metadata
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: sentence-classifiert
results: []
sentence-classifiert
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: 0.3410
- Precision: 0.9085
- Recall: 0.9068
- Accuracy: 0.9072
- F1: 0.9072
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 154 | 0.4158 | 0.8549 | 0.8445 | 0.8438 | 0.8443 |
| No log | 2.0 | 308 | 0.3426 | 0.8875 | 0.8804 | 0.8796 | 0.8787 |
| No log | 3.0 | 462 | 0.3594 | 0.8945 | 0.8856 | 0.8869 | 0.8868 |
| 0.3638 | 4.0 | 616 | 0.3302 | 0.9034 | 0.9008 | 0.9015 | 0.9014 |
| 0.3638 | 5.0 | 770 | 0.3410 | 0.9085 | 0.9068 | 0.9072 | 0.9072 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2