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https://huggingface.co/docs/diffusers/api/pipelines/stochastic_karras_ve | Stochastic Karras VE
Elucidating the Design Space of Diffusion-Based Generative Models is by Tero Karras, Miika Aittala, Timo Aila and Samuli Laine. This pipeline implements the stochastic sampling tailored to variance expanding (VE) models.
The abstract from the paper:
We argue that the theory and practice of diffusi... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/gligen | GLIGEN (Grounded Language-to-Image Generation)
The GLIGEN model was created by researchers and engineers from University of Wisconsin-Madison, Columbia University, and Microsoft. The StableDiffusionGLIGENPipeline and StableDiffusionGLIGENTextImagePipeline can generate photorealistic images conditioned on grounding inp... |
https://huggingface.co/docs/diffusers/api/pipelines/latent_diffusion_uncond | Unconditional Latent Diffusion
Unconditional Latent Diffusion was proposed in High-Resolution Image Synthesis with Latent Diffusion Models by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, BjΓΆrn Ommer.
The abstract from the paper is:
By decomposing the image formation process into a sequential applic... |
https://huggingface.co/docs/diffusers/api/pipelines/versatile_diffusion | Versatile Diffusion
Versatile Diffusion was proposed in Versatile Diffusion: Text, Images and Variations All in One Diffusion Model by Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi .
The abstract from the paper is:
The recent advances in diffusion models have set an impressive milestone in many gener... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/ldm3d_diffusion | Text-to-(RGB, depth)
LDM3D was proposed in LDM3D: Latent Diffusion Model for 3D by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unli... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl | Stable Diffusion XL
Stable Diffusion XL (SDXL) was proposed in SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis by Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas MΓΌller, Joe Penna, and Robin Rombach.
The abstract from the paper is:
We present SDXL, a latent d... |
https://huggingface.co/docs/diffusers/api/pipelines/value_guided_sampling | Value-guided planning
π§ͺ This is an experimental pipeline for reinforcement learning!
This pipeline is based on the Planning with Diffusion for Flexible Behavior Synthesis paper by Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine.
The abstract from the paper is:
Model-based reinforcement learning methods o... |
https://huggingface.co/docs/diffusers/api/pipelines/vq_diffusion | VQ Diffusion
Vector Quantized Diffusion Model for Text-to-Image Synthesis is by Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo.
The abstract from the paper is:
We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based ... |
https://huggingface.co/docs/diffusers/api/pipelines/unidiffuser | UniDiffuser
The UniDiffuser model was proposed in One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale by Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu.
The abstract from the paper is:
This paper proposes a unified diffusion framework (dubbed U... |
https://huggingface.co/docs/diffusers/api/pipelines/text_to_video_zero | Text2Video-Zero
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators is by Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, Zhangyang Wang, Shant Navasardyan, Humphrey Shi.
Text2Video-Zero enables zero-shot video generation using either:
A textual prompt
A prompt co... |
https://huggingface.co/docs/diffusers/api/pipelines/wuerstchen | WΓΌrstchen
WΓΌrstchen: Efficient Pretraining of Text-to-Image Models is by Pablo Pernias, Dominic Rampas, and Marc Aubreville.
The abstract from the paper is:
We introduce WΓΌrstchen, a novel technique for text-to-image synthesis that unites competitive performance with unprecedented cost-effectiveness and ease of traini... |
https://huggingface.co/docs/diffusers/api/pipelines/model_editing | Text-to-image model editing
Editing Implicit Assumptions in Text-to-Image Diffusion Models is by Hadas Orgad, Bahjat Kawar, and Yonatan Belinkov. This pipeline enables editing diffusion model weights, such that its assumptions of a given concept are changed. The resulting change is expected to take effect in all promp... |
https://huggingface.co/docs/diffusers/api/pipelines/text_to_video | π§ͺ This pipeline is for research purposes only.
Text-to-video
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation is by Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liang Wang, Yujun Shen, Deli Zhao, Jingren Zhou, Tieniu Tan.
