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---
license: cc-by-sa-3.0
language: ja
tags:
- question-answering
- extractive-qa
pipeline_tag:
- None
datasets:
- SkelterLabsInc/JaQuAD
metrics:
- Exact match
- F1 score
---
# BERT base Japanese - JaQuAD
## Description
A Japanese Question Answering model fine-tuned on [JaQuAD](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD).
Please refer [BERT base Japanese](https://huggingface.co/cl-tohoku/bert-base-japanese) for details about the pre-training model.
The codes for the fine-tuning are available at [SkelterLabsInc/JaQuAD](https://github.com/SkelterLabsInc/JaQuAD)
## Evaluation results
On the development set.
```shell
{"f1": 77.35, "exact_match": 61.01}
```
On the test set.
```shell
{"f1": 78.92, "exact_match": 63.38}
```
## Usage
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
question = 'アレクサンダー・グラハム・ベルは、どこで生まれたの?'
context = 'アレクサンダー・グラハム・ベルは、スコットランド生まれの科学者、発明家、工学者である。世界初の>実用的電話の発明で知られている。'
model = AutoModelForQuestionAnswering.from_pretrained(
'SkelterLabsInc/bert-base-japanese-jaquad')
tokenizer = AutoTokenizer.from_pretrained(
'SkelterLabsInc/bert-base-japanese-jaquad')
inputs = tokenizer(
question, context, add_special_tokens=True, return_tensors="pt")
input_ids = inputs["input_ids"].tolist()[0]
outputs = model(**inputs)
answer_start_scores = outputs.start_logits
answer_end_scores = outputs.end_logits
# Get the most likely beginning of answer with the argmax of the score.
answer_start = torch.argmax(answer_start_scores)
# Get the most likely end of answer with the argmax of the score.
# 1 is added to `answer_end` because the index pointed by score is inclusive.
answer_end = torch.argmax(answer_end_scores) + 1
answer = tokenizer.convert_tokens_to_string(
tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
# answer = 'スコットランド'
```
## License
The fine-tuned model is licensed under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.
## Citation
```bibtex
@misc{so2022jaquad,
title={{JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension}},
author={ByungHoon So and Kyuhong Byun and Kyungwon Kang and Seongjin Cho},
year={2022},
eprint={2202.01764},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |