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https://huggingface.co/docs/transformers/model_doc/idefics | IDEFICS
Overview
The IDEFICS model was proposed in OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh
... |
https://huggingface.co/papers/2306.16527 | Abstract
Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. We introduce ... |
https://huggingface.co/docs/transformers/model_doc/imagegpt | ImageGPT
Overview
The ImageGPT model was proposed in Generative Pretraining from Pixels by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. ImageGPT (iGPT) is a GPT-2-like model trained to predict the next pixel value, allowing for both unconditional and conditional image genera... |
https://huggingface.co/docs/transformers/model_doc/informer | Informer
Overview
The Informer model was proposed in Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
This method introduces a Probabilistic Attention mechanism to select the “active” querie... |
https://huggingface.co/docs/transformers/model_doc/layoutxlm | LayoutXLM
Overview
LayoutXLM was proposed in LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei. It’s a multilingual extension of the LayoutLMv2 model trained on 53 languages.
The abstr... |
https://huggingface.co/docs/transformers/model_doc/layoutlmv2 | LayoutLMV2
Overview
The LayoutLMV2 model was proposed in LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves LayoutLM to obtain s... |
https://huggingface.co/docs/transformers/model_doc/layoutlm | LayoutLM
Overview
The LayoutLM model was proposed in the paper LayoutLM: Pre-training of Text and Layout for Document Image Understanding by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. It’s a simple but effective pretraining method of text and layout for document image understanding and info... |
https://huggingface.co/docs/transformers/model_doc/jukebox | Jukebox
Overview
The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditioned on an artist, genres and lyrics.
T... |
https://huggingface.co/docs/transformers/model_doc/layoutlmv3 | LayoutLMv3
Overview
The LayoutLMv3 model was proposed in LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei. LayoutLMv3 simplifies LayoutLMv2 by using patch embeddings (as in ViT) instead of leveraging a CNN backbone, and pre-trains the... |
https://huggingface.co/docs/transformers/model_doc/instructblip | InstructBLIP
Overview
The InstructBLIP model was proposed in InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. InstructBLIP leverages the BLIP-2 architecture... |
https://huggingface.co/docs/transformers/model_doc/led | LED
Overview
The LED model was proposed in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
The abstract from the paper is the following:
Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the seque... |
https://huggingface.co/docs/transformers/model_doc/llama2 | Llama2
Overview
The Llama2 model was proposed in LLaMA: Open Foundation and Fine-Tuned Chat Models by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, G... |
https://huggingface.co/docs/transformers/model_doc/longt5 | LongT5
Overview
The LongT5 model was proposed in LongT5: Efficient Text-To-Text Transformer for Long Sequences by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. It’s an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 mo... |
https://huggingface.co/docs/transformers/model_doc/lilt | LiLT
Overview
The LiLT model was proposed in LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding by Jiapeng Wang, Lianwen Jin, Kai Ding. LiLT allows to combine any pre-trained RoBERTa text encoder with a lightweight Layout Transformer, to enable LayoutLM-like docum... |
https://huggingface.co/docs/transformers/model_doc/levit | LeViT
Overview
The LeViT model was proposed in LeViT: Introducing Convolutions to Vision Transformers by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze. LeViT improves the Vision Transformer (ViT) in performance and efficiency by a few architectural differences su... |
https://huggingface.co/docs/transformers/model_doc/llama | LLaMA
Overview
The LLaMA model was proposed in LLaMA: Open and Efficient Foundation Language Models by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillau... |
https://huggingface.co/docs/transformers/model_doc/longformer | Longformer
Overview
The Longformer model was presented in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
The abstract from the paper is the following:
Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically... |
https://huggingface.co/docs/transformers/model_doc/luke | LUKE
Overview
The LUKE model was proposed in LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda and Yuji Matsumoto. It is based on RoBERTa and adds entity embeddings as well as an entity-aware self-attention mechanism, which hel... |
https://huggingface.co/docs/transformers/model_doc/lxmert | LXMERT
Overview
The LXMERT model was proposed in LXMERT: Learning Cross-Modality Encoder Representations from Transformers by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders (one for the vision modality, one for the language modality, and then one to fuse both modalities) pretrained using a... |
https://huggingface.co/docs/transformers/model_doc/marian | MarianMT
Bugs: If you see something strange, file a Github Issue and assign @patrickvonplaten.
