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Models
The base classes [PreTrainedModel], [TFPreTrainedModel], and
[FlaxPreTrainedModel] implement the common methods for loading/saving a model either from a local
file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS
S3 repository).
[PreTrainedModel] and [TFPreTrainedModel] also implement a few methods which
are common among all the models to:
resize the input token embeddings when new tokens are added to the vocabulary
prune the attention heads of the model.
The other methods that are common to each model are defined in [~modeling_utils.ModuleUtilsMixin]
(for the PyTorch models) and [~modeling_tf_utils.TFModuleUtilsMixin] (for the TensorFlow models) or
for text generation, [~generation.GenerationMixin] (for the PyTorch models),
[~generation.TFGenerationMixin] (for the TensorFlow models) and
[~generation.FlaxGenerationMixin] (for the Flax/JAX models).
PreTrainedModel
[[autodoc]] PreTrainedModel
- push_to_hub
- all
Large model loading
In Transformers 4.20.0, the [~PreTrainedModel.from_pretrained] method has been reworked to accommodate large models using Accelerate. This requires Accelerate >= 0.9.0 and PyTorch >= 1.9.0. Instead of creating the full model, then loading the pretrained weights inside it (which takes twice the size of the model in RAM, one for the randomly initialized model, one for the weights), there is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded.
This option can be activated with low_cpu_mem_usage=True. The model is first created on the Meta device (with empty weights) and the state dict is then loaded inside it (shard by shard in the case of a sharded checkpoint). This way the maximum RAM used is the full size of the model only.
from transformers import AutoModelForSeq2SeqLM
t0pp = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", low_cpu_mem_usage=True)
Moreover, you can directly place the model on different devices if it doesn't fully fit in RAM (only works for inference for now). With device_map="auto", Accelerate will determine where to put each layer to maximize the use of your fastest devices (GPUs) and offload the rest on the CPU, or even the hard drive if you don't have enough GPU RAM (or CPU RAM). Even if the model is split across several devices, it will run as you would normally expect.
When passing a device_map, low_cpu_mem_usage is automatically set to True, so you don't need to specify it:
from transformers import AutoModelForSeq2SeqLM
t0pp = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", device_map="auto")
You can inspect how the model was split across devices by looking at its hf_device_map attribute:
py
t0pp.hf_device_map
python out
{'shared': 0,
'decoder.embed_tokens': 0,
'encoder': 0,
'decoder.block.0': 0,
'decoder.block.1': 1,
'decoder.block.2': 1,
'decoder.block.3': 1,
'decoder.block.4': 1,
'decoder.block.5': 1,
'decoder.block.6': 1,
'decoder.block.7': 1,
'decoder.block.8': 1,
'decoder.block.9': 1,
'decoder.block.10': 1,
'decoder.block.11': 1,
'decoder.block.12': 1,
'decoder.block.13': 1,
'decoder.block.14': 1,
'decoder.block.15': 1,
'decoder.block.16': 1,
'decoder.block.17': 1,
'decoder.block.18': 1,
'decoder.block.19': 1,
'decoder.block.20': 1,
'decoder.block.21': 1,
'decoder.block.22': 'cpu',
'decoder.block.23': 'cpu',
'decoder.final_layer_norm': 'cpu',
'decoder.dropout': 'cpu',
'lm_head': 'cpu'}
You can also write your own device map following the same format (a dictionary layer name to device). It should map all parameters of the model to a given device, but you don't have to detail where all the submodules of one layer go if that layer is entirely on the same device. For instance, the following device map would work properly for T0pp (as long as you have the GPU memory):
python
device_map = {"shared": 0, "encoder": 0, "decoder": 1, "lm_head": 1}
Another way to minimize the memory impact of your model is to instantiate it at a lower precision dtype (like torch.float16) or use direct quantization techniques as described below.
Model Instantiation dtype
Under Pytorch a model normally gets instantiated with torch.float32 format. This can be an issue if one tries to
load a model whose weights are in fp16, since it'd require twice as much memory. To overcome this limitation, you can
either explicitly pass the desired dtype using torch_dtype argument:
python
model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype=torch.float16)
or, if you want the model to always load in the most optimal memory pattern, you can use the special value "auto",
and then dtype will be automatically derived from the model's weights:
python
model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype="auto")
Models instantiated from scratch can also be told which dtype to use with:
python
config = T5Config.from_pretrained("t5")
model = AutoModel.from_config(config)
Due to Pytorch design, this functionality is only available for floating dtypes.
