[tune](deps): Bump transformers from 4.8.1 to 4.19.2 in /python/requirements/tune

[dependabot]

Bumps transformers from 4.8.1 to 4.19.2.

Release notes

Sourced from transformers's releases.

v4.19.2: Patch release

Patch release for the following PRs/commits:

• [OPT-fix #17229](huggingface/transformers#17229)
• [OPT - fix docstring and improve tests slighly #17228](huggingface/transformers#17228)
• [Align logits and labels in OPT #17237](huggingface/transformers#17237)

v4.19.1 Patch release

Fix Trainer for Datasets that don't have dict items #17239

v4.19.0: OPT, FLAVA, YOLOS, RegNet, TAPEX, Data2Vec vision, FSDP integration

Disclaimer: this release is the first release with no Python 3.6 support.

OPT

The OPT model was proposed in Open Pre-trained Transformer Language Models by Meta AI. OPT is a series of open-sourced large causal language models which perform similar in performance to GPT3.

• Add OPT by @​younesbelkada in #17088

FLAVA

The FLAVA model was proposed in FLAVA: A Foundational Language And Vision Alignment Model by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.

The paper aims at creating a single unified foundation model which can work across vision, language as well as vision-and-language multimodal tasks.

• [feat] Add FLAVA model by @​apsdehal in #16654

YOLOS

The YOLOS model was proposed in You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu. YOLOS proposes to just leverage the plain Vision Transformer (ViT) for object detection, inspired by DETR. It turns out that a base-sized encoder-only Transformer can also achieve 42 AP on COCO, similar to DETR and much more complex frameworks such as Faster R-CNN.

• Add YOLOS by @​NielsRogge in #16848

RegNet

The RegNet model was proposed in Designing Network Design Spaces by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.

The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.

• RegNet by @​FrancescoSaverioZuppichini in #16188

TAPEX

The TAPEX model was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. TAPEX pre-trains a BART model to solve synthetic SQL queries, after which it can be fine-tuned to answer natural language questions related to tabular data, as well as performing table fact checking.

• Add TAPEX by @​NielsRogge in #16473

Data2Vec: vision

... (truncated)

Commits

• 6e53542 Release: v4.19.2
• 687a7f8 Align logits and labels in OPT (#17237)
• f79af76 OPT - fix docstring and improve tests slighly (#17228)
• 219abba OPT-fix (#17229)
• a1651b5 Release: v4.19.1
• ca45659 Fix Trainer for Datasets that don't have dict items (#17239)
• a22db88 Release: v4.19.0
• 9f16a1c Update data2vec.mdx to include a Colab Notebook link (that shows fine-tuning)...
• a42242d migrate azure blob for beit checkpoints (#16902)
• b971c76 Add OPT (#17088)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #79.