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Example snippet for BertForQuestionAnswering

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Lysandre
2020-01-06 14:41:53 +01:00
parent 0ffc8eaf53
commit 74755c89b9
1 changed files with 1 additions and 2 deletions
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src/transformers/modeling_bert.py
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@@ -1392,8 +1392,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad') model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
input_text = "[CLS] " + question + " [SEP] " + text + " [SEP]" input_ids = tokenizer.encode(question, text)
input_ids = tokenizer.encode(input_text)
token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))] token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))]
start_scores, end_scores = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([token_type_ids])) start_scores, end_scores = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([token_type_ids]))
all_tokens = tokenizer.convert_ids_to_tokens(input_ids) all_tokens = tokenizer.convert_ids_to_tokens(input_ids)