ModuleUtilsMixin
[[autodoc]] modeling_utils.ModuleUtilsMixin
TFPreTrainedModel
[[autodoc]] TFPreTrainedModel
- push_to_hub
- all
TFModelUtilsMixin
[[autodoc]] modeling_tf_utils.TFModelUtilsMixin
FlaxPreTrainedModel
[[autodoc]] FlaxPreTrainedModel
- push_to_hub
- all
Pushing to the Hub
[[autodoc]] utils.PushToHubMixin
Sharded checkpoints
[[autodoc]] modeling_utils.load_sharded_checkpoint stringlengths 161 226k ⌀ |
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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 defined f... |
Before you begin, make sure you have all the necessary libraries installed:
pip install -q datasets transformers evaluate
We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:
from huggingface_hub import notebook... |
Before you begin, make sure you have all the necessary libraries installed:
pip install transformers datasets evaluate
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
from huggingface_hub import notebook_logi... |
Utilities for pipelines
This page lists all the utility functions the library provides for pipelines.
Most of those are only useful if you are studying the code of the models in the library.
Argument handling
[[autodoc]] pipelines.ArgumentHandler
[[autodoc]] pipelines.ZeroShotClassificationArgumentHandler
[[autodoc]] ... |
Train with a script
Along with the 🤗 Transformers notebooks, there are also example scripts demonstrating how to train a model for a task with PyTorch, TensorFlow, or JAX/Flax.
You will also find scripts we've used in our research projects and legacy examples which are mostly community contributed. These scripts are ... |
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 sequences will ... |
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 prepare... |
Training on Specialized Hardware
Note: Most of the strategies introduced in the single GPU section (such as mixed precision training or gradient accumulation) and multi-GPU section are generic and apply to training models in general so make sure to have a look at it before diving into this section.
This document wil... |
Feature Extractor
A feature extractor is in charge of preparing input features for audio or vision models. This includes feature extraction
from sequences, e.g., pre-processing audio files to Log-Mel Spectrogram features, feature extraction from images
e.g. cropping image image files, but also padding, normalization, ... |
ConvNeXt V2
Overview
The ConvNeXt V2 model was proposed in ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
ConvNeXt V2 is a pure convolutional model (ConvNet), inspired by the design of Visi... |
GPT-NeoX-Japanese
Overview
We introduce GPT-NeoX-Japanese, which is an autoregressive language model for Japanese, trained on top of https://github.com/EleutherAI/gpt-neox.
Japanese is a unique language with its large vocabulary and a combination of hiragana, katakana, and kanji writing scripts.
To address this ... |
CodeGen
Overview
The CodeGen model was proposed in A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong.
CodeGen is an autoregressive language model for program synthesis trained sequentially on The Pile, Bi... |
Big Transfer (BiT)
Overview
The BiT model was proposed in Big Transfer (BiT): General Visual Representation Learning by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
BiT is a simple recipe for scaling up pre-training of ResNet-like architectures (spe... |
UniSpeech
Overview
The UniSpeech model was proposed in UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael
Zeng, Xuedong Huang .
The abstract from the paper is the following:
In this paper, we propose ... |
I-BERT
Overview
The I-BERT model was proposed in I-BERT: Integer-only BERT Quantization by
Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney and Kurt Keutzer. It’s a quantized version of RoBERTa running
inference up to four times faster.
The abstract from the paper is the following:
Transformer based mode... |
DistilBERT
Overview
The DistilBERT model was proposed in the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
distilled version of BERT, and the paper DistilBERT, a
distilled version of BERT: smaller, faster, cheaper and lighter. DistilBERT is a
small, fast, cheap and light Transformer mode... |
UPerNet
Overview
The UPerNet model was proposed in Unified Perceptual Parsing for Scene Understanding
by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun. UPerNet is a general framework to effectively segment
a wide range of concepts from images, leveraging any vision backbone like ConvNeXt or Swin.
... |
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, Mich... |
The documentation page MODEL_DOC/SEW_D doesn’t exist in v4.31.0, but exists on the main version. Click here to redirect to the main version of the documentation. |
Open-Llama
Overview
The Open-Llama model was proposed in Open-Llama project by community developer s-JoL.
The model is mainly based on LLaMA with some modifications, incorporating memory-efficient attention from Xformers, stable embedding from Bloom, and shared input-output embedding from PaLM.
And the model is ... |
ConvBERT
Overview
The ConvBERT model was proposed in ConvBERT: Improving BERT with Span-based Dynamic Convolution by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng
Yan.
The abstract from the paper is the following:
Pre-trained language models like BERT and its variants have recently a... |
Data2Vec
Overview
The Data2Vec model was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
Data2Vec proposes a unified framework for self-supervised learning across different d... |
DeBERTa-v2
Overview
The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s
BERT model released in 2018 and Facebook’s RoBERTa model released in 2019.
