---
library_name: transformers
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
model-index:
- name: models_for_qa_cut
  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. -->

# models_for_qa_cut

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: 0.6446

## Model description
### 使用說明

```python
from transformers import pipeline

pipe = pipeline("question-answering", model="roberthsu2003/models_for_qa_cut")
answer = pipe(question="蔡英文何時卸任?",context="蔡英文於2024年5月卸任中華民國總統，交棒給時任副總統賴清德。卸任後較少公開露面，直至2024年10月她受邀訪問歐洲。[25]")
print(answer['answer'])
#'2024年5月'


context='台積電也承諾未來在台灣的各項投資不變，計劃未來在本國建造九座廠，包括新竹、高雄、台中、嘉義和台南等地，在2035年，台灣仍將生產高達80%的晶片。'''
answer = pipe(question='台積電未來要建立幾座廠',context=context)
print(answer['answer'])
answer = pipe(question='2035年在台灣生產的晶片比例?',context=context)
print(answer['answer'])
#九座
#80%
```

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6584        | 1.0   | 842  | 0.6412          |
| 0.4002        | 2.0   | 1684 | 0.6446          |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0