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[Docs] More general docstrings (#14028)

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Patrick von Platen 2021-10-16 00:48:37 +02:00 committed by GitHub
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commit f5af873617
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74 changed files with 463 additions and 437 deletions
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179
src/transformers/file_utils.py
View File

@ -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:

2
src/transformers/modeling_flax_utils.py
View File

@ -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,

12
src/transformers/models/albert/modeling_albert.py
View File

@ -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,

12
src/transformers/models/albert/modeling_tf_albert.py
View File

@ -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,

6
src/transformers/models/bart/modeling_bart.py
View File

@ -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,

2
src/transformers/models/bart/modeling_tf_bart.py
View File

@ -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,

12
src/transformers/models/bert/modeling_bert.py
View File

@ -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,

14
src/transformers/models/bert/modeling_tf_bert.py
View File

@ -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,

2
src/transformers/models/bert_generation/modeling_bert_generation.py
View File

@ -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,

12
src/transformers/models/big_bird/modeling_big_bird.py
View File

@ -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,

6
src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py
View File

@ -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,

2
src/transformers/models/blenderbot/modeling_tf_blenderbot.py
View File

@ -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,

2
src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py
View File

@ -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,

10
src/transformers/models/canine/modeling_canine.py
View File

@ -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,

12
src/transformers/models/convbert/modeling_convbert.py
View File

@ -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,

12
src/transformers/models/convbert/modeling_tf_convbert.py
View File

@ -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,

6
src/transformers/models/ctrl/modeling_ctrl.py
View File

@ -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,

6
src/transformers/models/ctrl/modeling_tf_ctrl.py
View File

@ -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,

10
src/transformers/models/deberta/modeling_deberta.py
View File

@ -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,

10
src/transformers/models/deberta/modeling_tf_deberta.py
View File

@ -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,

10
src/transformers/models/deberta_v2/modeling_deberta_v2.py
View File

@ -974,7 +974,7 @@ class DebertaV2Model(DebertaV2PreTrainedModel):
@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,
@ -1081,7 +1081,7 @@ class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):
@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,
@ -1222,7 +1222,7 @@ class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
@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,
@ -1318,7 +1318,7 @@ class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
@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,
@ -1406,7 +1406,7 @@ class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
@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,

10
src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py
View File

@ -1223,7 +1223,7 @@ class TFDebertaV2Model(TFDebertaV2PreTrainedModel):
@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,
@ -1296,7 +1296,7 @@ class TFDebertaV2ForMaskedLM(TFDebertaV2PreTrainedModel, TFMaskedLanguageModelin
@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,
@ -1399,7 +1399,7 @@ class TFDebertaV2ForSequenceClassification(TFDebertaV2PreTrainedModel, TFSequenc
@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,
@ -1497,7 +1497,7 @@ class TFDebertaV2ForTokenClassification(TFDebertaV2PreTrainedModel, TFTokenClass
@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,
@ -1591,7 +1591,7 @@ class TFDebertaV2ForQuestionAnswering(TFDebertaV2PreTrainedModel, TFQuestionAnsw
@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,

10
src/transformers/models/distilbert/modeling_distilbert.py
View File

@ -508,7 +508,7 @@ class DistilBertModel(DistilBertPreTrainedModel):
@add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -604,7 +604,7 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel):
@add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices"))
@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,
@ -702,7 +702,7 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
@add_start_docstrings_to_model_forward(DISTILBERT_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,
@ -818,7 +818,7 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
@add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, num_choices"))
@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,
@ -935,7 +935,7 @@ class DistilBertForTokenClassification(DistilBertPreTrainedModel):
@add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING)
@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,

12
src/transformers/models/distilbert/modeling_tf_distilbert.py
View File

@ -543,7 +543,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel):
@add_start_docstrings_to_model_forward(DISTILBERT_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,
@ -658,7 +658,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel, TFMaskedLanguageModel
@add_start_docstrings_to_model_forward(DISTILBERT_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,
@ -759,7 +759,7 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel, TFSeque
@add_start_docstrings_to_model_forward(DISTILBERT_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,
@ -854,7 +854,7 @@ class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel, TFTokenCla
@add_start_docstrings_to_model_forward(DISTILBERT_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,
@ -962,7 +962,7 @@ class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoic
DISTILBERT_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,
@ -1088,7 +1088,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel, TFQuestionAn
@add_start_docstrings_to_model_forward(DISTILBERT_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,

12
src/transformers/models/electra/modeling_electra.py
View File

@ -833,7 +833,7 @@ class ElectraModel(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_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,
@ -941,7 +941,7 @@ class ElectraForSequenceClassification(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_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,
@ -1136,7 +1136,7 @@ class ElectraForMaskedLM(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_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,
@ -1218,7 +1218,7 @@ class ElectraForTokenClassification(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_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,
@ -1307,7 +1307,7 @@ class ElectraForQuestionAnswering(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_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,
@ -1408,7 +1408,7 @@ class ElectraForMultipleChoice(ElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_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,

