README.md

metadata

license: apache-2.0
base_model: PlanTL-GOB-ES/bsc-bio-ehr-es
tags:
  - token-classification
  - generated_from_trainer
datasets:
  - Rodrigo1771/combined-train-distemist-dev-ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: output
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: Rodrigo1771/combined-train-distemist-dev-ner
          type: Rodrigo1771/combined-train-distemist-dev-ner
          config: CombinedTrainDisTEMISTDevNER
          split: validation
          args: CombinedTrainDisTEMISTDevNER
        metrics:
          - name: Precision
            type: precision
            value: 0.32197630636422075
          - name: Recall
            type: recall
            value: 0.8203088441740758
          - name: F1
            type: f1
            value: 0.4624414693662204
          - name: Accuracy
            type: accuracy
            value: 0.8601754843670617

output

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

• Loss: 0.6266
• Precision: 0.3220
• Recall: 0.8203
• F1: 0.4624
• Accuracy: 0.8602

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	0.9988	425	0.3834	0.2920	0.7819	0.4252	0.8517
0.3349	2.0	851	0.5730	0.2681	0.8070	0.4025	0.8221
0.1788	2.9988	1276	0.5796	0.2848	0.8009	0.4202	0.8338
0.1227	4.0	1702	0.6591	0.2996	0.8109	0.4376	0.8388
0.0856	4.9988	2127	0.6266	0.3220	0.8203	0.4624	0.8602
0.0597	6.0	2553	0.7859	0.3075	0.8112	0.4460	0.8476
0.0597	6.9988	2978	0.8297	0.3137	0.8166	0.4532	0.8508
0.0458	8.0	3404	0.8468	0.3135	0.8205	0.4536	0.8532
0.0343	8.9988	3829	0.9241	0.3085	0.8182	0.4481	0.8494
0.0292	9.9882	4250	0.9384	0.3100	0.8163	0.4494	0.8499

Framework versions

• Transformers 4.42.4
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1