Create README.md

README.md

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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 
+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
+
+TBA