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https://huggingface.co/docs/transformers/model_doc/blip-2 | 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 lightweight, 12-... |
https://huggingface.co/docs/transformers/model_doc/bit | 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 (specifical... |
https://huggingface.co/docs/transformers/model_doc/bridgetower | 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 cross-mod... |
https://huggingface.co/docs/transformers/model_doc/blenderbot-small | 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 was pr... |
https://huggingface.co/docs/transformers/model_doc/blenderbot | 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, Jason Wes... |
https://huggingface.co/docs/transformers/model_doc/big_bird | 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 sparse-a... |
https://huggingface.co/docs/transformers/model_doc/blip | BLIP
Overview
The BLIP model was proposed in BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
BLIP is a model that is able to perform various multi-modal tasks including
Visual Question Answering
Image-Text retri... |
https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus | BigBirdPegasus
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... |
https://huggingface.co/docs/transformers/model_doc/bloom | BLOOM
Overview
The BLOOM model has been proposed with its various versions through the BigScience Workshop. BigScience is inspired by other open science initiatives where researchers have pooled their time and resources to collectively achieve a higher impact. The architecture of BLOOM is essentially similar to GPT3 (a... |
https://huggingface.co/docs/transformers/philosophy | Philosophy
🤗 Transformers is an opinionated library built for:
machine learning researchers and educators seeking to use, study or extend large-scale Transformers models.
hands-on practitioners who want to fine-tune those models or serve them in production, or both.
engineers who just want to download a pretrained mod... |
https://huggingface.co/docs/transformers/pr_checks | Checks on a Pull Request
When you open a pull request on 🤗 Transformers, a fair number of checks will be run to make sure the patch you are adding is not breaking anything existing. Those checks are of four types:
regular tests
documentation build
code and documentation style
general repository consistency
In this doc... |
https://huggingface.co/docs/transformers/model_doc/clipseg | CLIPSeg
Overview
The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.
The abstract from the paper is the following:
Image segmentation is usually add... |
https://huggingface.co/docs/transformers/task_summary | What 🤗 Transformers can do
🤗 Transformers is a library of pretrained state-of-the-art models for natural language processing (NLP), computer vision, and audio and speech processing tasks. Not only does the library contain Transformer models, but it also has non-Transformer models like modern convolutional networks fo... |
https://huggingface.co/docs/transformers/glossary | Glossary
This glossary defines general machine learning and 🤗 Transformers terms to help you better understand the documentation.
A
attention mask
The attention mask is an optional argument used when batching sequences together.
This argument indicates to the model which tokens should be attended to, and which should ... |
https://huggingface.co/docs/transformers/model_doc/chinese_clip | 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 image-... |
https://huggingface.co/docs/transformers/model_doc/camembert | CamemBERT
Overview
The CamemBERT model was proposed in CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook’s RoBERTa model released in 2019. It is a mo... |
https://huggingface.co/docs/transformers/model_doc/clap | CLAP
Overview
The CLAP model was proposed in Large Scale Contrastive Language-Audio pretraining with feature fusion and keyword-to-caption augmentation by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
CLAP (Contrastive Language-Audio Pretraining) is a neural network trained on a ... |
https://huggingface.co/docs/transformers/model_doc/codegen | 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, BigQuery,... |
https://huggingface.co/docs/transformers/tasks_explained | How 🤗 Transformers solve tasks
In What 🤗 Transformers can do, you learned about natural language processing (NLP), speech and audio, computer vision tasks, and some important applications of them. This page will look closely at how models solve these tasks and explain what’s happening under the hood. There are many w... |
https://huggingface.co/docs/transformers/model_doc/clip | 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 (Contrastive La... |
https://huggingface.co/docs/transformers/model_doc/canine | CANINE
Overview
The CANINE model was proposed in CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. It’s among the first papers that trains a Transformer without using an explicit tokenization step (such as Byte Pair Enco... |
https://huggingface.co/oleksandryermilov | Oleksandr Yermilov
oleksandryermilov
olexandryermilov
Research interests
NLP, data privacy
Organizations
models
None public yet
datasets
None public yet |
https://huggingface.co/docs/transformers/model_summary | The Transformer model family
Since its introduction in 2017, the original Transformer model has inspired many new and exciting models that extend beyond natural language processing (NLP) tasks. There are models for predicting the folded structure of proteins, training a cheetah to run, and time series forecasting. With... |
https://huggingface.co/docs/transformers/tokenizer_summary | Summary of the tokenizers
On this page, we will have a closer look at tokenization.
