text stringlengths 5 58.6k | source stringclasses 470
values | url stringlengths 49 167 | source_section stringlengths 0 90 | file_type stringclasses 1
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|---|---|---|---|---|---|
[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqLMOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxseq2seqlmoutput | #flaxseq2seqlmoutput | .md | 458_57 |
[[autodoc]] modeling_flax_outputs.FlaxNextSentencePredictorOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxnextsentencepredictoroutput | #flaxnextsentencepredictoroutput | .md | 458_58 |
[[autodoc]] modeling_flax_outputs.FlaxSequenceClassifierOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxsequenceclassifieroutput | #flaxsequenceclassifieroutput | .md | 458_59 |
[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxseq2seqsequenceclassifieroutput | #flaxseq2seqsequenceclassifieroutput | .md | 458_60 |
[[autodoc]] modeling_flax_outputs.FlaxMultipleChoiceModelOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxmultiplechoicemodeloutput | #flaxmultiplechoicemodeloutput | .md | 458_61 |
[[autodoc]] modeling_flax_outputs.FlaxTokenClassifierOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxtokenclassifieroutput | #flaxtokenclassifieroutput | .md | 458_62 |
[[autodoc]] modeling_flax_outputs.FlaxQuestionAnsweringModelOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxquestionansweringmodeloutput | #flaxquestionansweringmodeloutput | .md | 458_63 |
[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput: No module named 'flax' | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxseq2seqquestionansweringmodeloutput | #flaxseq2seqquestionansweringmodeloutput | .md | 458_64 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/ | .md | 459_0 | |
Processors can mean two different things in the Transformers library:
- the objects that pre-process inputs for multi-modal models such as [Wav2Vec2](../model_doc/wav2vec2) (speech and text)
or [CLIP](../model_doc/clip) (text and vision)
- deprecated objects that were used in older versions of the library to preprocess... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#processors | #processors | .md | 459_1 |
Any multi-modal model will require an object to encode or decode the data that groups several modalities (among text,
vision and audio). This is handled by objects called processors, which group together two or more processing objects
such as tokenizers (for the text modality), image processors (for vision) and feature... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#multi-modal-processors | #multi-modal-processors | .md | 459_2 |
All processors follow the same architecture which is that of the
[`~data.processors.utils.DataProcessor`]. The processor returns a list of
[`~data.processors.utils.InputExample`]. These
[`~data.processors.utils.InputExample`] can be converted to
[`~data.processors.utils.InputFeatures`] in order to be fed to the model. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#deprecated-processors | #deprecated-processors | .md | 459_3 |
[General Language Understanding Evaluation (GLUE)](https://gluebenchmark.com/) is a benchmark that evaluates the
performance of models across a diverse set of existing NLU tasks. It was released together with the paper [GLUE: A
multi-task benchmark and analysis platform for natural language understanding](https://openr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#glue | #glue | .md | 459_4 |
[The Cross-Lingual NLI Corpus (XNLI)](https://www.nyu.edu/projects/bowman/xnli/) is a benchmark that evaluates the
quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on [*MultiNLI*](http://www.nyu.edu/projects/bowman/multinli/): pairs of text are labeled with textual entailment annotatio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#xnli | #xnli | .md | 459_5 |
[The Stanford Question Answering Dataset (SQuAD)](https://rajpurkar.github.io/SQuAD-explorer//) is a benchmark that
evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
(v1.1) was released together with the paper [SQuAD: 100,000+ Questions for Machine C... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#squad | #squad | .md | 459_6 |
Those processors are:
- [`~data.processors.utils.SquadV1Processor`]
- [`~data.processors.utils.SquadV2Processor`]
They both inherit from the abstract class [`~data.processors.utils.SquadProcessor`]
data.processors.squad.SquadProcessor
Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2P... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#processors | #processors | .md | 459_7 |
Here is an example using the processors as well as the conversion method using data files:
```python
# Loading a V2 processor
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)
# Loading a V1 processor
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#example-usage | #example-usage | .md | 459_8 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/ | .md | 460_0 | |
The [`Trainer`] class provides an API for feature-complete training in PyTorch, and it supports distributed training on multiple GPUs/TPUs, mixed precision for [NVIDIA GPUs](https://nvidia.github.io/apex/), [AMD GPUs](https://rocm.docs.amd.com/en/latest/rocm.html), and [`torch.amp`](https://pytorch.org/docs/stable/amp.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainer | #trainer | .md | 460_1 |
Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.
