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perf_train_cpu_many.md	# Efficient Training on Multiple CPUs When training on a single CPU is too slow, we can use multiple CPUs. This guide focuses on PyTorch-based DDP enabling distributed CPU training efficiently. ## Intel® oneCCL Bindings for PyTorch [Intel® oneCCL](https://github.com/oneapi-src/oneCCL) (collective communications li...
bertology.md	# BERTology There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT (that some call "BERTology"). Some good examples of this field are: - BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick: https://arxiv.org/abs/1...
training.md	# Fine-tune a pretrained model [[open-in-colab]] There are significant benefits to using a pretrained model. It reduces computation costs, your carbon footprint, and allows you to use state-of-the-art models without having to train one from scratch. 🤗 Transformers provides access to thousands of pretrained models ...
tf_xla.md	# XLA Integration for TensorFlow Models [[open-in-colab]] Accelerated Linear Algebra, dubbed XLA, is a compiler for accelerating the runtime of TensorFlow Models. From the [official documentation](https://www.tensorflow.org/xla): XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra tha...
run_scripts.md	# Train with a script Along with the 🤗 Transformers [notebooks](./noteboks/README), there are also example scripts demonstrating how to train a model for a task with [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch), [TensorFlow](https://github.com/huggingface/transformers/tree/main/...
generation_strategies.md	# Text generation strategies Text generation is essential to many NLP tasks, such as open-ended text generation, summarization, translation, and more. It also plays a role in a variety of mixed-modality applications that have text as an output like speech-to-text and vision-to-text. Some of the models that can gen...
multilingual.md	# Multilingual models for inference [[open-in-colab]] There are several multilingual models in 🤗 Transformers, and their inference usage differs from monolingual models. Not all multilingual model usage is different though. Some models, like [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multi...
community.md	# Community This page regroups resources around 🤗 Transformers developed by the community. ## Community resources: \| Resource \| Description \| Author \| \|:----------\|:-------------\|------:\| \| [Hugging Face Transformers Glossary Flashcards](https://www.darigovresearch.com/huggingface-transf...
task_summary.md	# What 🤗 Transformers can do 🤗 Transformers is a library of pretrained state-of-the-art models for natural language processing (NLP), computer vision, and audio and speech processing tasks. Not only does the library contain Transformer models, but it also has non-Transformer models like modern convolutional networ...
chat_templating.md	# Templates for Chat Models ## Introduction An increasingly common use case for LLMs is chat. In a chat context, rather than continuing a single string of text (as is the case with a standard language model), the model instead continues a conversation that consists of one or more messages, each of which i...
perf_torch_compile.md	# Optimize inference using torch.compile() This guide aims to provide a benchmark on the inference speed-ups introduced with [`torch.compile()`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for [computer vision models in 🤗 Transformers](https://huggingface.co/models?pipeline_tag=image-cla...
hpo_train.md	# Hyperparameter Search using Trainer API 🤗 Transformers provides a [`Trainer`] class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The [`Trainer`] provides API for hyperparameter search. This doc shows how to enable it in example....
glossary.md	# Glossary This glossary defines general machine learning and 🤗 Transformers terms to help you better understand the documentation. ## A ### attention mask The attention mask is an optional argument used when batching sequences together. This argument indicates to the model which tokens should be attended to, ...
troubleshooting.md	# Troubleshoot Sometimes errors occur, but we are here to help! This guide covers some of the most common issues we've seen and how you can resolve them. However, this guide isn't meant to be a comprehensive collection of every 🤗 Transformers issue. For more help with troubleshooting your issue, try: 1. Asking for...
accelerate.md	# Distributed training with 🤗 Accelerate As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude. At Hugging Face, we created the [🤗 Accelerate](https://huggingface.co/docs/accelerate) library to help ...
index.md	# 🤗 Transformers State-of-the-art Machine Learning for [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), and [JAX](https://jax.readthedocs.io/en/latest/). 🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can r...
add_new_model.md	# How to add a model to 🤗 Transformers? The 🤗 Transformers library is often able to offer new models thanks to community contributors. But this can be a challenging project and requires an in-depth knowledge of the 🤗 Transformers library and the model to implement. At Hugging Face, we're trying to empower more of...
