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Add PvT-v2 Model (huggingface#26812)
* Added pytests for pvt-v2, all passed * Added pvt_v2 to docs/source/end/model_doc * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Reverted batch eval changes for PR * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat. Added additional type support for image size in config * Fixed config backbone compat * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Reverted batch eval changes for PR * Updated index.md * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat * Ran fix-copies * Fixed PvtV2Backbone tests * Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py * Fixed backbone stuff and fixed tests: all passing * Ran make fixup * Made modifications for code checks * Remove ONNX config from configuration_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Use explicit image size dict in test_modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Make image_size optional in test_modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Remove _ntuple use in modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Remove reference to fp16_enabled * Model modules now take config as first argument even when not used * Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling" * All LayerNorm now instantiates with config.layer_norm_eps * Added docstring for depth-wise conv layer * PvtV2Config now only takes Union[int, Tuple[int, int]] for image size * Refactored PVTv2 in prep for gradient checkpointing * Gradient checkpointing ready to test * Removed override of _set_gradient_checkpointing * Cleaned out old code * Applied code fixup * Applied code fixup * Began debug of pvt_v2 tests * Leave handling of num_labels to base pretrained config class * Deactivated gradient checkpointing tests until it is fixed * Removed PvtV2ImageProcessor which duped PvtImageProcessor * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Added pvt_v2 to docs/source/end/model_doc * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Reverted batch eval changes for PR * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat. Added additional type support for image size in config * Fixed config backbone compat * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Reverted batch eval changes for PR * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat * Ran fix-copies * Fixed PvtV2Backbone tests * Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py * Fixed backbone stuff and fixed tests: all passing * Ran make fixup * Made modifications for code checks * Remove ONNX config from configuration_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Use explicit image size dict in test_modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Make image_size optional in test_modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Remove _ntuple use in modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Remove reference to fp16_enabled * Model modules now take config as first argument even when not used * Replaced abbreviations for "SR" and "AP" with explicit "spatialreduction" and "averagepooling" * All LayerNorm now instantiates with config.layer_norm_eps * Added docstring for depth-wise conv layer * PvtV2Config now only takes Union[int, Tuple[int, int]] for image size * Refactored PVTv2 in prep for gradient checkpointing * Gradient checkpointing ready to test * Removed override of _set_gradient_checkpointing * Cleaned out old code * Applied code fixup * Applied code fixup * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Ran fix-copies and fixup. All checks passed * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Reverted batch eval changes for PR * Fixed config docstring. Added channels property * Fixed config backbone compat * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Ran fix-copies and fixup. All checks passed * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Fixed config backbone compat * Ran fix-copies * Began debug of pvt_v2 tests * Leave handling of num_labels to base pretrained config class * Deactivated gradient checkpointing tests until it is fixed * Removed PvtV2ImageProcessor which duped PvtImageProcessor * Fixed issue from rebase * Fixed issue from rebase * Set tests for gradient checkpointing to skip those using reentrant since it isn't supported * Fixed issue from rebase * Fixed issue from rebase * Changed model name in docs * Removed duplicate PvtV2Backbone * Work around type switching issue in tests * Fix model name in config comments * Update docs/source/en/model_doc/pvt_v2.md Co-authored-by: Arthur <[email protected]> * Changed name of variable from 'attn_reduce' to 'sr_type' * Changed name of variable from 'attn_reduce' to 'sr_type' * Changed from using 'sr_type' to 'linear_attention' for clarity * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Removed old code * Changed from using 'sr_type' to 'linear_attention' for clarity * Fixed Class names to be more descriptive * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Removed outdated code * Moved paper abstract to single line in pvt_v2.md * Added usage tips to pvt_v2.md * Simplified module inits by passing layer_idx * Fixed typing for hidden_act in PvtV2Config * Removed unusued import * Add pvt_v2 to docs/source/en/_toctree.yml * Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive. * Updated documentation in docs/source/en/model_doc/pvt_v2.md to be more comprehensive. * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Move function parameters to single line Co-authored-by: amyeroberts <[email protected]> * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Update year of copyright to 2024 Co-authored-by: amyeroberts <[email protected]> * Update src/transformers/models/pvt_v2/modeling_pvt_v2.py Make code more explicit Co-authored-by: amyeroberts <[email protected]> * Updated sr_ratio to be more explicit spatial_reduction_ratio * Removed excess type hints in modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Move params to single line in modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Removed needless comment in modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Update copyright date in pvt_v2.md Co-authored-by: amyeroberts <[email protected]> * Moved params to single line in modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Updated copyright date in configuration_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Cleaned comments in modeling_pvt_v2.py Co-authored-by: amyeroberts <[email protected]> * Renamed spatial_reduction Conv2D operation * Revert "Update src/transformers/models/pvt_v2/modeling_pvt_v2.py " This reverts commit c4a0441. * Updated conversion script to reflect module name change * Deprecated reshape_last_stage option in config * Removed unused imports * Code formatting * Fixed outdated decorators on test_inference_fp16 * Added "Copied from" comments in test_modeling_pvt_v2.py * Fixed import listing * Updated model name * Force empty commit for PR refresh * Fixed linting issue * Removed # Copied from comments * Added PVTv2 to README_fr.md * Ran make fix-copies * Replace all FoamoftheSea hub references with OpenGVLab * Fixed out_indices and out_features logic in configuration_pvt_v2.py * Made ImageNet weight conversion verification optional in convert_pvt_v2_to_pytorch.py * Ran code fixup * Fixed order of parent classes in PvtV2Config to fix the to_dict method override --------- Co-authored-by: amyeroberts <[email protected]> Co-authored-by: Arthur <[email protected]>
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‎README.md