The abstract from the paper is:
A diffusion probabilistic mod... |
https://huggingface.co/docs/diffusers/api/pipelines/unclip | UnCLIP
Hierarchical Text-Conditional Image Generation with CLIP Latents is by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen. The UnCLIP model in π€ Diffusers comes from kakaobrainβs karlo.
The abstract from the paper is following:
Contrastive models like CLIP have been shown to learn robust repre... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/adapter | Text-to-Image Generation with Adapter Conditioning
Overview
T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models by Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie.
Using the pretrained models we can provide control images (for exa... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_unclip | Stable unCLIP
Stable unCLIP checkpoints are finetuned from Stable Diffusion 2.1 checkpoints to condition on CLIP image embeddings. Stable unCLIP still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used for text guided image variation. When combined with an unCLIP prior, it c... |
https://huggingface.co/docs/diffusers/api/schedulers/cm_stochastic_iterative | CMStochasticIterativeScheduler
Consistency Models by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever introduced a multistep and onestep scheduler (Algorithm 1) that is capable of generating good samples in one or a small number of steps.
The abstract from the paper is:
Diffusion models have made significan... |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/self_attention_guidance | undefined |
https://huggingface.co/docs/diffusers/stable_diffusion/image_variation | undefined |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion_2 | undefined |
https://huggingface.co/docs/diffusers/stable_diffusion/latent_upscale | undefined |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion_safe | undefined |
https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/model_editing | undefined |
https://huggingface.co/docs/diffusers/api/schedulers/deis | DEISMultistepScheduler
Diffusion Exponential Integrator Sampler (DEIS) is proposed in Fast Sampling of Diffusion Models with Exponential Integrator by Qinsheng Zhang and Yongxin Chen. DEISMultistepScheduler is a fast high order solver for diffusion ordinary differential equations (ODEs).
This implementation modifies ... |
https://huggingface.co/docs/diffusers/api/schedulers/ddim_inverse | step
< source >
( model_output: FloatTensor timestep: int sample: FloatTensor eta: float = 0.0 use_clipped_model_output: bool = False variance_noise: typing.Optional[torch.FloatTensor] = None return_dict: bool = True ) β ~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput or tuple
Parameters
model_output (t... |
https://huggingface.co/docs/diffusers/stable_unclip | undefined |
https://huggingface.co/docs/huggingface_hub/package_reference/utilities | Utilities
Configure logging
The huggingface_hub package exposes a logging utility to control the logging level of the package itself. You can import it as such:
from huggingface_hub import logging
Then, you may define the verbosity in order to update the amount of logs youβll see:
from huggingface_hub import logging
l... |
https://huggingface.co/docs/diffusers/api/schedulers/multistep_dpm_solver_inverse | DPMSolverMultistepInverse
DPMSolverMultistepInverse is the inverted scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li... |
https://huggingface.co/docs/huggingface_hub/package_reference/hf_file_system | Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes
Filesystem API
The HfFileSystem class provides a pythonic file interface to the Hugging Face Hub based on... |
https://huggingface.co/docs/diffusers/api/schedulers/overview | Schedulers
π€ Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a modelβs output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is ... |
https://huggingface.co/docs/huggingface_hub/package_reference/tensorboard | TensorBoard logger
TensorBoard is a visualization toolkit for machine learning experimentation. TensorBoard allows tracking and visualizing metrics such as loss and accuracy, visualizing the model graph, viewing histograms, displaying images and much more. TensorBoard is well integrated with the Hugging Face Hub. The H... |
https://huggingface.co/docs/huggingface_hub/package_reference/webhooks_server | Webhooks Server
Webhooks are a foundation for MLOps-related features. They allow you to listen for new changes on specific repos or to all repos belonging to particular users/organizations youβre interested in following. To learn more about webhooks on the Huggingface Hub, you can read the Webhooks guide.
Check out thi... |
https://huggingface.co/docs/diffusers/api/schedulers/ddpm | Diffusers documentation
DDPMScheduler
DDPMScheduler
Denoising Diffusion Probabilistic Models (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the context of the π€ Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the p... |
https://huggingface.co/docs/diffusers/api/schedulers/ddim | DDIMScheduler
Denoising Diffusion Implicit Models (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.
