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DistillBert Documentation Code Example fixes

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drc10723
2019-10-03 21:22:36 +05:30
committed by Victor SANH
parent e2ae9c0b73
commit e1b2949ae6
2 changed files with 3 additions and 5 deletions
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2
transformers/modeling_distilbert.py
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@@ -649,7 +649,7 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
start_positions = torch.tensor([1]) start_positions = torch.tensor([1])
end_positions = torch.tensor([3]) end_positions = torch.tensor([3])
outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions) outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
loss, start_scores, end_scores = outputs[:2] loss, start_scores, end_scores = outputs[:3]
""" """
def __init__(self, config): def __init__(self, config):

6
transformers/modeling_tf_distilbert.py
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@@ -603,7 +603,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-uncased') model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids) outputs = model(input_ids)
prediction_scores = outputs[0] prediction_scores = outputs[0]
""" """
@@ -715,9 +715,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased') model = TFDistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
start_positions = tf.constant([1]) outputs = model(input_ids)
end_positions = tf.constant([3])
outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
start_scores, end_scores = outputs[:2] start_scores, end_scores = outputs[:2]
""" """