Bump transformers[torch] from 4.8.2 to 4.24.0 in /server

[dependabot]

Bumps transformers[torch] from 4.8.2 to 4.24.0.

Release notes

Sourced from transformers[torch]'s releases.

v4.24.0: ESM-2/ESMFold, LiLT, Flan-T5, Table Transformer and Contrastive search decoding

ESM-2/ESMFold

ESM-2 and ESMFold are new state-of-the-art Transformer protein language and folding models from Meta AI's Fundamental AI Research Team (FAIR). ESM-2 is trained with a masked language modeling objective, and it can be easily transferred to sequence and token classification tasks for proteins. Checkpoints exist in various sizes, from 8 million parameters up to a huge 15 billion parameter model.

ESMFold is a state-of-the-art single sequence protein folding model which produces high accuracy predictions significantly faster. Unlike previous protein folding tools like AlphaFold2 and openfold, ESMFold uses a pretrained protein language model to generate token embeddings that are used as input to the folding model, and so does not require a multiple sequence alignment (MSA) of related proteins as input. As a result, proteins can be folded in a single forward pass of the model without requiring any external databases or search/alignment tools to be present at inference time. This hugely reduces the time and compute requirements for folding.

Transformer protein language models were introduced in the paper Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus.

ESMFold was introduced in the paper Language models of protein sequences at the scale of evolution enable accurate structure prediction by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, and Alexander Rives.

• Add ESMFold by @​Rocketknight1 in #19977
• TF port of ESM by @​Rocketknight1 in #19587

LiLT

LiLT allows to combine any pre-trained RoBERTa text encoder with a lightweight Layout Transformer, to enable LayoutLM-like document understanding for many languages.

It was proposed in LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding by Jiapeng Wang, Lianwen Jin, Kai Ding.

• Add LiLT by @​NielsRogge in #19450

Flan-T5

FLAN-T5 is an enhanced version of T5 that has been finetuned on a mixture of tasks.

It was released in the paper Scaling Instruction-Finetuned Language Models by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei.

• Add flan-t5 documentation page by @​younesbelkada in #19892

Table Transformer

Table Transformer is a model that can perform table extraction and table structure recognition from unstructured documents based on the DETR architecture.

It was proposed in PubTables-1M: Towards comprehensive table extraction from unstructured documents by Brandon Smock, Rohith Pesala, Robin Abraham.

• Add table transformer [v2] by @​NielsRogge in #19614

Contrastive search decoding

Contrastive search decoding is a new state-of-the-art generation method which aims at reducing the repetitive patterns in which generation models often fall.

It was introduced in A Contrastive Framework for Neural Text Generation by Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, Nigel Collier.

• Adding the state-of-the-art contrastive search decoding methods for the codebase of generation_utils.py by @​gmftbyGMFTBY in #19477

Safety and security

We continue to explore the new serialization format not using Pickle via the safetensors library, this time by adding support for TensorFlow models. More checkpoints have been converted to this format. Support is still experimental.

... (truncated)

Commits

• 94b3f54 Unpin PyTorch for the release
• 8f95346 Add ESMFold code sample (#20000)
• 502d3b6 Remove pin temporarily to get tests
• 0e654e0 Added onnx config whisper (#19525)
• 1ebb3f7 Release v4.24.0
• 9c13b66 Unpin PyTorch
• 7f9b7b3 Add ESMFold (#19977)
• 4c9e0f0 Add support for gradient checkpointing (#19990)
• 8214a9f Pin torch to < 1.13 temporarily (#19989)
• 6aede2d Tranformers documentation translation to Italian #17459 (#19988)
• Additional commits viewable in compare view

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Superseded by #71.