benchmarks.md	# Benchmarks Hugging Face's Benchmarking tools are deprecated and it is advised to use external Benchmarking libraries to measure the speed and memory complexity of Transformer models. [[open-in-colab]] Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benc...
debugging.md	# Debugging ## Multi-GPU Network Issues Debug When training or inferencing with `DistributedDataParallel` and multiple GPU, if you run into issue of inter-communication between processes and/or nodes, you can use the following script to diagnose network issues. ```bash wget https://raw.githubusercontent.com/huggi...
model_memory_anatomy.md	# Model training anatomy To understand performance optimization techniques that one can apply to improve efficiency of model training speed and memory utilization, it's helpful to get familiar with how GPU is utilized during training, and how compute intensity varies depending on an operation performed. Let's s...
tflite.md	# Export to TFLite [TensorFlow Lite](https://www.tensorflow.org/lite/guide) is a lightweight framework for deploying machine learning models on resource-constrained devices, such as mobile phones, embedded systems, and Internet of Things (IoT) devices. TFLite is designed to optimize and run models efficiently on...
performance.md	# Performance and Scalability Training large transformer models and deploying them to production present various challenges. During training, the model may require more GPU memory than available or exhibit slow training speed. In the deployment phase, the model can struggle to handle the required throughput in ...
tokenizer_summary.md	# Summary of the tokenizers [[open-in-colab]] On this page, we will have a closer look at tokenization. As we saw in [the preprocessing tutorial](preprocessing), tokenizing a text is splitting it into words or subwords, which then are converted to ids through a look-up table. Converting words or subwords to ids i...
perf_infer_cpu.md	# CPU inference With some optimizations, it is possible to efficiently run large model inference on a CPU. One of these optimization techniques involves compiling the PyTorch code into an intermediate format for high-performance environments like C++. The other technique fuses multiple operations into one kernel to ...
create_a_model.md	# Create a custom architecture An [`AutoClass`](model_doc/auto) automatically infers the model architecture and downloads pretrained configuration and weights. Generally, we recommend using an `AutoClass` to produce checkpoint-agnostic code. But users who want more control over specific model parameters can create a...
model_sharing.md	# Share a model The last two tutorials showed how you can fine-tune a model with PyTorch, Keras, and 🤗 Accelerate for distributed setups. The next step is to share your model with the community! At Hugging Face, we believe in openly sharing knowledge and resources to democratize artificial intelligence for everyone...
fast_tokenizers.md	# Use tokenizers from 🤗 Tokenizers The [`PreTrainedTokenizerFast`] depends on the [🤗 Tokenizers](https://huggingface.co/docs/tokenizers) library. The tokenizers obtained from the 🤗 Tokenizers library can be loaded very simply into 🤗 Transformers. Before getting in the specifics, let's first start by creating a...
perf_hardware.md	# Custom hardware for training The hardware you use to run model training and inference can have a big effect on performance. For a deep dive into GPUs make sure to check out Tim Dettmer's excellent [blog post](https://timdettmers.com/2020/09/07/which-gpu-for-deep-learning/). Let's have a look at some practical adv...
pr_checks.md	# Checks on a Pull Request When you open a pull request on 🤗 Transformers, a fair number of checks will be run to make sure the patch you are adding is not breaking anything existing. Those checks are of four types: - regular tests - documentation build - code and documentation style - general repository consis...
perf_train_tpu.md	# Training on TPUs Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [multi-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it before diving into...
serialization.md	# Export to ONNX Deploying 🤗 Transformers models in production environments often requires, or can benefit from exporting the models into a serialized format that can be loaded and executed on specialized runtimes and hardware. 🤗 Optimum is an extension of Transformers that enables exporting models from PyTorch...
installation.md	# Installation Install 🤗 Transformers for whichever deep learning library you're working with, setup your cache, and optionally configure 🤗 Transformers to run offline. 🤗 Transformers is tested on Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, and Flax. Follow the installation instructions below for the deep lear...
