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Unverified Commit f5af8736 authored by Patrick von Platen's avatar Patrick von Platen Committed by GitHub
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[Docs] More general docstrings (#14028) 

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* finish

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* finish
parent 47489a69
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Showing with 209 additions and 120 deletions
src/transformers/file_utils.py
View file @ f5af8736
......@@ -791,10 +791,10 @@ def _prepare_output_docstrings(output_type, config_class):
PT_TOKEN_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
......@@ -808,10 +808,10 @@ PT_TOKEN_CLASSIFICATION_SAMPLE = r"""
PT_QUESTION_ANSWERING_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
......@@ -828,10 +828,10 @@ PT_QUESTION_ANSWERING_SAMPLE = r"""
PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
......@@ -844,10 +844,10 @@ PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
PT_MASKED_LM_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")
......@@ -861,10 +861,10 @@ PT_MASKED_LM_SAMPLE = r"""
PT_BASE_MODEL_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
......@@ -876,10 +876,10 @@ PT_BASE_MODEL_SAMPLE = r"""
PT_MULTIPLE_CHOICE_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
......@@ -899,9 +899,9 @@ PT_CAUSAL_LM_SAMPLE = r"""
Example::
>>> import torch
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
......@@ -910,6 +910,79 @@ PT_CAUSAL_LM_SAMPLE = r"""
>>> logits = outputs.logits
"""
PT_SPEECH_BASE_MODEL_SAMPLE = r"""
Example::
>>> from transformers import {processor_class}, {model_class}
>>> from datasets import load_dataset
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
"""
PT_SPEECH_CTC_SAMPLE = r"""
Example::
>>> from transformers import {processor_class}, {model_class}
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> logits = model(**inputs).logits
>>> predicted_ids = torch.argmax(logits, dim=-1)
>>> # transcribe speech
>>> transcription = processor.batch_decode(predicted_ids)
>>> # compute loss
>>> with processor.as_target_processor():
... inputs["labels"] = processor(dataset[0]["text"], return_tensors="pt").input_ids
>>> loss = model(**inputs).loss
"""
PT_SPEECH_SEQ_CLASS_SAMPLE = r"""
Example::
>>> from transformers import {processor_class}, {model_class}
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> feature_extractor = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt")
>>> logits = model(**inputs).logits
>>> predicted_class_ids = torch.argmax(logits, dim=-1)
>>> predicted_label = model.config.id2label[predicted_class_ids]
>>> # compute loss - target_label is e.g. "down"
>>> target_label = model.config.id2label[0]
>>> inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
>>> loss = model(**inputs).loss
"""
PT_SAMPLE_DOCSTRINGS = {
"SequenceClassification": PT_SEQUENCE_CLASSIFICATION_SAMPLE,
"QuestionAnswering": PT_QUESTION_ANSWERING_SAMPLE,
......@@ -918,16 +991,19 @@ PT_SAMPLE_DOCSTRINGS = {
"MaskedLM": PT_MASKED_LM_SAMPLE,
"LMHead": PT_CAUSAL_LM_SAMPLE,
"BaseModel": PT_BASE_MODEL_SAMPLE,
"SpeechBaseModel": PT_SPEECH_BASE_MODEL_SAMPLE,
"CTC": PT_SPEECH_CTC_SAMPLE,
"AudioClassification": PT_SPEECH_SEQ_CLASS_SAMPLE,
}
TF_TOKEN_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
......@@ -942,10 +1018,10 @@ TF_TOKEN_CLASSIFICATION_SAMPLE = r"""
TF_QUESTION_ANSWERING_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
......@@ -961,10 +1037,10 @@ TF_QUESTION_ANSWERING_SAMPLE = r"""
TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
......@@ -978,10 +1054,10 @@ TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
