From 985bba90961803c0f83dcb20d3139c0d4a9bcee3 Mon Sep 17 00:00:00 2001 From: Chengxi Guo Date: Tue, 27 Oct 2020 19:29:25 +0800 Subject: [PATCH] fix doc bug (#8082) Signed-off-by: mymusise --- src/transformers/file_utils.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/transformers/file_utils.py b/src/transformers/file_utils.py index 23283b0856..e51d9f827e 100644 --- a/src/transformers/file_utils.py +++ b/src/transformers/file_utils.py @@ -651,7 +651,7 @@ TF_TOKEN_CLASSIFICATION_SAMPLE = r""" >>> import tensorflow as tf >>> 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") >>> input_ids = inputs["input_ids"] @@ -669,7 +669,7 @@ TF_QUESTION_ANSWERING_SAMPLE = r""" >>> import tensorflow as tf >>> 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" >>> input_dict = tokenizer(question, text, return_tensors='tf') @@ -688,7 +688,7 @@ TF_SEQUENCE_CLASSIFICATION_SAMPLE = r""" >>> import tensorflow as tf >>> 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["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1 @@ -705,7 +705,7 @@ TF_MASKED_LM_SAMPLE = r""" >>> import tensorflow as tf >>> 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["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 >>> 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") >>> outputs = model(inputs) @@ -737,7 +737,7 @@ TF_MULTIPLE_CHOICE_SAMPLE = r""" >>> import tensorflow as tf >>> 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." >>> choice0 = "It is eaten with a fork and a knife." @@ -758,7 +758,7 @@ TF_CAUSAL_LM_SAMPLE = r""" >>> import tensorflow as tf >>> 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") >>> outputs = model(inputs)