Transformer-QG-on-SQuADImplement Question Generator with SOTA pre-trained Language Models (RoBERTa, BERT, GPT, BART, T5, etc.)
Stars: ✭ 28 (+40%)
Mutual labels: bert, question-generation, roberta
classyclassy is a simple-to-use library for building high-performance Machine Learning models in NLP.
Stars: ✭ 61 (+205%)
Mutual labels: sequence-to-sequence, bert
BertvizTool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)
Stars: ✭ 3,443 (+17115%)
Mutual labels: bert, roberta
Chinese Bert WwmPre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）
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Mutual labels: bert, roberta
Roberta zhRoBERTa中文预训练模型: RoBERTa for Chinese
Stars: ✭ 1,953 (+9665%)
Mutual labels: bert, roberta
vietnamese-robertaA Robustly Optimized BERT Pretraining Approach for Vietnamese
Stars: ✭ 22 (+10%)
Mutual labels: bert, roberta
KLUE📖 Korean NLU Benchmark
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Mutual labels: bert, roberta
question generatorAn NLP system for generating reading comprehension questions
Stars: ✭ 188 (+840%)
Mutual labels: bert, question-generation
roberta-wwm-base-distillthis is roberta wwm base distilled model which was distilled from roberta wwm by roberta wwm large
Stars: ✭ 61 (+205%)
Mutual labels: bert, roberta
Albert zhA LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS, 海量中文预训练ALBERT模型
Stars: ✭ 3,500 (+17400%)
Mutual labels: bert, roberta
CLUE pytorchCLUE baseline pytorch CLUE的pytorch版本基线
Stars: ✭ 72 (+260%)
Mutual labels: bert, roberta
Clue中文语言理解测评基准 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard
Stars: ✭ 2,425 (+12025%)
Mutual labels: bert, roberta
ercEmotion recognition in conversation
Stars: ✭ 34 (+70%)
Mutual labels: bert, roberta
text2textText2Text: Cross-lingual natural language processing and generation toolkit
Stars: ✭ 188 (+840%)
Mutual labels: bert, question-generation
beirA Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
Stars: ✭ 738 (+3590%)
Mutual labels: bert, question-generation
Text-SummarizationAbstractive and Extractive Text summarization using Transformers.
Stars: ✭ 38 (+90%)
Mutual labels: bert, roberta
berserkerBerserker - BERt chineSE woRd toKenizER
Stars: ✭ 17 (-15%)
Mutual labels: sequence-to-sequence, bert