notebooks.md	null
perf_train_tpu_tf.md	# Training on TPU with TensorFlow If you don't need long explanations and just want TPU code samples to get started with, check out [our TPU example notebook!](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) ### What is a TPU? A TPU is a **Tensor Processing ...
llm_tutorial_optimization.md	# Optimizing LLMs for Speed and Memory [[open-in-colab]] Large Language Models (LLMs) such as GPT3/4, [Falcon](https://huggingface.co/tiiuae/falcon-40b), and [Llama](https://huggingface.co/meta-llama/Llama-2-70b-hf) are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as ess...
philosophy.md	# Philosophy 🤗 Transformers is an opinionated library built for: - machine learning researchers and educators seeking to use, study or extend large-scale Transformers models. - hands-on practitioners who want to fine-tune those models or serve them in production, or both. - engineers who just want to download a ...
perf_train_gpu_one.md	# Methods and tools for efficient training on a single GPU This guide demonstrates practical techniques that you can use to increase the efficiency of your model's training by optimizing memory utilization, speeding up the training, or both. If you'd like to understand how GPU is utilized during training, please...
testing.md	# Testing Let's take a look at how 🤗 Transformers models are tested and how you can write new tests and improve the existing ones. There are 2 test suites in the repository: 1. `tests` -- tests for the general API 2. `examples` -- tests primarily for various applications that aren't part of the API ## How trans...
custom_models.md	# Sharing custom models The 🤗 Transformers library is designed to be easily extensible. Every model is fully coded in a given subfolder of the repository with no abstraction, so you can easily copy a modeling file and tweak it to your needs. If you are writing a brand new model, it might be easier to start from s...
big_models.md	# Instantiating a big model When you want to use a very big pretrained model, one challenge is to minimize the use of the RAM. The usual workflow from PyTorch is: 1. Create your model with random weights. 2. Load your pretrained weights. 3. Put those pretrained weights in your random model. Step 1 and 2 both re...
attention.md	# Attention mechanisms Most transformer models use full attention in the sense that the attention matrix is square. It can be a big computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and use a sparse version of the attention matrix to speed up traini...
pipeline_tutorial.md	# Pipelines for inference The [`pipeline`] makes it simple to use any model from the [Hub](https://huggingface.co/models) for inference on any language, computer vision, speech, and multimodal tasks. Even if you don't have experience with a specific modality or aren't familiar with the underlying code behind the mod...
custom_tools.md	# Custom Tools and Prompts If you are not aware of what tools and agents are in the context of transformers, we recommend you read the [Transformers Agents](transformers_agents) page first. Transformers Agents is an experimental API that is subject to change at any time. Results returned by the agents can vary as...
perplexity.md	# Perplexity of fixed-length models [[open-in-colab]] Perplexity (PPL) is one of the most common metrics for evaluating language models. Before diving in, we should note that the metric applies specifically to classical language models (sometimes called autoregressive or causal language models) and is not well de...
model_summary.md	# The Transformer model family Since its introduction in 2017, the [original Transformer](https://arxiv.org/abs/1706.03762) model has inspired many new and exciting models that extend beyond natural language processing (NLP) tasks. There are models for [predicting the folded structure of proteins](https://huggingfac...
sagemaker.md	# Run training on Amazon SageMaker The documentation has been moved to [hf.co/docs/sagemaker](https://huggingface.co/docs/sagemaker). This page will be removed in `transformers` 5.0. ### Table of Content - [Train Hugging Face models on Amazon SageMaker with the SageMaker Python SDK](https://huggingface.co/docs/sa...
contributing.md	null
perf_train_cpu.md	# Efficient Training on CPU This guide focuses on training large models efficiently on CPU. ## Mixed precision with IPEX IPEX is optimized for CPUs with AVX-512 or above, and functionally works for CPUs with only AVX2. So, it is expected to bring performance benefit for Intel CPU generations with AVX-512 or above ...
transformers_agents.md	# Transformers Agents Transformers Agents is an experimental API which is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models are prone to change. Transformers version v4.29.0, building on the concept of tools and agents. You can play with in [this colab](ht...