As we saw in the preprocessing tutorial, tokenizing a text is splitting it into words or subwords, which then are converted to ids through a look-up table. Converting words or subwords to ids is straightforward, so in this summary, we w... |
https://huggingface.co/docs/transformers/attention | Attention mechanisms
Most transformer models use full attention in the sense that the attention matrix is square. It can be a big computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and use a sparse version of the attention matrix to speed up training.
LSH... |
https://huggingface.co/docs/transformers/model_memory_anatomy | Model training anatomy
To understand performance optimization techniques that one can apply to improve efficiency of model training speed and memory utilization, it’s helpful to get familiar with how GPU is utilized during training, and how compute intensity varies depending on an operation performed.
Let’s start by ex... |
https://huggingface.co/docs/transformers/pipeline_webserver | Using pipelines for a webserver
Creating an inference engine is a complex topic, and the "best" solution will most likely depend on your problem space. Are you on CPU or GPU? Do you want the lowest latency, the highest throughput, support for many models, or just highly optimize 1 specific model? There are many ways to... |
https://huggingface.co/docs/transformers/pad_truncation | 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 h... |
https://huggingface.co/docs/transformers/bertology | BERTology
There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT (that some call “BERTology”). Some good examples of this field are:
BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick: https://arxiv.org/abs/1905.05950
Ar... |
https://huggingface.co/docs/transformers/perplexity | Perplexity of fixed-length models
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 for masked language ... |
https://huggingface.co/docs/transformers/main_classes/callback | Callbacks
Callbacks are objects that can customize the behavior of the training loop in the PyTorch Trainer (this feature is not yet implemented in TensorFlow) that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms…) and take decisions (like early stopping).
Callb... |
https://huggingface.co/docs/transformers/main_classes/keras_callbacks | When training a Transformers model with Keras, there are some library-specific callbacks available to automate common tasks:
class transformers.KerasMetricCallback
< source >
( metric_fn: typing.Callable eval_dataset: typing.Union[tensorflow.python.data.ops.dataset_ops.DatasetV2, numpy.ndarray, tensorflow.python.framew... |
https://huggingface.co/docs/transformers/main_classes/configuration | The base class PretrainedConfig implements the common methods for loading/saving a configuration either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace’s AWS S3 repository).
Each derived config class implements model specific attributes. Comm... |
https://huggingface.co/docs/transformers/main_classes/logging | Logging
🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.
Currently the default verbosity of the library is WARNING.
To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity to the INFO level.
impor... |
https://huggingface.co/docs/transformers/main_classes/agent | Agents & Tools
Transformers Agents is an experimental API which is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models are prone to change.
To learn more about agents and tools make sure to read the introductory guide. This page contains the API docs for the underlyin... |
https://huggingface.co/docs/transformers/main_classes/onnx | Exporting 🤗 Transformers models to ONNX
🤗 Transformers provides a transformers.onnx package that enables you to convert model checkpoints to an ONNX graph by leveraging configuration objects.
See the guide on exporting 🤗 Transformers models for more details.
ONNX Configurations
We provide three abstract classes that... |
https://huggingface.co/docs/transformers/main_classes/text_generation | Generation
Each framework has a generate method for text generation implemented in their respective GenerationMixin class:
PyTorch generate() is implemented in GenerationMixin.
TensorFlow generate() is implemented in TFGenerationMixin.
Flax/JAX generate() is implemented in FlaxGenerationMixin.
Regardless of your framew... |
https://huggingface.co/docs/transformers/main_classes/model | 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 TFPre... |
https://huggingface.co/docs/transformers/main_classes/data_collator | Data Collator
Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of the same type as the elements of train_dataset or eval_dataset.