Translations should be similar, but not identical to output in the test set linked to in each model card.
Tips:
A framework for translation models, using the same models as BART.
Implementation Notes
Each model is about 298 M... |
https://huggingface.co/docs/transformers/model_doc/mctct | M-CTC-T
This model is in maintenance mode only, so we won’t accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0. You can do so by running the following command: pip install -U transformers==4.30.0.
Overview
The M-CTC-T... |
https://huggingface.co/docs/transformers/model_doc/markuplm | MarkupLM
Overview
The MarkupLM model was proposed in MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei. MarkupLM is BERT, but applied to HTML pages instead of raw text documents. The model incorporates additional embedding layers to i... |
https://huggingface.co/docs/transformers/model_doc/mask2former | Mask2Former
Overview
The Mask2Former model was proposed in Masked-attention Mask Transformer for Universal Image Segmentation by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar. Mask2Former is a unified framework for panoptic, instance and semantic segmentation and features significant... |
https://huggingface.co/docs/transformers/model_doc/m2m_100 | M2M100
Overview
The M2M100 model was proposed in Beyond English-Centric Multilingual Machine Translation by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov... |
https://huggingface.co/docs/transformers/model_doc/matcha | MatCha
Overview
MatCha has been proposed in the paper MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering, from Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos.
The abstract of the paper s... |
https://huggingface.co/docs/transformers/model_doc/mega | MEGA
Overview
The MEGA model was proposed in Mega: Moving Average Equipped Gated Attention by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer. MEGA proposes a new approach to self-attention with each encoder layer having a multi-headed exponential moving ... |
https://huggingface.co/docs/transformers/model_doc/maskformer | MaskFormer
This is a recently introduced model so the API hasn’t been tested extensively. There may be some bugs or slight breaking changes to fix it in the future. If you see something strange, file a Github Issue.
Overview
The MaskFormer model was proposed in Per-Pixel Classification is Not All You Need for Semantic ... |
https://huggingface.co/docs/transformers/model_doc/megatron-bert | MegatronBERT
Overview
The MegatronBERT model was proposed in Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
The abstract from the paper is the following:
Recent work in language m... |
https://huggingface.co/docs/transformers/model_doc/mbart | MBart and MBart-50
DISCLAIMER: If you see something strange, file a Github Issue and assign @patrickvonplaten
Overview of MBart
The MBart model was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan Ghazvininejad, Mike Lew... |
https://huggingface.co/docs/transformers/model_doc/megatron_gpt2 | MegatronGPT2
Overview
The MegatronGPT2 model was proposed in Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
The abstract from the paper is the following:
Recent work in language m... |
https://huggingface.co/docs/transformers/model_doc/mluke | mLUKE
Overview
The mLUKE model was proposed in mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka. It’s a multilingual extension of the LUKE model trained on the basis of XLM-RoBERTa.
It is based on XLM-RoBERTa and adds entity embeddi... |
https://huggingface.co/docs/transformers/model_doc/mistral | Mistral
Overview
Mistral-7B-v0.1 is Mistral AI’s first Large Language Model (LLM).
Model Details
Mistral-7B-v0.1 is a decoder-based LM with the following architectural choices:
Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
GQA (Grouped Q... |
https://huggingface.co/docs/transformers/model_doc/mgp-str | MGP-STR
Overview
The MGP-STR model was proposed in Multi-Granularity Prediction for Scene Text Recognition by Peng Wang, Cheng Da, and Cong Yao. MGP-STR is a conceptually simple yet powerful vision Scene Text Recognition (STR) model, which is built upon the Vision Transformer (ViT). To integrate linguistic knowledge, M... |
https://huggingface.co/docs/transformers/model_doc/mms | MMS
Overview
The MMS model was proposed in Scaling Speech Technology to 1,000+ Languages by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Aul... |
https://huggingface.co/docs/transformers/model_doc/mobilenet_v1 | MobileNet V1
Overview
The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
The abstract from the paper is the following:
We presen... |
https://huggingface.co/docs/transformers/model_doc/mobilenet_v2 | MobileNet V2
Overview
The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
The abstract from the paper is the following:
In this paper we describe a new mobile architecture, MobileNetV2, that improves... |
https://huggingface.co/docs/transformers/model_doc/mobilebert | MobileBERT
Overview
The MobileBERT model was proposed in MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. It’s a bidirectional transformer based on the BERT model, which is compressed and accelerated using several ap... |
https://huggingface.co/docs/transformers/model_doc/mobilevit | MobileViT
Overview
The MobileViT model was proposed in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari. MobileViT introduces a new layer that replaces local processing in convolutions with global processing using transformers.