Args:
model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*):
The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed.
<Tip>
[`Trainer`] is optimized to wor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainerapi-reference | #trainerapi-reference | .md | 460_2 |
Seq2SeqTrainer
- evaluate
- predict | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#seq2seqtrainer | #seq2seqtrainer | .md | 460_3 |
TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
itself**.
Using [`HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
command line.
Parameters:
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#trainingarguments | #trainingarguments | .md | 460_4 |
TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
itself**.
Using [`HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
command line.
Parameters:
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/trainer.md | https://huggingface.co/docs/transformers/en/main_classes/trainer/#seq2seqtrainingarguments | #seq2seqtrainingarguments | .md | 460_5 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/ | .md | 461_0 | |
Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of
the same type as the elements of `train_dataset` or `eval_dataset`.
To be able to build batches, data collators may apply some processing (like padding). Some of them (like
[`DataCollatorForLanguageMo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#data-collator | #data-collator | .md | 461_1 |
data.data_collator.default_data_collator
Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:
- `label`: handles a single value (int or float) per object
- `label_ids`: handles a list of values per object
Does not do any additional pre... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#default-data-collator | #default-data-collator | .md | 461_2 |
data.data_collator.DefaultDataCollator
Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:
- `label`: handles a single value (int or float) per object
- `label_ids`: handles a list of values per object
Does not do any additional prepr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#defaultdatacollator | #defaultdatacollator | .md | 461_3 |
data.data_collator.DataCollatorWithPadding
Data collator that will dynamically pad the inputs received.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
Select a ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#datacollatorwithpadding | #datacollatorwithpadding | .md | 461_4 |
data.data_collator.DataCollatorForTokenClassification
Data collator that will dynamically pad the inputs received, as well as the labels.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optiona... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#datacollatorfortokenclassification | #datacollatorfortokenclassification | .md | 461_5 |
data.data_collator.DataCollatorForSeq2Seq
Data collator that will dynamically pad the inputs received, as well as the labels.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
model ([`PreTrainedModel`], *optional*):
The model that is being trained. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#datacollatorforseq2seq | #datacollatorforseq2seq | .md | 461_6 |
data.data_collator.DataCollatorForLanguageModeling
Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
mlm ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#datacollatorforlanguagemodeling | #datacollatorforlanguagemodeling | .md | 461_7 |
data.data_collator.DataCollatorForWholeWordMask
Data collator used for language modeling that masks entire words.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for masked language modeling
<Tip>
This collator relies on details of the implementation of subword tokeniz... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#datacollatorforwholewordmask | #datacollatorforwholewordmask | .md | 461_8 |
data.data_collator.DataCollatorForPermutationLanguageModeling
Data collator used for permutation language modeling.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for permutation language modeling with procedures specific to XLNet
- numpy_mask_tokens
- tf_mask_tokens
- ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#datacollatorforpermutationlanguagemodeling | #datacollatorforpermutationlanguagemodeling | .md | 461_9 |
data.data_collator.DataCollatorWithFlattening
Data collator used for padding free approach. Does the following:
- concatate the entire mini batch into single long sequence [1, total_tokens]
- uses `separator_id` to separate sequences within the concatenated `labels`, default value is -100
- no padding will be added... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/data_collator.md | https://huggingface.co/docs/transformers/en/main_classes/data_collator/#datacollatorwithflattening | #datacollatorwithflattening | .md | 461_10 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/deepspeed.md | https://huggingface.co/docs/transformers/en/main_classes/deepspeed/ | .md | 462_0 | |
[DeepSpeed](https://github.com/microsoft/DeepSpeed), powered by Zero Redundancy Optimizer (ZeRO), is an optimization library for training and fitting very large models onto a GPU. It is available in several ZeRO stages, where each stage progressively saves more GPU memory by partitioning the optimizer state, gradients,... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/deepspeed.md | https://huggingface.co/docs/transformers/en/main_classes/deepspeed/#deepspeed | #deepspeed | .md | 462_1 |
integrations.HfDeepSpeedConfig
This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage.
