transformers/docs/source/en/index.md
NielsRogge 836921fdeb
Add UDOP (#22940)
* First draft

* More improvements

* More improvements

* More fixes

* Fix copies

* More improvements

* More fixes

* More improvements

* Convert checkpoint

* More improvements, set up tests

* Fix more tests

* Add UdopModel

* More improvements

* Fix equivalence test

* More fixes

* Redesign model

* Extend conversion script

* Use real inputs for conversion script

* Add image processor

* Improve conversion script

* Add UdopTokenizer

* Add fast tokenizer

* Add converter

* Update README's

* Add processor

* Add fully fledged tokenizer

* Add fast tokenizer

* Use processor in conversion script

* Add tokenizer tests

* Fix one more test

* Fix more tests

* Fix tokenizer tests

* Enable fast tokenizer tests

* Fix more tests

* Fix additional_special_tokens of fast tokenizer

* Fix tokenizer tests

* Fix more tests

* Fix equivalence test

* Rename image to pixel_values

* Rename seg_data to bbox

* More renamings

* Remove vis_special_token

* More improvements

* Add docs

* Fix copied from

* Update slow tokenizer

* Update fast tokenizer design

* Make text input optional

* Add first draft of processor tests

* Fix more processor tests

* Fix decoder_start_token_id

* Fix test_initialization

* Add integration test

* More improvements

* Improve processor, add test

* Add more copied from

* Add more copied from

* Add more copied from

* Add more copied from

* Remove print statement

* Update README and auto mapping

* Delete files

* Delete another file

* Remove code

* Fix test

* Fix docs

* Remove asserts

* Add doc tests

* Include UDOP in exotic model tests

* Add expected tesseract decodings

* Add sentencepiece

* Use same design as T5

* Add UdopEncoderModel

* Add UdopEncoderModel to tests

* More fixes

* Fix fast tokenizer

* Fix one more test

* Remove parallelisable attribute

* Fix copies

* Remove legacy file

* Copy from T5Tokenizer

* Fix rebase

* More fixes, copy from T5

* More fixes

* Fix init

* Use ArthurZ/udop for tests

* Make all model tests pass

* Remove UdopForConditionalGeneration from auto mapping

* Fix more tests

* fixups

* more fixups

* fix the tokenizers

* remove un-necessary changes

* nits

* nits

* replace truncate_sequences_boxes with truncate_sequences for fix-copies

* nit current path

* add a test for input ids

* ids that we should get taken from c9f7a32f57

* nits converting

* nits

* apply ruff

* nits

* nits

* style

* fix slow order of addition

* fix udop fast range as well

* fixup

* nits

* Add docstrings

* Fix gradient checkpointing

* Update code examples

* Skip tests

* Update integration test

* Address comment

* Make fixup

* Remove extra ids from tokenizer

* Skip test

* Apply suggestions from code review

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update year

* Address comment

* Address more comments

* Address comments

* Add copied from

* Update CI

* Rename script

* Update model id

* Add AddedToken, skip tests

* Update CI

* Fix doc tests

* Do not use Tesseract for the doc tests

* Remove kwargs

* Add original inputs

* Update casting

* Fix doc test

* Update question

* Update question

* Use LayoutLMv3ImageProcessor

* Update organization

* Improve docs

* Update forward signature

* Make images optional

* Remove deprecated device argument

* Add comment, add add_prefix_space

* More improvements

* Remove kwargs

---------

Co-authored-by: ArthurZucker <arthur.zucker@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
2024-03-04 18:49:02 +01:00

38 KiB

🤗 Transformers

State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.

🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:

📝 Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
🖼️ Computer Vision: image classification, object detection, and segmentation.
🗣️ Audio: automatic speech recognition and audio classification.
🐙 Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

🤗 Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. This provides the flexibility to use a different framework at each stage of a model's life; train a model in three lines of code in one framework, and load it for inference in another. Models can also be exported to a format like ONNX and TorchScript for deployment in production environments.

Join the growing community on the Hub, forum, or Discord today!

If you are looking for custom support from the Hugging Face team

HuggingFace Expert Acceleration Program

Contents

The documentation is organized into five sections:

  • GET STARTED provides a quick tour of the library and installation instructions to get up and running.

  • TUTORIALS are a great place to start if you're a beginner. This section will help you gain the basic skills you need to start using the library.

  • HOW-TO GUIDES show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model.

  • CONCEPTUAL GUIDES offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.

  • API describes all classes and functions:

    • MAIN CLASSES details the most important classes like configuration, model, tokenizer, and pipeline.
    • MODELS details the classes and functions related to each model implemented in the library.
    • INTERNAL HELPERS details utility classes and functions used internally.