12
src/transformers/models/electra/modeling_tf_electra.py
View File

@ -950,7 +950,7 @@ class TFElectraModel(TFElectraPreTrainedModel):
@add_start_docstrings_to_model_forward(ELECTRA_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=TFBaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
@ -1211,7 +1211,7 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
@add_start_docstrings_to_model_forward(ELECTRA_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,
@ -1336,7 +1336,7 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
@add_start_docstrings_to_model_forward(ELECTRA_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,
@ -1444,7 +1444,7 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss)
@add_start_docstrings_to_model_forward(ELECTRA_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,
@ -1584,7 +1584,7 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
@add_start_docstrings_to_model_forward(ELECTRA_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,
@ -1681,7 +1681,7 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
@add_start_docstrings_to_model_forward(ELECTRA_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,

2
src/transformers/models/flaubert/modeling_flaubert.py
View File

@ -148,7 +148,7 @@ class FlaubertModel(XLMModel):
@add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,

4
src/transformers/models/flaubert/modeling_tf_flaubert.py
View File

@ -236,7 +236,7 @@ class TFFlaubertModel(TFFlaubertPreTrainedModel):
@add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING)
@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,
@ -820,7 +820,7 @@ class TFFlaubertWithLMHeadModel(TFFlaubertPreTrainedModel):
@add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFFlaubertWithLMHeadModelOutput,
config_class=_CONFIG_FOR_DOC,

12
src/transformers/models/fnet/modeling_fnet.py
View File

@ -545,7 +545,7 @@ class FNetModel(FNetPreTrainedModel):
@add_start_docstrings_to_model_forward(FNET_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=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -733,7 +733,7 @@ class FNetForMaskedLM(FNetPreTrainedModel):
@add_start_docstrings_to_model_forward(FNET_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,
@ -889,7 +889,7 @@ class FNetForSequenceClassification(FNetPreTrainedModel):
@add_start_docstrings_to_model_forward(FNET_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,
@ -961,7 +961,7 @@ class FNetForMultipleChoice(FNetPreTrainedModel):
@add_start_docstrings_to_model_forward(FNET_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,
@ -1042,7 +1042,7 @@ class FNetForTokenClassification(FNetPreTrainedModel):
@add_start_docstrings_to_model_forward(FNET_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,
@ -1111,7 +1111,7 @@ class FNetForQuestionAnswering(FNetPreTrainedModel):
@add_start_docstrings_to_model_forward(FNET_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,

2
src/transformers/models/fsmt/modeling_fsmt.py
View File

@ -1007,7 +1007,7 @@ class FSMTModel(PretrainedFSMTModel):
@add_start_docstrings_to_model_forward(FSMT_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,

14
src/transformers/models/funnel/modeling_funnel.py
View File

@ -910,7 +910,7 @@ class FunnelBaseModel(FunnelPreTrainedModel):
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small-base",
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -987,7 +987,7 @@ class FunnelModel(FunnelPreTrainedModel):
@add_start_docstrings_to_model_forward(FUNNEL_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=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1174,7 +1174,7 @@ class FunnelForMaskedLM(FunnelPreTrainedModel):
@add_start_docstrings_to_model_forward(FUNNEL_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,
@ -1248,7 +1248,7 @@ class FunnelForSequenceClassification(FunnelPreTrainedModel):
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small-base",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -1338,7 +1338,7 @@ class FunnelForMultipleChoice(FunnelPreTrainedModel):
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small-base",
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1424,7 +1424,7 @@ class FunnelForTokenClassification(FunnelPreTrainedModel):
@add_start_docstrings_to_model_forward(FUNNEL_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,
@ -1506,7 +1506,7 @@ class FunnelForQuestionAnswering(FunnelPreTrainedModel):
@add_start_docstrings_to_model_forward(FUNNEL_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,

14
src/transformers/models/funnel/modeling_tf_funnel.py
View File

@ -1126,7 +1126,7 @@ class TFFunnelBaseModel(TFFunnelPreTrainedModel):
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small-base",
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1187,7 +1187,7 @@ class TFFunnelModel(TFFunnelPreTrainedModel):
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small",
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1337,7 +1337,7 @@ class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss)
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small",
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
@ -1426,7 +1426,7 @@ class TFFunnelForSequenceClassification(TFFunnelPreTrainedModel, TFSequenceClass
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small-base",
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -1525,7 +1525,7 @@ class TFFunnelForMultipleChoice(TFFunnelPreTrainedModel, TFMultipleChoiceLoss):
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small-base",
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1655,7 +1655,7 @@ class TFFunnelForTokenClassification(TFFunnelPreTrainedModel, TFTokenClassificat
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small",
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -1747,7 +1747,7 @@ class TFFunnelForQuestionAnswering(TFFunnelPreTrainedModel, TFQuestionAnsweringL
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="funnel-transformer/small",
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,

