metadata

license: apache-2.0
base_model: PlanTL-GOB-ES/bsc-bio-ehr-es
tags:
  - token-classification
  - generated_from_trainer
datasets:
  - Rodrigo1771/symptemist-ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: output
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: Rodrigo1771/symptemist-ner
          type: Rodrigo1771/symptemist-ner
          config: SympTEMIST NER
          split: validation
          args: SympTEMIST NER
        metrics:
          - name: Precision
            type: precision
            value: 0.6675139806812405
          - name: Recall
            type: recall
            value: 0.7186644772851669
          - name: F1
            type: f1
            value: 0.6921454928835002
          - name: Accuracy
            type: accuracy
            value: 0.9483461131252205

output

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

Loss: 0.2747
Precision: 0.6675
Recall: 0.7187
F1: 0.6921
Accuracy: 0.9483

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	1.0	150	0.1504	0.5091	0.6409	0.5675	0.9456
No log	2.0	300	0.1547	0.5881	0.6995	0.639	0.9462
No log	3.0	450	0.1618	0.6237	0.6984	0.6589	0.9476
0.126	4.0	600	0.1920	0.6154	0.7181	0.6628	0.9451
0.126	5.0	750	0.2102	0.6561	0.7028	0.6786	0.9488
0.126	6.0	900	0.2414	0.6443	0.7088	0.6750	0.9467
0.0251	7.0	1050	0.2500	0.6588	0.7061	0.6816	0.9492
0.0251	8.0	1200	0.2642	0.6440	0.7307	0.6846	0.9474
0.0251	9.0	1350	0.2747	0.6675	0.7187	0.6921	0.9483
0.0091	10.0	1500	0.2767	0.6595	0.7187	0.6878	0.9488

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1