---
base_model: bert-base-chinese
tags:
- generated_from_keras_callback
model-index:
- name: AIYIYA/my_aa
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# AIYIYA/my_aa

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:
- Train Loss: 0.7596
- Validation Loss: 1.4913
- Train Accuracy: 0.6753
- Epoch: 29

## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 280, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 3.4316     | 3.2876          | 0.2078         | 0     |
| 3.0452     | 3.0083          | 0.2338         | 1     |
| 2.6954     | 2.7106          | 0.3766         | 2     |
| 2.3815     | 2.4910          | 0.4935         | 3     |
| 2.0499     | 2.3035          | 0.5584         | 4     |
| 1.8322     | 2.1419          | 0.5844         | 5     |
| 1.6292     | 1.9997          | 0.6104         | 6     |
| 1.4675     | 1.8933          | 0.6234         | 7     |
| 1.3115     | 1.8016          | 0.5974         | 8     |
| 1.2088     | 1.7273          | 0.6364         | 9     |
| 1.1053     | 1.6728          | 0.6623         | 10    |
| 1.0254     | 1.6284          | 0.6364         | 11    |
| 0.9600     | 1.6252          | 0.6494         | 12    |
| 0.9058     | 1.5662          | 0.6623         | 13    |
| 0.8675     | 1.5423          | 0.6623         | 14    |
| 0.8434     | 1.5208          | 0.6753         | 15    |
| 0.8356     | 1.5140          | 0.6753         | 16    |
| 0.8070     | 1.5024          | 0.6753         | 17    |
| 0.7749     | 1.4941          | 0.6753         | 18    |
| 0.7805     | 1.4913          | 0.6753         | 19    |
| 0.7764     | 1.4913          | 0.6753         | 20    |
| 0.7630     | 1.4913          | 0.6753         | 21    |
| 0.7806     | 1.4913          | 0.6753         | 22    |
| 0.7665     | 1.4913          | 0.6753         | 23    |
| 0.7803     | 1.4913          | 0.6753         | 24    |
| 0.7778     | 1.4913          | 0.6753         | 25    |
| 0.7781     | 1.4913          | 0.6753         | 26    |
| 0.7798     | 1.4913          | 0.6753         | 27    |
| 0.7845     | 1.4913          | 0.6753         | 28    |
| 0.7596     | 1.4913          | 0.6753         | 29    |


### Framework versions

- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3