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Bump transformers from 4.16.2 to 4.22.2 #73

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@dependabot dependabot bot commented on behalf of github Oct 3, 2022

Bumps transformers from 4.16.2 to 4.22.2.

Release notes

Sourced from transformers's releases.

# v4.22.2 Patch release

Fixes a bug where a cached tokenizer/model was not accessible anymore offline (either forcing offline mode or because of an internet issue).

v4.22.1: Patch release:

Patch release for the following PRs:

v4.22.0: Swin Transformer v2, VideoMAE, Donut, Pegasus-X, X-CLIP, ERNIE

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.

Swin Transformer v2 improves the original Swin Transformer using 3 main techniques: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) a log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.

VideoMAE

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.

VideoMAE is an extension of ViTMAE for video.

Donut

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.

Pegasus-X

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.

X-CLIP

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.

... (truncated)

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Bumps [transformers](https://github.com/huggingface/transformers) from 4.16.2 to 4.22.2.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.16.2...v4.22.2)

---
updated-dependencies:
- dependency-name: transformers
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Oct 3, 2022
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dependabot bot commented on behalf of github Oct 17, 2022

Superseded by #78.

@dependabot dependabot bot closed this Oct 17, 2022
@dependabot dependabot bot deleted the dependabot/pip/transformers-4.22.2 branch October 17, 2022 12:33
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