The abstract from the... |
https://huggingface.co/docs/transformers/model_doc/mobilevitv2 | MobileViTV2
Overview
The MobileViTV2 model was proposed in Separable Self-attention for Mobile Vision Transformers by Sachin Mehta and Mohammad Rastegari.
MobileViTV2 is the second version of MobileViT, constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.
The abstract fro... |
https://huggingface.co/docs/transformers/model_doc/mpt | MPT
Overview
The MPT model was proposed by the MosaicML team and released with multiple sizes and finetuned variants. The MPT models is a series of open source and commercially usable LLMs pre-trained on 1T tokens.
MPT models are GPT-style decoder-only transformers with several improvements: performance-optimized layer... |
https://huggingface.co/docs/transformers/model_doc/mra | MRA
Overview
The MRA model was proposed in Multi Resolution Analysis (MRA) for Approximate Self-Attention by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, and Vikas Singh.
The abstract from the paper is the following:
Transformers have emerged as a preferred model for many tasks in natural langugage processin... |
https://huggingface.co/docs/transformers/model_doc/mvp | MVP
Overview
The MVP model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
According to the abstract,
MVP follows a standard Transformer encoder-decoder architecture.
MVP is supervised pre-trained using labeled datasets.
M... |
https://huggingface.co/docs/transformers/model_doc/mt5 | mT5
Overview
The mT5 model was presented in mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
The abstract from the paper is the following:
The recent “Text-to-Text Transfer Transformer” ... |
https://huggingface.co/docs/transformers/model_doc/mpnet | MPNet
Overview
The MPNet model was proposed in MPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of masked language modeling and pe... |
https://huggingface.co/docs/transformers/model_doc/musicgen | MusicGen
Overview
The MusicGen model was proposed in the paper Simple and Controllable Music Generation by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.
MusicGen is a single stage auto-regressive Transformer model capable of generating high-quality music s... |
https://huggingface.co/docs/transformers/model_doc/nat | Neighborhood Attention Transformer
Overview
NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.
It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern.
The abstract from the paper is the fo... |
https://huggingface.co/docs/transformers/model_doc/nllb-moe | NLLB-MOE
Overview
The NLLB model was presented in No Language Left Behind: Scaling Human-Centered Machine Translation by Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Y... |
https://huggingface.co/docs/transformers/model_doc/nystromformer | Nyströmformer
Overview
The Nyströmformer model was proposed in Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, and Vikas Singh.
The abstract from the paper is the following:
Transformers have emerged as a ... |
https://huggingface.co/docs/transformers/model_doc/nezha | Nezha
Overview
The Nezha model was proposed in NEZHA: Neural Contextualized Representation for Chinese Language Understanding by Junqiu Wei et al.
The abstract from the paper is the following:
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its ... |
https://huggingface.co/docs/transformers/model_doc/open-llama | Open-Llama
This model is in maintenance mode only, so we won’t accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.31.0. You can do so by running the following command: pip install -U transformers==4.31.0.
This model differ... |
https://huggingface.co/docs/transformers/model_doc/nougat | Nougat
Overview
The Nougat model was proposed in Nougat: Neural Optical Understanding for Academic Documents by Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic. Nougat uses the same architecture as Donut, meaning an image Transformer encoder and an autoregressive text Transformer decoder to translate sc... |
https://huggingface.co/docs/transformers/model_doc/nllb | NLLB
DISCLAIMER: The default behaviour for the tokenizer has recently been fixed (and thus changed)!
