SUMIN/HuggingFace_transformer
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fix doc bug (#8082)

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Signed-off-by: mymusise <mymusise1@gmail.com>
This commit is contained in:
Chengxi Guo
2020-10-27 19:29:25 +08:00
committed by GitHub
parent 08f534d2da
commit 985bba9096
1 changed files with 7 additions and 7 deletions
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14
src/transformers/file_utils.py
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@@ -651,7 +651,7 @@ TF_TOKEN_CLASSIFICATION_SAMPLE = r"""
>>> import tensorflow as tf >>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}') >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)) >>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> input_ids = inputs["input_ids"] >>> input_ids = inputs["input_ids"]
@@ -669,7 +669,7 @@ TF_QUESTION_ANSWERING_SAMPLE = r"""
>>> import tensorflow as tf >>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}') >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)) >>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> 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_dict = tokenizer(question, text, return_tensors='tf') >>> input_dict = tokenizer(question, text, return_tensors='tf')
@@ -688,7 +688,7 @@ TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
>>> import tensorflow as tf >>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}') >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)) >>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1 >>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1
@@ -705,7 +705,7 @@ TF_MASKED_LM_SAMPLE = r"""
>>> import tensorflow as tf >>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}') >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)) >>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf") >>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf")
>>> inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"] >>> inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
@@ -722,7 +722,7 @@ TF_BASE_MODEL_SAMPLE = r"""
>>> import tensorflow as tf >>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}') >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)) >>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs) >>> outputs = model(inputs)
@@ -737,7 +737,7 @@ TF_MULTIPLE_CHOICE_SAMPLE = r"""
>>> import tensorflow as tf >>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}') >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)) >>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife." >>> choice0 = "It is eaten with a fork and a knife."
@@ -758,7 +758,7 @@ TF_CAUSAL_LM_SAMPLE = r"""
>>> import tensorflow as tf >>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}') >>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)) >>> model = {model_class}.from_pretrained('{checkpoint}', return_dict=True)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs) >>> outputs = model(inputs)