torchscript.md	# Export to TorchScript This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities with variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming releases, with more code examples, a more flexible implementation, and ben...
perf_train_special.md	# Training on Specialized Hardware Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [multi-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it be...
autoclass_tutorial.md	# Load pretrained instances with an AutoClass With so many different Transformer architectures, it can be challenging to create one for your checkpoint. As a part of 🤗 Transformers core philosophy to make the library easy, simple and flexible to use, an `AutoClass` automatically infers and loads the correct archite...
perf_train_gpu_many.md	# Efficient Training on Multiple GPUs If training a model on a single GPU is too slow or if the model's weights do not fit in a single GPU's memory, transitioning to a multi-GPU setup may be a viable option. Prior to making this transition, thoroughly explore all the strategies covered in the [Methods and tools ...
quicktour.md	# Quick tour [[open-in-colab]] Get up and running with 🤗 Transformers! Whether you're a developer or an everyday user, this quick tour will help you get started and show you how to use the [`pipeline`] for inference, load a pretrained model and preprocessor with an [AutoClass](./model_doc/auto), and quickly train ...
pad_truncation.md	# Padding and truncation Batched inputs are often different lengths, so they can't be converted to fixed-size tensors. Padding and truncation are strategies for dealing with this problem, to create rectangular tensors from batches of varying lengths. Padding adds a special padding token to ensure shorter sequenc...
preprocessing.md	# Preprocess [[open-in-colab]] Before you can train a model on a dataset, it needs to be preprocessed into the expected model input format. Whether your data is text, images, or audio, they need to be converted and assembled into batches of tensors. 🤗 Transformers provides a set of preprocessing classes to help pr...
llm_tutorial.md	# Generation with LLMs [[open-in-colab]] LLMs, or Large Language Models, are the key component behind text generation. In a nutshell, they consist of large pretrained transformer models trained to predict the next word (or, more precisely, token) given some input text. Since they predict one token at a time, you ne...
perf_infer_gpu_one.md	# GPU inference GPUs are the standard choice of hardware for machine learning, unlike CPUs, because they are optimized for memory bandwidth and parallelism. To keep up with the larger sizes of modern models or to run these large models on existing and older hardware, there are several optimizations you can use to sp...
pipeline_webserver.md	# Using pipelines for a webserver Creating an inference engine is a complex topic, and the "best" solution will most likely depend on your problem space. Are you on CPU or GPU? Do you want the lowest latency, the highest throughput, support for many models, or just highly optimize 1 specific model? There are ma...
peft.md	# Load adapters with 🤗 PEFT [[open-in-colab]] [Parameter-Efficient Fine Tuning (PEFT)](https://huggingface.co/blog/peft) methods freeze the pretrained model parameters during fine-tuning and add a small number of trainable parameters (the adapters) on top of it. The adapters are trained to learn task-specific info...
add_new_pipeline.md	# How to create a custom pipeline? In this guide, we will see how to create a custom pipeline and share it on the [Hub](hf.co/models) or add it to the 🤗 Transformers library. First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes, dictionaries or wh...
tasks_explained.md	# How 🤗 Transformers solve tasks In [What 🤗 Transformers can do](task_summary), you learned about natural language processing (NLP), speech and audio, computer vision tasks, and some important applications of them. This page will look closely at how models solve these tasks and explain what's happening under the h...
add_tensorflow_model.md	# How to convert a 🤗 Transformers model to TensorFlow? Having multiple frameworks available to use with 🤗 Transformers gives you flexibility to play their strengths when designing your application, but it implies that compatibility must be added on a per-model basis. The good news is that adding TensorFlow compa...
tasks/audio_classification.md	# Audio classification [[open-in-colab]] Audio classification - just like with text - assigns a class label output from the input data. The only difference is instead of text inputs, you have raw audio waveforms. Some practical applications of audio classification include identifying speaker intent, language classi...
tasks/prompting.md	# LLM prompting guide [[open-in-colab]] Large Language Models such as Falcon, LLaMA, etc. are pretrained transformer models initially trained to predict the next token given some input text. They typically have billions of parameters and have been trained on trillions of tokens for an extended period of time. A...
