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TooT-PLM-P2S
This model is a fine-tuned version of ElnaggarLab/ankh-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1649
- Q3 Accuracy: 0.5470
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: 0.001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Q3 Accuracy |
|---|---|---|---|---|
| 1.0993 | 0.08 | 50 | 0.3957 | 0.4526 |
| 0.4077 | 0.16 | 100 | 0.3564 | 0.5574 |
| 0.3475 | 0.24 | 150 | 0.3082 | 0.4327 |
| 0.2474 | 0.31 | 200 | 0.2171 | 0.5477 |
| 0.2567 | 0.39 | 250 | 0.3058 | 0.4924 |
| 0.2948 | 0.47 | 300 | 0.1828 | 0.5470 |
| 0.2148 | 0.55 | 350 | 0.1873 | 0.5470 |
| 0.209 | 0.63 | 400 | 0.1832 | 0.5470 |
| 0.1761 | 0.71 | 450 | 0.1791 | 0.5103 |
| 0.1791 | 0.79 | 500 | 0.1700 | 0.5470 |
| 0.1641 | 0.87 | 550 | 0.1684 | 0.5470 |
| 0.1653 | 0.94 | 600 | 0.1649 | 0.5470 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1