Supported models and frameworks

The table below represents the current support in the library for each of those models, whether they have a Python tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via Flax), PyTorch, and/or TensorFlow.

Model PyTorch support TensorFlow support Flax Support
ALBERT
ALIGN
AltCLIP
Audio Spectrogram Transformer
Autoformer
Bark
BART
BARThez
BARTpho
BEiT
BERT
Bert Generation
BertJapanese
BERTweet
BigBird
BigBird-Pegasus
BioGpt
BiT
Blenderbot
BlenderbotSmall
BLIP
BLIP-2
BLOOM
BORT
BridgeTower
BROS
ByT5
CamemBERT
CANINE
Chinese-CLIP
CLAP
CLIP
CLIPSeg
CLVP
CodeGen
CodeLlama
Conditional DETR
ConvBERT
ConvNeXT
ConvNeXTV2
CPM
CPM-Ant
CTRL
CvT
Data2VecAudio
Data2VecText
Data2VecVision
DeBERTa
DeBERTa-v2
Decision Transformer
Deformable DETR
DeiT
DePlot
Depth Anything
DETA
DETR
DialoGPT
DiNAT
DINOv2
DistilBERT
DiT
DonutSwin
DPR
DPT
EfficientFormer
EfficientNet
ELECTRA
EnCodec
Encoder decoder
ERNIE
ErnieM
ESM
FairSeq Machine-Translation
Falcon
FastSpeech2Conformer
FLAN-T5
FLAN-UL2
FlauBERT
FLAVA
FNet
FocalNet
Funnel Transformer
Fuyu
Gemma
GIT
GLPN
GPT Neo
GPT NeoX
GPT NeoX Japanese
GPT-J
GPT-Sw3
GPTBigCode
GPTSAN-japanese
Graphormer
GroupViT
HerBERT
Hubert
I-BERT
IDEFICS
ImageGPT
Informer
InstructBLIP
Jukebox
KOSMOS-2
LayoutLM
LayoutLMv2
LayoutLMv3
LayoutXLM
LED
LeViT
LiLT
LLaMA
Llama2
LLaVa
Longformer
LongT5
LUKE
LXMERT
M-CTC-T
M2M100
MADLAD-400
Marian
MarkupLM
Mask2Former
MaskFormer
MatCha
mBART
mBART-50
MEGA
Megatron-BERT
Megatron-GPT2
MGP-STR
Mistral
Mixtral
mLUKE
MMS
MobileBERT
MobileNetV1
MobileNetV2
MobileViT
MobileViTV2
MPNet
MPT
MRA
MT5
MusicGen
MVP
NAT
Nezha
NLLB
NLLB-MOE
Nougat
Nyströmformer
OneFormer
OpenAI GPT
OpenAI GPT-2
OpenLlama
OPT
OWL-ViT
OWLv2
PatchTSMixer
PatchTST
Pegasus
PEGASUS-X
Perceiver
Persimmon
Phi
PhoBERT
Pix2Struct
PLBart
PoolFormer
Pop2Piano
ProphetNet
PVT
QDQBert
Qwen2
RAG
REALM
Reformer
RegNet
RemBERT
ResNet
RetriBERT
RoBERTa
RoBERTa-PreLayerNorm
RoCBert
RoFormer
RWKV
SAM
SeamlessM4T
SeamlessM4Tv2
SegFormer
SegGPT
SEW
SEW-D
SigLIP
Speech Encoder decoder
Speech2Text
SpeechT5
Splinter
SqueezeBERT
StableLm
Starcoder2
SwiftFormer
Swin Transformer
Swin Transformer V2
Swin2SR
SwitchTransformers
T5
T5v1.1
Table Transformer
TAPAS
TAPEX
Time Series Transformer
TimeSformer
Trajectory Transformer
Transformer-XL
TrOCR
TVLT
TVP
UDOP
UL2
UMT5
UniSpeech
UniSpeechSat
UnivNet
UPerNet
VAN
VideoMAE
ViLT
VipLlava
Vision Encoder decoder
VisionTextDualEncoder
VisualBERT
ViT
ViT Hybrid
VitDet
ViTMAE
ViTMatte
ViTMSN
VITS
ViViT
Wav2Vec2
Wav2Vec2-BERT
Wav2Vec2-Conformer
Wav2Vec2Phoneme
WavLM
Whisper
X-CLIP
X-MOD
XGLM
XLM
XLM-ProphetNet
XLM-RoBERTa
XLM-RoBERTa-XL
XLM-V
XLNet
XLS-R
XLSR-Wav2Vec2
YOLOS
YOSO