The previous version adds [self.eos_token_id, self.cur_lang_code] at the end of the token sequence for both target and source tokenization. This is wrong as the NLLB paper mentions (page 48, 6.1.1. Model Architecture) :... |
https://huggingface.co/docs/transformers/model_doc/oneformer | OneFormer
Overview
The OneFormer model was proposed in OneFormer: One Transformer to Rule Universal Image Segmentation by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi. OneFormer is a universal image segmentation framework that can be trained on a single panoptic dataset to perform sema... |
https://huggingface.co/s-JoL | 4 62
Phy
s-JoL
Research interests
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models
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datasets
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https://huggingface.co/docs/transformers/master/model_doc/opt | OPT
Overview
The OPT model was proposed in Open Pre-trained Transformer Language Models by Meta AI. OPT is a series of open-sourced large causal language models which perform similar in performance to GPT3.
The abstract from the paper is the following:
Large language models, which are often trained for hundreds of thou... |
https://huggingface.co/docs/transformers/model_doc/pegasus | Pegasus
DISCLAIMER: If you see something strange, file a Github Issue and assign @patrickvonplaten.
Overview
The Pegasus model was proposed in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
According to the a... |
https://huggingface.co/docs/transformers/model_doc/owlvit | OWL-ViT
Overview
The OWL-ViT (short for Vision Transformer for Open-World Localization) was proposed in Simple Open-Vocabulary Object Detection with Vision Transformers by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa De... |
https://huggingface.co/docs/transformers/model_doc/perceiver | Perceiver
Overview
The Perceiver IO model was proposed in Perceiver IO: A General Architecture for Structured Inputs & Outputs by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Bo... |
https://huggingface.co/docs/transformers/model_doc/pegasus_x | PEGASUS-X
Overview
The PEGASUS-X model was proposed in Investigating Efficiently Extending Transformers for Long Input Summarization by Jason Phang, Yao Zhao and Peter J. Liu.
PEGASUS-X (PEGASUS eXtended) extends the PEGASUS models for long input summarization through additional long input pretraining and using stagger... |
https://huggingface.co/docs/transformers/model_doc/persimmon | Persimmon
Overview
The Persimmon model was created by ADEPT, and authored by Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani.
The authors introduced Persimmon-8B, a decoder model based on the classic transformers architecture, with query and key norma... |
https://huggingface.co/docs/transformers/model_doc/phobert | PhoBERT
Overview
The PhoBERT model was proposed in PhoBERT: Pre-trained language models for Vietnamese by Dat Quoc Nguyen, Anh Tuan Nguyen.
The abstract from the paper is the following:
We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-tra... |
https://huggingface.co/docs/transformers/model_doc/pop2piano | Pop2Piano
Overview
The Pop2Piano model was proposed in Pop2Piano : Pop Audio-based Piano Cover Generation by Jongho Choi and Kyogu Lee.
Piano covers of pop music are widely enjoyed, but generating them from music is not a trivial task. It requires great expertise with playing piano as well as knowing different characte... |
https://huggingface.co/docs/transformers/model_doc/pix2struct | Pix2Struct
Overview
The Pix2Struct model was proposed in Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
The abstract from the paper is... |
https://huggingface.co/docs/transformers/model_doc/poolformer | PoolFormer
Overview
The PoolFormer model was proposed in MetaFormer is Actually What You Need for Vision by Sea AI Labs. Instead of designing complicated token mixer to achieve SOTA performance, the target of this work is to demonstrate the competence of transformer models largely stem from the general architecture Met... |
https://huggingface.co/docs/transformers/model_doc/pvt | Pyramid Vision Transformer (PVT)
Overview
The PVT model was proposed in Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. The PVT is a type of vision transformer that utiliz... |
https://huggingface.co/docs/transformers/model_doc/realm.html | undefined |
https://huggingface.co/docs/transformers/model_doc/qdqbert | QDQBERT
Overview
The QDQBERT model can be referenced in Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
The abstract from the paper is the following:
Quantization techniques can reduce the size of Deep ... |
https://huggingface.co/docs/transformers/model_doc/prophetnet | ProphetNet
DISCLAIMER: If you see something strange, file a Github Issue and assign @patrickvonplaten
Overview
The ProphetNet model was proposed in ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zho... |
https://huggingface.co/docs/transformers/model_doc/rag | RAG
Overview
Retrieval-augmented generation (“RAG”) models combine the powers of pretrained dense retrieval (DPR) and sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate outputs. The retriever and seq2seq modules are initialized from pretrained models, ... |
https://huggingface.co/docs/transformers/model_doc/plbart | PLBart
DISCLAIMER: If you see something strange, file a Github Issue and assign @gchhablani.