It builds on RoBERTa with disentangled atte... |
Blenderbot Small
Note that BlenderbotSmallModel and
BlenderbotSmallForConditionalGeneration are only used in combination with the checkpoint
facebook/blenderbot-90M. Larger Blenderbot checkpoints should
instead be used with BlenderbotModel and
BlenderbotForConditionalGeneration
Overview
The Blender chatbot model... |
BEiT
Overview
The BEiT model was proposed in BEiT: BERT Pre-Training of Image Transformers by
Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of
Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to... |
ELECTRA
Overview
The ELECTRA model was proposed in the paper ELECTRA: Pre-training Text Encoders as Discriminators Rather Than
Generators. ELECTRA is a new pretraining approach which trains two
transformer models: the generator and the discriminator. The generator’s role is to replace tokens in a sequence, and
i... |
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 ... |
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 efficie... |
EfficientFormer
Overview
The EfficientFormer model was proposed in EfficientFormer: Vision Transformers at MobileNet Speed
by Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren. EfficientFormer proposes a
dimension-consistent pure transformer that can be run on ... |
Audio Spectrogram Transformer
Overview
The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass.
The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-... |
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. Lo... |
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 ... |
ALIGN
Overview
The ALIGN model was proposed in Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. ALIGN is a multi-modal vision and language model. It c... |
BORT
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 BOR... |
Convolutional Vision Transformer (CvT)
Overview
The CvT model was proposed in CvT: Introducing Convolutions to Vision Transformers by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang. The Convolutional vision Transformer (CvT) improves the Vision Transformer (ViT) in performanc... |
DPR
Overview
Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was
introduced in Dense Passage Retrieval for Open-Domain Question Answering by
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih.
T... |
BridgeTower
Overview
The BridgeTower model was proposed in BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The goal of this model is to build a
bridge between each uni-modal encoder and the cr... |
XLM
Overview
The XLM model was proposed in Cross-lingual Language Model Pretraining by
Guillaume Lample, Alexis Conneau. It’s a transformer pretrained using one of the following objectives:
a causal language modeling (CLM) objective (next token prediction),
a masked language modeling (MLM) objective (BERT-like),... |
TAPAS
Overview
The TAPAS model was proposed in TAPAS: Weakly Supervised Table Parsing via Pre-training
by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos. It’s a BERT-based model specifically
designed (and pre-trained) for answering questions about tabular ... |
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 ... |
TimeSformer
Overview
The TimeSformer model was proposed in TimeSformer: Is Space-Time Attention All You Need for Video Understanding? by Facebook Research.
This work is a milestone in action-recognition field being the first video transformer. It inspired many transformer based video understanding and classifica... |
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, Mos... |
BigBird
Overview
The BigBird model was proposed in Big Bird: Transformers for Longer Sequences by
Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a s... |
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 a... |
CLIP
Overview
The CLIP model was proposed in Learning Transferable Visual Models From Natural Language Supervision by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh,
Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. CLIP
(Contras... |
DiT
Overview
DiT was proposed in DiT: Self-supervised Pre-training for Document Image Transformer by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
DiT applies the self-supervised objective of BEiT (BERT pre-training of Image Transformers) to 42 million document images, allowing for state-of-t... |
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 Transform... |
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 Architectu... |
X-CLIP
Overview
The X-CLIP model was proposed in Expanding Language-Image Pretrained Models for General Video Recognition by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
X-CLIP is a minimal extension of CLIP for video. The model consists of a text en... |
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... |
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 differe... |
BertJapanese
Overview
The BERT models trained on Japanese text.
There are models with two different tokenization methods:
Tokenize with MeCab and WordPiece. This requires some extra dependencies, fugashi which is a wrapper around MeCab.
Tokenize into characters.
To use MecabTokenizer, you should pip install tran... |
with TrOCR is by checking the tutorial
notebooks, which show how to use the model
at inference time as well as fine-tuning on custom data.
TrOCR is pre-trained in 2 stages before being fine-tuned on downstream datasets. It achieves state-of-the-art results
on both printed (e.g. the SROIE dataset and handwritten (e.g. ... |
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 ... |
FLAVA
Overview
The FLAVA model was proposed in FLAVA: A Foundational Language And Vision Alignment Model by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
The paper aims at creating a single unified foundation mod... |
Chinese-CLIP
Overview
The Chinese-CLIP model was proposed in Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou.
Chinese-CLIP is an implementation of CLIP (Radford et al., 2021) on a large-scale dataset of Chinese... |
Bark
Overview
Bark is a transformer-based text-to-speech model proposed by Suno AI in suno-ai/bark.
Bark is made of 4 main models:
BarkSemanticModel (also referred to as the ‘text’ model): a causal auto-regressive transformer model that takes as input tokenized text, and predicts semantic text tokens that captu... |
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 Transf... |
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 timesta... |
XLM-ProphetNet
DISCLAIMER: If you see something strange, file a Github Issue and assign
@patrickvonplaten
Overview
The XLM-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
... |
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 lan... |
Hubert
Overview
Hubert was proposed in HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan
Salakhutdinov, Abdelrahman Mohamed.