To be able to build batches, data collators may apply some processing (like padding). Some of them (like DataCollatorForLang... |
https://huggingface.co/docs/transformers/model_doc/encodec | 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 consis... |
https://huggingface.co/docs/transformers/master/model_doc/dpt | DPT
Overview
The DPT model was proposed in Vision Transformers for Dense Prediction by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun. DPT is a model that leverages the Vision Transformer (ViT) as backbone for dense prediction tasks like semantic segmentation and depth estimation.
The abstract from the paper is the fo... |
https://huggingface.co/docs/transformers/model_doc/efficientformer | 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 mobile d... |
https://huggingface.co/docs/transformers/model_doc/efficientnet | EfficientNet
Overview
The EfficientNet model was proposed in EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le. EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster tha... |
https://huggingface.co/docs/transformers/main_classes/optimizer_schedules | Optimization
The .optimization module provides:
an optimizer with weight decay fixed that can be used to fine-tuned models, and
several schedules in the form of schedule objects that inherit from _LRSchedule:
a gradient accumulation class to accumulate the gradients of multiple batches
AdamW (PyTorch)
class transformer... |
https://huggingface.co/docs/transformers/model_doc/encoder-decoder | Encoder Decoder Models
Overview
The EncoderDecoderModel can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for seque... |
https://huggingface.co/docs/transformers/main_classes/output | Model outputs
All models have outputs that are instances of subclasses of ModelOutput. Those are data structures containing all the information returned by the model, but that can also be used as tuples or dictionaries.
Let’s see how this looks in an example:
from transformers import BertTokenizer, BertForSequenceClass... |
https://huggingface.co/docs/transformers/model_doc/electra | 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 is there... |
https://huggingface.co/docs/transformers/model_doc/ernie | ERNIE
Overview
ERNIE is a series of powerful models proposed by baidu, especially in Chinese tasks, including [ERNIE1.0](https://arxiv.org/abs/1904.09223), [ERNIE2.0](https://ojs.aaai.org/index.php/AAAI/article/view/6428), [ERNIE3.0](https://arxiv.org/abs/2107.02137), [ERNIE-Gram](https://arxiv.org/abs/2010.12148), [ER... |
https://huggingface.co/docs/transformers/model_doc/flan-ul2 | FLAN-UL2
Overview
Flan-UL2 is an encoder decoder model based on the T5 architecture. It uses the same configuration as the UL2 model released earlier last year. It was fine tuned using the “Flan” prompt tuning and dataset collection. Similiar to Flan-T5, one can directly use FLAN-UL2 weights without finetuning the mode... |
https://huggingface.co/docs/transformers/model_doc/falcon | Falcon
Overview
Falcon is a class of causal decoder-only models built by TII. The largest Falcon checkpoints have been trained on >=1T tokens of text, with a particular emphasis on the RefinedWeb corpus. They are made available under the Apache 2.0 license.
Falcon’s architecture is modern and optimized for inference, w... |
https://huggingface.co/docs/transformers/model_doc/flan-t5 | FLAN-T5
Overview
FLAN-T5 was released in the paper Scaling Instruction-Finetuned Language Models - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.
One can directly use FLAN-T5 weights without finetuning the model:
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> mode... |
https://huggingface.co/docs/transformers/model_doc/esm | ESM
Overview
This page provides code and pre-trained weights for Transformer protein language models from Meta AI's Fundamental AI Research Team, providing the state-of-the-art ESMFold and ESM-2, and the previously released ESM-1b and ESM-1v. Transformer protein language models were introduced in the paper [Biological ... |
https://huggingface.co/docs/transformers/model_doc/flaubert | FlauBERT
Overview
The FlauBERT model was proposed in the paper FlauBERT: Unsupervised Language Model Pre-training for French by Hang Le et al. It’s a transformer model pretrained using a masked language modeling (MLM) objective (like BERT).
