Update README.md

README.md

@@ -43,22 +43,27 @@ 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')
+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")
+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
+# 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 
+# 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'
+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