Update README.md

README.md

@@ -78,7 +78,7 @@ qa_input = {
 'context': 'My name is Sarah and I live in London'
 }
 res = nlp(qa_input)
-# {'score': 0.9844420552253723, 'start': 30, 'end': 37, 'answer': ' London'}
+# {'score': 0.984, 'start': 30, 'end': 37, 'answer': ' London'}
 
 # b) Load model & tokenizer
 model = AutoModelForQuestionAnswering.from_pretrained(model_name)
@@ -87,11 +87,14 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 question = 'Where do I live?'
 context = 'My name is Sarah and I live in London'
 encoding = tokenizer(question, context, return_tensors="pt")
-
-start_scores, end_scores = model(encoding["input_ids"], attention_mask=encoding["attention_mask"], return_dict=False)
+start_scores, end_scores = model(
+  encoding["input_ids"],
+  attention_mask=encoding["attention_mask"],
+  return_dict=False
+)
 
 all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
-answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1]
+answer_tokens = all_tokens[torch.argmax(start_scores):torch.argmax(end_scores) + 1]
 answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
 # 'London'
 ```