metadata
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BioBERT_JNLPBA_NER
results: []
BioBERT_JNLPBA_NER
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1450
- Precision: 0.9613
- Recall: 0.9549
- F1: 0.9581
- Accuracy: 0.9516
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2743 | 1.0 | 582 | 0.1504 | 0.9575 | 0.9536 | 0.9556 | 0.9494 |
| 0.1418 | 2.0 | 1164 | 0.1473 | 0.9624 | 0.9532 | 0.9578 | 0.9510 |
| 0.1247 | 3.0 | 1746 | 0.1450 | 0.9613 | 0.9549 | 0.9581 | 0.9516 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0