Overview of PLBart
The PLBART model was proposed in Unified Pre-training for Program Understanding and Generation by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang. This is a BART-like model which can be use... |
https://huggingface.co/docs/transformers/model_doc/regnet | RegNet
Overview
The RegNet model was proposed in Designing Network Design Spaces by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce... |
https://huggingface.co/docs/transformers/model_doc/resnet | ResNet
Overview
The ResNet model was proposed in Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. Our implementation follows the small changes made by Nvidia, we apply the stride=2 for downsampling in bottleneck’s 3x3 conv and not in the first 1x1. This is generally ... |
https://huggingface.co/docs/transformers/model_doc/reformer | Reformer
DISCLAIMER: This model is still a work in progress, if you see something strange, file a Github Issue.
Overview
The Reformer model was proposed in the paper Reformer: The Efficient Transformer by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
The abstract from the paper is the following:
Large Transformer mode... |
https://huggingface.co/docs/transformers/model_doc/roberta-prelayernorm | RoBERTa-PreLayerNorm
Overview
The RoBERTa-PreLayerNorm model was proposed in fairseq: A Fast, Extensible Toolkit for Sequence Modeling by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli. It is identical to using the --encoder-normalize-before flag in fairseq.
The ... |
https://huggingface.co/docs/transformers/model_doc/rembert | RemBERT
Overview
The RemBERT model was proposed in Rethinking Embedding Coupling in Pre-trained Language Models by Hyung Won Chung, Thibault Févry, Henry Tsai, Melvin Johnson, Sebastian Ruder.
The abstract from the paper is the following:
We re-evaluate the standard practice of sharing weights between input and output ... |
https://huggingface.co/docs/transformers/model_doc/roberta | RoBERTa
Overview
The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google’s BERT model released in 2018.
It builds on BERT and mod... |
https://huggingface.co/docs/transformers/model_doc/roc_bert | RoCBert
Overview
The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It’s a pretrained Chinese language model that is robust under various forms of adversarial attacks.
The abstract from the paper i... |
https://huggingface.co/docs/transformers/model_doc/segformer | SegFormer
Overview
The SegFormer model was proposed in SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo. The model consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to a... |
https://huggingface.co/docs/transformers/model_doc/sam | SAM
Overview
SAM (Segment Anything Model) was proposed in Segment Anything by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
The model can be used to predict segmentation masks of any object of i... |
https://huggingface.co/docs/transformers/model_doc/sew | SEW
Overview
SEW (Squeezed and Efficient Wav2Vec) was proposed in Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
The abstract from the paper is the following:
This paper is a study of performance-effi... |
https://huggingface.co/docs/transformers/model_doc/rwkv | RWKV
Overview
The RWKV model was proposed in this repo
It suggests a tweak in the traditional Transformer attention to make it linear. This way, the model can be used as recurrent network: passing inputs for timestamp 0 and timestamp 1 together is the same as passing inputs at timestamp 0, then inputs at timestamp 1 al... |
https://huggingface.co/docs/transformers/model_doc/sew_d | undefined |
https://huggingface.co/docs/transformers/model_doc/roformer | RoFormer
Overview
The RoFormer model was proposed in RoFormer: Enhanced Transformer with Rotary Position Embedding by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
The abstract from the paper is the following:
Position encoding in transformer architecture provides supervision for dependency modelin... |
https://huggingface.co/docs/transformers/model_doc/speecht5 | SpeechT5
Overview
The SpeechT5 model was proposed in SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
The abstract from the paper is the fo... |
https://huggingface.co/docs/transformers/model_doc/speech_to_text | Speech2Text
Overview
The Speech2Text model was proposed in fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. It’s a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Trans... |
https://huggingface.co/docs/transformers/model_doc/speech_to_text_2 | Speech2Text2
Overview
