Title: Transformers: State-of-the-Art Natural Language Processing

Type Software Wolf, Thomas, Debut, Lysandre, Sanh, Victor, Chaumond, Julien, Delangue, Clement, Moi, Anthony, Cistac, Perric, Ma, Clara, Jernite, Yacine, Plu, Julien, Xu, Canwen, Le Scao, Teven, Gugger, Sylvain, Drame, Mariama, Lhoest, Quentin, Rush, Alexander M. (2020): Transformers: State-of-the-Art Natural Language Processing. Zenodo. Software. https://zenodo.org/record/7080024

Authors: Wolf, Thomas ; Debut, Lysandre ; Sanh, Victor ; Chaumond, Julien ; Delangue, Clement ; Moi, Anthony ; Cistac, Perric ; Ma, Clara ; Jernite, Yacine ; Plu, Julien ; Xu, Canwen ; Le Scao, Teven ; Gugger, Sylvain ; Drame, Mariama ; Lhoest, Quentin ; Rush, Alexander M. ;

Links

• Item record in Zenodo
• Digital object URL

Summary

Swin Transformer v2

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 VideoMAE model was proposed in VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Zhan Tong, Yibing Song, Jue Wang, Limin Wang. VideoMAE extends masked auto encoders (MAE) to video, claiming state-of-the-art performance on several video classification benchmarks.

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 Transformer decoder to perform document understanding tasks such as document image classification, form understanding and visual question answering.

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 staggered block-local attention with global tokens in the encoder.

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 encoder, a cross-frame vision encoder, a multi-frame integration Transformer, and a video-specific prompt generator.

ERNIE is a series of powerful models proposed by baidu, especially in Chinese tasks, including ERNIE1.0, ERNIE2.0, ERNIE3.0, ERNIE-Gram, ERNIE-health, etc. These models are contributed by nghuyong and the official code can be found in PaddleNLP (in PaddlePaddle).

MobileViT and LayoutLMv3 are now available in TensorFlow.

A new question answering head was added for the LayoutLM model.

Two new pipelines are available in transformers: a document question answering pipeline, as well as an image to text generation pipeline.

There is now Mac M1 support in PyTorch in transformers in pipelines and the Trainer.

Starting from version v4.22.0, we'll now officially support PyTorch and TensorFlow versions that were released up to two years ago. Versions older than two years-old will not be supported going forward.

We're making this change as we begin actively testing transformers compatibility on older versions. This project can be followed here.

The generate method now starts enforcing stronger validation in order to ensure proper usage.

The as_target_tokenizer and as_target_processor context managers have been deprecated. The new API is to use the call method of the tokenizer/processor with keyword arguments. For instance:

becomes

Bits and bytes is now integrated within transformers. This feature can reduce the size of large models by up to 2, with low loss in precision.

Models that have sharded checkpoints in PyTorch can be loaded in Flax.

The TensorFlow examples have been rewritten to support all recent features developped in the past months.

DeBERTa-v2 is now trainable with XLA.

More information

• DOI: 10.5281/zenodo.7080024

Dates

• Publication date: 2020
• Issued: October 01, 2020

Notes

Rights

• info:eu-repo/semantics/openAccess Open Access

Format

electronic resource

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