8
src/transformers/models/gpt2/modeling_gpt2.py
View File

@ -732,7 +732,7 @@ class GPT2Model(GPT2PreTrainedModel):
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@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,
@ -1009,7 +1009,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
@ -1338,7 +1338,7 @@ class GPT2ForSequenceClassification(GPT2PreTrainedModel):
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="microsoft/DialogRPT-updown",
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
@ -1457,7 +1457,7 @@ class GPT2ForTokenClassification(GPT2PreTrainedModel):
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="microsoft/DialogRPT-updown",
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,

6
src/transformers/models/gpt2/modeling_tf_gpt2.py
View File

@ -587,7 +587,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel):
@add_start_docstrings_to_model_forward(GPT2_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,
@ -679,7 +679,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel, TFCausalLanguageModelingLoss):
@add_start_docstrings_to_model_forward(GPT2_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,
@ -959,7 +959,7 @@ class TFGPT2ForSequenceClassification(TFGPT2PreTrainedModel, TFSequenceClassific
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="microsoft/DialogRPT-updown",
output_type=TFSequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,

6
src/transformers/models/gpt_neo/modeling_gpt_neo.py
View File

@ -497,7 +497,7 @@ class GPTNeoModel(GPTNeoPreTrainedModel):
@add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING)
@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,
@ -713,7 +713,7 @@ class GPTNeoForCausalLM(GPTNeoPreTrainedModel):
@add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
@ -827,7 +827,7 @@ class GPTNeoForSequenceClassification(GPTNeoPreTrainedModel):
@add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,

6
src/transformers/models/gptj/modeling_gptj.py
View File

@ -491,7 +491,7 @@ class GPTJModel(GPTJPreTrainedModel):
@add_start_docstrings_to_model_forward(GPTJ_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=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
@ -743,7 +743,7 @@ class GPTJForCausalLM(GPTJPreTrainedModel):
@add_start_docstrings_to_model_forward(GPTJ_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=CausalLMOutputWithPast,
config_class=_CONFIG_FOR_DOC,
@ -864,7 +864,7 @@ class GPTJForSequenceClassification(GPTJPreTrainedModel):
@add_start_docstrings_to_model_forward(GPTJ_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=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,

12
src/transformers/models/ibert/modeling_ibert.py
View File

@ -772,7 +772,7 @@ class IBertModel(IBertPreTrainedModel):
@add_start_docstrings_to_model_forward(IBERT_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,
@ -875,7 +875,7 @@ class IBertForMaskedLM(IBertPreTrainedModel):
@add_start_docstrings_to_model_forward(IBERT_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,
@ -983,7 +983,7 @@ class IBertForSequenceClassification(IBertPreTrainedModel):
@add_start_docstrings_to_model_forward(IBERT_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,
@ -1066,7 +1066,7 @@ class IBertForMultipleChoice(IBertPreTrainedModel):
@add_start_docstrings_to_model_forward(IBERT_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,
@ -1160,7 +1160,7 @@ class IBertForTokenClassification(IBertPreTrainedModel):
@add_start_docstrings_to_model_forward(IBERT_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,
@ -1269,7 +1269,7 @@ class IBertForQuestionAnswering(IBertPreTrainedModel):
@add_start_docstrings_to_model_forward(IBERT_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,

6
src/transformers/models/led/modeling_led.py
View File

@ -2174,7 +2174,7 @@ class LEDModel(LEDPreTrainedModel):
@add_start_docstrings_to_model_forward(LED_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,
@ -2468,7 +2468,7 @@ class LEDForSequenceClassification(LEDPreTrainedModel):
@add_start_docstrings_to_model_forward(LED_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,
@ -2578,7 +2578,7 @@ class LEDForQuestionAnswering(LEDPreTrainedModel):
@add_start_docstrings_to_model_forward(LED_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,

2
src/transformers/models/led/modeling_tf_led.py
View File

@ -2230,7 +2230,7 @@ class TFLEDModel(TFLEDPreTrainedModel):
@add_start_docstrings_to_model_forward(LED_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=TFLEDSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,

6
src/transformers/models/longformer/modeling_longformer.py
View File

@ -1822,7 +1822,7 @@ class LongformerForSequenceClassification(LongformerPreTrainedModel):
@add_start_docstrings_to_model_forward(LONGFORMER_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=LongformerSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -2084,7 +2084,7 @@ class LongformerForTokenClassification(LongformerPreTrainedModel):
@add_start_docstrings_to_model_forward(LONGFORMER_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=LongformerTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -2176,7 +2176,7 @@ class LongformerForMultipleChoice(LongformerPreTrainedModel):
LONGFORMER_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=LongformerMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,

