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<!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# General Utilities
This page lists all of Transformers general utility functions that are found in the file `file_utils.py`.
Most of those are only useful if you are studying the general code in the library.
## Enums and namedtuples
[[autodoc]] file_utils.ExplicitEnum
[[autodoc]] file_utils.PaddingStrategy
[[autodoc]] file_utils.TensorType
## Special Decorators
[[autodoc]] file_utils.add_start_docstrings
[[autodoc]] file_utils.add_start_docstrings_to_model_forward
[[autodoc]] file_utils.add_end_docstrings
[[autodoc]] file_utils.add_code_sample_docstrings
[[autodoc]] file_utils.replace_return_docstrings
## Special Properties
[[autodoc]] file_utils.cached_property
## Other Utilities
[[autodoc]] file_utils._LazyModule

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..
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
General Utilities
-----------------------------------------------------------------------------------------------------------------------
This page lists all of Transformers general utility functions that are found in the file ``file_utils.py``.
Most of those are only useful if you are studying the general code in the library.
Enums and namedtuples
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.file_utils.ExplicitEnum
.. autoclass:: transformers.file_utils.PaddingStrategy
.. autoclass:: transformers.file_utils.TensorType
Special Decorators
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autofunction:: transformers.file_utils.add_start_docstrings
.. autofunction:: transformers.file_utils.add_start_docstrings_to_model_forward
.. autofunction:: transformers.file_utils.add_end_docstrings
.. autofunction:: transformers.file_utils.add_code_sample_docstrings
.. autofunction:: transformers.file_utils.replace_return_docstrings
Special Properties
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.file_utils.cached_property
Other Utilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.file_utils._LazyModule