A `weakref` of this object is stored in the module's globals to be able to access the config from areas where
things like the Trainer object is not available (e.g. `from_pr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/deepspeed.md | https://huggingface.co/docs/transformers/en/main_classes/deepspeed/#hfdeepspeedconfig | #hfdeepspeedconfig | .md | 462_2 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/configuration.md | https://huggingface.co/docs/transformers/en/main_classes/configuration/ | .md | 463_0 | |
The base class [`PretrainedConfig`] implements the common methods for loading/saving a configuration
either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
from HuggingFace's AWS S3 repository).
Each derived config class implements model specific attributes... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/configuration.md | https://huggingface.co/docs/transformers/en/main_classes/configuration/#configuration | #configuration | .md | 463_1 |
Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
methods for loading/downloading/saving configurations.
<Tip>
A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
initialize a model does **not** ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/configuration.md | https://huggingface.co/docs/transformers/en/main_classes/configuration/#pretrainedconfig | #pretrainedconfig | .md | 463_2 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/logging.md | https://huggingface.co/docs/transformers/en/main_classes/logging/ | .md | 464_0 | |
🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.
Currently the default verbosity of the library is `WARNING`.
To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity
to the INFO level.
```py... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/logging.md | https://huggingface.co/docs/transformers/en/main_classes/logging/#logging | #logging | .md | 464_1 |
Python has two logging systems that are often used in conjunction: `logging`, which is explained above, and `warnings`,
which allows further classification of warnings in specific buckets, e.g., `FutureWarning` for a feature or path
that has already been deprecated and `DeprecationWarning` to indicate an upcoming depre... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/logging.md | https://huggingface.co/docs/transformers/en/main_classes/logging/#logging-vs-warnings | #logging-vs-warnings | .md | 464_2 |
[[autodoc]] logging.set_verbosity: No module named 'transformers.logging'_error: No module named 'transformers.logging'
[[autodoc]] logging.set_verbosity: No module named 'transformers.logging'_warning: No module named 'transformers.logging'
[[autodoc]] logging.set_verbosity: No module named 'transformers.logging'_... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/logging.md | https://huggingface.co/docs/transformers/en/main_classes/logging/#base-setters | #base-setters | .md | 464_3 |
[[autodoc]] logging.get_verbosity: No module named 'transformers.logging'
[[autodoc]] logging.set_verbosity: No module named 'transformers.logging'
[[autodoc]] logging.get_logger: No module named 'transformers.logging'
[[autodoc]] logging.enable_default_handler: No module named 'transformers.logging'
[[autodoc]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/logging.md | https://huggingface.co/docs/transformers/en/main_classes/logging/#other-functions | #other-functions | .md | 464_4 |
<!--Copyright 2022 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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/image_processor.md | https://huggingface.co/docs/transformers/en/main_classes/image_processor/ | .md | 465_0 | |
An image processor is in charge of preparing input features for vision models and post processing their outputs. This includes transformations such as resizing, normalization, and conversion to PyTorch, TensorFlow, Flax and Numpy tensors. It may also include model specific post-processing such as converting logits to s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/image_processor.md | https://huggingface.co/docs/transformers/en/main_classes/image_processor/#image-processor | #image-processor | .md | 465_1 |
image_processing_utils.ImageProcessingMixin
This is an image processor mixin used to provide saving/loading functionality for sequential and image feature
extractors.