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1. **[Pop2Piano](https://huggingface.co/docs/transformers/model_doc/pop2piano)** released with the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi and Kyogu Lee.
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1. **[ProphetNet](https://huggingface.co/docs/transformers/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.
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1. **[PVT](https://huggingface.co/docs/transformers/model_doc/pvt)** (from Nanjing University, The University of Hong Kong etc.) released with the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
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1. **[PVTv2](https://huggingface.co/docs/transformers/main/model_doc/pvt_v2)** (from Shanghai AI Laboratory, Nanjing University, The University of Hong Kong etc.) released with the paper [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/abs/2106.13797) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
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1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
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1. **[Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2)** (from the Qwen team, Alibaba Group) released with the paper [Qwen Technical Report](https://arxiv.org/abs/2309.16609) by Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou and Tianhang Zhu.
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1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.

‎README_es.md

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1. **[Pop2Piano](https://huggingface.co/docs/transformers/model_doc/pop2piano)** released with the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi, Kyogu Lee.
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1. **[ProphetNet](https://huggingface.co/docs/transformers/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.
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1. **[PVT](https://huggingface.co/docs/transformers/model_doc/pvt)** (from Nanjing University, The University of Hong Kong etc.) released with the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
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1. **[PVTv2](https://huggingface.co/docs/transformers/main/model_doc/pvt_v2)** (from Shanghai AI Laboratory, Nanjing University, The University of Hong Kong etc.) released with the paper [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/abs/2106.13797) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
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1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
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1. **[Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2)** (from the Qwen team, Alibaba Group) released with the paper [Qwen Technical Report](https://arxiv.org/abs/2309.16609) by Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou and Tianhang Zhu.
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1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.

‎README_fr.md

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1. **[Pop2Piano](https://huggingface.co/docs/transformers/model_doc/pop2piano)** a été publié dans l'article [Pop2Piano : Génération de reprises de morceaux de piano basée sur l'audio pop](https://arxiv.org/abs/2211.00895) par Jongho Choi et Kyogu Lee.
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1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (de Microsoft Research) a été publié dans l'article [ProphetNet : Prédire les N-grammes futurs pour l'entraînement préalable de séquences à séquences](https://arxiv.org/abs/2001.04063) par Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang et Ming Zhou.
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1. **[PVT](https://huggingface.co/docs/transformers/model_doc/pvt)** (de l'Université de Nankin, l'Université de Hong Kong, etc.) a été publié dans l'article [Pyramid Vision Transformer : Une colonne vertébrale polyvalente pour la prédiction dense sans convolutions](https://arxiv.org/pdf/2102.12122.pdf) par Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo et Ling Shao.
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1. **[PVTv2](https://huggingface.co/docs/transformers/main/model_doc/pvt_v2)** (de Shanghai AI Laboratory, Nanjing University, The University of Hong Kong etc.) publié dans l'article [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/abs/2106.13797) parWenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.
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1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (de NVIDIA) a été publié dans l'article [Quantification entière pour l'inférence d'apprentissage profond : Principes et évaluation empirique](https://arxiv.org/abs/2004.09602) par Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev et Paulius Micikevicius.
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1. **[Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2)** (de l'équipe Qwen, Alibaba Group) a été publié avec le rapport technique [Qwen](https://arxiv.org/abs/2309.16609) par Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou et Tianhang Zhu.
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1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (de Facebook) a été publié dans l'article [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) par Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.