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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Utilities for Generation
This page lists all the utility functions used by [`~generation_utils.GenerationMixin.generate`],
[`~generation_utils.GenerationMixin.greedy_search`],
[`~generation_utils.GenerationMixin.sample`],
[`~generation_utils.GenerationMixin.beam_search`],
[`~generation_utils.GenerationMixin.beam_sample`], and
[`~generation_utils.GenerationMixin.group_beam_search`].
Most of those are only useful if you are studying the code of the generate methods in the library.
## Generate Outputs
The output of [`~generation_utils.GenerationMixin.generate`] is an instance of a subclass of
[`~file_utils.ModelOutput`]. This output is a data structure containing all the information returned
by [`~generation_utils.GenerationMixin.generate`], but that can also be used as tuple or dictionary.
Here's an example:
```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
```
The `generation_output` object is a [`~generation_utils.GreedySearchDecoderOnlyOutput`], as we can
see in the documentation of that class below, it means it has the following attributes:
- `sequences`: the generated sequences of tokens
- `scores` (optional): the prediction scores of the language modelling head, for each generation step
- `hidden_states` (optional): the hidden states of the model, for each generation step
- `attentions` (optional): the attention weights of the model, for each generation step
Here we have the `scores` since we passed along `output_scores=True`, but we don't have `hidden_states` and
`attentions` because we didn't pass `output_hidden_states=True` or `output_attentions=True`.
You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
will get `None`. Here for instance `generation_output.scores` are all the generated prediction scores of the
language modeling head, and `generation_output.attentions` is `None`.
When using our `generation_output` object as a tuple, it only keeps the attributes that don't have `None` values.
Here, for instance, it has two elements, `loss` then `logits`, so
```python
generation_output[:2]
```
will return the tuple `(generation_output.sequences, generation_output.scores)` for instance.
When using our `generation_output` object as a dictionary, it only keeps the attributes that don't have `None`
values. Here, for instance, it has two keys that are `sequences` and `scores`.
We document here all output types.
### GreedySearchOutput
[[autodoc]] generation_utils.GreedySearchDecoderOnlyOutput
[[autodoc]] generation_utils.GreedySearchEncoderDecoderOutput
[[autodoc]] generation_flax_utils.FlaxGreedySearchOutput
### SampleOutput
[[autodoc]] generation_utils.SampleDecoderOnlyOutput
[[autodoc]] generation_utils.SampleEncoderDecoderOutput
[[autodoc]] generation_flax_utils.FlaxSampleOutput
### BeamSearchOutput
[[autodoc]] generation_utils.BeamSearchDecoderOnlyOutput
[[autodoc]] generation_utils.BeamSearchEncoderDecoderOutput
### BeamSampleOutput
[[autodoc]] generation_utils.BeamSampleDecoderOnlyOutput
[[autodoc]] generation_utils.BeamSampleEncoderDecoderOutput
## LogitsProcessor
A [`LogitsProcessor`] can be used to modify the prediction scores of a language model head for
generation.
[[autodoc]] LogitsProcessor
- __call__
[[autodoc]] LogitsProcessorList
- __call__
[[autodoc]] LogitsWarper
- __call__
[[autodoc]] MinLengthLogitsProcessor
- __call__
[[autodoc]] TemperatureLogitsWarper
- __call__
[[autodoc]] RepetitionPenaltyLogitsProcessor
- __call__
[[autodoc]] TopPLogitsWarper
- __call__
[[autodoc]] TopKLogitsWarper
- __call__
[[autodoc]] NoRepeatNGramLogitsProcessor
- __call__
[[autodoc]] NoBadWordsLogitsProcessor
- __call__
[[autodoc]] PrefixConstrainedLogitsProcessor
- __call__
[[autodoc]] HammingDiversityLogitsProcessor
- __call__
[[autodoc]] ForcedBOSTokenLogitsProcessor
- __call__
[[autodoc]] ForcedEOSTokenLogitsProcessor
- __call__
[[autodoc]] InfNanRemoveLogitsProcessor
- __call__
[[autodoc]] FlaxLogitsProcessor
- __call__
[[autodoc]] FlaxLogitsProcessorList
- __call__
[[autodoc]] FlaxLogitsWarper
- __call__
[[autodoc]] FlaxTemperatureLogitsWarper
- __call__
[[autodoc]] FlaxTopPLogitsWarper
- __call__
[[autodoc]] FlaxTopKLogitsWarper
- __call__
[[autodoc]] FlaxForcedBOSTokenLogitsProcessor
- __call__
[[autodoc]] FlaxForcedEOSTokenLogitsProcessor
- __call__
[[autodoc]] FlaxMinLengthLogitsProcessor
- __call__
## StoppingCriteria
A [`StoppingCriteria`] can be used to change when to stop generation (other than EOS token).
[[autodoc]] StoppingCriteria
- __call__
[[autodoc]] StoppingCriteriaList
- __call__
[[autodoc]] MaxLengthCriteria
- __call__
[[autodoc]] MaxTimeCriteria
- __call__
## BeamSearch
[[autodoc]] BeamScorer
- process
- finalize
[[autodoc]] BeamSearchScorer
- process
- finalize
## Utilities
[[autodoc]] top_k_top_p_filtering
[[autodoc]] tf_top_k_top_p_filtering