tasks/asr.md	# Automatic speech recognition [[open-in-colab]] Automatic speech recognition (ASR) converts a speech signal to text, mapping a sequence of audio inputs to text outputs. Virtual assistants like Siri and Alexa use ASR models to help users everyday, and there are many other useful user-facing applications like live c...
tasks/token_classification.md	# Token classification [[open-in-colab]] Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization. This guide ...
tasks/image_to_image.md	# Image-to-Image Task Guide [[open-in-colab]] Image-to-Image task is the task where an application receives an image and outputs another image. This has various subtasks, including image enhancement (super resolution, low light enhancement, deraining and so on), image inpainting, and more. This guide will show yo...
tasks/text-to-speech.md	# Text to speech [[open-in-colab]] Text-to-speech (TTS) is the task of creating natural-sounding speech from text, where the speech can be generated in multiple languages and for multiple speakers. Several text-to-speech models are currently available in 🤗 Transformers, such as [Bark](../model_doc/bark), [MMS]...
tasks/question_answering.md	# Question answering [[open-in-colab]] Question answering tasks return an answer given a question. If you've ever asked a virtual assistant like Alexa, Siri or Google what the weather is, then you've used a question answering model before. There are two common types of question answering tasks: - Extractive: extra...
tasks/multiple_choice.md	# Multiple choice [[open-in-colab]] A multiple choice task is similar to question answering, except several candidate answers are provided along with a context and the model is trained to select the correct answer. This guide will show you how to: 1. Finetune [BERT](https://huggingface.co/bert-base-uncased) on th...
tasks/monocular_depth_estimation.md	# Monocular depth estimation Monocular depth estimation is a computer vision task that involves predicting the depth information of a scene from a single image. In other words, it is the process of estimating the distance of objects in a scene from a single camera viewpoint. Monocular depth estimation has various...
tasks/sequence_classification.md	# Text classification [[open-in-colab]] Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, which as...
tasks/semantic_segmentation.md	# Image Segmentation [[open-in-colab]] Image segmentation models separate areas corresponding to different areas of interest in an image. These models work by assigning a label to each pixel. There are several types of segmentation: semantic segmentation, instance segmentation, and panoptic segmentation. In this g...
tasks/object_detection.md	# Object detection [[open-in-colab]] Object detection is the computer vision task of detecting instances (such as humans, buildings, or cars) in an image. Object detection models receive an image as input and output coordinates of the bounding boxes and associated labels of the detected objects. An image can conta...
tasks/video_classification.md	# Video classification [[open-in-colab]] Video classification is the task of assigning a label or class to an entire video. Videos are expected to have only one class for each video. Video classification models take a video as input and return a prediction about which class the video belongs to. These models can be...
tasks/zero_shot_object_detection.md	# Zero-shot object detection [[open-in-colab]] Traditionally, models used for [object detection](object_detection) require labeled image datasets for training, and are limited to detecting the set of classes from the training data. Zero-shot object detection is supported by the [OWL-ViT](../model_doc/owlvit) mode...
tasks/language_modeling.md	# Causal language modeling [[open-in-colab]] There are two types of language modeling, causal and masked. This guide illustrates causal language modeling. Causal language models are frequently used for text generation. You can use these models for creative applications like choosing your own text adventure or an ...
tasks/masked_language_modeling.md	# Masked language modeling [[open-in-colab]] Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally. This means the model has full access to the tokens on the left and right. Masked language modeling is great for tasks that require a good contextual unde...
tasks/zero_shot_image_classification.md	# Zero-shot image classification [[open-in-colab]] Zero-shot image classification is a task that involves classifying images into different categories using a model that was not explicitly trained on data containing labeled examples from those specific categories. Traditionally, image classification requires trai...
tasks/translation.md	# Translation [[open-in-colab]] Translation converts a sequence of text from one language to another. It is one of several tasks you can formulate as a sequence-to-sequence problem, a powerful framework for returning some output from an input, like translation or summarization. Translation systems are commonly used...
