---
library_name: transformers
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_based_bert-base-chinese
  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

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: 0.0429
- Precision: 0.9487
- Recall: 0.9514
- F1: 0.9501
- Accuracy: 0.9957

## 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: 16
- eval_batch_size: 16
- 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: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0294        | 1.0   | 1981  | 0.0255          | 0.8782    | 0.9345 | 0.9055 | 0.9923   |
| 0.0169        | 2.0   | 3962  | 0.0214          | 0.9167    | 0.9362 | 0.9263 | 0.9942   |
| 0.0113        | 3.0   | 5943  | 0.0213          | 0.9206    | 0.9464 | 0.9333 | 0.9948   |
| 0.0069        | 4.0   | 7924  | 0.0228          | 0.9224    | 0.9518 | 0.9369 | 0.9949   |
| 0.0044        | 5.0   | 9905  | 0.0228          | 0.9267    | 0.9432 | 0.9349 | 0.9950   |
| 0.0039        | 6.0   | 11886 | 0.0255          | 0.9323    | 0.9416 | 0.9369 | 0.9949   |
| 0.0024        | 7.0   | 13867 | 0.0300          | 0.9385    | 0.9443 | 0.9414 | 0.9951   |
| 0.0028        | 8.0   | 15848 | 0.0276          | 0.9323    | 0.9491 | 0.9407 | 0.9952   |
| 0.0022        | 9.0   | 17829 | 0.0297          | 0.9371    | 0.9459 | 0.9415 | 0.9952   |
| 0.0019        | 10.0  | 19810 | 0.0306          | 0.9318    | 0.9479 | 0.9397 | 0.9950   |
| 0.0012        | 11.0  | 21791 | 0.0326          | 0.9298    | 0.9500 | 0.9398 | 0.9951   |
| 0.0016        | 12.0  | 23772 | 0.0344          | 0.9329    | 0.9496 | 0.9412 | 0.9951   |
| 0.0011        | 13.0  | 25753 | 0.0355          | 0.9290    | 0.9511 | 0.9399 | 0.9951   |
| 0.0009        | 14.0  | 27734 | 0.0307          | 0.9405    | 0.9461 | 0.9433 | 0.9954   |
| 0.0009        | 15.0  | 29715 | 0.0336          | 0.9420    | 0.9430 | 0.9425 | 0.9953   |
| 0.0006        | 16.0  | 31696 | 0.0351          | 0.9357    | 0.9446 | 0.9401 | 0.9950   |
| 0.0007        | 17.0  | 33677 | 0.0326          | 0.9358    | 0.9484 | 0.9420 | 0.9952   |
| 0.0008        | 18.0  | 35658 | 0.0345          | 0.9269    | 0.9549 | 0.9407 | 0.9951   |
| 0.0007        | 19.0  | 37639 | 0.0347          | 0.9352    | 0.9523 | 0.9437 | 0.9953   |
| 0.0005        | 20.0  | 39620 | 0.0344          | 0.9381    | 0.9509 | 0.9445 | 0.9956   |
| 0.0006        | 21.0  | 41601 | 0.0360          | 0.9375    | 0.9498 | 0.9436 | 0.9953   |
| 0.0004        | 22.0  | 43582 | 0.0365          | 0.9429    | 0.9473 | 0.9451 | 0.9954   |
| 0.0005        | 23.0  | 45563 | 0.0362          | 0.9388    | 0.9495 | 0.9441 | 0.9952   |
| 0.0004        | 24.0  | 47544 | 0.0384          | 0.9410    | 0.9444 | 0.9427 | 0.9951   |
| 0.0003        | 25.0  | 49525 | 0.0361          | 0.9445    | 0.9479 | 0.9461 | 0.9956   |
| 0.0003        | 26.0  | 51506 | 0.0372          | 0.9459    | 0.9466 | 0.9463 | 0.9955   |
| 0.0004        | 27.0  | 53487 | 0.0390          | 0.9432    | 0.9461 | 0.9446 | 0.9954   |
| 0.0003        | 28.0  | 55468 | 0.0381          | 0.9412    | 0.9466 | 0.9439 | 0.9955   |
| 0.0004        | 29.0  | 57449 | 0.0404          | 0.9312    | 0.9540 | 0.9424 | 0.9952   |
| 0.0002        | 30.0  | 59430 | 0.0397          | 0.9389    | 0.9509 | 0.9449 | 0.9955   |
| 0.0003        | 31.0  | 61411 | 0.0388          | 0.9413    | 0.9488 | 0.9450 | 0.9954   |
| 0.0002        | 32.0  | 63392 | 0.0409          | 0.9411    | 0.9473 | 0.9442 | 0.9953   |
| 0.0003        | 33.0  | 65373 | 0.0418          | 0.9443    | 0.9509 | 0.9476 | 0.9955   |
| 0.0002        | 34.0  | 67354 | 0.0413          | 0.9390    | 0.9529 | 0.9459 | 0.9955   |
| 0.0001        | 35.0  | 69335 | 0.0418          | 0.9371    | 0.9516 | 0.9443 | 0.9953   |
| 0.0001        | 36.0  | 71316 | 0.0427          | 0.9414    | 0.9525 | 0.9469 | 0.9955   |
| 0.0001        | 37.0  | 73297 | 0.0439          | 0.9359    | 0.9538 | 0.9448 | 0.9953   |
| 0.0001        | 38.0  | 75278 | 0.0418          | 0.9429    | 0.9509 | 0.9469 | 0.9955   |
| 0.0           | 39.0  | 77259 | 0.0410          | 0.9511    | 0.9482 | 0.9497 | 0.9957   |
| 0.0001        | 40.0  | 79240 | 0.0410          | 0.9473    | 0.9507 | 0.9490 | 0.9956   |
| 0.0           | 41.0  | 81221 | 0.0442          | 0.9444    | 0.9532 | 0.9488 | 0.9956   |
| 0.0001        | 42.0  | 83202 | 0.0419          | 0.9427    | 0.9522 | 0.9474 | 0.9956   |
| 0.0001        | 43.0  | 85183 | 0.0423          | 0.9506    | 0.9477 | 0.9491 | 0.9956   |
| 0.0001        | 44.0  | 87164 | 0.0426          | 0.9478    | 0.9498 | 0.9488 | 0.9956   |
| 0.0001        | 45.0  | 89145 | 0.0426          | 0.9425    | 0.9523 | 0.9474 | 0.9956   |
| 0.0           | 46.0  | 91126 | 0.0435          | 0.9433    | 0.9509 | 0.9471 | 0.9956   |
| 0.0           | 47.0  | 93107 | 0.0437          | 0.9458    | 0.9500 | 0.9479 | 0.9956   |
| 0.0           | 48.0  | 95088 | 0.0436          | 0.9458    | 0.9516 | 0.9487 | 0.9957   |
| 0.0           | 49.0  | 97069 | 0.0428          | 0.9483    | 0.9507 | 0.9495 | 0.9957   |
| 0.0           | 50.0  | 99050 | 0.0429          | 0.9487    | 0.9514 | 0.9501 | 0.9957   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu126
- Datasets 3.2.0
- Tokenizers 0.21.0