“中文长文本分类、短句子分类、多标签分类、两句子相似度（Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short），字词句向量嵌入层（embeddings）和网络层（graph）构建基类，FastText，TextCNN，CharCNN，TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN”

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GitPedia Card440/002

ChineseGLUE

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

TurboTransformers

Tencent

C++albertbert

“a fast and user-friendly runtime for transformer inference (Bert, Albert, GPT2, Decoders, etc) on CPU and GPU.”

CLUENER2020

CLUEbenchmark

Pythonalbertbert

“CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition”

TencentPretrain

Tencent

Pythonalbertbart

“Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo ”

CLUECorpus2020

CLUEbenchmark

albertbert

“Large-scale Pre-training Corpus for Chinese 100G 中文预训练语料 ”

Bert-Multi-Label-Text-Classification

lonePatient

Pythonalbertbert

“This repo contains a PyTorch implementation of a pretrained BERT model for multi-label text classification.”

curated-transformers

explosion

Pythonalbertbert

“🤖 A PyTorch library of curated Transformer models and their composable components”

CLUEPretrainedModels

CLUEbenchmark

Pythonalbertbert

“高质量中文预训练模型集合：最先进大模型、最快小模型、相似度专门模型”

albert_pytorch

lonePatient

Pythonalbertbert

“A Lite Bert For Self-Supervised Learning Language Representations”

Macropodus

yongzhuo

Pythonalbertcalulator

“自然语言处理工具Macropodus，基于Albert+BiLSTM+CRF深度学习网络架构，中文分词，词性标注，命名实体识别，新词发现，关键词，文本摘要，文本相似度，科学计算器，中文数字阿拉伯数字(罗马数字)转换，中文繁简转换，拼音转换。tookit(tool) of NLP，CWS(chinese word segnment)，POS(Part-Of-Speech Tagging)，NER(name entity recognition)，Find(new words discovery)，Keyword(keyword extraction)，Summarize(text summarization)，Sim(text similarity)，Calculate(scientific calculator)，Chi2num(chinese number to arabic number)”

BERT-Relation-Extraction

plkmo

Pythonalbertbert

“PyTorch implementation for "Matching the Blanks: Distributional Similarity for Relation Learning" paper”