syssec-utd / py36-pylingual-v1-segmenter

huggingface.co
Total runs: 141
24-hour runs: 0
7-day runs: 3
30-day runs: -99
Model's Last Updated: February 05 2025
token-classification

Introduction of py36-pylingual-v1-segmenter

Model Details of py36-pylingual-v1-segmenter

py36-pylingual-v1-segmenter

This model is a fine-tuned version of syssec-utd/py36-pylingual-v1-mlm on the syssec-utd/segmentation-py36-pylingual-v1-tokenized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0006
  • Precision: 1.0
  • Recall: 1.0
  • F1: 1.0
  • Accuracy: 1.0
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
  • optimizer: Use OptimizerNames.ADAMW_TORCH 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
0.0051 1.0 90840 0.0014 1.0 1.0 1.0 1.0
0.0029 2.0 181680 0.0006 1.0 1.0 1.0 1.0
Framework versions
  • Transformers 4.48.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.21.0

Runs of syssec-utd py36-pylingual-v1-segmenter on huggingface.co

141
Total runs
0
24-hour runs
0
3-day runs
3
7-day runs
-99
30-day runs

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py36-pylingual-v1-segmenter huggingface.co

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py36-pylingual-v1-segmenter install

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