ner_based_bert-base-chinese_withBadcase_replaceSpace

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ner_based_bert-base-chinese_withBadcase_replaceSpace

This model is a fine-tuned version of google-bert/bert-base-chinese on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0138
• Precision: 0.9505
• Recall: 0.9655
• F1: 0.9579
• Accuracy: 0.9969

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: 128
• eval_batch_size: 128
• seed: 42
• 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: cosine
• lr_scheduler_warmup_steps: 20
• training_steps: 6520
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.54.0
• Pytorch 2.7.0+cu128
• Datasets 4.0.0
• Tokenizers 0.21.4