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megagonlabs / bunkai

Sentence boundary disambiguation tool for Japanese texts (日本語文境界判定器)
https://pypi.org/project/bunkai/
Apache License 2.0
185 stars 11 forks source link

Bump transformers from 4.22.2 to 4.23.1 #169

Closed dependabot[bot] closed 2 years ago

dependabot[bot] commented 2 years ago

Bumps transformers from 4.22.2 to 4.23.1.

Release notes

Sourced from transformers's releases.

v4.23.1 Patch release

Fix a revert introduced by mistake making the "automatic-speech-recognition" for Whisper.

v4.23.0: Whisper, Deformable DETR, Conditional DETR, MarkupLM, MSN, safetensors

Whisper

The Whisper model was proposed in Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.

Whisper is an encoder-decoder Transformer trained on 680,000 hours of labeled (transcribed) audio. The model shows impressive performance and robustness in a zero-shot setting, in multiple languages.

Deformable DETR

The Deformable DETR model was proposed in Deformable DETR: Deformable Transformers for End-to-End Object Detection by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.

Deformable DETR mitigates the slow convergence issues and limited feature spatial resolution of the original DETR by leveraging a new deformable attention module which only attends to a small set of key sampling points around a reference.

Conditional DETR

The Conditional DETR model was proposed in Conditional DETR for Fast Training Convergence by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.

Conditional DETR presents a conditional cross-attention mechanism for fast DETR training. Conditional DETR converges 6.7× to 10× faster than DETR.

Time Series Transformer

The Time Series Transformer model is a vanilla encoder-decoder Transformer for time series forecasting.

The model is trained in a similar way to how one would train an encoder-decoder Transformer (like T5 or BART) for machine translation; i.e. teacher forcing is used. At inference time, one can autoregressively generate samples, one time step at a time.

:warning: This is a recently introduced model and modality, so the API hasn't been tested extensively. There may be some bugs or slight breaking changes to fix it in the future. If you see something strange, file a Github Issue.

Masked Siamese Networks

The ViTMSN model was proposed in Masked Siamese Networks for Label-Efficient Learning by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.

MSN (masked siamese networks) consists of a joint-embedding architecture to match the prototypes of masked patches with that of the unmasked patches. With this setup, the method yields excellent performance in the low-shot and extreme low-shot regimes for image classification, outperforming other self-supervised methods such as DINO. For instance, with 1% of ImageNet-1K labels, the method achieves 75.7% top-1 accuracy.

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