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..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Utilities for Generation
-----------------------------------------------------------------------------------------------------------------------
This page lists all the utility functions used by :meth:`~transformers.generation_utils.GenerationMixin.generate`,
:meth:`~transformers.generation_utils.GenerationMixin.greedy_search`,
:meth:`~transformers.generation_utils.GenerationMixin.sample`,
:meth:`~transformers.generation_utils.GenerationMixin.beam_search`,
:meth:`~transformers.generation_utils.GenerationMixin.beam_sample`, and
:meth:`~transformers.generation_utils.GenerationMixin.group_beam_search`.
Most of those are only useful if you are studying the code of the generate methods in the library.
Generate Outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The output of :meth:`~transformers.generation_utils.GenerationMixin.generate` is an instance of a subclass of
:class:`~transformers.file_utils.ModelOutput`. This output is a data structure containing all the information returned
by :meth:`~transformers.generation_utils.GenerationMixin.generate`, but that can also be used as tuple or dictionary.
Here's an example:
.. code-block::
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
The ``generation_output`` object is a :class:`~transformers.generation_utils.GreedySearchDecoderOnlyOutput`, as we can
see in the documentation of that class below, it means it has the following attributes:
- ``sequences``: the generated sequences of tokens
- ``scores`` (optional): the prediction scores of the language modelling head, for each generation step
- ``hidden_states`` (optional): the hidden states of the model, for each generation step
- ``attentions`` (optional): the attention weights of the model, for each generation step
Here we have the ``scores`` since we passed along ``output_scores=True``, but we don't have ``hidden_states`` and
``attentions`` because we didn't pass ``output_hidden_states=True`` or ``output_attentions=True``.
You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
will get ``None``. Here for instance ``generation_output.scores`` are all the generated prediction scores of the
language modeling head, and ``generation_output.attentions`` is ``None``.
When using our ``generation_output`` object as a tuple, it only keeps the attributes that don't have ``None`` values.
Here, for instance, it has two elements, ``loss`` then ``logits``, so
.. code-block::
generation_output[:2]
will return the tuple ``(generation_output.sequences, generation_output.scores)`` for instance.
When using our ``generation_output`` object as a dictionary, it only keeps the attributes that don't have ``None``
values. Here, for instance, it has two keys that are ``sequences`` and ``scores``.
We document here all output types.
GreedySearchOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.generation_utils.GreedySearchDecoderOnlyOutput
:members:
.. autoclass:: transformers.generation_utils.GreedySearchEncoderDecoderOutput
:members:
.. autoclass:: transformers.generation_flax_utils.FlaxGreedySearchOutput
:members:
SampleOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.generation_utils.SampleDecoderOnlyOutput
:members:
.. autoclass:: transformers.generation_utils.SampleEncoderDecoderOutput
:members:
.. autoclass:: transformers.generation_flax_utils.FlaxSampleOutput
:members:
BeamSearchOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.generation_utils.BeamSearchDecoderOnlyOutput
:members:
.. autoclass:: transformers.generation_utils.BeamSearchEncoderDecoderOutput
:members:
BeamSampleOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.generation_utils.BeamSampleDecoderOnlyOutput
:members:
.. autoclass:: transformers.generation_utils.BeamSampleEncoderDecoderOutput
:members:
LogitsProcessor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A :class:`~transformers.LogitsProcessor` can be used to modify the prediction scores of a language model head for
generation.
.. autoclass:: transformers.LogitsProcessor
:members: __call__
.. autoclass:: transformers.LogitsProcessorList
:members: __call__
.. autoclass:: transformers.LogitsWarper
:members: __call__
.. autoclass:: transformers.MinLengthLogitsProcessor
:members: __call__
.. autoclass:: transformers.TemperatureLogitsWarper
:members: __call__
.. autoclass:: transformers.RepetitionPenaltyLogitsProcessor
:members: __call__
.. autoclass:: transformers.TopPLogitsWarper
:members: __call__
.. autoclass:: transformers.TopKLogitsWarper
:members: __call__
.. autoclass:: transformers.NoRepeatNGramLogitsProcessor
:members: __call__
.. autoclass:: transformers.NoBadWordsLogitsProcessor
:members: __call__
.. autoclass:: transformers.PrefixConstrainedLogitsProcessor
:members: __call__
.. autoclass:: transformers.HammingDiversityLogitsProcessor
:members: __call__
.. autoclass:: transformers.ForcedBOSTokenLogitsProcessor
:members: __call__
.. autoclass:: transformers.ForcedEOSTokenLogitsProcessor
:members: __call__
.. autoclass:: transformers.InfNanRemoveLogitsProcessor
:members: __call__
.. autoclass:: transformers.FlaxLogitsProcessor
:members: __call__
.. autoclass:: transformers.FlaxLogitsProcessorList
:members: __call__
.. autoclass:: transformers.FlaxLogitsWarper
:members: __call__
.. autoclass:: transformers.FlaxTemperatureLogitsWarper
:members: __call__
.. autoclass:: transformers.FlaxTopPLogitsWarper
:members: __call__
.. autoclass:: transformers.FlaxTopKLogitsWarper
:members: __call__
.. autoclass:: transformers.FlaxForcedBOSTokenLogitsProcessor
:members: __call__
.. autoclass:: transformers.FlaxForcedEOSTokenLogitsProcessor
:members: __call__
.. autoclass:: transformers.FlaxMinLengthLogitsProcessor
:members: __call__
StoppingCriteria
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A :class:`~transformers.StoppingCriteria` can be used to change when to stop generation (other than EOS token).
.. autoclass:: transformers.StoppingCriteria
:members: __call__
.. autoclass:: transformers.StoppingCriteriaList
:members: __call__
.. autoclass:: transformers.MaxLengthCriteria
:members: __call__
.. autoclass:: transformers.MaxTimeCriteria
:members: __call__
BeamSearch
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.BeamScorer
:members: process, finalize
.. autoclass:: transformers.BeamSearchScorer
:members: process, finalize
Utilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autofunction:: transformers.top_k_top_p_filtering
.. autofunction:: transformers.tf_top_k_top_p_filtering