TF_MASKED_LM_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf")
......@@ -995,10 +1071,10 @@ TF_MASKED_LM_SAMPLE = r"""
TF_BASE_MODEL_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
......@@ -1010,10 +1086,10 @@ TF_BASE_MODEL_SAMPLE = r"""
TF_MULTIPLE_CHOICE_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
......@@ -1031,10 +1107,10 @@ TF_MULTIPLE_CHOICE_SAMPLE = r"""
TF_CAUSAL_LM_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
......@@ -1056,9 +1132,9 @@ TF_SAMPLE_DOCSTRINGS = {
FLAX_TOKEN_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors='jax')
......@@ -1070,9 +1146,9 @@ FLAX_TOKEN_CLASSIFICATION_SAMPLE = r"""
FLAX_QUESTION_ANSWERING_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
......@@ -1086,9 +1162,9 @@ FLAX_QUESTION_ANSWERING_SAMPLE = r"""
FLAX_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors='jax')
......@@ -1100,9 +1176,9 @@ FLAX_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
FLAX_MASKED_LM_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors='jax')
......@@ -1114,9 +1190,9 @@ FLAX_MASKED_LM_SAMPLE = r"""
FLAX_BASE_MODEL_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors='jax')
......@@ -1128,9 +1204,9 @@ FLAX_BASE_MODEL_SAMPLE = r"""
FLAX_MULTIPLE_CHOICE_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
......@@ -1146,9 +1222,9 @@ FLAX_MULTIPLE_CHOICE_SAMPLE = r"""
FLAX_CAUSAL_LM_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> from transformers import {processor_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> tokenizer = {processor_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
......@@ -1170,7 +1246,14 @@ FLAX_SAMPLE_DOCSTRINGS = {
def add_code_sample_docstrings(
*docstr, tokenizer_class=None, checkpoint=None, output_type=None, config_class=None, mask=None, model_cls=None
*docstr,
processor_class=None,
checkpoint=None,
output_type=None,
config_class=None,
mask=None,
model_cls=None,
modality=None
):
def docstring_decorator(fn):
# model_class defaults to function's class if not specified otherwise
......@@ -1183,9 +1266,11 @@ def add_code_sample_docstrings(
else:
sample_docstrings = PT_SAMPLE_DOCSTRINGS
doc_kwargs = dict(model_class=model_class, tokenizer_class=tokenizer_class, checkpoint=checkpoint)
doc_kwargs = dict(model_class=model_class, processor_class=processor_class, checkpoint=checkpoint)
if "SequenceClassification" in model_class:
if "SequenceClassification" in model_class and modality == "audio":
code_sample = sample_docstrings["AudioClassification"]
elif "SequenceClassification" in model_class:
code_sample = sample_docstrings["SequenceClassification"]
elif "QuestionAnswering" in model_class:
code_sample = sample_docstrings["QuestionAnswering"]
......@@ -1198,6 +1283,10 @@ def add_code_sample_docstrings(
code_sample = sample_docstrings["MaskedLM"]
elif "LMHead" in model_class or "CausalLM" in model_class:
code_sample = sample_docstrings["LMHead"]
elif "CTC" in model_class:
code_sample = sample_docstrings["CTC"]
elif "Model" in model_class and modality == "audio":
code_sample = sample_docstrings["SpeechBaseModel"]
elif "Model" in model_class or "Encoder" in model_class:
code_sample = sample_docstrings["BaseModel"]
else:
......
src/transformers/modeling_flax_utils.py
View file @ f5af8736
......@@ -528,7 +528,7 @@ def overwrite_call_docstring(model_class, docstring):
def append_call_sample_docstring(model_class, tokenizer_class, checkpoint, output_type, config_class, mask=None):
model_class.__call__ = copy_func(model_class.__call__)
model_class.__call__ = add_code_sample_docstrings(
tokenizer_class=tokenizer_class,
processor_class=tokenizer_class,
checkpoint=checkpoint,
output_type=output_type,
config_class=config_class,
......
