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library_name: transformers
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
model-index:
- name: models_for_qa_slide
results: []
datasets:
- roberthsu2003/for_MRC_QA
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. -->
# models_for_qa_slide
This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese)
使用的資料集是roberthsu2003/for_MRC_QA
## Model description
Question&Answering
使用overflow滑動視窗的策略
## 使用方式
```python
from transformers import pipeline
pipe = pipeline("question-answering", model="roberthsu2003/models_for_qa_slide")
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'])
-----------
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'])
```
## 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
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |