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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 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. The original code can be found here. |
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Resources |
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Text classification task guide |
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Token classification task guide |
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Question answering task guide |
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Masked language modeling task guide |
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Multiple choice task guide |
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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 |