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Update transformers requirement from <4.17,>=4.1 to >=4.1,<4.18 #5583

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Mar 11, 2022

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Updates the requirements on transformers to permit the latest version.

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.

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.

PoolFormer

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

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.

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.

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.

... (truncated)

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Updates the requirements on [transformers](https://github.com/huggingface/transformers) to permit the latest version.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.1.0...v4.17.0)

---
updated-dependencies:
- dependency-name: transformers
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Mar 4, 2022
@AkshitaB AkshitaB merged commit ab37da7 into main Mar 11, 2022
@AkshitaB AkshitaB deleted the dependabot/pip/transformers-gte-4.1-and-lt-4.18 branch March 11, 2022 04:59
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