10
src/transformers/models/longformer/modeling_tf_longformer.py
View File

@ -2088,7 +2088,7 @@ class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModel
@add_start_docstrings_to_model_forward(LONGFORMER_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=TFLongformerMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
@ -2197,7 +2197,7 @@ class TFLongformerForQuestionAnswering(TFLongformerPreTrainedModel, TFQuestionAn
@add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="allenai/longformer-large-4096-finetuned-triviaqa",
output_type=TFLongformerQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -2366,7 +2366,7 @@ class TFLongformerForSequenceClassification(TFLongformerPreTrainedModel, TFSeque
@add_start_docstrings_to_model_forward(LONGFORMER_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=TFLongformerSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -2492,7 +2492,7 @@ class TFLongformerForMultipleChoice(TFLongformerPreTrainedModel, TFMultipleChoic
LONGFORMER_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=TFLongformerMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -2645,7 +2645,7 @@ class TFLongformerForTokenClassification(TFLongformerPreTrainedModel, TFTokenCla
@add_start_docstrings_to_model_forward(LONGFORMER_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=TFLongformerTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,

4
src/transformers/models/lxmert/modeling_lxmert.py
View File

@ -901,7 +901,7 @@ class LxmertModel(LxmertPreTrainedModel):
@add_start_docstrings_to_model_forward(LXMERT_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=LxmertModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1384,7 +1384,7 @@ class LxmertForQuestionAnswering(LxmertPreTrainedModel):
@add_start_docstrings_to_model_forward(LXMERT_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=LxmertForQuestionAnsweringOutput,
config_class=_CONFIG_FOR_DOC,

2
src/transformers/models/lxmert/modeling_tf_lxmert.py
View File

@ -950,7 +950,7 @@ class TFLxmertModel(TFLxmertPreTrainedModel):
@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFLxmertModelOutput,
config_class=_CONFIG_FOR_DOC,

2
src/transformers/models/m2m_100/modeling_m2m_100.py
View File

@ -1123,7 +1123,7 @@ class M2M100Model(M2M100PreTrainedModel):
@add_start_docstrings_to_model_forward(M2M_100_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,

2
src/transformers/models/marian/modeling_tf_marian.py
View File

@ -1224,7 +1224,7 @@ class TFMarianModel(TFMarianPreTrainedModel):
@add_start_docstrings_to_model_forward(MARIAN_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,

6
src/transformers/models/mbart/modeling_mbart.py
View File

@ -1134,7 +1134,7 @@ class MBartModel(MBartPreTrainedModel):
@add_start_docstrings_to_model_forward(MBART_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,
@ -1405,7 +1405,7 @@ class MBartForSequenceClassification(MBartPreTrainedModel):
@add_start_docstrings_to_model_forward(MBART_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,
@ -1518,7 +1518,7 @@ class MBartForQuestionAnswering(MBartPreTrainedModel):
@add_start_docstrings_to_model_forward(MBART_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,

2
src/transformers/models/mbart/modeling_tf_mbart.py
View File

@ -1208,7 +1208,7 @@ class TFMBartModel(TFMBartPreTrainedModel):
@add_start_docstrings_to_model_forward(MBART_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,

12
src/transformers/models/megatron_bert/modeling_megatron_bert.py
View File

@ -873,7 +873,7 @@ class MegatronBertModel(MegatronBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MEGATRON_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,
@ -1282,7 +1282,7 @@ class MegatronBertForMaskedLM(MegatronBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MEGATRON_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,
@ -1480,7 +1480,7 @@ class MegatronBertForSequenceClassification(MegatronBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MEGATRON_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,
@ -1566,7 +1566,7 @@ class MegatronBertForMultipleChoice(MegatronBertPreTrainedModel):
MEGATRON_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,
@ -1661,7 +1661,7 @@ class MegatronBertForTokenClassification(MegatronBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MEGATRON_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,
@ -1751,7 +1751,7 @@ class MegatronBertForQuestionAnswering(MegatronBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MEGATRON_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,

12
src/transformers/models/mobilebert/modeling_mobilebert.py
View File

@ -817,7 +817,7 @@ class MobileBertModel(MobileBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MOBILEBERT_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,
@ -1032,7 +1032,7 @@ class MobileBertForMaskedLM(MobileBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MOBILEBERT_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,
@ -1222,7 +1222,7 @@ class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MOBILEBERT_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,
@ -1322,7 +1322,7 @@ class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MOBILEBERT_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,
@ -1427,7 +1427,7 @@ class MobileBertForMultipleChoice(MobileBertPreTrainedModel):
MOBILEBERT_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,
@ -1526,7 +1526,7 @@ class MobileBertForTokenClassification(MobileBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MOBILEBERT_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,

12
src/transformers/models/mobilebert/modeling_tf_mobilebert.py
View File

@ -933,7 +933,7 @@ class TFMobileBertModel(TFMobileBertPreTrainedModel):
@add_start_docstrings_to_model_forward(MOBILEBERT_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,
@ -1124,7 +1124,7 @@ class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModel
@add_start_docstrings_to_model_forward(MOBILEBERT_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,
@ -1348,7 +1348,7 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque
@add_start_docstrings_to_model_forward(MOBILEBERT_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,
@ -1456,7 +1456,7 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn
@add_start_docstrings_to_model_forward(MOBILEBERT_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,
@ -1591,7 +1591,7 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
MOBILEBERT_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,
@ -1742,7 +1742,7 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla
@add_start_docstrings_to_model_forward(MOBILEBERT_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,

