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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_based_bert-base-chinese_withBadcase_replaceSpace
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_bert-base-chinese_withBadcase_replaceSpace
This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/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
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1507 | 1.0 | 652 | 0.0249 | 0.8949 | 0.9105 | 0.9026 | 0.9928 |
| 0.0256 | 2.0 | 1304 | 0.0189 | 0.9186 | 0.9245 | 0.9215 | 0.9945 |
| 0.0195 | 3.0 | 1956 | 0.0169 | 0.9237 | 0.9470 | 0.9352 | 0.9952 |
| 0.0131 | 4.0 | 2608 | 0.0161 | 0.9299 | 0.9499 | 0.9398 | 0.9956 |
| 0.0114 | 5.0 | 3260 | 0.0149 | 0.9311 | 0.9607 | 0.9457 | 0.9959 |
| 0.01 | 6.0 | 3912 | 0.0146 | 0.9395 | 0.9600 | 0.9497 | 0.9962 |
| 0.0072 | 7.0 | 4564 | 0.0139 | 0.9480 | 0.9562 | 0.9521 | 0.9965 |
| 0.0065 | 8.0 | 5216 | 0.0133 | 0.9431 | 0.9655 | 0.9542 | 0.9966 |
| 0.0059 | 9.0 | 5868 | 0.0134 | 0.9501 | 0.9640 | 0.9570 | 0.9968 |
| 0.0042 | 10.0 | 6520 | 0.0138 | 0.9505 | 0.9655 | 0.9579 | 0.9969 |
### Framework versions
- Transformers 4.54.0
- Pytorch 2.7.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
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