README.md

metadata

library_name: transformers
license: apache-2.0
base_model: PlanTL-GOB-ES/bsc-bio-ehr-es
tags:
  - generated_from_trainer
datasets:
  - distemist-85-ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: output
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: distemist-85-ner
          type: distemist-85-ner
          config: DisTEMIST NER
          split: validation
          args: DisTEMIST NER
        metrics:
          - name: Precision
            type: precision
            value: 0.7898351648351648
          - name: Recall
            type: recall
            value: 0.8072063640617688
          - name: F1
            type: f1
            value: 0.7984262902105993
          - name: Accuracy
            type: accuracy
            value: 0.9768286487154489

output

This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on the distemist-85-ner dataset. It achieves the following results on the evaluation set:

• Loss: 0.1497
• Precision: 0.7898
• Recall: 0.8072
• F1: 0.7984
• Accuracy: 0.9768

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	0.9990	499	0.0739	0.7271	0.7953	0.7596	0.9731
0.105	2.0	999	0.0908	0.7436	0.7890	0.7656	0.9729
0.0448	2.9990	1498	0.0930	0.7676	0.7990	0.7830	0.9744
0.0255	4.0	1998	0.1052	0.7806	0.7983	0.7894	0.9757
0.0164	4.9990	2497	0.1100	0.7756	0.8007	0.7879	0.9750
0.0112	6.0	2997	0.1266	0.7869	0.8124	0.7994	0.9768
0.0073	6.9990	3496	0.1288	0.7929	0.8009	0.7969	0.9763
0.0054	8.0	3996	0.1424	0.8032	0.8049	0.8040	0.9765
0.0038	8.9990	4495	0.1455	0.7901	0.8042	0.7971	0.9765
0.0028	9.9900	4990	0.1497	0.7898	0.8072	0.7984	0.9768

Framework versions

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