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
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