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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Custom Layers and Utilities
This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
Most of those are only useful if you are studying the code of the models in the library.
## Pytorch custom modules
[[autodoc]] modeling_utils.Conv1D
[[autodoc]] modeling_utils.PoolerStartLogits
- forward
[[autodoc]] modeling_utils.PoolerEndLogits
- forward
[[autodoc]] modeling_utils.PoolerAnswerClass
- forward
[[autodoc]] modeling_utils.SquadHeadOutput
[[autodoc]] modeling_utils.SQuADHead
- forward
[[autodoc]] modeling_utils.SequenceSummary
- forward
## PyTorch Helper Functions
[[autodoc]] apply_chunking_to_forward
[[autodoc]] modeling_utils.find_pruneable_heads_and_indices
[[autodoc]] modeling_utils.prune_layer
[[autodoc]] modeling_utils.prune_conv1d_layer
[[autodoc]] modeling_utils.prune_linear_layer
## TensorFlow custom layers
[[autodoc]] modeling_tf_utils.TFConv1D
[[autodoc]] modeling_tf_utils.TFSharedEmbeddings
- call
[[autodoc]] modeling_tf_utils.TFSequenceSummary
## TensorFlow loss functions
[[autodoc]] modeling_tf_utils.TFCausalLanguageModelingLoss
[[autodoc]] modeling_tf_utils.TFMaskedLanguageModelingLoss
[[autodoc]] modeling_tf_utils.TFMultipleChoiceLoss
[[autodoc]] modeling_tf_utils.TFQuestionAnsweringLoss
[[autodoc]] modeling_tf_utils.TFSequenceClassificationLoss
[[autodoc]] modeling_tf_utils.TFTokenClassificationLoss
## TensorFlow Helper Functions
[[autodoc]] modeling_tf_utils.get_initializer
[[autodoc]] modeling_tf_utils.keras_serializable
[[autodoc]] modeling_tf_utils.shape_list

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Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Custom Layers and Utilities
-----------------------------------------------------------------------------------------------------------------------
This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
Most of those are only useful if you are studying the code of the models in the library.
Pytorch custom modules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.modeling_utils.Conv1D
.. autoclass:: transformers.modeling_utils.PoolerStartLogits
:members: forward
.. autoclass:: transformers.modeling_utils.PoolerEndLogits
:members: forward
.. autoclass:: transformers.modeling_utils.PoolerAnswerClass
:members: forward
.. autoclass:: transformers.modeling_utils.SquadHeadOutput
.. autoclass:: transformers.modeling_utils.SQuADHead
:members: forward
.. autoclass:: transformers.modeling_utils.SequenceSummary
:members: forward
PyTorch Helper Functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autofunction:: transformers.apply_chunking_to_forward
.. autofunction:: transformers.modeling_utils.find_pruneable_heads_and_indices
.. autofunction:: transformers.modeling_utils.prune_layer
.. autofunction:: transformers.modeling_utils.prune_conv1d_layer
.. autofunction:: transformers.modeling_utils.prune_linear_layer
TensorFlow custom layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.modeling_tf_utils.TFConv1D
.. autoclass:: transformers.modeling_tf_utils.TFSharedEmbeddings
:members: call
.. autoclass:: transformers.modeling_tf_utils.TFSequenceSummary
TensorFlow loss functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.modeling_tf_utils.TFCausalLanguageModelingLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFMultipleChoiceLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFQuestionAnsweringLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFSequenceClassificationLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFTokenClassificationLoss
:members:
TensorFlow Helper Functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autofunction:: transformers.modeling_tf_utils.get_initializer
.. autofunction:: transformers.modeling_tf_utils.keras_serializable
.. autofunction:: transformers.modeling_tf_utils.shape_list