- from_pretrained
- save_pretrained | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/image_processor.md | https://huggingface.co/docs/transformers/en/main_classes/image_processor/#imageprocessingmixin | #imageprocessingmixin | .md | 465_2 |
Holds the output of the [`~SequenceFeatureExtractor.pad`] and feature extractor specific `__call__` methods.
This class is derived from a python dictionary and can be used as a dictionary.
Args:
data (`dict`, *optional*):
Dictionary of lists/arrays/tensors returned by the __call__/pad methods ('input_values', 'atte... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/image_processor.md | https://huggingface.co/docs/transformers/en/main_classes/image_processor/#batchfeature | #batchfeature | .md | 465_3 |
image_processing_utils.BaseImageProcessor | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/image_processor.md | https://huggingface.co/docs/transformers/en/main_classes/image_processor/#baseimageprocessor | #baseimageprocessor | .md | 465_4 |
image_processing_utils_fast.BaseImageProcessorFast | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/image_processor.md | https://huggingface.co/docs/transformers/en/main_classes/image_processor/#baseimageprocessorfast | #baseimageprocessorfast | .md | 465_5 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/callback.md | https://huggingface.co/docs/transformers/en/main_classes/callback/ | .md | 466_0 | |
Callbacks are objects that can customize the behavior of the training loop in the PyTorch
[`Trainer`] (this feature is not yet implemented in TensorFlow) that can inspect the training loop
state (for progress reporting, logging on TensorBoard or other ML platforms...) and take decisions (like early
stopping).
Callbac... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/callback.md | https://huggingface.co/docs/transformers/en/main_classes/callback/#callbacks | #callbacks | .md | 466_1 |
Here is the list of the available [`TrainerCallback`] in the library:
integrations.CometCallback
A [`TrainerCallback`] that sends the logs to [Comet ML](https://www.comet.com/site/).
- setup
A [`TrainerCallback`] that handles the default flow of the training loop for logs, evaluation and checkpoints.
A bare [... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/callback.md | https://huggingface.co/docs/transformers/en/main_classes/callback/#available-callbacks | #available-callbacks | .md | 466_2 |
A class for objects that will inspect the state of the training loop at some events and take some decisions. At
each of those events the following arguments are available:
Args:
args ([`TrainingArguments`]):
The training arguments used to instantiate the [`Trainer`].
state ([`TrainerState`]):
The current state of the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/callback.md | https://huggingface.co/docs/transformers/en/main_classes/callback/#trainercallback | #trainercallback | .md | 466_3 |
A class containing the [`Trainer`] inner state that will be saved along the model and optimizer when checkpointing
and passed to the [`TrainerCallback`].
<Tip>
In all this class, one step is to be understood as one update step. When using gradient accumulation, one update
step may require several forward and backwa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/callback.md | https://huggingface.co/docs/transformers/en/main_classes/callback/#trainerstate | #trainerstate | .md | 466_4 |
A class that handles the [`Trainer`] control flow. This class is used by the [`TrainerCallback`] to activate some
switches in the training loop.
Args:
should_training_stop (`bool`, *optional*, defaults to `False`):
Whether or not the training should be interrupted.
