| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: TurboPascal/ChineseModernBert |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: ner_based_ChineseModernBert_without_phone |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # ner_based_ChineseModernBert_without_phone |
|
|
| This model is a fine-tuned version of [TurboPascal/ChineseModernBert](https://huggingface.co/TurboPascal/ChineseModernBert) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0239 |
| - Precision: 0.9365 |
| - Recall: 0.9401 |
| - F1: 0.9383 |
| - Accuracy: 0.9960 |
|
|
| ## 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: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 512 |
| - 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 |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 163 | 0.0310 | 0.8354 | 0.8581 | 0.8466 | 0.9907 | |
| | No log | 2.0 | 326 | 0.0202 | 0.8915 | 0.9244 | 0.9077 | 0.9942 | |
| | No log | 3.0 | 489 | 0.0174 | 0.9211 | 0.9297 | 0.9254 | 0.9953 | |
| | 0.0784 | 4.0 | 652 | 0.0167 | 0.9226 | 0.9374 | 0.9300 | 0.9956 | |
| | 0.0784 | 5.0 | 815 | 0.0182 | 0.9305 | 0.9434 | 0.9369 | 0.9959 | |
| | 0.0784 | 6.0 | 978 | 0.0193 | 0.9329 | 0.9438 | 0.9383 | 0.9960 | |
| | 0.0046 | 7.0 | 1141 | 0.0220 | 0.9333 | 0.9430 | 0.9381 | 0.9960 | |
| | 0.0046 | 8.0 | 1304 | 0.0221 | 0.9328 | 0.9437 | 0.9382 | 0.9959 | |
| | 0.0046 | 9.0 | 1467 | 0.0235 | 0.9320 | 0.9452 | 0.9385 | 0.9960 | |
| | 0.0011 | 10.0 | 1630 | 0.0239 | 0.9365 | 0.9401 | 0.9383 | 0.9960 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.54.0 |
| - Pytorch 2.7.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.4 |
|
|
