py314-pylingual-v9-segmenter
This model is a fine-tuned version of syssec-utd/py314-pylingual-v9-mlm on the syssec-utd/segmentation-py314-pylingual-v9-tokenized dataset. It achieves the following results on the evaluation set:
- Loss: 0.0169
- Precision: 0.9912
- Recall: 0.9920
- F1: 0.9916
- Accuracy: 0.9980
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: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 192
- total_eval_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0235 | 1.0 | 27274 | 0.0207 | 0.9907 | 0.9915 | 0.9911 | 0.9978 |
| 0.0125 | 2.0 | 54548 | 0.0169 | 0.9912 | 0.9920 | 0.9916 | 0.9980 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.12.0+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2