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corrected typo in example for t5 model input argument

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patrickvonplaten 2019-12-19 01:26:01 +01:00 committed by Lysandre Debut
parent 8efc6dd544
commit ed6ba93912
2 changed files with 4 additions and 4 deletions
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4
transformers/modeling_t5.py
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@ -693,7 +693,7 @@ class T5Model(T5PreTrainedModel):
tokenizer = T5Tokenizer.from_pretrained('t5-small') tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5Model.from_pretrained('t5-small') model = T5Model.from_pretrained('t5-small')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids) outputs = model(input_ids=input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
""" """
@ -798,7 +798,7 @@ class T5WithLMHeadModel(T5PreTrainedModel):
tokenizer = T5Tokenizer.from_pretrained('t5-small') tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5WithLMHeadModel.from_pretrained('t5-small') model = T5WithLMHeadModel.from_pretrained('t5-small')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, lm_labels=input_ids) outputs = model(input_ids=input_ids, lm_labels=input_ids)
loss, prediction_scores = outputs[:2] loss, prediction_scores = outputs[:2]
""" """

4
transformers/modeling_tf_t5.py
View File

@ -610,7 +610,7 @@ class TFT5Model(TFT5PreTrainedModel):
tokenizer = T5Tokenizer.from_pretrained('t5-small') tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = TFT5Model.from_pretrained('t5-small') model = TFT5Model.from_pretrained('t5-small')
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) outputs = model(input_ids=input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
""" """
@ -701,7 +701,7 @@ class TFT5WithLMHeadModel(TFT5PreTrainedModel):
tokenizer = T5Tokenizer.from_pretrained('t5-small') tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = TFT5WithLMHeadModel.from_pretrained('t5-small') model = TFT5WithLMHeadModel.from_pretrained('t5-small')
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) outputs = model(input_ids=input_ids)
prediction_scores = outputs[0] prediction_scores = outputs[0]
""" """