[tune](deps): Bump transformers from 4.9.1 to 4.10.2 in /python/requirements/tune by dependabot[bot] · Pull Request #53 · DmitriGekhtman/ray

dependabot · 2021-09-11T07:04:28Z

Bumps transformers from 4.9.1 to 4.10.2.

Release notes

v4.10.2: Patch release

[Wav2Vec2] Fix dtype 64 bug #13517 (@patrickvonplaten)

v4.10.1: Patch release

[Wav2Vec2] Fix normalization for non-padded tensors #13512 (@patrickvonplaten)

Fixing backward compatiblity for non prefixed tokens (B-, I-). #13493 (@Narsil)

Fixing #13381 #13400 (@Narsil)

v4.10.0: LayoutLM-v2, LayoutXLM, BEiT

LayoutLM-v2 and LayoutXLM

Four new models are released as part of the LatourLM-v2 implementation: LayoutLMv2ForSequenceClassification, LayoutLMv2Model, LayoutLMv2ForTokenClassification and LayoutLMv2ForQuestionAnswering, in PyTorch.

The LayoutLMV2 model was proposed in LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves LayoutLM to obtain state-of-the-art results across several document image understanding benchmarks:

Add LayoutLMv2 + LayoutXLM #12604 (@NielsRogge)

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=layoutlmv2

BEiT

Three new models are released as part of the BEiT implementation: BeitModel, BeitForMaskedImageModeling, and BeitForImageClassification, in PyTorch.

The BEiT model was proposed in BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predict the class of an image (as done in the original ViT paper), BEiT models are pre-trained to predict visual tokens from the codebook of OpenAI’s DALL-E model given masked patches.

Add BEiT #12994 (@NielsRogge)

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

Speech improvements

The Wav2Vec2 and HuBERT models now have a sequence classification head available.

Add Wav2Vec2 & Hubert ForSequenceClassification #13153 (@anton-l)

DeBERTa in TensorFlow (@kamalkraj)

The DeBERTa and DeBERTa-v2 models have been converted from PyTorch to TensorFlow.

Deberta tf #12972 (@kamalkraj)

Deberta_v2 tf #13120 (@kamalkraj)

Flax model additions

EncoderDecoder, DistilBERT, and ALBERT, now have support in Flax!

FlaxEncoderDecoder allowing Bert2Bert and Bert2GPT2 in Flax #13008 (@ydshieh)

FlaxDistilBERT #13324 (@kamalkraj)

FlaxAlBERT #13294 (@kamalkraj)

... (truncated)

Commits

a5fc344 [Wav2Vec2] Fix dtype 64 bug (#13517)
2c51442 Release: v4.10.2
28e2787 Release: 4.10.1
e5e0452 Fixing #13381 (#13400)
4afbd7e Fixing backward compatiblity for non prefixed tokens (B-, I-). (#13493)
60eb416 [Wav2Vec2] Fix normalization for non-padded tensors (#13512)
d12bbe4 Release: v4.10.0
642e193 [GitHub Runner] Fix flax runner (#13357)
c76de10 Add generate kwargs to Seq2SeqTrainingArguments (#13339)
702f4a4 Fixed CLM model still using MODEL_FOR_MASKED_LM_MAPPING (#13002)
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Bumps [transformers](https://github.com/huggingface/transformers) from 4.9.1 to 4.10.2. - [Release notes](https://github.com/huggingface/transformers/releases) - [Commits](huggingface/transformers@v4.9.1...v4.10.2) --- updated-dependencies: - dependency-name: transformers dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com>

dependabot · 2021-09-19T17:58:00Z