12
src/transformers/models/mpnet/modeling_mpnet.py
View File

@ -511,7 +511,7 @@ class MPNetModel(MPNetPreTrainedModel):
@add_start_docstrings_to_model_forward(MPNET_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,
@ -593,7 +593,7 @@ class MPNetForMaskedLM(MPNetPreTrainedModel):
@add_start_docstrings_to_model_forward(MPNET_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,
@ -695,7 +695,7 @@ class MPNetForSequenceClassification(MPNetPreTrainedModel):
@add_start_docstrings_to_model_forward(MPNET_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,
@ -777,7 +777,7 @@ class MPNetForMultipleChoice(MPNetPreTrainedModel):
@add_start_docstrings_to_model_forward(MPNET_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,
@ -869,7 +869,7 @@ class MPNetForTokenClassification(MPNetPreTrainedModel):
@add_start_docstrings_to_model_forward(MPNET_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,
@ -977,7 +977,7 @@ class MPNetForQuestionAnswering(MPNetPreTrainedModel):
@add_start_docstrings_to_model_forward(MPNET_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,

12
src/transformers/models/mpnet/modeling_tf_mpnet.py
View File

@ -684,7 +684,7 @@ class TFMPNetModel(TFMPNetPreTrainedModel):
@add_start_docstrings_to_model_forward(MPNET_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,
@ -813,7 +813,7 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
@add_start_docstrings_to_model_forward(MPNET_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,
@ -934,7 +934,7 @@ class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassif
@add_start_docstrings_to_model_forward(MPNET_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,
@ -1040,7 +1040,7 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, TFMultipleChoiceLoss):
@add_start_docstrings_to_model_forward(MPNET_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,
@ -1172,7 +1172,7 @@ class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificatio
@add_start_docstrings_to_model_forward(MPNET_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,
@ -1271,7 +1271,7 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos
@add_start_docstrings_to_model_forward(MPNET_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,

6
src/transformers/models/openai/modeling_openai.py
View File

@ -433,7 +433,7 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -552,7 +552,7 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
@ -756,7 +756,7 @@ class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel):
@add_start_docstrings_to_model_forward(OPENAI_GPT_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,

6
src/transformers/models/openai/modeling_tf_openai.py
View File

@ -522,7 +522,7 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@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,
@ -598,7 +598,7 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel, TFCausalLanguageModelin
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutput,
config_class=_CONFIG_FOR_DOC,
@ -856,7 +856,7 @@ class TFOpenAIGPTForSequenceClassification(TFOpenAIGPTPreTrainedModel, TFSequenc
@add_start_docstrings_to_model_forward(OPENAI_GPT_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,

2
src/transformers/models/pegasus/modeling_tf_pegasus.py
View File

@ -1233,7 +1233,7 @@ class TFPegasusModel(TFPegasusPreTrainedModel):
@add_start_docstrings_to_model_forward(PEGASUS_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,

10
src/transformers/models/reformer/modeling_reformer.py
View File

@ -1992,7 +1992,7 @@ class ReformerModel(ReformerPreTrainedModel):
@add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=ReformerModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -2198,7 +2198,7 @@ class ReformerModelWithLMHead(ReformerPreTrainedModel):
@add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
@ -2313,7 +2313,7 @@ class ReformerForMaskedLM(ReformerPreTrainedModel):
@add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
@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,
@ -2394,7 +2394,7 @@ class ReformerForSequenceClassification(ReformerPreTrainedModel):
@add_start_docstrings_to_model_forward(REFORMER_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,
@ -2512,7 +2512,7 @@ class ReformerForQuestionAnswering(ReformerPreTrainedModel):
@add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
@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,

12
src/transformers/models/rembert/modeling_rembert.py
View File

@ -781,7 +781,7 @@ class RemBertModel(RemBertPreTrainedModel):
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
@ -933,7 +933,7 @@ class RemBertForMaskedLM(RemBertPreTrainedModel):
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
@ -1175,7 +1175,7 @@ class RemBertForSequenceClassification(RemBertPreTrainedModel):
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -1259,7 +1259,7 @@ class RemBertForMultipleChoice(RemBertPreTrainedModel):
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1351,7 +1351,7 @@ class RemBertForTokenClassification(RemBertPreTrainedModel):
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -1439,7 +1439,7 @@ class RemBertForQuestionAnswering(RemBertPreTrainedModel):
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,