The abstract from the paper is:
Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps ... |
https://huggingface.co/docs/huggingface_hub/package_reference/cards | Repository Cards
The huggingface_hub library provides a Python interface to create, share, and update Model/Dataset Cards. Visit the dedicated documentation page for a deeper view of what Model Cards on the Hub are, and how they work under the hood. You can also check out our Model Cards guide to get a feel for how you... |
https://huggingface.co/docs/huggingface_hub/guides/download | Download files from the Hub
The huggingface_hub library provides functions to download files from the repositories stored on the Hub. You can use these functions independently or integrate them into your own library, making it more convenient for your users to interact with the Hub. This guide will show you how to:
Dow... |
https://huggingface.co/docs/huggingface_hub/package_reference/space_runtime | Managing your Space runtime
Check the HfApi documentation page for the reference of methods to manage your Space on the Hub.
Duplicate a Space: duplicate_space()
Fetch current runtime: get_space_runtime()
Manage secrets: add_space_secret() and delete_space_secret()
Manage hardware: request_space_hardware()
Manage state... |
https://huggingface.co/docs/huggingface_hub/installation | Installation
Before you start, you will need to setup your environment by installing the appropriate packages.
huggingface_hub is tested on Python 3.8+.
Install with pip
It is highly recommended to install huggingface_hub in a virtual environment. If you are unfamiliar with Python virtual environments, take a look at t... |
https://huggingface.co/docs/huggingface_hub/quick-start | Quickstart
The Hugging Face Hub is the go-to place for sharing machine learning models, demos, datasets, and metrics. huggingface_hub library helps you interact with the Hub without leaving your development environment. You can create and manage repositories easily, download and upload files, and get useful model and d... |
https://huggingface.co/docs/huggingface_hub/guides/hf_file_system | Interact with the Hub through the Filesystem API
In addition to the HfApi, the huggingface_hub library provides HfFileSystem, a pythonic fsspec-compatible file interface to the Hugging Face Hub. The HfFileSystem builds of top of the HfApi and offers typical filesystem style operations like cp, mv, ls, du, glob, get_fil... |
https://huggingface.co/docs/huggingface_hub/guides/overview | Hub Python Library documentation
How-to guides
Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes
How-to guides
In this section, you will find practical gu... |
https://huggingface.co/docs/huggingface_hub/guides/search | Search the Hub
In this tutorial, you will learn how to search models, datasets and spaces on the Hub using huggingface_hub.
How to list repositories ?
huggingface_hub library includes an HTTP client HfApi to interact with the Hub. Among other things, it can list models, datasets and spaces stored on the Hub:
>>> from h... |
https://huggingface.co/docs/huggingface_hub/guides/community | Interact with Discussions and Pull Requests
The huggingface_hub library provides a Python interface to interact with Pull Requests and Discussions on the Hub. Visit the dedicated documentation page for a deeper view of what Discussions and Pull Requests on the Hub are, and how they work under the hood.
Retrieve Discuss... |
https://huggingface.co/docs/huggingface_hub/package_reference/community | Hub Python Library documentation
Interacting with Discussions and Pull Requests
Interacting with Discussions and Pull Requests
Check the HfApi documentation page for the reference of methods enabling interaction with Pull Requests and Discussions on the Hub.
get_repo_discussions()
get_discussion_details()
create_discu... |
https://huggingface.co/docs/huggingface_hub/guides/model-cards | Create and share Model Cards
The huggingface_hub library provides a Python interface to create, share, and update Model Cards. Visit the dedicated documentation page for a deeper view of what Model Cards on the Hub are, and how they work under the hood.
