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corrected typo in example for t5 model input argument
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@ -693,7 +693,7 @@ class T5Model(T5PreTrainedModel):
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tokenizer = T5Tokenizer.from_pretrained('t5-small')
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model = T5Model.from_pretrained('t5-small')
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input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
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outputs = model(input_ids)
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outputs = model(input_ids=input_ids)
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last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
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"""
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@ -798,7 +798,7 @@ class T5WithLMHeadModel(T5PreTrainedModel):
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tokenizer = T5Tokenizer.from_pretrained('t5-small')
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model = T5WithLMHeadModel.from_pretrained('t5-small')
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input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
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outputs = model(input_ids, lm_labels=input_ids)
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outputs = model(input_ids=input_ids, lm_labels=input_ids)
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loss, prediction_scores = outputs[:2]
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"""
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@ -610,7 +610,7 @@ class TFT5Model(TFT5PreTrainedModel):
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tokenizer = T5Tokenizer.from_pretrained('t5-small')
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model = TFT5Model.from_pretrained('t5-small')
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input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
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outputs = model(input_ids)
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outputs = model(input_ids=input_ids)
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last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
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"""
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@ -701,7 +701,7 @@ class TFT5WithLMHeadModel(TFT5PreTrainedModel):
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tokenizer = T5Tokenizer.from_pretrained('t5-small')
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model = TFT5WithLMHeadModel.from_pretrained('t5-small')
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input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
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outputs = model(input_ids)
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outputs = model(input_ids=input_ids)
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prediction_scores = outputs[0]
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"""
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