build(deps-dev): bump transformers from 4.12.5 to 4.26.0

dependabot[bot] commented 1 year ago

Bumps transformers from 4.12.5 to 4.26.0.

Release notes

PyTorch 2.0 support, Audio Spectogram Transformer, Jukebox, Switch Transformers and more

PyTorch 2.0 stack support

We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line.

This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures.

Note that to get the best performance, we recommend:

using an Ampere GPU (or more recent)

sticking to fixed shaped for now (so use --pad_to_max_length in our examples)

Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498

Audio Spectrogram Transformer

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-of-the-art results for audio classification.

Add Audio Spectogram Transformer by @NielsRogge in #19981

Jukebox

The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.

Add Jukebox model (replaces #16875) by @ArthurZucker in #17826

Switch Transformers

The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer.

It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.

Add Switch transformers by @younesbelkada and @ArthurZucker in #19323

RocBert

The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It’s a pretrained Chinese language model that is robust under various forms of adversarial attacks.

Add RocBert by @sww9370 in #20013

CLIPSeg

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.

Add CLIPSeg by @NielsRogge in #20066

NAT and DiNAT

NAT

NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.

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Commits

820c46a Hotifx remove tuple for git config image processor. (#21278)
a280cdd Fix MaskFormerImageProcessor.post_process_instance_segmentation (#21256)
baf0df1 Release: v4.26.0
fd5cdae Models docstring (#21225)
9e86c4e Supported pipeline tasks update (#21268)
d8415ba [Whisper] fix all issues with unk token (#21250)
c18b4fb Add class properties with warnings (#21195)
b80b221 [ci-daily] Fix pipeline tests (#21257)
275ad9d Add: TensorFlow example for semantic segmentation task guide (#21223)
2218dac Notebook examples grouping and update (#21265)
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