CeLLaTe-tapt-pubmedbert-tokenizer-adapted_v2_lr-1e5
This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on the Mardiyyah/TAPT_CeLLaTe2.0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.1540
- Accuracy: 0.7656
- Perplexity: 3.1710
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3407
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity |
|---|---|---|---|---|---|
| 1.3604 | 1.0 | 14 | 1.2099 | 0.7589 | 3.3532 |
| 1.379 | 2.0 | 28 | 1.2093 | 0.7611 | 3.3510 |
| 1.3573 | 3.0 | 42 | 1.2053 | 0.7575 | 3.3377 |
| 1.3451 | 4.0 | 56 | 1.2265 | 0.7551 | 3.4092 |
| 1.3249 | 5.0 | 70 | 1.2203 | 0.7572 | 3.3881 |
| 1.3186 | 6.0 | 84 | 1.2113 | 0.7549 | 3.3579 |
| 1.3065 | 7.0 | 98 | 1.1578 | 0.7646 | 3.1829 |
| 1.3073 | 8.0 | 112 | 1.2125 | 0.7521 | 3.3620 |
| 1.2854 | 9.0 | 126 | 1.1526 | 0.7605 | 3.1665 |
| 1.2812 | 10.0 | 140 | 1.1805 | 0.7573 | 3.2559 |
| 1.2996 | 11.0 | 154 | 1.1601 | 0.7548 | 3.1903 |
| 1.2608 | 12.0 | 168 | 1.1775 | 0.7599 | 3.2463 |
| 1.2642 | 13.0 | 182 | 1.2410 | 0.7528 | 3.4592 |
| 1.2615 | 14.0 | 196 | 1.1971 | 0.7567 | 3.3107 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.21.0