[tune](deps): Bump transformers from 4.3.2 to 4.6.0 in /python/requirements

[dependabot]

Bumps transformers from 4.3.2 to 4.6.0.

Release notes

Sourced from transformers's releases.

v4.6.0: ViT, DeiT, CLIP, LUKE, BigBirdPegasus, MegatronBERT

Transformers aren't just for text - they can handle a huge range of input types, and there's been a flurry of papers and new models in the last few months applying them to vision tasks that had traditionally been dominated by convolutional networks. With this release, we're delighted to announce that several state-of-the-art pretrained vision and multimodal text+vision transformer models are now accessible in the huggingface/transformers repo. Give them a try!

ViT (@​NielsRogge)

Two new models are released as part of the ViT implementation: ViTModel and ViTForImageClassification, in PyTorch.

ViT is an image transformer-based model obtaining state-of-the-art results on image classification tasks. It was the first paper that successfully trained a Transformer encoder on ImageNet, attaining very good results compared to familiar convolutional architectures.

The Vision Transformer (ViT) model was proposed in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=vit

DeiT (@​NielsRogge)

Three new models are released as part of the DeiT implementation: DeiTModel, DeiTForImageClassification and DeiTForImageClassificationWithTeacher, in PyTorch.

DeiT is an image transformer model similar to the ViT model. DeiT (data-efficient image transformers) models are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original ViT models.

The DeiT model was proposed in Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=deit

• Add DeiT (PyTorch) #11056 (@​NielsRogge)

CLIP (@​patil-suraj)

Three new models are released as part of the CLIP implementation: CLIPModel, CLIPVisionModel and CLIPTextModel, in PyTorch.

CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3.

The CLIP model was proposed in Learning Transferable Visual Models From Natural Language Supervision by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=clip

• CLIP #11445 (@​patil-suraj)

BigBirdPegasus (@​vasudevgupta7)

BigBird is a sparse-attention-based transformer that extends Transformer based models, such as BERT to much longer sequences. In addition to sparse attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it has been shown that applying sparse, global, and random attention approximates full attention while being computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context, BigBird has shown improved performance on various long document NLP tasks, such as question answering and summarization, compared to BERT or RoBERTa.

The BigBird model was proposed in Big Bird: Transformers for Longer Sequences by Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others.

• Add BigBirdPegasus #10991 (@​vasudevgupta7)

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=bigbird_pegasus

LUKE (@​NielsRogge, @​ikuyamada)

... (truncated)

Commits

• 64e7856 Release: v4.6.0
• fd6204b [Lazy init] Force fall back to slow init for composite models (#11705)
• 5c1cda9 fix example in config doc (#11696)
• 77f4c46 remove defaults to None if optional (#11703)
• 6797cdc Updates README and fixes bug (#11701)
• f063c56 Fix clip docs (#11694)
• 8719afa CLIP (#11445)
• 4ce6bcc Adds Flax BERT finetuning example on GLUE (#11564)
• f13f1f8 Test checkpointing (#11682)
• d9b2862 Fix TF Roberta for mixed precision training (#11675)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #19.