Load a Model Card from the Hub
To load an existing card from the H... |
https://huggingface.co/docs/huggingface_hub/guides/upload | Upload files to the Hub
Sharing your files and work is an important aspect of the Hub. The huggingface_hub offers several options for uploading your files to the Hub. You can use these functions independently or integrate them into your library, making it more convenient for your users to interact with the Hub. This gu... |
https://huggingface.co/docs/huggingface_hub/guides/manage-spaces | Manage your Space
In this guide, we will see how to manage your Space runtime (secrets, hardware, and storage) using huggingface_hub.
A simple example: configure secrets and hardware.
Here is an end-to-end example to create and setup a Space on the Hub.
1. Create a Space on the Hub.
>>> from huggingface_hub import HfAp... |
https://huggingface.co/docs/huggingface_hub/package_reference/cache | Cache-system reference
The caching system was updated in v0.8.0 to become the central cache-system shared across libraries that depend on the Hub. Read the cache-system guide for a detailed presentation of caching at HF.
Helpers
try_to_load_from_cache
huggingface_hub.try_to_load_from_cache
< source >
( repo_id: str fil... |
https://huggingface.co/docs/huggingface_hub/guides/repository | Create and manage a repository
The Hugging Face Hub is a collection of git repositories. Git is a widely used tool in software development to easily version projects when working collaboratively. This guide will show you how to interact with the repositories on the Hub, especially:
Create and delete a repository.
Manag... |
https://huggingface.co/docs/huggingface_hub/guides/inference | Run Inference on servers
Inference is the process of using a trained model to make predictions on new data. As this process can be compute-intensive, running on a dedicated server can be an interesting option. The huggingface_hub library provides an easy way to call a service that runs inference for hosted models. Ther... |
https://huggingface.co/docs/huggingface_hub/guides/integrations | Integrate any ML framework with the Hub
The Hugging Face Hub makes hosting and sharing models with the community easy. It supports dozens of libraries in the Open Source ecosystem. We are always working on expanding this support to push collaborative Machine Learning forward. The huggingface_hub library plays a key rol... |
https://huggingface.co/docs/huggingface_hub/package_reference/login | Hub Python Library documentation
Login and logout
Login and logout
The huggingface_hub library allows users to programmatically login and logout the machine to the Hub.
login
huggingface_hub.login
< source >
( token: typing.Optional[str] = None add_to_git_credential: bool = False new_session: bool = True write_permiss... |
https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables | Environment variables
huggingface_hub can be configured using environment variables.
If you are unfamiliar with environment variable, here are generic articles about them on macOS and Linux and on Windows.
This page will guide you through all environment variables specific to huggingface_hub and their meaning.
Generic
... |
https://huggingface.co/docs/huggingface_hub/package_reference/overview | Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes
Overview
This section contains an exhaustive and technical description of huggingface_hub classes and met... |
https://huggingface.co/docs/huggingface_hub/package_reference/mixins | Mixins & serialization methods
Mixins
The huggingface_hub library offers a range of mixins that can be used as a parent class for your objects, in order to provide simple uploading and downloading functions. Check out our integration guide to learn how to integrate any ML framework with the Hub.
Generic
A generic mixin... |
https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http | Git vs HTTP paradigm
The huggingface_hub library is a library for interacting with the Hugging Face Hub, which is a collections of git-based repositories (models, datasets or Spaces). There are two main ways to access the Hub using huggingface_hub.
The first approach, the so-called βgit-basedβ approach, is led by the R... |
https://huggingface.co/docs/huggingface_hub/guides/manage-cache | Manage huggingface_hub cache-system
Understand caching
The Hugging Face Hub cache-system is designed to be the central cache shared across libraries that depend on the Hub. It has been updated in v0.8.0 to prevent re-downloading same files between revisions.
The caching system is designed as follows:
<CACHE_DIR>
ββ <MO... |
https://huggingface.co/docs/huggingface_hub/package_reference/repository | The Repository class is a helper class that wraps git and git-lfs commands. It provides tooling adapted for managing repositories which can be very large.
