Nezha Pytorch implementation (#17776)
* wip * rebase * all tests pass * rebase * ready for PR * address comments * fix styles * add require_torch to pipeline test * remove remote image to improve CI consistency * address comments; fix tf/flax tests * address comments; fix tf/flax tests * fix tests; add alias * repo consistency tests * Update src/transformers/pipelines/visual_question_answering.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * address comments * Update src/transformers/pipelines/visual_question_answering.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * merge * wip * wip * wip * most basic tests passes * all tests pass now * relative embedding * wip * running make fixup * remove bert changes * fix doc * fix doc * fix issues * fix doc * address comments * fix CI * remove redundant copied from * address comments * fix broken test Co-authored-by: Sijun He <sijunhe@Sijuns-MacBook-Pro.local> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
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Sijun He
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title: MPNet
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- local: model_doc/mt5
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title: MT5
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- local: model_doc/nezha
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title: NEZHA
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- local: model_doc/nystromformer
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title: Nyströmformer
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- local: model_doc/openai-gpt
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@@ -122,6 +122,7 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
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1. **[MobileBERT](model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
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1. **[MPNet](model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
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1. **[MT5](model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
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1. **[Nezha](model_doc/nezha)** (from Huawei Noah’s Ark Lab) released with the paper [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
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1. **[Nyströmformer](model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
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1. **[OPT](master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
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1. **[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.
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@@ -248,6 +249,7 @@ Flax), PyTorch, and/or TensorFlow.
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| MobileBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
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| MPNet | ✅ | ✅ | ✅ | ✅ | ❌ |
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| MT5 | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Nezha | ❌ | ❌ | ✅ | ❌ | ❌ |
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| Nyströmformer | ❌ | ❌ | ✅ | ❌ | ❌ |
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| OpenAI GPT | ✅ | ✅ | ✅ | ✅ | ❌ |
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| OpenAI GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅ |
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docs/source/en/model_doc/nezha.mdx
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docs/source/en/model_doc/nezha.mdx
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<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Nezha
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## Overview
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The Nezha model was proposed in [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei et al.
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The abstract from the paper is the following:
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*The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks
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due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
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In this technical report, we present our practice of pre-training language models named NEZHA (NEural contextualiZed
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representation for CHinese lAnguage understanding) on Chinese corpora and finetuning for the Chinese NLU tasks.
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The current version of NEZHA is based on BERT with a collection of proven improvements, which include Functional
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Relative Positional Encoding as an effective positional encoding scheme, Whole Word Masking strategy,
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Mixed Precision Training and the LAMB Optimizer in training the models. The experimental results show that NEZHA
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achieves the state-of-the-art performances when finetuned on several representative Chinese tasks, including
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named entity recognition (People's Daily NER), sentence matching (LCQMC), Chinese sentiment classification (ChnSenti)
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and natural language inference (XNLI).*
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This model was contributed by [sijunhe](https://huggingface.co/sijunhe). The original code can be found [here](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-PyTorch).
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## NezhaConfig
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[[autodoc]] NezhaConfig
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## NezhaModel
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[[autodoc]] NezhaModel
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- forward
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## NezhaForPreTraining
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[[autodoc]] NezhaForPreTraining
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- forward
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## NezhaForMaskedLM
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[[autodoc]] NezhaForMaskedLM
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- forward
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## NezhaForNextSentencePrediction
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[[autodoc]] NezhaForNextSentencePrediction
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- forward
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## NezhaForSequenceClassification
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[[autodoc]] NezhaForSequenceClassification
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- forward
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## NezhaForMultipleChoice
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[[autodoc]] NezhaForMultipleChoice
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- forward
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## NezhaForTokenClassification
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[[autodoc]] NezhaForTokenClassification
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- forward
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## NezhaForQuestionAnswering
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[[autodoc]] NezhaForQuestionAnswering
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- forward
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