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[doc] :class: hunt (#14955)

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* [doc] :class: hunt

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix the fix + style

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Stas Bekman 2021-12-27 17:17:38 -08:00 committed by GitHub
parent 2c5597f6c7
commit 10fd4fa1a6
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7 changed files with 11 additions and 13 deletions
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2
src/transformers/feature_extraction_sequence_utils.py
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@ -139,7 +139,7 @@ class SequenceFeatureExtractor(FeatureExtractionMixin):
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of :class:`~transformers.BatchFeature` or list of :class:`~transformers.BatchFeature` to this method "
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature` to this method "
f"that includes {self.model_input_names[0]}, but you provided {list(processed_features.keys())}"
)

10
src/transformers/models/auto/configuration_auto.py
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@ -411,8 +411,8 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = _LazyLoadAllMappings(CONFIG_ARCHIVE_MAP_MAPP
def _get_class_name(model_class: Union[str, List[str]]):
if isinstance(model_class, (list, tuple)):
return " or ".join([f":class:`~transformers.{c}`" for c in model_class if c is not None])
return f":class:`~transformers.{model_class}`"
return " or ".join([f"[`{c}`]" for c in model_class if c is not None])
return f"[`{model_class}`]"
def _list_model_options(indent, config_to_class=None, use_model_types=True):
@ -420,9 +420,7 @@ def _list_model_options(indent, config_to_class=None, use_model_types=True):
raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
if use_model_types:
if config_to_class is None:
model_type_to_name = {
model_type: f":class:`~transformers.{config}`" for model_type, config in CONFIG_MAPPING_NAMES.items()
}
model_type_to_name = {model_type: f"[`{config}`]" for model_type, config in CONFIG_MAPPING_NAMES.items()}
else:
model_type_to_name = {
model_type: _get_class_name(model_class)
@ -443,7 +441,7 @@ def _list_model_options(indent, config_to_class=None, use_model_types=True):
config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items()
}
lines = [
f"{indent}- :class:`~transformers.{config_name}` configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
f"{indent}- [`{config_name}`] configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
for config_name in sorted(config_to_name.keys())
]
return "\n".join(lines)

4
src/transformers/models/mmbt/modeling_mmbt.py
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@ -94,9 +94,9 @@ MMBT_START_DOCSTRING = r"""
config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration.
transformer (:class: *~nn.Module*): A text transformer that is used by MMBT.
transformer (`nn.Module`): A text transformer that is used by MMBT.
It should have embeddings, encoder, and pooler attributes.
encoder (:class: *~nn.Module*): Encoder for the second modality.
encoder (`nn.Module`): Encoder for the second modality.
It should take in a batch of modal inputs and return k, n dimension embeddings.
"""

2
src/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py
View File

@ -737,7 +737,7 @@ class Speech2Text2DecoderWrapper(Speech2Text2PreTrainedModel):
@add_start_docstrings(
"The Speech2Text2 Decoder with a language modeling head. Can be used as the decoder part of :class:`~transformers.EncoderDecoderModel` and :class:`~transformers.SpeechEncoderDecoder`.",
"The Speech2Text2 Decoder with a language modeling head. Can be used as the decoder part of [`EncoderDecoderModel`] and [`SpeechEncoderDecoder`].",
SPEECH_TO_TEXT_2_START_DOCSTRING,
)
class Speech2Text2ForCausalLM(Speech2Text2PreTrainedModel):

2
src/transformers/models/trocr/modeling_trocr.py
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@ -770,7 +770,7 @@ class TrOCRDecoderWrapper(TrOCRPreTrainedModel):
@add_start_docstrings(
"The TrOCR Decoder with a language modeling head. Can be used as the decoder part of :class:`~transformers.EncoderDecoderModel` and :class:`~transformers.VisionEncoderDecoder`.",
"The TrOCR Decoder with a language modeling head. Can be used as the decoder part of [`EncoderDecoderModel`] and [`VisionEncoderDecoder`].",
TROCR_START_DOCSTRING,
)
class TrOCRForCausalLM(TrOCRPreTrainedModel):

2
tests/test_modeling_rag.py
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@ -84,7 +84,7 @@ def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
def require_retrieval(test_case):
"""
Decorator marking a test that requires a set of dependencies necessary for pefrorm retrieval with
:class:`~transformers.RagRetriever`.
[`RagRetriever`].
These tests are skipped when respective libraries are not installed.

2
tests/test_modeling_tf_rag.py
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@ -44,7 +44,7 @@ TOLERANCE = 1e-3
def require_retrieval(test_case):
"""
Decorator marking a test that requires a set of dependencies necessary for pefrorm retrieval with
:class:`~transformers.RagRetriever`.
[`RagRetriever`].
These tests are skipped when respective libraries are not installed.