The abstract from the paper is the following:
Language models have become a key... |
https://huggingface.co/docs/transformers/model_doc/flava | 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 model whic... |
https://huggingface.co/docs/transformers/model_doc/ernie_m | ErnieM
Overview
The ErnieM model was proposed in ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
The abstract from the paper is the following:
Recent studies have demonstrated tha... |
https://huggingface.co/docs/transformers/model_doc/auto | Auto Classes
In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the from_pretrained() method. AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path to the pretrained weights... |
https://huggingface.co/docs/transformers/model_doc/fnet | FNet
Overview
The FNet model was proposed in FNet: Mixing Tokens with Fourier Transforms by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon. The model replaces the self-attention layer in a BERT model with a fourier transform which returns only the real parts of the transform. The model is significantl... |
https://huggingface.co/docs/transformers/model_doc/focalnet | 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 claim that... |
https://huggingface.co/docs/transformers/model_doc/glpn | GLPN
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 GLPN model was proposed in Global-Local Path Networks for Monocular Depth Estimation with Vertica... |
https://huggingface.co/docs/transformers/model_doc/openai-gpt | OpenAI GPT
Overview
OpenAI GPT model was proposed in Improving Language Understanding by Generative Pre-Training by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It’s a causal (unidirectional) transformer pre-trained using language modeling on a large corpus will long range dependencies, the Toront... |
https://huggingface.co/docs/transformers/model_doc/git | GIT
Overview
The GIT model was proposed in GIT: A Generative Image-to-text Transformer for Vision and Language by Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang. GIT is a decoder-only Transformer that leverages CLIP’s vision encoder to condition the model on v... |
https://huggingface.co/docs/transformers/model_doc/gpt_neox | GPT-NeoX
Overview
We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge, the largest dense autoregressive model that has publicly availabl... |
https://huggingface.co/docs/transformers/model_doc/gpt_neox_japanese | 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 distinc... |
https://huggingface.co/docs/transformers/model_doc/gpt_neo | GPT Neo
Overview
The GPTNeo model was released in the EleutherAI/gpt-neo repository by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. It is a GPT2 like causal language model trained on the Pile dataset.
The architecture is similar to GPT2 except that GPT Neo uses local attention in every other layer w... |
https://huggingface.co/docs/transformers/model_doc/funnel | 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 convolution... |
https://huggingface.co/docs/transformers/model_doc/gpt2 | OpenAI GPT2
Overview
OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever from OpenAI. It’s a causal (unidirectional) transformer pretrained using language modeling on a very large corpus of ~40 GB of... |
https://huggingface.co/docs/transformers/model_doc/llama_code | undefined |
https://huggingface.co/docs/transformers/model_doc/convnext | 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 from th... |
https://huggingface.co/docs/transformers/model_doc/convbert | 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 achieved... |
https://huggingface.co/docs/transformers/model_doc/gptj | 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 RAM: 1... |
https://huggingface.co/docs/transformers/model_doc/conditional_detr | Conditional DETR
Overview
The Conditional DETR model was proposed in Conditional DETR for Fast Training Convergence by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang. Conditional DETR presents a conditional cross-attention mechanism for fast DETR training. Conditional DE... |
https://huggingface.co/docs/transformers/model_doc/convnextv2 | 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 Vision Tran... |
https://huggingface.co/docs/transformers/model_doc/cpmant | CPMAnt
Overview
CPM-Ant is an open-source Chinese pre-trained language model (PLM) with 10B parameters. It is also the first milestone of the live training process of CPM-Live. The training process is cost-effective and environment-friendly. CPM-Ant also achieves promising results with delta tuning on the CUGE benchmar... |
https://huggingface.co/docs/transformers/model_doc/cpm | CPM
Overview
The CPM model was proposed in CPM: A Large-scale Generative Chinese Pre-trained Language Model by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Su... |
https://huggingface.co/docs/transformers/model_doc/ctrl | 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 ~140 GB ... |
https://huggingface.co/docs/transformers/model_doc/cvt | 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 performance and e... |
https://huggingface.co/docs/transformers/model_doc/decision_transformer | 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 followi... |