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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Utilities for pipelines
This page lists all the utility functions the library provides for pipelines.
Most of those are only useful if you are studying the code of the models in the library.
## Argument handling
[[autodoc]] pipelines.ArgumentHandler
[[autodoc]] pipelines.ZeroShotClassificationArgumentHandler
[[autodoc]] pipelines.QuestionAnsweringArgumentHandler
## Data format
[[autodoc]] pipelines.PipelineDataFormat
[[autodoc]] pipelines.CsvPipelineDataFormat
[[autodoc]] pipelines.JsonPipelineDataFormat
[[autodoc]] pipelines.PipedPipelineDataFormat
## Utilities
[[autodoc]] pipelines.PipelineException

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..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Utilities for pipelines
-----------------------------------------------------------------------------------------------------------------------
This page lists all the utility functions the library provides for pipelines.
Most of those are only useful if you are studying the code of the models in the library.
Argument handling
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.pipelines.ArgumentHandler
.. autoclass:: transformers.pipelines.ZeroShotClassificationArgumentHandler
.. autoclass:: transformers.pipelines.QuestionAnsweringArgumentHandler
Data format
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.pipelines.PipelineDataFormat
:members:
.. autoclass:: transformers.pipelines.CsvPipelineDataFormat
:members:
.. autoclass:: transformers.pipelines.JsonPipelineDataFormat
:members:
.. autoclass:: transformers.pipelines.PipedPipelineDataFormat
:members:
Utilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.pipelines.PipelineException

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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Utilities for Tokenizers
This page lists all the utility functions used by the tokenizers, mainly the class
[`~tokenization_utils_base.PreTrainedTokenizerBase`] that implements the common methods between
[`PreTrainedTokenizer`] and [`PreTrainedTokenizerFast`] and the mixin
[`~tokenization_utils_base.SpecialTokensMixin`].
Most of those are only useful if you are studying the code of the tokenizers in the library.
## PreTrainedTokenizerBase
[[autodoc]] tokenization_utils_base.PreTrainedTokenizerBase
- __call__
- all
## SpecialTokensMixin
[[autodoc]] tokenization_utils_base.SpecialTokensMixin
## Enums and namedtuples
[[autodoc]] tokenization_utils_base.TruncationStrategy
[[autodoc]] tokenization_utils_base.CharSpan
[[autodoc]] tokenization_utils_base.TokenSpan

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..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Utilities for Tokenizers
-----------------------------------------------------------------------------------------------------------------------
This page lists all the utility functions used by the tokenizers, mainly the class
:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` that implements the common methods between
:class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` and the mixin
:class:`~transformers.tokenization_utils_base.SpecialTokensMixin`.
Most of those are only useful if you are studying the code of the tokenizers in the library.
PreTrainedTokenizerBase
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.tokenization_utils_base.PreTrainedTokenizerBase
:special-members: __call__
:members:
SpecialTokensMixin
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.tokenization_utils_base.SpecialTokensMixin
:members:
Enums and namedtuples
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.tokenization_utils_base.TruncationStrategy
.. autoclass:: transformers.tokenization_utils_base.CharSpan
.. autoclass:: transformers.tokenization_utils_base.TokenSpan

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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# Utilities for Trainer
This page lists all the utility functions used by [`Trainer`].
Most of those are only useful if you are studying the code of the Trainer in the library.
## Utilities
[[autodoc]] EvalPrediction
[[autodoc]] IntervalStrategy
[[autodoc]] set_seed
[[autodoc]] torch_distributed_zero_first
## Callbacks internals
[[autodoc]] trainer_callback.CallbackHandler
## Distributed Evaluation
[[autodoc]] trainer_pt_utils.DistributedTensorGatherer
## Distributed Evaluation
[[autodoc]] HfArgumentParser
## Debug Utilities
[[autodoc]] debug_utils.DebugUnderflowOverflow

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..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Utilities for Trainer
-----------------------------------------------------------------------------------------------------------------------
This page lists all the utility functions used by :class:`~transformers.Trainer`.
Most of those are only useful if you are studying the code of the Trainer in the library.
Utilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.EvalPrediction
.. autoclass:: transformers.IntervalStrategy
.. autofunction:: transformers.set_seed
.. autofunction:: transformers.torch_distributed_zero_first
Callbacks internals
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.trainer_callback.CallbackHandler
Distributed Evaluation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.trainer_pt_utils.DistributedTensorGatherer
:members:
Distributed Evaluation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.HfArgumentParser
Debug Utilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.debug_utils.DebugUnderflowOverflow