tasks/summarization.md	# Summarization [[open-in-colab]] Summarization creates a shorter version of a document or an article that captures all the important information. Along with translation, it is another example of a task that can be formulated as a sequence-to-sequence task. Summarization can be: - Extractive: extract the most rele...
tasks/knowledge_distillation_for_image_classification.md	# Knowledge Distillation for Computer Vision [[open-in-colab]] Knowledge distillation is a technique used to transfer knowledge from a larger, more complex model (teacher) to a smaller, simpler model (student). To distill knowledge from one model to another, we take a pre-trained teacher model trained on a certain ...
tasks/idefics.md	# Image tasks with IDEFICS [[open-in-colab]] While individual tasks can be tackled by fine-tuning specialized models, an alternative approach that has recently emerged and gained popularity is to use large models for a diverse set of tasks without fine-tuning. For instance, large language models can handle such...
tasks/image_classification.md	# Image classification [[open-in-colab]] Image classification assigns a label or class to an image. Unlike text or audio classification, the inputs are the pixel values that comprise an image. There are many applications for image classification, such as detecting damage after a natural disaster, monitoring crop ...
tasks/visual_question_answering.md	# Visual Question Answering [[open-in-colab]] Visual Question Answering (VQA) is the task of answering open-ended questions based on an image. The input to models supporting this task is typically a combination of an image and a question, and the output is an answer expressed in natural language. Some notewort...
tasks/image_captioning.md	# Image captioning [[open-in-colab]] Image captioning is the task of predicting a caption for a given image. Common real world applications of it include aiding visually impaired people that can help them navigate through different situations. Therefore, image captioning helps to improve content accessibility for...
tasks/document_question_answering.md	# Document Question Answering [[open-in-colab]] Document Question Answering, also referred to as Document Visual Question Answering, is a task that involves providing answers to questions posed about document images. The input to models supporting this task is typically a combination of an image and a question, a...
internal/pipelines_utils.md	# 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 ...
internal/time_series_utils.md	# Time Series Utilities This page lists all the utility functions and classes that can be used for Time Series based models. Most of those are only useful if you are studying the code of the time series models or you wish to add to the collection of distributional output classes. ## Distributional Output [[autodo...
internal/modeling_utils.md	# 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]] pytorch_utils.Conv1D [[autodoc]] model...
internal/file_utils.md	# General Utilities This page lists all of Transformers general utility functions that are found in the file `utils.py`. Most of those are only useful if you are studying the general code in the library. ## Enums and namedtuples [[autodoc]] utils.ExplicitEnum [[autodoc]] utils.PaddingStrategy [[autodoc]] utils....
internal/tokenization_utils.md	# 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.SpecialToke...
internal/audio_utils.md	# Utilities for `FeatureExtractors` This page lists all the utility functions that can be used by the audio [`FeatureExtractor`] in order to compute special features from a raw audio using common algorithms such as Short Time Fourier Transform or log mel spectrogram. Most of those are only useful if you are stu...
internal/generation_utils.md	# Utilities for Generation This page lists all the utility functions used by [`~generation.GenerationMixin.generate`], [`~generation.GenerationMixin.greedy_search`], [`~generation.GenerationMixin.contrastive_search`], [`~generation.GenerationMixin.sample`], [`~generation.GenerationMixin.beam_search`], [`~genera...
internal/trainer_utils.md	# 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]] enable_full_determinism [[autodoc]] set_seed [[autod...
internal/image_processing_utils.md	# Utilities for Image Processors This page lists all the utility functions used by the image processors, mainly the functional transformations used to process the images. Most of those are only useful if you are studying the code of the image processors in the library. ## Image Transformations [[autodoc]] image_...
model_doc/bert.md	# BERT ## Overview The BERT model was proposed in [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer pretrained using a combination of masked langua...
model_doc/squeezebert.md	# SqueezeBERT ## Overview The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, Kurt W. Keutzer. It's a bidirectional transformer similar to the BERT model. The ...
model_doc/flaubert.md	# FlauBERT ## Overview The FlauBERT model was proposed in the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le et al. It's a transformer model pretrained using a masked language modeling (MLM) objective (like BERT). The abstract from the paper is t...

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