README.md

---
library_name: transformers
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: for_classification
  results: []
license: apache-2.0
datasets:
- roberthsu2003/data_for_classification
language:
- zh
---

<!-- 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. -->

# for_classification

This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2366
- Accuracy: 0.9189
- F1: 0.9415

## 模型實作

```python
from transformers import pipeline

id2_label = {'LABEL_0':"負評",'LABEL_1':"正評"}
pipe = pipeline('text-classification', model="roberthsu2003/for_classification")

sen="服務人員都很親切"
print(sen,id2_label[pipe(sen)[0]['label']])

sen1="服務人員都不親切"
print(sen1,id2_label[pipe(sen1)[0]['label']])
```

## 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: 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: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2886        | 1.0   | 110  | 0.2269          | 0.9009   | 0.9272 |
| 0.1799        | 2.0   | 220  | 0.2218          | 0.9112   | 0.9356 |
| 0.1395        | 3.0   | 330  | 0.2366          | 0.9189   | 0.9415 |


### Framework versions

- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1