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Release v4.12.0
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2021-10-28 12:09:45 -04:00
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docs/source/conf.py
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@@ -27,7 +27,8 @@ author = "huggingface"
# The short X.Y version
version = ""
# The full version, including alpha/beta/rc tags
release = "4.12.0.dev0"
release = "4.12.0"

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@@ -105,17 +105,17 @@ Supported models
3. :doc:`BARThez <model_doc/barthez>` (from École polytechnique) released with the paper `BARThez: a Skilled Pretrained
French Sequence-to-Sequence Model <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P.
Tixier, Michalis Vazirgiannis.
4. `BARTpho <https://huggingface.co/transformers/master/model_doc/bartpho.html>`__ (from VinAI Research) released with
the paper `BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese <https://arxiv.org/abs/2109.09701>`__ by
Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
4. :doc:`BARTpho <model_doc/bartpho>` (from VinAI Research) released with the paper `BARTpho: Pre-trained
Sequence-to-Sequence Models for Vietnamese <https://arxiv.org/abs/2109.09701>`__ by Nguyen Luong Tran, Duong Minh Le
and Dat Quoc Nguyen.
5. :doc:`BEiT <model_doc/beit>` (from Microsoft) released with the paper `BEiT: BERT Pre-Training of Image Transformers
<https://arxiv.org/abs/2106.08254>`__ by Hangbo Bao, Li Dong, Furu Wei.
6. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang,
Kenton Lee and Kristina Toutanova.
7. `BERTweet <https://huggingface.co/transformers/master/model_doc/bertweet.html>`__ (from VinAI Research) released
with the paper `BERTweet: A pre-trained language model for English Tweets
<https://aclanthology.org/2020.emnlp-demos.2/>`__ by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
7. :doc:`BERTweet <model_doc/bertweet>` (from VinAI Research) released with the paper `BERTweet: A pre-trained language
model for English Tweets <https://aclanthology.org/2020.emnlp-demos.2/>`__ by Dat Quoc Nguyen, Thanh Vu and Anh Tuan
Nguyen.
8. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
Narayan, Aliaksei Severyn.
@@ -261,9 +261,9 @@ Supported models
54. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__ by Jingqing Zhang, Yao Zhao,
Mohammad Saleh and Peter J. Liu.
55. `PhoBERT <https://huggingface.co/transformers/master/model_doc/phobert.html>`__ (from VinAI Research) released with
the paper `PhoBERT: Pre-trained language models for Vietnamese
<https://www.aclweb.org/anthology/2020.findings-emnlp.92/>`__ by Dat Quoc Nguyen and Anh Tuan Nguyen.
55. :doc:`PhoBERT <model_doc/phobert>` (from VinAI Research) released with the paper `PhoBERT: Pre-trained language
models for Vietnamese <https://www.aclweb.org/anthology/2020.findings-emnlp.92/>`__ by Dat Quoc Nguyen and Anh Tuan
Nguyen.
56. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
@@ -278,10 +278,9 @@ Supported models
60. :doc:`RoFormer <model_doc/roformer>` (from ZhuiyiTechnology), released together with the paper a `RoFormer:
Enhanced Transformer with Rotary Position Embedding <https://arxiv.org/pdf/2104.09864v1.pdf>`__ by Jianlin Su and
Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
61. `SegFormer <https://huggingface.co/transformers/master/model_doc/segformer.html>`__ (from NVIDIA) released with the
paper `SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
<https://arxiv.org/abs/2105.15203>`__ by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping
Luo.
61. :doc:`SegFormer <model_doc/segformer>` (from NVIDIA) released with the paper `SegFormer: Simple and Efficient
Design for Semantic Segmentation with Transformers <https://arxiv.org/abs/2105.15203>`__ by Enze Xie, Wenhai Wang,
Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
62. :doc:`SEW <model_doc/sew>` (from ASAPP) released with the paper `Performance-Efficiency Trade-offs in Unsupervised
Pre-training for Speech Recognition <https://arxiv.org/abs/2109.06870>`__ by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu
Han, Kilian Q. Weinberger, Yoav Artzi.
@@ -314,18 +313,16 @@ Supported models
71. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
72. `TrOCR <https://huggingface.co/transformers/master/model_doc/trocr.html>`__ (from Microsoft), released together
with the paper `TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
<https://arxiv.org/abs/2109.10282>`__ by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
Zhoujun Li, Furu Wei.
73. `UniSpeech <https://huggingface.co/transformers/master/model_doc/unispeech.html>`__ (from Microsoft Research)
released with the paper `UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
<https://arxiv.org/abs/2101.07597>`__ by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei,
Michael Zeng, Xuedong Huang.
74. `UniSpeechSat <https://huggingface.co/transformers/master/model_doc/unispeech_sat.html>`__ (from Microsoft
Research) released with the paper `UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE
PRE-TRAINING <https://arxiv.org/abs/2110.05752>`__ by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen,
Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
72. :doc:`TrOCR <model_doc/trocr>` (from Microsoft), released together with the paper `TrOCR: Transformer-based Optical
Character Recognition with Pre-trained Models <https://arxiv.org/abs/2109.10282>`__ by Minghao Li, Tengchao Lv, Lei
Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
73. :doc:`UniSpeech <model_doc/unispeech>` (from Microsoft Research) released with the paper `UniSpeech: Unified Speech
Representation Learning with Labeled and Unlabeled Data <https://arxiv.org/abs/2101.07597>`__ by Chengyi Wang, Yu
Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
74. :doc:`UniSpeechSat <model_doc/unispeech_sat>` (from Microsoft Research) released with the paper `UNISPEECH-SAT:
UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING <https://arxiv.org/abs/2110.05752>`__ by
Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li,
Xiangzhan Yu.
75. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`__ by Alexey Dosovitskiy,
Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias