py315-pylingual-v2-segmenter
This model is a fine-tuned version of syssec-utd/py315-pylingual-v2-mlm on the syssec-utd/segmentation-py315-pylingual-v2-tokenized dataset. It achieves the following results on the evaluation set:
- Loss: 0.0066
- Precision: 0.9910
- Recall: 0.9917
- F1: 0.9913
- Accuracy: 0.9977
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: 28
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0085 | 1.0 | 74922 | 0.0066 | 0.9905 | 0.9912 | 0.9908 | 0.9976 |
| 0.0041 | 2.0 | 149844 | 0.0066 | 0.9910 | 0.9917 | 0.9913 | 0.9977 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1