CeLLaTe-tapt-pubmedbert-tokenizer-adapted-wwmask
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: 1.2144
- Accuracy: 0.7569
- Perplexity: 3.3682
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: 3e-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 |
|---|---|---|---|---|---|
| 1.4325 | 1.0 | 14 | 1.2906 | 0.7533 | 3.6351 |
| 1.4038 | 2.0 | 28 | 1.2569 | 0.7560 | 3.5146 |
| 1.3795 | 3.0 | 42 | 1.2207 | 0.7535 | 3.3895 |
| 1.3515 | 4.0 | 56 | 1.1725 | 0.7632 | 3.2301 |
| 1.3173 | 5.0 | 70 | 1.1807 | 0.7625 | 3.2565 |
| 1.271 | 6.0 | 84 | 1.1901 | 0.7561 | 3.2874 |
| 1.2655 | 7.0 | 98 | 1.1605 | 0.7615 | 3.1915 |
| 1.2681 | 8.0 | 112 | 1.1396 | 0.7630 | 3.1255 |
| 1.2362 | 9.0 | 126 | 1.1118 | 0.7709 | 3.0397 |
| 1.2196 | 10.0 | 140 | 1.1419 | 0.7619 | 3.1328 |
| 1.2191 | 11.0 | 154 | 1.1508 | 0.7569 | 3.1606 |
| 1.1892 | 12.0 | 168 | 1.1398 | 0.7642 | 3.1263 |
| 1.1819 | 13.0 | 182 | 1.1509 | 0.7616 | 3.1612 |
| 1.1469 | 14.0 | 196 | 1.1664 | 0.7601 | 3.2103 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.21.0