The Speech2Text2 model is used together with Wav2Vec2 for Speech Translation models proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
Speech2Text2 is a decoder-only transformer model... |
https://huggingface.co/docs/transformers/model_doc/splinter | Splinter
Overview
The Splinter model was proposed in Few-Shot Question Answering by Pretraining Span Selection by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy. Splinter is an encoder-only transformer (similar to BERT) pretrained using the recurring span selection task on a large corpus comprising... |
https://huggingface.co/docs/transformers/model_doc/swiftformer | SwiftFormer
Overview
The SwiftFormer model was proposed in SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
The SwiftFormer paper introduces a novel efficient addi... |
https://huggingface.co/docs/transformers/model_doc/swin | Swin Transformer
Overview
The Swin Transformer was proposed in Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
The abstract from the paper is the following:
This paper presents a new vision Transformer, ca... |
https://huggingface.co/docs/transformers/model_doc/squeezebert | SqueezeBERT
Overview
The SqueezeBERT model was proposed in SqueezeBERT: What can computer vision teach NLP about efficient neural networks? by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, Kurt W. Keutzer. It’s a bidirectional transformer similar to the BERT model. The key difference between the BERT architecture a... |
https://huggingface.co/docs/transformers/model_doc/swinv2 | Swin Transformer V2
Overview
The Swin Transformer V2 model was proposed in Swin Transformer V2: Scaling Up Capacity and Resolution by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
The abstract from the paper is the following:
Large-scal... |
https://huggingface.co/docs/transformers/model_doc/vitmatte | ViTMatte
Overview
The ViTMatte model was proposed in Boosting Image Matting with Pretrained Plain Vision Transformers by Jingfeng Yao, Xinggang Wang, Shusheng Yang, Baoyuan Wang. ViTMatte leverages plain Vision Transformers for the task of image matting, which is the process of accurately estimating the foreground obje... |
https://huggingface.co/docs/transformers/model_doc/swin2sr | Swin2SR
Overview
The Swin2SR model was proposed in Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte. Swin2R improves the SwinIR model by incorporating Swin Transformer v2 layers which mitigates issues such as training instabil... |
https://huggingface.co/docs/transformers/model_doc/vits | VITS
Overview
The VITS model was proposed in Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech by Jaehyeon Kim, Jungil Kong, Juhee Son.
VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) is an end-to-end speech synthesis model that predicts a s... |
https://huggingface.co/docs/transformers/model_doc/vit_msn | ViTMSN
Overview
The ViTMSN model was proposed in Masked Siamese Networks for Label-Efficient Learning by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas. The paper presents a joint-embedding architecture to match the prototypes... |
https://huggingface.co/docs/transformers/model_doc/vivit | Video Vision Transformer (ViViT)
Overview
The Vivit model was proposed in ViViT: A Video Vision Transformer by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid. The paper proposes one of the first successful pure-transformer based set of models for video understanding.
The abstract ... |
https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer | Wav2Vec2-Conformer
Overview
The Wav2Vec2-Conformer was added to an updated version of fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
The official results of the model can be found in Table 3 and Table 4 of the paper.
The Wa... |
https://huggingface.co/docs/transformers/model_doc/wav2vec2 | Wav2Vec2
Overview
The Wav2Vec2 model was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
The abstract from the paper is the following:
We show for the first time that learning powerful representations from spee... |
https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme | Wav2Vec2Phoneme
Overview
The Wav2Vec2Phoneme model was proposed in Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al., 2021 by Qiantong Xu, Alexei Baevski, Michael Auli.
The abstract from the paper is the following:
Recent progress in self-training, self-supervised pretraining and unsupervised ... |
https://huggingface.co/docs/transformers/model_doc/wavlm | WavLM
Overview
The WavLM model was proposed in WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu,... |
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