The abstract from the paper is the following:
Self-supervised appro... |
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 m... |
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
com... |
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, wh... |
GPT-J
Overview
The GPT-J model was released in the kingoflolz/mesh-transformer-jax repository by Ben Wang and Aran Komatsuzaki. It is a GPT-2-like
causal language model trained on the Pile dataset.
This model was contributed by Stella Biderman.
Tips:
To load GPT-J in float32 one would need at least 2x model size... |
Graphormer
Overview
The Graphormer model was proposed in Do Transformers Really Perform Bad for Graph Representation? by
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen and Tie-Yan Liu. It is a Graph Transformer model, modified to allow computations on graphs instead of te... |
with ViLT is by checking the example notebooks
(which showcase both inference and fine-tuning on custom data).
ViLT is a model that takes both pixel_values and input_ids as input. One can use ViltProcessor to prepare data for the model.
This processor wraps a image processor (for the image modality) and a tokenizer (f... |
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, M... |
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.... |
DeBERTa
Overview
The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s
BERT model released in 2018 and Facebook’s RoBERTa model released in 2019.
It builds on RoBERTa with disentangled attenti... |
Transformer XL
Overview
The Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan
Salakhutdinov. It’s a causal (uni-directional) transformer with relative positioning (sinusoïdal) e... |
The documentation page MODEL_DOC/REALM.HTML doesn’t exist in v4.31.0, but exists on the main version. Click here to redirect to the main version of the documentation. |
Time Series Transformer
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 Time Series Transformer model is a vanilla encoder-decoder Transformer ... |
DeiT
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 DeiT model was proposed in Training data-efficient image transformers & distillation throu... |
ViTMAE
Overview
The ViTMAE model was proposed in Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li,
Piotr Dollár, Ross Girshick. The paper shows that, by pre-training a Vision Transformer (ViT) to reconstruct pixel values for masked patches, one can get results ... |
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 ... |
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 ... |
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:
Lar... |
TVLT
Overview
The TVLT model was proposed in TVLT: Textless Vision-Language Transformer
by Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal (the first three authors contributed equally). The Textless Vision-Language Transformer (TVLT) is a model that uses raw visual and audio inputs for vision-and-language repre... |
BertGeneration
Overview
The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using
EncoderDecoderModel as proposed in Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
The abstract from the paper is the fol... |
GPT-Sw3
Overview
The GPT-Sw3 model was first proposed in
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish
by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman,
Fredrik Carlsson, Magnus Sahlgren.
Since that first... |
Decision Transformer
Overview
The Decision Transformer model was proposed in Decision Transformer: Reinforcement Learning via Sequence Modeling
by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
The abstract from the paper is the ... |
ConvNeXT
Overview
The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.
The abstract ... |
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 i... |
Funnel Transformer
Overview
The Funnel Transformer model was proposed in the paper Funnel-Transformer: Filtering out Sequential Redundancy for
Efficient Language Processing. It is a bidirectional transformer model, like
BERT, but with a pooling operation after each block of layers, a bit like in traditional conv... |
Blenderbot
DISCLAIMER: If you see something strange, file a Github Issue .
Overview
The Blender chatbot model was proposed in Recipes for building an open-domain chatbot Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Ja... |
BLIP-2
Overview
The BLIP-2 model was proposed in BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models by
Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi. BLIP-2 leverages frozen pre-trained image encoders and large language models (LLMs) by training a lightweig... |
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 ... |
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... |
UniSpeech-SAT
Overview
The UniSpeech-SAT model was proposed in UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware
Pre-Training by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen,
Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu .
The abstract from the paper is ... |
FocalNet
Overview
The FocalNet model was proposed in Focal Modulation Networks by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
FocalNets completely replace self-attention (used in models like ViT and Swin) by a focal modulation mechanism for modeling token interactions in vision.
The authors cla... |
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 p... |
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 laye... |
TAPEX
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 TA... |
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 emerge... |
EnCodec
Overview
The EnCodec neural codec model was proposed in High Fidelity Neural Audio Compression by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
The abstract from the paper is the following:
We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks. It... |
YOLOS
Overview
The YOLOS model was proposed in You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
YOLOS proposes to just leverage the plain Vision Transformer (ViT) for objec... |
SEW-D
Overview
SEW-D (Squeezed and Efficient Wav2Vec with Disentangled attention) 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:
Th... |
CTRL
Overview
CTRL model was proposed in CTRL: A Conditional Transformer Language Model for Controllable Generation by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong and
Richard Socher. It’s a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
of ~... |
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