src/transformers/models/albert/modeling_albert.py
View file @ f5af8736
......@@ -665,7 +665,7 @@ class AlbertModel(AlbertPreTrainedModel):
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
......@@ -916,7 +916,7 @@ class AlbertForMaskedLM(AlbertPreTrainedModel):
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -995,7 +995,7 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1101,7 +1101,7 @@ class AlbertForTokenClassification(AlbertPreTrainedModel):
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1191,7 +1191,7 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel):
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1290,7 +1290,7 @@ class AlbertForMultipleChoice(AlbertPreTrainedModel):
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/albert/modeling_tf_albert.py
View file @ f5af8736
......@@ -783,7 +783,7 @@ class TFAlbertModel(TFAlbertPreTrainedModel):
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
......@@ -1000,7 +1000,7 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1105,7 +1105,7 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1214,7 +1214,7 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1315,7 +1315,7 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1443,7 +1443,7 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/bart/modeling_bart.py
View file @ f5af8736
......@@ -1130,7 +1130,7 @@ class BartModel(BartPretrainedModel):
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1400,7 +1400,7 @@ class BartForSequenceClassification(BartPretrainedModel):
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1512,7 +1512,7 @@ class BartForQuestionAnswering(BartPretrainedModel):
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/bart/modeling_tf_bart.py
View file @ f5af8736
......@@ -1196,7 +1196,7 @@ class TFBartModel(TFBartPretrainedModel):
@add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/bert/modeling_bert.py
View file @ f5af8736
......@@ -886,7 +886,7 @@ class BertModel(BertPreTrainedModel):
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
......@@ -1302,7 +1302,7 @@ class BertForMaskedLM(BertPreTrainedModel):
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1501,7 +1501,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1600,7 +1600,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1698,7 +1698,7 @@ class BertForTokenClassification(BertPreTrainedModel):
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1788,7 +1788,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/bert/modeling_tf_bert.py
View file @ f5af8736
......@@ -1064,7 +1064,7 @@ class TFBertModel(TFBertPreTrainedModel):
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
......@@ -1335,7 +1335,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1451,7 +1451,7 @@ class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
}
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
......@@ -1704,7 +1704,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassific
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1814,7 +1814,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1973,7 +1973,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2080,7 +2080,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/bert_generation/modeling_bert_generation.py
View file @ f5af8736
......@@ -300,7 +300,7 @@ class BertGenerationEncoder(BertGenerationPreTrainedModel):
@add_start_docstrings_to_model_forward(BERT_GENERATION_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/big_bird/modeling_big_bird.py
View file @ f5af8736
......@@ -1974,7 +1974,7 @@ class BigBirdModel(BigBirdPreTrainedModel):
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
......@@ -2380,7 +2380,7 @@ class BigBirdForMaskedLM(BigBirdPreTrainedModel):
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2646,7 +2646,7 @@ class BigBirdForSequenceClassification(BigBirdPreTrainedModel):
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2743,7 +2743,7 @@ class BigBirdForMultipleChoice(BigBirdPreTrainedModel):
BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2838,7 +2838,7 @@ class BigBirdForTokenClassification(BigBirdPreTrainedModel):
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2946,7 +2946,7 @@ class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="google/bigbird-base-trivia-itc",
output_type=BigBirdForQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py
View file @ f5af8736
......@@ -2338,7 +2338,7 @@ class BigBirdPegasusModel(BigBirdPegasusPreTrainedModel):
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2611,7 +2611,7 @@ class BigBirdPegasusForSequenceClassification(BigBirdPegasusPreTrainedModel):
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2724,7 +2724,7 @@ class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel):
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/blenderbot/modeling_tf_blenderbot.py
View file @ f5af8736
......@@ -1206,7 +1206,7 @@ class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
@add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py
View file @ f5af8736
......@@ -1194,7 +1194,7 @@ class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