14
src/transformers/models/rembert/modeling_tf_rembert.py
View File

@ -956,7 +956,7 @@ class TFRemBertModel(TFRemBertPreTrainedModel):
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=TFBaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
@ -1078,7 +1078,7 @@ class TFRemBertForMaskedLM(TFRemBertPreTrainedModel, TFMaskedLanguageModelingLos
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=TFMaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
@ -1186,7 +1186,7 @@ class TFRemBertForCausalLM(TFRemBertPreTrainedModel, TFCausalLanguageModelingLos
}
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=TFCausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
@ -1329,7 +1329,7 @@ class TFRemBertForSequenceClassification(TFRemBertPreTrainedModel, TFSequenceCla
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -1435,7 +1435,7 @@ class TFRemBertForMultipleChoice(TFRemBertPreTrainedModel, TFMultipleChoiceLoss)
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1579,7 +1579,7 @@ class TFRemBertForTokenClassification(TFRemBertPreTrainedModel, TFTokenClassific
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
@ -1675,7 +1675,7 @@ class TFRemBertForQuestionAnswering(TFRemBertPreTrainedModel, TFQuestionAnswerin
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="rembert",
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,

12
src/transformers/models/roberta/modeling_roberta.py
View File

@ -739,7 +739,7 @@ class RobertaModel(RobertaPreTrainedModel):
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1058,7 +1058,7 @@ class RobertaForMaskedLM(RobertaPreTrainedModel):
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1171,7 +1171,7 @@ class RobertaForSequenceClassification(RobertaPreTrainedModel):
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1267,7 +1267,7 @@ class RobertaForMultipleChoice(RobertaPreTrainedModel):
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1364,7 +1364,7 @@ class RobertaForTokenClassification(RobertaPreTrainedModel):
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1476,7 +1476,7 @@ class RobertaForQuestionAnswering(RobertaPreTrainedModel):
@add_start_docstrings_to_model_forward(ROBERTA_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,

14
src/transformers/models/roberta/modeling_tf_roberta.py
View File

@ -933,7 +933,7 @@ class TFRobertaModel(TFRobertaPreTrainedModel):
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1108,7 +1108,7 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLos
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1222,7 +1222,7 @@ class TFRobertaForCausalLM(TFRobertaPreTrainedModel, TFCausalLanguageModelingLos
@add_start_docstrings_to_model_forward(ROBERTA_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=TFCausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
@ -1392,7 +1392,7 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1503,7 +1503,7 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss)
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1641,7 +1641,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific
@add_start_docstrings_to_model_forward(ROBERTA_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,
@ -1742,7 +1742,7 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin
@add_start_docstrings_to_model_forward(ROBERTA_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,

12
src/transformers/models/roformer/modeling_roformer.py
View File

@ -839,7 +839,7 @@ class RoFormerModel(RoFormerPreTrainedModel):
@add_start_docstrings_to_model_forward(ROFORMER_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,
@ -987,7 +987,7 @@ class RoFormerForMaskedLM(RoFormerPreTrainedModel):
@add_start_docstrings_to_model_forward(ROFORMER_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,
@ -1246,7 +1246,7 @@ class RoFormerForSequenceClassification(RoFormerPreTrainedModel):
@add_start_docstrings_to_model_forward(ROFORMER_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,
@ -1328,7 +1328,7 @@ class RoFormerForMultipleChoice(RoFormerPreTrainedModel):
ROFORMER_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,
@ -1418,7 +1418,7 @@ class RoFormerForTokenClassification(RoFormerPreTrainedModel):
@add_start_docstrings_to_model_forward(ROFORMER_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,
@ -1505,7 +1505,7 @@ class RoFormerForQuestionAnswering(RoFormerPreTrainedModel):
@add_start_docstrings_to_model_forward(ROFORMER_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,

14
src/transformers/models/roformer/modeling_tf_roformer.py
View File

@ -814,7 +814,7 @@ class TFRoFormerModel(TFRoFormerPreTrainedModel):
@add_start_docstrings_to_model_forward(ROFORMER_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,
@ -886,7 +886,7 @@ class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingL
@add_start_docstrings_to_model_forward(ROFORMER_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,
@ -978,7 +978,7 @@ class TFRoFormerForCausalLM(TFRoFormerPreTrainedModel, TFCausalLanguageModelingL
return self.mlm.predictions
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutput,
config_class=_CONFIG_FOR_DOC,
@ -1103,7 +1103,7 @@ class TFRoFormerForSequenceClassification(TFRoFormerPreTrainedModel, TFSequenceC
@add_start_docstrings_to_model_forward(ROFORMER_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,
@ -1208,7 +1208,7 @@ class TFRoFormerForMultipleChoice(TFRoFormerPreTrainedModel, TFMultipleChoiceLos
ROFORMER_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,
@ -1344,7 +1344,7 @@ class TFRoFormerForTokenClassification(TFRoFormerPreTrainedModel, TFTokenClassif
@add_start_docstrings_to_model_forward(ROFORMER_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,
@ -1437,7 +1437,7 @@ class TFRoFormerForQuestionAnswering(TFRoFormerPreTrainedModel, TFQuestionAnswer
@add_start_docstrings_to_model_forward(ROFORMER_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,

2
src/transformers/models/speech_to_text/modeling_speech_to_text.py
View File

@ -1138,7 +1138,7 @@ class Speech2TextModel(Speech2TextPreTrainedModel):
@add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_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,

