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* 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>
76 lines
2.7 KiB
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76 lines
2.7 KiB
Plaintext
<!--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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# 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 |