If `True`, this variable will not be set back to `... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/callback.md | https://huggingface.co/docs/transformers/en/main_classes/callback/#trainercontrol | #trainercontrol | .md | 466_5 |
<!--Copyright 2023 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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/backbones.md | https://huggingface.co/docs/transformers/en/main_classes/backbones/ | .md | 467_0 | |
A backbone is a model used for feature extraction for higher level computer vision tasks such as object detection and image classification. Transformers provides an [`AutoBackbone`] class for initializing a Transformers backbone from pretrained model weights, and two utility classes:
* [`~utils.BackboneMixin`] enable... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/backbones.md | https://huggingface.co/docs/transformers/en/main_classes/backbones/#backbone | #backbone | .md | 467_1 |
AutoBackbone | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/backbones.md | https://huggingface.co/docs/transformers/en/main_classes/backbones/#autobackbone | #autobackbone | .md | 467_2 |
utils.BackboneMixin | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/backbones.md | https://huggingface.co/docs/transformers/en/main_classes/backbones/#backbonemixin | #backbonemixin | .md | 467_3 |
utils.BackboneConfigMixin
A Mixin to support handling the `out_features` and `out_indices` attributes for the backbone configurations. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/backbones.md | https://huggingface.co/docs/transformers/en/main_classes/backbones/#backboneconfigmixin | #backboneconfigmixin | .md | 467_4 |
models.timm_backbone.TimmBackbone
Wrapper class for timm models to be used as backbones. This enables using the timm models interchangeably with the
other models in the library keeping the same API. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/backbones.md | https://huggingface.co/docs/transformers/en/main_classes/backbones/#timmbackbone | #timmbackbone | .md | 467_5 |
models.timm_backbone.TimmBackbone
Wrapper class for timm models to be used as backbones. This enables using the timm models interchangeably with the
other models in the library keeping the same API.
Config | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/backbones.md | https://huggingface.co/docs/transformers/en/main_classes/backbones/#timmbackboneconfig | #timmbackboneconfig | .md | 467_6 |
<!--Copyright 2023 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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/ | .md | 468_0 | |
Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Transformers supports the AWQ and GPTQ quantiz... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#quantization | #quantization | .md | 468_1 |
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `quanto`.
Args:
weights (`str`, *optional*, defaults to `"int8"`):
The target dtype for the weights after quantization. Supported values are ("float8","int8","int4","int2")
activations (`str`, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#quantoconfig | #quantoconfig | .md | 468_2 |
This is a wrapper class about `aqlm` parameters.
Args:
in_group_size (`int`, *optional*, defaults to 8):
The group size along the input dimension.
out_group_size (`int`, *optional*, defaults to 1):
The group size along the output dimension. It's recommended to always use 1.
num_codebooks (`int`, *optional*, defaults ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#aqlmconfig | #aqlmconfig | .md | 468_3 |
This is a wrapper class about `vptq` parameters.
Args:
enable_proxy_error (`bool`, *optional*, defaults to `False`): calculate proxy error for each layer
config_for_layers (`Dict`, *optional*, defaults to `{}`): quantization params for each layer
shared_layer_config (`Dict`, *optional*, defaults to `{}`): shared quan... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#vptqconfig | #vptqconfig | .md | 468_4 |
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `auto-awq` library awq quantization relying on auto_awq backend.
Args:
bits (`int`, *optional*, defaults to 4):
The number of bits to quantize to.
group_size (`int`, *optional*, defaults to 128... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#awqconfig | #awqconfig | .md | 468_5 |
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `eetq`.
Args:
weights (`str`, *optional*, defaults to `"int8"`):
The target dtype for the weights. Supported value is only "int8"
modules_to_not_convert (`list`, *optional*, default to `None`):... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#eetqconfig | #eetqconfig | .md | 468_6 |
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `optimum` api for gptq quantization relying on auto_gptq backend.
Args:
bits (`int`):
The number of bits to quantize to, supported numbers are (2, 3, 4, 8).
tokenizer (`str` or `PreTrainedToken... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#gptqconfig | #gptqconfig | .md | 468_7 |
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `bitsandbytes`.
This replaces `load_in_8bit` or `load_in_4bit`therefore both options are mutually exclusive.
