DiscEvalDiscourse Based Evaluation of Language Understanding
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classyclassy is a simple-to-use library for building high-performance Machine Learning models in NLP.
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Clue中文语言理解测评基准 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard
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ChineseglueLanguage Understanding Evaluation benchmark for Chinese: datasets, baselines, pre-trained models,corpus and leaderboard
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Mutual labels: glue, bert
tfbert基于tensorflow1.x的预训练模型调用，支持单机多卡、梯度累积，XLA加速，混合精度。可灵活训练、验证、预测。
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Mutual labels: bert, electra
WSDM-Cup-2019[ACM-WSDM] 3rd place solution at WSDM Cup 2019, Fake News Classification on Kaggle.
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Mutual labels: bert, natural-language-understanding
Tokenizers💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
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AiSpaceAiSpace: Better practices for deep learning model development and deployment For Tensorflow 2.0
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Mutual labels: bert, electra
Pycorrectorpycorrector is a toolkit for text error correction. 文本纠错，Kenlm，Seq2Seq_Attention，BERT，MacBERT，ELECTRA，ERNIE，Transformer等模型实现，开箱即用。
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Mt DnnMulti-Task Deep Neural Networks for Natural Language Understanding
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label-studio-transformersLabel data using HuggingFace's transformers and automatically get a prediction service
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Kevinpro-NLP-demoAll NLP you Need Here. 个人实现了一些好玩的NLP demo，目前包含13个NLP应用的pytorch实现
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bert extension tfBERT Extension in TensorFlow
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text2classMulti-class text categorization using state-of-the-art pre-trained contextualized language models, e.g. BERT
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Fill-the-GAP[ACL-WS] 4th place solution to gendered pronoun resolution challenge on Kaggle
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Filipino-Text-BenchmarksOpen-source benchmark datasets and pretrained transformer models in the Filipino language.
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Bert As ServiceMapping a variable-length sentence to a fixed-length vector using BERT model
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Transformers🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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CLUE pytorchCLUE baseline pytorch CLUE的pytorch版本基线
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Person reid baseline pytorchPytorch ReID: A tiny, friendly, strong pytorch implement of object re-identification baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial
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Mutual labels: baseline