It is the recommended tool as soon as any git operation is involved, or when collaboration will be a point of focus with the repository itself.
class huggingface_hub... |
https://huggingface.co/docs/huggingface_hub/package_reference/file_download | Downloading files
Download a single file
hf_hub_download
huggingface_hub.hf_hub_download
< source >
( repo_id: str filename: str subfolder: typing.Optional[str] = None repo_type: typing.Optional[str] = None revision: typing.Optional[str] = None endpoint: typing.Optional[str] = None library_name: typing.Optional[str] = ... |
https://huggingface.co/docs/huggingface_hub/guides/webhooks_server | Webhooks Server
Webhooks are a foundation for MLOps-related features. They allow you to listen for new changes on specific repos or to all repos belonging to particular users/organizations youβre interested in following. This guide will explain how to leverage huggingface_hub to create a server listening to webhooks an... |
https://huggingface.co/docs/huggingface_hub/package_reference/hf_api | Below is the documentation for the HfApi class, which serves as a Python wrapper for the Hugging Face Hubβs API.
All methods from the HfApi are also accessible from the packageβs root directly. Both approaches are detailed below.
Using the root method is more straightforward but the HfApi class gives you more flexibili... |
https://huggingface.co/docs/tokenizers/training_from_memory | Training from memory
In the Quicktour, we saw how to build and train a tokenizer using text files, but we can actually use any Python Iterator. In this section weβll see a few different ways of training our tokenizer.
For all the examples listed below, weβll use the same Tokenizer and Trainer, built as following:
from... |
https://huggingface.co/docs/tokenizers/pipeline | The tokenization pipeline
When calling Tokenizer.encode or Tokenizer.encode_batch, the input text(s) go through the following pipeline:
normalization
pre-tokenization
model
post-processing
Weβll see in details what happens during each of those steps in detail, as well as when you want to decode <decoding> some token i... |
https://huggingface.co/docs/tokenizers/quicktour | Quicktour
Letβs have a quick look at the π€ Tokenizers library features. The library provides an implementation of todayβs most used tokenizers that is both easy to use and blazing fast.
Build a tokenizer from scratch
To illustrate how fast the π€ Tokenizers library is, letβs train a new tokenizer on wikitext-103 (51... |
https://huggingface.co/docs/huggingface_hub/package_reference/inference_client | Inference
Inference is the process of using a trained model to make predictions on new data. As this process can be compute-intensive, running on a dedicated server can be an interesting option. The huggingface_hub library provides an easy way to call a service that runs inference for hosted models. There are several s... |
https://huggingface.co/docs/tokenizers/installation | π€ Tokenizers is tested on Python 3.5+.
You should install π€ Tokenizers in a virtual environment. If youβre unfamiliar with Python virtual environments, check out the user guide. Create a virtual environment with the version of Python youβre going to use and activate it.
Installation with pip
π€ Tokenizers can be ins... |
https://huggingface.co/docs/tokenizers/api/input-sequences | These types represent all the different kinds of sequence that can be used as input of a Tokenizer. Globally, any sequence can be either a string or a list of strings, according to the operating mode of the tokenizer: raw text vs pre-tokenized.
TextInputSequence
tokenizers.TextInputSequence
A str that represents an in... |
https://huggingface.co/docs/tokenizers/components | Components
When building a Tokenizer, you can attach various types of components to this Tokenizer in order to customize its behavior. This page lists most provided components.
Normalizers
A Normalizer is in charge of pre-processing the input string in order to normalize it as relevant for a given use case. Some comm... |
https://huggingface.co/docs/tokenizers/api/visualizer | Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes
Visualizer
Annotation
class tokenizers.tools.Annotation
< source >
( start: int end: int label: str )
... |
https://huggingface.co/docs/tokenizers/api/normalizers | Tokenizers documentation
Normalizers
Normalizers
BertNormalizer
class tokenizers.normalizers.BertNormalizer
( clean_text = True handle_chinese_chars = True strip_accents = None lowercase = True )
Parameters
clean_text (bool, optional, defaults to True) β Whether to clean the text, by removing any control character... |
https://huggingface.co/docs/tokenizers/api/decoders | Tokenizers documentation
Decoders
Decoders
BPEDecoder
class tokenizers.decoders.BPEDecoder
( suffix = '</w>' )
Parameters
suffix (str, optional, defaults to </w>) β The suffix that was used to caracterize an end-of-word. This suffix will be replaced by whitespaces during the decoding
BPEDecoder Decoder
ByteLevel ... |
https://huggingface.co/docs/tokenizers/api/encode-inputs | These types represent all the different kinds of input that a Tokenizer accepts when using encode_batch().
