CeLLaTe-tapt-pubmedbert-tokenizer-original-spanmask
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: 2.1721
- Accuracy: 0.5857
- Perplexity: 8.7763
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 |
|---|---|---|---|---|---|
| 2.7396 | 1.0 | 14 | 2.3642 | 0.5703 | 10.6359 |
| 2.5077 | 2.0 | 28 | 2.2759 | 0.5742 | 9.7369 |
| 2.4329 | 3.0 | 42 | 2.1662 | 0.5936 | 8.7251 |
| 2.3886 | 4.0 | 56 | 2.1748 | 0.5859 | 8.8000 |
| 2.378 | 5.0 | 70 | 2.1150 | 0.5914 | 8.2897 |
| 2.3618 | 6.0 | 84 | 2.1638 | 0.5821 | 8.7040 |
| 2.322 | 7.0 | 98 | 2.1467 | 0.5910 | 8.5563 |
| 2.299 | 8.0 | 112 | 2.1630 | 0.5827 | 8.6973 |
| 2.3081 | 9.0 | 126 | 2.0821 | 0.5989 | 8.0216 |
| 2.2939 | 10.0 | 140 | 2.1986 | 0.5835 | 9.0125 |
| 2.2826 | 11.0 | 154 | 2.1773 | 0.5870 | 8.8226 |
| 2.2581 | 12.0 | 168 | 2.1368 | 0.5880 | 8.4727 |
| 2.2392 | 13.0 | 182 | 2.1805 | 0.5857 | 8.8505 |
| 2.2305 | 14.0 | 196 | 2.1175 | 0.5906 | 8.3106 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.21.0