Precision
            type: precision
            value: 0.6764102564102564
          - name: Recall
            type: recall
            value: 0.7219485495347564
          - name: F1
            type: f1
            value: 0.6984379136881121
          - name: Accuracy
            type: accuracy
            value: 0.9500465205813469

output

This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on the Rodrigo1771/symptemist-fasttext-8-ner dataset. It achieves the following results on the evaluation set:

• Loss: 0.3073
• Precision: 0.6764
• Recall: 0.7219
• F1: 0.6984
• Accuracy: 0.9500

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
No log	0.9975	203	0.1501	0.5960	0.6338	0.6143	0.9468
No log	2.0	407	0.1761	0.6529	0.6940	0.6729	0.9492
0.1312	2.9975	610	0.1995	0.6322	0.7170	0.6720	0.9470
0.1312	4.0	814	0.2182	0.6446	0.7137	0.6774	0.9483
0.0248	4.9975	1017	0.2461	0.6251	0.7219	0.6701	0.9449
0.0248	6.0	1221	0.2695	0.6410	0.7302	0.6827	0.9469
0.0248	6.9975	1424	0.2829	0.6529	0.7340	0.6911	0.9470
0.0081	8.0	1628	0.2982	0.6711	0.7181	0.6938	0.9494
0.0081	8.9975	1831	0.3073	0.6764	0.7219	0.6984	0.9500
0.0038	9.9754	2030	0.3079	0.6713	0.7165	0.6931	0.9500

Framework versions

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