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-8-ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: output
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: Rodrigo1771/symptemist-8-ner
          type: Rodrigo1771/symptemist-8-ner
          config: SympTEMIST NER
          split: validation
          args: SympTEMIST NER
        metrics:
          - name: Precision
            type: precision
            value: 0.6832101372756072
          - name: Recall
            type: recall
            value: 0.7082649151614668
          - name: F1
            type: f1
            value: 0.6955119591507659
          - name: Accuracy
            type: accuracy
            value: 0.9498058968847252

output

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

• Loss: 0.3003
• Precision: 0.6832
• Recall: 0.7083
• F1: 0.6955
• Accuracy: 0.9498

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	0.9976	209	0.1511	0.5818	0.6694	0.6226	0.9457
No log	2.0	419	0.1794	0.6113	0.7017	0.6534	0.9465
0.1282	2.9976	628	0.2102	0.6572	0.6979	0.6769	0.9470
0.1282	4.0	838	0.2329	0.6605	0.6984	0.6789	0.9489
0.0255	4.9976	1047	0.2591	0.6646	0.6973	0.6806	0.9491
0.0255	6.0	1257	0.2737	0.6599	0.6968	0.6778	0.9491
0.0255	6.9976	1466	0.2876	0.6687	0.7094	0.6884	0.9492
0.0085	8.0	1676	0.3003	0.6832	0.7083	0.6955	0.9498
0.0085	8.9976	1885	0.3072	0.6697	0.7170	0.6926	0.9492
0.004	9.9761	2090	0.3125	0.6711	0.7137	0.6918	0.9492

Framework versions

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