readme: ---
  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}
  }
  ```