syssec-utd / py314-pylingual-v2-segmenter

huggingface.co
Total runs: 1
24-hour runs: 0
7-day runs: 0
30-day runs: 1
Model's Last Updated: December 18 2025
token-classification

Introduction of py314-pylingual-v2-segmenter

Model Details of py314-pylingual-v2-segmenter

py314-pylingual-v2-segmenter

This model is a fine-tuned version of syssec-utd/py314-pylingual-v2-mlm on the syssec-utd/segmentation-py314-pylingual-v2-tokenized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0022
  • Precision: 0.9953
  • Recall: 0.9960
  • F1: 0.9956
  • Accuracy: 0.9987
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: 48
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 3
  • total_train_batch_size: 144
  • 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
0.0092 1.0 33414 0.0024 0.9948 0.9942 0.9945 0.9985
0.0052 2.0 66828 0.0022 0.9953 0.9960 0.9956 0.9987
Framework versions
  • Transformers 4.57.3
  • Pytorch 2.9.1+cu128
  • Datasets 4.4.1
  • Tokenizers 0.22.1

Runs of syssec-utd py314-pylingual-v2-segmenter on huggingface.co

1
Total runs
0
24-hour runs
0
3-day runs
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7-day runs
1
30-day runs

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