https://huggingface.co/docs/transformers/model_doc/deberta-v2 | 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 attention a... |
https://huggingface.co/docs/transformers/model_doc/data2vec | 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 data mod... |
https://huggingface.co/docs/transformers/model_doc/deberta | 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 attention and ... |
https://huggingface.co/docs/transformers/model_doc/deplot | DePlot
Overview
DePlot was proposed in the paper DePlot: One-shot visual language reasoning by plot-to-table translation from Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
The abstract of the paper states the ... |
https://huggingface.co/docs/transformers/model_doc/deformable_detr | Deformable DETR
Overview
The Deformable DETR model was proposed in Deformable DETR: Deformable Transformers for End-to-End Object Detection by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai. Deformable DETR mitigates the slow convergence issues and limited feature spatial resolution of the original ... |
https://huggingface.co/docs/transformers/model_doc/dialogpt | DialoGPT
Overview
DialoGPT was proposed in DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan. It’s a GPT2 Model trained on 147M conversation-like exchanges extract... |
https://huggingface.co/docs/transformers/model_doc/deta | DETA
Overview
The DETA model was proposed in NMS Strikes Back by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl. DETA (short for Detection Transformers with Assignment) improves Deformable DETR by replacing the one-to-one bipartite Hungarian matching loss with one-to-many label assignments used in... |
https://huggingface.co/docs/transformers/model_doc/deit | 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 through atte... |
https://huggingface.co/docs/transformers/model_doc/dinat | Dilated Neighborhood Attention Transformer
Overview
DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi.
It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.
The abstract from the... |
https://huggingface.co/docs/transformers/model_doc/distilbert | 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 model train... |
https://huggingface.co/docs/transformers/model_doc/dinov2 | DINOv2
Overview
The DINOv2 model was proposed in DINOv2: Learning Robust Visual Features without Supervision by Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, ... |
https://huggingface.co/docs/transformers/model_doc/detr | DETR
Overview
The DETR model was proposed in End-to-End Object Detection with Transformers by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end ... |
https://huggingface.co/docs/transformers/model_doc/dit | 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-the-art ... |
https://huggingface.co/docs/transformers/model_doc/gptsan-japanese | GPTSAN-japanese
Overview
The GPTSAN-japanese model was released in the repository by Toshiyuki Sakamoto (tanreinama).
GPTSAN is a Japanese language model using Switch Transformer. It has the same structure as the model introduced as Prefix LM in the T5 paper, and support both Text Generation and Masked Language Modelin... |
https://huggingface.co/docs/transformers/model_doc/donut | Donut
Overview
The Donut model was proposed in OCR-free Document Understanding Transformer by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park. Donut consists of an image Transformer encoder and an autoregressive text Transforme... |
https://huggingface.co/docs/transformers/model_doc/gpt-sw3 | 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 paper ... |
https://huggingface.co/docs/transformers/model_doc/graphormer | 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 text seque... |
https://huggingface.co/docs/transformers/model_doc/gpt_bigcode | GPTBigCode
Overview
The GPTBigCode model was proposed in SantaCoder: don’t reach for the stars! by BigCode. The listed authors are: Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn... |
https://huggingface.co/docs/transformers/model_doc/dpr | 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.
The abst... |
https://huggingface.co/docs/transformers/model_doc/herbert | HerBERT
Overview
The HerBERT model was proposed in KLEJ: Comprehensive Benchmark for Polish Language Understanding by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, and Ireneusz Gawlik. It is a BERT-based Language Model trained on Polish Corpora using only MLM objective with dynamic masking of whole words.
The abstract... |
https://huggingface.co/docs/transformers/model_doc/groupvit | GroupViT
Overview
The GroupViT model was proposed in GroupViT: Semantic Segmentation Emerges from Text Supervision by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang. Inspired by CLIP, GroupViT is a vision-language model that can perform zero-shot semantic segmentation on a... |
https://huggingface.co/docs/transformers/model_doc/ibert | 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 models, lik... |
https://huggingface.co/docs/transformers/model_doc/hubert | 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 approaches f... |
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