‎README_hd.md

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1. **[Pop2Piano](https://huggingface.co/docs/transformers/model_doc/pop2piano)** released with the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi, Kyogu Lee.
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1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (माइक्रोसॉफ्ट रिसर्च से) साथ में पेपर [ProphetNet: प्रेडिक्टिंग फ्यूचर एन-ग्राम फॉर सीक्वेंस-टू-सीक्वेंस प्री-ट्रेनिंग](https://arxiv.org/abs/2001.04063) यू यान, वीज़ेन क्यूई, येयुन गोंग, दयाहेंग लियू, नान डुआन, जिउशेंग चेन, रुओफ़ेई झांग और मिंग झोउ द्वारा पोस्ट किया गया।
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1. **[PVT](https://huggingface.co/docs/transformers/model_doc/pvt)** (Nanjing University, The University of Hong Kong etc. से) Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. द्वाराअनुसंधान पत्र [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf) के साथ जारी किया गया
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1. **[PVTv2](https://huggingface.co/docs/transformers/main/model_doc/pvt_v2)** (Shanghai AI Laboratory, Nanjing University, The University of Hong Kong etc. से) Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. द्वाराअनुसंधान पत्र [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/abs/2106.13797) के साथ जारी किया गया
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1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (NVIDIA से) साथ वाला पेपर [डीप लर्निंग इंफ़ेक्शन के लिए इंटीजर क्वांटिज़ेशन: प्रिंसिपल्स एंड एम्पिरिकल इवैल्यूएशन](https://arxiv.org/abs/2004.09602) हाओ वू, पैट्रिक जुड, जिआओजी झांग, मिखाइल इसेव और पॉलियस माइकेविसियस द्वारा।
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1. **[Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2)** (the Qwen team, Alibaba Group से) Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou and Tianhang Zhu. द्वाराअनुसंधान पत्र [Qwen Technical Report](https://arxiv.org/abs/2309.16609) के साथ जारी किया गया
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1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (फेसबुक से) साथ में कागज [रिट्रीवल-ऑगमेंटेड जेनरेशन फॉर नॉलेज-इंटेंसिव एनएलपी टास्क](https://arxiv.org/abs/2005.11401) पैट्रिक लुईस, एथन पेरेज़, अलेक्जेंड्रा पिक्टस, फैबियो पेट्रोनी, व्लादिमीर कारपुखिन, नमन गोयल, हेनरिक कुटलर, माइक लुईस, वेन-ताउ यिह, टिम रॉकटाशेल, सेबस्टियन रिडेल, डौवे कीला द्वारा।

‎README_ja.md

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1. **[Pop2Piano](https://huggingface.co/docs/transformers/model_doc/pop2piano)** released with the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi, Kyogu Lee.
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1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (Microsoft Research から) Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou から公開された研究論文: [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063)
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1. **[PVT](https://huggingface.co/docs/transformers/model_doc/pvt)** (Nanjing University, The University of Hong Kong etc. から) Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. から公開された研究論文 [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf)
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1. **[PVTv2](https://huggingface.co/docs/transformers/main/model_doc/pvt_v2)** (Shanghai AI Laboratory, Nanjing University, The University of Hong Kong etc. から) Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. から公開された研究論文 [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/abs/2106.13797)
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1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (NVIDIA から) Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius から公開された研究論文: [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602)
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1. **[Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2)** (the Qwen team, Alibaba Group から) Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou and Tianhang Zhu. から公開された研究論文 [Qwen Technical Report](https://arxiv.org/abs/2309.16609)
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1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (Facebook から) Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela から公開された研究論文: [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)

‎README_ko.md

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1. **[Pop2Piano](https://huggingface.co/docs/transformers/model_doc/pop2piano)** released with the paper [Pop2Piano : Pop Audio-based Piano Cover Generation](https://arxiv.org/abs/2211.00895) by Jongho Choi, Kyogu Lee.
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1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (Microsoft Research 에서) Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 의 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 논문과 함께 발표했습니다.
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1. **[PVT](https://huggingface.co/docs/transformers/model_doc/pvt)** (Nanjing University, The University of Hong Kong etc. 에서 제공)은 Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.의 [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/pdf/2102.12122.pdf)논문과 함께 발표했습니다.
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1. **[PVTv2](https://huggingface.co/docs/transformers/main/model_doc/pvt_v2)** (Shanghai AI Laboratory, Nanjing University, The University of Hong Kong etc. 에서 제공)은 Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao.의 [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://arxiv.org/abs/2106.13797)논문과 함께 발표했습니다.
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1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (NVIDIA 에서) Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius 의 [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) 논문과 함께 발표했습니다.
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1. **[Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2)** (the Qwen team, Alibaba Group 에서 제공)은 Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou and Tianhang Zhu.의 [Qwen Technical Report](https://arxiv.org/abs/2309.16609)논문과 함께 발표했습니다.
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1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (Facebook 에서) Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela 의 [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) 논문과 함께 발표했습니다.

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