@add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/canine/modeling_canine.py
View file @ f5af8736
......@@ -1096,7 +1096,7 @@ class CanineModel(CaninePreTrainedModel):
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CanineModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
......@@ -1277,7 +1277,7 @@ class CanineForSequenceClassification(CaninePreTrainedModel):
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1373,7 +1373,7 @@ class CanineForMultipleChoice(CaninePreTrainedModel):
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1465,7 +1465,7 @@ class CanineForTokenClassification(CaninePreTrainedModel):
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1552,7 +1552,7 @@ class CanineForQuestionAnswering(CaninePreTrainedModel):
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/convbert/modeling_convbert.py
View file @ f5af8736
......@@ -793,7 +793,7 @@ class ConvBertModel(ConvBertPreTrainedModel):
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
......@@ -896,7 +896,7 @@ class ConvBertForMaskedLM(ConvBertPreTrainedModel):
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -999,7 +999,7 @@ class ConvBertForSequenceClassification(ConvBertPreTrainedModel):
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1096,7 +1096,7 @@ class ConvBertForMultipleChoice(ConvBertPreTrainedModel):
CONVBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1191,7 +1191,7 @@ class ConvBertForTokenClassification(ConvBertPreTrainedModel):
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1278,7 +1278,7 @@ class ConvBertForQuestionAnswering(ConvBertPreTrainedModel):
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/convbert/modeling_tf_convbert.py
View file @ f5af8736
......@@ -754,7 +754,7 @@ class TFConvBertModel(TFConvBertPreTrainedModel):
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -886,7 +886,7 @@ class TFConvBertForMaskedLM(TFConvBertPreTrainedModel, TFMaskedLanguageModelingL
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1010,7 +1010,7 @@ class TFConvBertForSequenceClassification(TFConvBertPreTrainedModel, TFSequenceC
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1119,7 +1119,7 @@ class TFConvBertForMultipleChoice(TFConvBertPreTrainedModel, TFMultipleChoiceLos
CONVBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1257,7 +1257,7 @@ class TFConvBertForTokenClassification(TFConvBertPreTrainedModel, TFTokenClassif
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1352,7 +1352,7 @@ class TFConvBertForQuestionAnswering(TFConvBertPreTrainedModel, TFQuestionAnswer
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/ctrl/modeling_ctrl.py
View file @ f5af8736
......@@ -355,7 +355,7 @@ class CTRLModel(CTRLPreTrainedModel):
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
......@@ -516,7 +516,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithPast,
config_class=_CONFIG_FOR_DOC,
......@@ -619,7 +619,7 @@ class CTRLForSequenceClassification(CTRLPreTrainedModel):
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/ctrl/modeling_tf_ctrl.py
View file @ f5af8736
......@@ -543,7 +543,7 @@ class TFCTRLModel(TFCTRLPreTrainedModel):
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
......@@ -671,7 +671,7 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel, TFCausalLanguageModelingLoss):
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutputWithPast,
config_class=_CONFIG_FOR_DOC,
......@@ -795,7 +795,7 @@ class TFCTRLForSequenceClassification(TFCTRLPreTrainedModel, TFSequenceClassific
@add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/deberta/modeling_deberta.py
View file @ f5af8736
......@@ -866,7 +866,7 @@ class DebertaModel(DebertaPreTrainedModel):
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -972,7 +972,7 @@ class DebertaForMaskedLM(DebertaPreTrainedModel):
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1112,7 +1112,7 @@ class DebertaForSequenceClassification(DebertaPreTrainedModel):
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1207,7 +1207,7 @@ class DebertaForTokenClassification(DebertaPreTrainedModel):
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1294,7 +1294,7 @@ class DebertaForQuestionAnswering(DebertaPreTrainedModel):
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/deberta/modeling_tf_deberta.py
View file @ f5af8736
......@@ -1101,7 +1101,7 @@ class TFDebertaModel(TFDebertaPreTrainedModel):
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1173,7 +1173,7 @@ class TFDebertaForMaskedLM(TFDebertaPreTrainedModel, TFMaskedLanguageModelingLos
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1275,7 +1275,7 @@ class TFDebertaForSequenceClassification(TFDebertaPreTrainedModel, TFSequenceCla
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1372,7 +1372,7 @@ class TFDebertaForTokenClassification(TFDebertaPreTrainedModel, TFTokenClassific
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1465,7 +1465,7 @@ class TFDebertaForQuestionAnswering(TFDebertaPreTrainedModel, TFQuestionAnswerin
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
......
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