Currently only supports `LLM.int8()`, `FP4`, and `NF4` quantization. If more metho... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | #bitsandbytesconfig | .md | 468_8 |
quantizers.base.HfQuantizer
Abstract class of the HuggingFace quantizer. Supports for now quantizing HF transformers models for inference and/or quantization.
This class is used only for transformers.PreTrainedModel.from_pretrained and cannot be easily used outside the scope of that method
yet.
Attributes
quantizat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#hfquantizer | #hfquantizer | .md | 468_9 |
HiggsConfig is a configuration class for quantization using the HIGGS method.
Args:
bits (int, *optional*, defaults to 4):
Number of bits to use for quantization. Can be 2, 3 or 4. Default is 4.
p (int, *optional*, defaults to 2):
Quantization grid dimension. 1 and 2 are supported. 2 is always better in practice. Def... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#higgsconfig | #higgsconfig | .md | 468_10 |
This is wrapper around hqq's BaseQuantizeConfig.
Args:
nbits (`int`, *optional*, defaults to 4):
Number of bits. Supported values are (8, 4, 3, 2, 1).
group_size (`int`, *optional*, defaults to 64):
Group-size value. Supported values are any value that is divisble by weight.shape[axis]).
view_as_float (`bool`, *optio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#hqqconfig | #hqqconfig | .md | 468_11 |
This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using fbgemm fp8 quantization.
Args:
activation_scale_ub (`float`, *optional*, defaults to 1200.0):
The activation scale upper bound. This is used when quantizing the input activation.
modules_to_not... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#fbgemmfp8config | #fbgemmfp8config | .md | 468_12 |
This is a wrapper class that handles compressed-tensors quantization config options.
It is a wrapper around `compressed_tensors.QuantizationConfig`
Args:
config_groups (`typing.Dict[str, typing.Union[ForwardRef('QuantizationScheme'), typing.List[str]]]`, *optional*):
dictionary mapping group name to a quantization sche... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#compressedtensorsconfig | #compressedtensorsconfig | .md | 468_13 |
This is a config class for torchao quantization/sparsity techniques.
Args:
quant_type (`str`):
The type of quantization we want to use, currently supporting: `int4_weight_only`, `int8_weight_only` and `int8_dynamic_activation_int8_weight`.
modules_to_not_convert (`list`, *optional*, default to `None`):
The list of mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#torchaoconfig | #torchaoconfig | .md | 468_14 |
BitNetConfig(modules_to_not_convert: Optional[List] = None, **kwargs) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitnetconfig | #bitnetconfig | .md | 468_15 |
<!--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 agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/ | .md | 469_0 | |
🤗 Transformers provides a `transformers.onnx` package that enables you to
convert model checkpoints to an ONNX graph by leveraging configuration objects.
See the [guide](../serialization) on exporting 🤗 Transformers models for more
details. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#exporting--transformers-models-to-onnx | #exporting--transformers-models-to-onnx | .md | 469_1 |
We provide three abstract classes that you should inherit from, depending on the
type of model architecture you wish to export:
* Encoder-based models inherit from [`~onnx.config.OnnxConfig`]
* Decoder-based models inherit from [`~onnx.config.OnnxConfigWithPast`]
* Encoder-decoder models inherit from [`~onnx.config.O... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnx-configurations | #onnx-configurations | .md | 469_2 |
onnx.config.OnnxConfig
Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnxconfig | #onnxconfig | .md | 469_3 |
onnx.config.OnnxConfig
Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
WithPast | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnxconfigwithpast | #onnxconfigwithpast | .md | 469_4 |
onnx.config.OnnxSeq2SeqConfigWithPast | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnxseq2seqconfigwithpast | #onnxseq2seqconfigwithpast | .md | 469_5 |
Each ONNX configuration is associated with a set of _features_ that enable you
to export models for different types of topologies or tasks. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnx-features | #onnx-features | .md | 469_6 |
onnx.features.FeaturesManager | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#featuresmanager | #featuresmanager | .md | 469_7 |
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