4
src/transformers/models/splinter/modeling_splinter.py
View File

@ -635,7 +635,7 @@ class SplinterModel(SplinterPreTrainedModel):
@add_start_docstrings_to_model_forward(SPLINTER_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,
@ -836,7 +836,7 @@ class SplinterForQuestionAnswering(SplinterPreTrainedModel):
@add_start_docstrings_to_model_forward(SPLINTER_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,

12
src/transformers/models/squeezebert/modeling_squeezebert.py
View File

@ -571,7 +571,7 @@ class SqueezeBertModel(SqueezeBertPreTrainedModel):
@add_start_docstrings_to_model_forward(SQUEEZEBERT_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,
@ -664,7 +664,7 @@ class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel):
@add_start_docstrings_to_model_forward(SQUEEZEBERT_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,
@ -743,7 +743,7 @@ class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel):
@add_start_docstrings_to_model_forward(SQUEEZEBERT_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,
@ -842,7 +842,7 @@ class SqueezeBertForMultipleChoice(SqueezeBertPreTrainedModel):
SQUEEZEBERT_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,
@ -934,7 +934,7 @@ class SqueezeBertForTokenClassification(SqueezeBertPreTrainedModel):
@add_start_docstrings_to_model_forward(SQUEEZEBERT_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,
@ -1021,7 +1021,7 @@ class SqueezeBertForQuestionAnswering(SqueezeBertPreTrainedModel):
@add_start_docstrings_to_model_forward(SQUEEZEBERT_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,

6
src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py
View File

@ -883,7 +883,7 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTransfoXLModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -975,7 +975,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTransfoXLLMHeadModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1091,7 +1091,7 @@ class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenc
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTransfoXLSequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,

6
src/transformers/models/transfo_xl/modeling_transfo_xl.py
View File

@ -871,7 +871,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TransfoXLModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1052,7 +1052,7 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TransfoXLLMHeadModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1174,7 +1174,7 @@ class TransfoXLForSequenceClassification(TransfoXLPreTrainedModel):
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TransfoXLSequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,

107
src/transformers/models/wav2vec2/modeling_wav2vec2.py
View File

@ -29,6 +29,7 @@ from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...file_utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
@ -43,6 +44,7 @@ logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "Wav2Vec2Config"
_CHECKPOINT_FOR_DOC = "facebook/wav2vec2-base-960h"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/wav2vec2-base-960h",
@ -1118,7 +1120,13 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
return hidden_states
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Wav2Vec2BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
)
def forward(
self,
input_values,
@ -1128,30 +1136,6 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
output_hidden_states=None,
return_dict=None,
):
"""
Returns:
Example::
>>> from transformers import Wav2Vec2Processor, Wav2Vec2Model
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
>>> def map_to_array(batch):
>>> speech, _ = sf.read(batch["file"])
>>> batch["speech"] = speech
>>> return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1502,7 +1486,12 @@ class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
self.wav2vec2.feature_extractor._freeze_parameters()
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutput, config_class=_CONFIG_FOR_DOC)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_values,
@ -1518,41 +1507,6 @@ class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
the sequence length of the output logits. Indices are selected in ``[-100, 0, ..., config.vocab_size -
1]``. All labels set to ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ...,
config.vocab_size - 1]``.
Returns:
Example::
>>> import torch
>>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
>>> def map_to_array(batch):
>>> speech, _ = sf.read(batch["file"])
>>> batch["speech"] = speech
>>> return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values # Batch size 1
>>> logits = model(input_values).logits
>>> predicted_ids = torch.argmax(logits, dim=-1)
>>> transcription = processor.decode(predicted_ids[0])
>>> # compute loss
>>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"
>>> # wrap processor as target processor to encode labels
>>> with processor.as_target_processor():
>>> labels = processor(target_transcription, return_tensors="pt").input_ids
>>> loss = model(input_values, labels=labels).loss
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
@ -1647,7 +1601,13 @@ class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
param.requires_grad = False
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
@add_code_sample_docstrings(
processor_class="Wav2Vec2FeatureExtractor",
checkpoint="superb/wav2vec2-base-superb-ks",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
)
def forward(
self,
input_values,
@ -1662,29 +1622,6 @@ class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Example::
>>> import torch
>>> from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
>>> from datasets import load_dataset
>>> processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
>>> model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks")
>>> ds = load_dataset("anton-l/superb_dummy", "ks", split="test")
>>> input_values = processor(ds["speech"][4], return_tensors="pt").input_values # Batch size 1
>>> logits = model(input_values).logits
>>> predicted_class_ids = torch.argmax(logits, dim=-1)
>>> # compute loss
>>> target_label = "down"
>>> labels = torch.tensor([model.config.label2id[target_label]])
>>> loss = model(input_values, labels=labels).loss
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict

