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中文语言理解测评基准 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard

Mt Dnn ⭐ 2,154

Multi-Task Deep Neural Networks for Natural Language Understanding

Chineseglue ⭐ 1,765

Language Understanding Evaluation benchmark for Chinese: datasets, baselines, pre-trained models,corpus and leaderboard

Albert_pytorch ⭐ 423

A Lite Bert For Self-Supervised Learning Language Representations

Sembert ⭐ 264

Semantics-aware BERT for Language Understanding (AAAI 2020)

Bert Of Theseus ⭐ 186

⛵️The official PyTorch implementation for "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing" (EMNLP 2020).

This is the implementation of the paper AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning (https://arxiv.org/abs/2205.12410).

Pkd For Bert Model Compression ⭐ 82

pytorch implementation for Patient Knowledge Distillation for BERT Model Compression

Electra_pytorch ⭐ 73

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Clue_pytorch ⭐ 61

CLUE baseline pytorch CLUE的pytorch版本基线

Glue Bert4keras ⭐ 47

基于bert4keras的GLUE基准代码

Code for the paper "PALBERT: Teaching ALBERT to Ponder", NeurIPS 2022 Spotlight

AiSpace: Better practices for deep learning model development and deployment For Tensorflow 2.0

IndoLEM is a comprehensive Indonesian NLU benchmark, comprising three pillars NLP task: morpho-syntax, semantic, and discourse. Presented in COLING 2020.

Pragmeval ⭐ 18

Discourse Based Evaluation of Language Understanding

In MT-BERT we reproduce a neural language understanding model which implements a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple NLU tasks.

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