Update generate_answers.py

generate_answers.py

@@ -1,15 +1,15 @@
-from transformers import BertTokenizer, BertForQuestionAnswering
-import torch
-
-model_path = "D:/code/bert_easy/bert-large-uncased-whole-word-masking-finetuned-squad"
-tokenizer = BertTokenizer.from_pretrained(model_path)
-model = BertForQuestionAnswering.from_pretrained(model_path)
-
-def get_answer(question, context):
-    """Answers a question using BERT on given context."""
-    inputs = tokenizer(question, context, return_tensors="pt", truncation=True, max_length=512)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    start = torch.argmax(outputs.start_logits)
-    end = torch.argmax(outputs.end_logits) + 1
-    return tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start:end]))
+from transformers import BertTokenizer, BertForQuestionAnswering
+import torch
+
+model_path = "bert-large-uncased-whole-word-masking-finetuned-squad"
+tokenizer = BertTokenizer.from_pretrained(model_path)
+model = BertForQuestionAnswering.from_pretrained(model_path)
+
+def get_answer(question, context):
+    """Answers a question using BERT on given context."""
+    inputs = tokenizer(question, context, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    start = torch.argmax(outputs.start_logits)
+    end = torch.argmax(outputs.end_logits) + 1
+    return tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start:end]))