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base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: classifier_adapter
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. -->
# classifier_adapter
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0386
- Accuracy: 0.9875
- Precision: 0.8841
- Recall: 0.7947
- F1: 0.8283
- Ap: 0.8850
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ap |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log | 0.38 | 100 | 0.1590 | 0.9571 | 0.0 | 0.0 | 0.0 | 0.1046 |
| No log | 0.75 | 200 | 0.1578 | 0.9571 | 0.0 | 0.0 | 0.0 | 0.1808 |
| No log | 1.13 | 300 | 0.1185 | 0.9653 | 0.0899 | 0.0599 | 0.0680 | 0.4391 |
| No log | 1.51 | 400 | 0.0898 | 0.9724 | 0.2199 | 0.1409 | 0.1617 | 0.6479 |
| 0.1405 | 1.89 | 500 | 0.0774 | 0.9750 | 0.3319 | 0.2273 | 0.2575 | 0.7417 |
| 0.1405 | 2.26 | 600 | 0.0683 | 0.9771 | 0.4118 | 0.3002 | 0.3294 | 0.7791 |
| 0.1405 | 2.64 | 700 | 0.0616 | 0.9804 | 0.6207 | 0.4336 | 0.4810 | 0.8187 |
| 0.1405 | 3.02 | 800 | 0.0556 | 0.9821 | 0.7210 | 0.4875 | 0.5435 | 0.8380 |
| 0.1405 | 3.4 | 900 | 0.0519 | 0.9830 | 0.7329 | 0.5224 | 0.5839 | 0.8566 |
| 0.0598 | 3.77 | 1000 | 0.0486 | 0.9846 | 0.7818 | 0.6063 | 0.6615 | 0.8629 |
| 0.0598 | 4.15 | 1100 | 0.0469 | 0.9853 | 0.8223 | 0.6807 | 0.7248 | 0.8633 |
| 0.0598 | 4.53 | 1200 | 0.0457 | 0.9856 | 0.8521 | 0.7235 | 0.7663 | 0.8666 |
| 0.0598 | 4.91 | 1300 | 0.0439 | 0.9859 | 0.8436 | 0.6955 | 0.7435 | 0.8753 |
| 0.0598 | 5.28 | 1400 | 0.0424 | 0.9862 | 0.8715 | 0.6964 | 0.7496 | 0.8739 |
| 0.0399 | 5.66 | 1500 | 0.0415 | 0.9869 | 0.8695 | 0.7621 | 0.7994 | 0.8772 |
| 0.0399 | 6.04 | 1600 | 0.0416 | 0.9865 | 0.8700 | 0.7670 | 0.8039 | 0.8853 |
| 0.0399 | 6.42 | 1700 | 0.0401 | 0.9871 | 0.8687 | 0.7686 | 0.8047 | 0.8846 |
| 0.0399 | 6.79 | 1800 | 0.0405 | 0.9867 | 0.8734 | 0.7851 | 0.8167 | 0.8848 |
| 0.0399 | 7.17 | 1900 | 0.0410 | 0.9865 | 0.8600 | 0.7708 | 0.8057 | 0.8770 |
| 0.0315 | 7.55 | 2000 | 0.0393 | 0.9873 | 0.8869 | 0.7718 | 0.8158 | 0.8819 |
| 0.0315 | 7.92 | 2100 | 0.0385 | 0.9871 | 0.8747 | 0.7861 | 0.8196 | 0.8856 |
| 0.0315 | 8.3 | 2200 | 0.0386 | 0.9877 | 0.8863 | 0.7856 | 0.8227 | 0.8857 |
| 0.0315 | 8.68 | 2300 | 0.0390 | 0.9869 | 0.8695 | 0.7949 | 0.8221 | 0.8830 |
| 0.0315 | 9.06 | 2400 | 0.0391 | 0.9872 | 0.8685 | 0.8081 | 0.8311 | 0.8830 |
| 0.026 | 9.43 | 2500 | 0.0386 | 0.9875 | 0.8841 | 0.7947 | 0.8283 | 0.8850 |
| 0.026 | 9.81 | 2600 | 0.0390 | 0.9871 | 0.8615 | 0.8064 | 0.8264 | 0.8840 |
| 0.026 | 10.19 | 2700 | 0.0386 | 0.9873 | 0.8689 | 0.8023 | 0.8264 | 0.8859 |
| 0.026 | 10.57 | 2800 | 0.0386 | 0.9873 | 0.8737 | 0.7986 | 0.8265 | 0.8860 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Tokenizers 0.15.2
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