metadata
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: sentence-classifier
results: []
sentence-classifier
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: 0.3291
- Precision: 0.9236
- Recall: 0.9217
- Accuracy: 0.9219
- F1: 0.9221
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 | 0.3536 | 0.8783 | 0.8745 | 0.8747 | 0.8753 |
| No log | 2.0 | 308 | 0.2784 | 0.9132 | 0.9105 | 0.9105 | 0.9109 |
| No log | 3.0 | 462 | 0.2928 | 0.9189 | 0.9160 | 0.9162 | 0.9165 |
| 0.3402 | 4.0 | 616 | 0.3098 | 0.9239 | 0.9223 | 0.9227 | 0.9228 |
| 0.3402 | 5.0 | 770 | 0.3291 | 0.9236 | 0.9217 | 0.9219 | 0.9221 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1