TextEncodeInput
tokenizers.TextEncodeInput
Represents a textual input for encoding. Can be either:
A single sequence: TextInputSequence
A pair of sequences:
A Tuple of TextInputSequence
Or a List of TextInputSequ... |
https://huggingface.co/docs/tokenizers/api/trainers | Tokenizers documentation
Trainers
Trainers
BpeTrainer
class tokenizers.trainers.BpeTrainer
( )
Parameters
vocab_size (int, optional) β The size of the final vocabulary, including all tokens and alphabet.
min_frequency (int, optional) β The minimum frequency a pair should have in order to be merged.
show_progress... |
https://huggingface.co/docs/tokenizers/api/added-tokens | Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes
Added Tokens
AddedToken
class tokenizers.AddedToken
( content single_word = False lstrip = False rstrip... |
https://huggingface.co/docs/tokenizers/api/post-processors | class tokenizers.processors.BertProcessing
( sep cls )
Parameters
sep (Tuple[str, int]) β A tuple with the string representation of the SEP token, and its id
cls (Tuple[str, int]) β A tuple with the string representation of the CLS token, and its id
This post-processor takes care of adding the special tokens needed... |
https://huggingface.co/tasks/audio-classification | Tasks
Audio Classification
Audio classification is the task of assigning a label or class to a given audio. It can be used for recognizing which command a user is giving or the emotion of a statement, as well as identifying a speaker.
Inputs
Your browser does not support the audio element.
Audio Classification Model ... |
https://huggingface.co/docs/tokenizers/api/pre-tokenizers | BertPreTokenizer
class tokenizers.pre_tokenizers.BertPreTokenizer
( )
BertPreTokenizer
This pre-tokenizer splits tokens on spaces, and also on punctuation. Each occurence of a punctuation character will be treated separately.
ByteLevel
class tokenizers.pre_tokenizers.ByteLevel
( add_prefix_space = True use_regex = T... |
https://huggingface.co/tasks/audio-to-audio | Tasks
Audio-to-Audio
Audio-to-Audio is a family of tasks in which the input is an audio and the output is one or multiple generated audios. Some example tasks are speech enhancement and source separation.
Inputs
Your browser does not support the audio element.
Output
Your browser does not support the audio element. Y... |
https://huggingface.co/tasks/video-classification | Tasks
Video Classification
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.
Inputs
Video Classification Model
A... |
https://huggingface.co/tasks/unconditional-image-generation | Tasks
Unconditional Image Generation
Unconditional image generation is the task of generating images with no condition in any context (like a prompt text or another image). Once trained, the model will create images that resemble its training data distribution.
Inputs
Number of images to generate:
4
Unconditional Imag... |
https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&library=transformers.js | Active filters: automatic-speech-recognition, transformers.js |
https://huggingface.co/tasks/automatic-speech-recognition | Tasks
Automatic Speech Recognition
Automatic Speech Recognition (ASR), also known as Speech to Text (STT), is the task of transcribing a given audio to text. It has many applications, such as voice user interfaces.
Inputs
Your browser does not support the audio element.
Automatic Speech Recognition Model
Output
Tran... |
https://huggingface.co/tasks/tabular-classification | Tasks
Tabular Classification
Tabular classification is the task of classifying a target category (a group) based on set of attributes.
Tabular Classification Model
About Tabular Classification
About the Task
Tabular classification is the task of assigning a label or class given a limited number of attributes. For ex... |
https://huggingface.co/models?pipeline_tag=audio-classification&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/tasks/image-to-text | Tasks
Image-to-Text
Image to text models output a text from a given image. Image captioning or optical character recognition can be considered as the most common applications of image to text.
