metadata
base_model: dmis-lab/biobert-base-cased-v1.2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BioBERT-full-finetuned-ner-pablo
results: []
BioBERT-full-finetuned-ner-pablo
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1114
- Precision: 0.7951
- Recall: 0.7809
- F1: 0.7879
- Accuracy: 0.9690
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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1541 | 0.9998 | 2608 | 0.1456 | 0.6888 | 0.7147 | 0.7015 | 0.9601 |
| 0.1073 | 2.0 | 5217 | 0.1244 | 0.7397 | 0.7450 | 0.7423 | 0.9645 |
| 0.0744 | 2.9994 | 7824 | 0.1114 | 0.7951 | 0.7809 | 0.7879 | 0.9690 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1