[tune](deps): Bump transformers from 4.8.1 to 4.17.0 in /python/requirements/tune

[dependabot]

Bumps transformers from 4.8.1 to 4.17.0.

Release notes

Sourced from transformers's releases.

v4.17.0: XGLM, ConvNext, PoolFormer, PLBart, Data2Vec, MaskFormer and code in the Hub

New models

XGLM

The XGLM model was proposed in Few-shot Learning with Multilingual Language Models by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.

XGLM is a GPT3-like multilingual model trained on a balanced corpus covering a diverse set of languages.

• Add XGLM models by @​patil-suraj in huggingface/transformers#14876

ConvNext

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.

• Add ConvNeXT by @​NielsRogge in huggingface/transformers#15277
• Add TFConvNextModel by @​sayakpaul in huggingface/transformers#15750

PoolFormer

The PoolFormer model was proposed in MetaFormer is Actually What You Need for Vision by Sea AI Labs.

• Add PoolFormer by @​heytanay in huggingface/transformers#15531

PLBart

The PLBART model was proposed in Unified Pre-training for Program Understanding and Generation by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.

This is a BART-like model which can be used to perform code-summarization, code-generation, and code-translation tasks. The pre-trained model plbart-base has been trained using multilingual denoising task on Java, Python and English.

• Add PLBart by @​gchhablani in huggingface/transformers#13269
• Add missing PLBart entry in README by @​gchhablani in huggingface/transformers#15721

Data2Vec

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 modalities - text, audio and images. Importantly, predicted targets for pre-training are contextualized latent representations of the inputs, rather than modality-specific, context-independent targets.

• Add Data2Vec by @​edugp in huggingface/transformers#15507

Maskformer

The MaskFormer model was proposed in Per-Pixel Classification is Not All You Need for Semantic Segmentation by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.

MaskFormer addresses semantic segmentation with a mask classification paradigm instead of performing classic pixel-level classification.

• Maskformer by @​FrancescoSaverioZuppichini in huggingface/transformers#15682

... (truncated)

Commits

• 198c335 [Doctests] Fix ignore bug and add more doc tests (#15911)
• 8529a85 [Fix link in pipeline doc] (#15906)
• 7e8ae01 Release: v4.17.0
• 3d22428 Update delete-dev-doc job to match build-dev-doc (#15891)
• 89be34c Fix SegformerForImageClassification (#15895)
• 130b987 [XGLM] run sampling test on CPU to be deterministic (#15892)
• baab5e7 TF generate refactor - Sample (#15793)
• 96ae92b [SegFormer] Add deprecation warning (#15889)
• 8fd4731 Fix Bug in FlaxWav2Vec2 Slow Test (#15887)
• d83d22f Maskformer (#15682)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #66.