All Git Users → yongzhuo

6 open source projects by yongzhuo

1. Nlg Yongzhuo
中文文本生成(NLG)之文本摘要(text summarization)工具包, 语料数据(corpus data), 抽取式摘要 Extractive text summary of Lead3、keyword、textrank、text teaser、word significance、LDA、LSI、NMF。(graph,feature,topic model,summarize tool or tookit)
2. Macadam
Macadam是一个以Tensorflow(Keras)和bert4keras为基础,专注于文本分类、序列标注和关系抽取的自然语言处理工具包。支持RANDOM、WORD2VEC、FASTTEXT、BERT、ALBERT、ROBERTA、NEZHA、XLNET、ELECTRA、GPT-2等EMBEDDING嵌入; 支持FineTune、FastText、TextCNN、CharCNN、BiRNN、RCNN、DCNN、CRNN、DeepMoji、SelfAttention、HAN、Capsule等文本分类算法; 支持CRF、Bi-LSTM-CRF、CNN-LSTM、DGCNN、Bi-LSTM-LAN、Lattice-LSTM-Batch、MRC等序列标注算法。
3. Nlp xiaojiang
自然语言处理(nlp),小姜机器人(闲聊检索式chatbot),BERT句向量-相似度(Sentence Similarity),XLNET句向量-相似度(text xlnet embedding),文本分类(Text classification), 实体提取(ner,bert+bilstm+crf),数据增强(text augment, data enhance),同义句同义词生成,句子主干提取(mainpart),中文汉语短文本相似度,文本特征工程,keras-http-service调用
4. Keras Textclassification
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
5. Macropodus
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)
6. Pytorch-NLU
Pytorch-NLU,一个中文文本分类、序列标注工具包,支持中文长文本、短文本的多类、多标签分类任务,支持中文命名实体识别、词性标注、分词等序列标注任务。 Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech ta…
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