Inputs
Output
Detailed description
a herd of giraffes and zebras grazing in a field
About Image-to-Text
Use Cases
Image Capti... |
https://huggingface.co/models?pipeline_tag=document-question-answering&library=transformers.js | Active filters: document-question-answering, transformers.js |
https://huggingface.co/tasks/visual-question-answering | Tasks
Visual Question Answering
Visual Question Answering is the task of answering open-ended questions based on an image. They output natural language responses to natural language questions.
Inputs
Question
What is in this image?
Visual Question Answering Model
About Visual Question Answering
Use Cases
Aid the Vis... |
https://huggingface.co/tasks/document-question-answering | Tasks
Document Question Answering
Document Question Answering (also known as Document Visual Question Answering) is the task of answering questions on document images. Document question answering models take a (document, question) pair as input and return an answer in natural language. Models usually rely on multi-mod... |
https://huggingface.co/tasks/feature-extraction | Tasks
Feature Extraction
Feature extraction refers to the process of transforming raw data into numerical features that can be processed while preserving the information in the original dataset.
Inputs
Input
India, officially the Republic of India, is a country in South Asia.
About Feature Extraction
About the Task
F... |
https://huggingface.co/docs/tokenizers/api/models | Models
BPE
class tokenizers.models.BPE
( vocab = None merges = None cache_capacity = None dropout = None unk_token = None continuing_subword_prefix = None end_of_word_suffix = None fuse_unk = None byte_fallback = False )
Parameters
vocab (Dict[str, int], optional) β A dictionnary of string keys and their ids {"am":... |
https://huggingface.co/tasks/text-to-speech | Tasks
Text-to-Speech
Text-to-Speech (TTS) is the task of generating natural sounding speech given text input. TTS models can be extended to have a single model that generates speech for multiple speakers and multiple languages.
Inputs
Input
I love audio models on the Hub!
Output
Your browser does not support the audio... |
https://huggingface.co/models?pipeline_tag=zero-shot-image-classification&library=transformers.js | Active filters: zero-shot-image-classification, transformers.js |
https://huggingface.co/tasks/tabular-regression | Tasks
Tabular Regression
Tabular regression is the task of predicting a numerical value given a set of attributes.
About Tabular Regression
About the Task
Tabular regression is the task of predicting a numerical value given a set of attributes/features. Tabular meaning that data is stored in a table (like an excel sh... |
https://huggingface.co/tasks/text-to-image | Tasks
Text-to-Image
Generates images from input text. These models can be used to generate and modify images based on text prompts.
Inputs
Input
A city above clouds, pastel colors, Victorian style
Output
About Text-to-Image
Use Cases
Data Generation
Businesses can generate data for their their use cases by inputting... |
https://huggingface.co/models?pipeline_tag=image-to-text&library=transformers.js | Natural Language Processing
Natural Language Processing |
https://huggingface.co/tasks/reinforcement-learning | Tasks
Reinforcement Learning
Reinforcement learning is the computational approach of learning from action by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback
Inputs
State
Red traffic light, pedestrians are about to pass.
Reinforcement Learning Model
Outp... |
https://huggingface.co/docs/tokenizers/api/encoding | class tokenizers.Encoding
( )
The Encoding represents the output of a Tokenizer.
The attention mask
This indicates to the LM which tokens should be attended to, and which should not. This is especially important when batching sequences, where we need to applying padding.
The generated IDs
The IDs are the main input to... |
https://huggingface.co/docs/transformers.js/installation | Installation
To install via NPM, run:
npm i @xenova/transformers
Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using ES Modules, you can import the library with:
<script type="module"> import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/trans... |
https://huggingface.co/docs/transformers.js/tutorials/electron | Transformers.js documentation
Building an Electron application
Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes |
https://huggingface.co/docs/transformers.js/tutorials/browser-extension | Transformers.js documentation
Building a browser extension
Join the Hugging Face community
and get access to the augmented documentation experience
Collaborate on models, datasets and Spaces
Faster examples with accelerated inference
Switch between documentation themes |
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