12
src/transformers/models/xlm/modeling_tf_xlm.py
View File

@ -703,7 +703,7 @@ class TFXLMModel(TFXLMPreTrainedModel):
@add_start_docstrings_to_model_forward(XLM_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,
@ -856,7 +856,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
@add_start_docstrings_to_model_forward(XLM_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=TFXLMWithLMHeadModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -946,7 +946,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat
@add_start_docstrings_to_model_forward(XLM_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,
@ -1072,7 +1072,7 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
@add_start_docstrings_to_model_forward(XLM_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,
@ -1222,7 +1222,7 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos
@add_start_docstrings_to_model_forward(XLM_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,
@ -1327,7 +1327,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL
@add_start_docstrings_to_model_forward(XLM_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,

12
src/transformers/models/xlm/modeling_xlm.py
View File

@ -488,7 +488,7 @@ class XLMModel(XLMPreTrainedModel):
@add_start_docstrings_to_model_forward(XLM_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=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -710,7 +710,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
@add_start_docstrings_to_model_forward(XLM_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,
@ -789,7 +789,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
@add_start_docstrings_to_model_forward(XLM_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,
@ -889,7 +889,7 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
@add_start_docstrings_to_model_forward(XLM_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,
@ -1112,7 +1112,7 @@ class XLMForTokenClassification(XLMPreTrainedModel):
@add_start_docstrings_to_model_forward(XLM_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,
@ -1205,7 +1205,7 @@ class XLMForMultipleChoice(XLMPreTrainedModel):
@add_start_docstrings_to_model_forward(XLM_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,

10
src/transformers/models/xlnet/modeling_tf_xlnet.py
View File

@ -1160,7 +1160,7 @@ class TFXLNetModel(TFXLNetPreTrainedModel):
@add_start_docstrings_to_model_forward(XLNET_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=TFXLNetModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1429,7 +1429,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif
@add_start_docstrings_to_model_forward(XLNET_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=TFXLNetForSequenceClassificationOutput,
config_class=_CONFIG_FOR_DOC,
@ -1555,7 +1555,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
@add_start_docstrings_to_model_forward(XLNET_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=TFXLNetForMultipleChoiceOutput,
config_class=_CONFIG_FOR_DOC,
@ -1704,7 +1704,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio
@add_start_docstrings_to_model_forward(XLNET_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=TFXLNetForTokenClassificationOutput,
config_class=_CONFIG_FOR_DOC,
@ -1811,7 +1811,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
@add_start_docstrings_to_model_forward(XLNET_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=TFXLNetForQuestionAnsweringSimpleOutput,
config_class=_CONFIG_FOR_DOC,

10
src/transformers/models/xlnet/modeling_xlnet.py
View File

@ -1069,7 +1069,7 @@ class XLNetModel(XLNetPreTrainedModel):
@add_start_docstrings_to_model_forward(XLNET_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=XLNetModelOutput,
config_class=_CONFIG_FOR_DOC,
@ -1497,7 +1497,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
@add_start_docstrings_to_model_forward(XLNET_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=XLNetForSequenceClassificationOutput,
config_class=_CONFIG_FOR_DOC,
@ -1604,7 +1604,7 @@ class XLNetForTokenClassification(XLNetPreTrainedModel):
@add_start_docstrings_to_model_forward(XLNET_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=XLNetForTokenClassificationOutput,
config_class=_CONFIG_FOR_DOC,
@ -1701,7 +1701,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
@add_start_docstrings_to_model_forward(XLNET_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=XLNetForMultipleChoiceOutput,
config_class=_CONFIG_FOR_DOC,
@ -1804,7 +1804,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
@add_start_docstrings_to_model_forward(XLNET_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=XLNetForQuestionAnsweringSimpleOutput,
config_class=_CONFIG_FOR_DOC,

16
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py
View File

@ -945,7 +945,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -1068,7 +1068,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -1177,7 +1177,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelca
}
@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,
@ -1344,7 +1344,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -1450,7 +1450,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -1593,7 +1593,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -1689,7 +1689,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -2941,7 +2941,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,

18
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py
View File

@ -795,7 +795,7 @@ class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelna
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -953,7 +953,7 @@ class {{cookiecutter.camelcase_modelname}}ForMaskedLM({{cookiecutter.camelcase_m
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -1221,7 +1221,7 @@ class {{cookiecutter.camelcase_modelname}}ForSequenceClassification({{cookiecutt
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -1301,7 +1301,7 @@ class {{cookiecutter.camelcase_modelname}}ForMultipleChoice({{cookiecutter.camel
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -1391,7 +1391,7 @@ class {{cookiecutter.camelcase_modelname}}ForTokenClassification({{cookiecutter.
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -1478,7 +1478,7 @@ class {{cookiecutter.camelcase_modelname}}ForQuestionAnswering({{cookiecutter.ca
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -2646,7 +2646,7 @@ class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelna
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -2921,7 +2921,7 @@ class {{cookiecutter.camelcase_modelname}}ForSequenceClassification({{cookiecutt
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,
@ -3022,7 +3022,7 @@ class {{cookiecutter.camelcase_modelname}}ForQuestionAnswering({{cookiecutter.ca
@add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_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,