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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, TextGCNnlp_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)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)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。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、抽取式文本摘要等序列标注任务。 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 speechatglm-maths
chatglm-6b微调/LORA/PPO/推理, 样本为自动生成的整数/小数加减乘除运算, 可gpu/cpuChatGLM2-SFT
ChatGLM2-6B微调, SFT/LoRA, instruction finetuneLLM-SFT
中文大模型微调(LLM-SFT), 数学指令数据集MWP-Instruct, 支持模型(ChatGLM-6B, LLaMA, Bloom-7B, baichuan-7B), 支持(LoRA, QLoRA, DeepSpeed, UI, TensorboardX), 支持(微调, 推理, 测评, 接口)等.Qwen-SFT
阿里通义千问(Qwen-7B-Chat/Qwen-7B), 微调/LORA/推理layoutlmv3-layoutxlm-chinese
chinese document classification of layoutlmv3 and layoutxlmTookit-Sihui
Tookit-Sihui, a tool of some common algorithm, AI文本混合科学计算器(calculator-sihui), 句子词频-逆文本频率(TF-IDF),搜索BM25, 前缀树搜索关键词(trietree), 模板匹配-递归函数(func_recursive),中文数字转阿拉伯数字(chinese to number),阿拉伯数字转汉语数字, HMM, CRFLlama2-SFT
Llama2-SFT, Llama-2-7B微调(transformers)/LORA(peft)/推理gemma-sft
Gemma-SFT, gemma-2b/gemma-7b微调(finetune,transformers)/LORA(peft)/推理(inference)char_CNN_text_classification_Chinese2Pinyin
char_CNN_text_classification_Chinese2Pinyin,中文转拼音实例-基于字符的卷积神经网络-超短文本分类-主要代码为lc222的github项目,有HTTP访问等pytorch-loss
pytorch版损失函数,改写自科学空间文章,【通过互信息思想来缓解类别不平衡问题】、【将“softmax+交叉熵”推广到多标签分类问题】ChatGLM3-SFT
chatglm3-6b, 微调/LORA/推理/单机多卡/deepspeed/支持多轮对话InternLM-SFT
InternLM-7B微调, SFT/LoRA, instruction finetuneMacroGPT-Pretrain
macrogpt大模型全量预训练(1b3,32层), 多卡deepspeed/单卡adafactorOpen-Information-Extraction-System
中文开放信息抽取系统, open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)yongzhuo_spider
爬取政务Government数据、菜谱cookbook数据、百科QA问答数据等,爬虫Word-Dict
构建中文词频词典-搜索引擎式切词(create chinese word dict of freq by segnment of search)Text-Analysis
文本数据分析, Text-AnalysisJavaLearning
A project of Java Learning、webmagic、mongo、arango、redis、mysqlleetcode-in-out
leetcode一些热门题型的python代码,包括输入输出。leetcode of hot, which Includes input and output.near-synonym
near-synonym, 中文反义词/近义词(antonym/synonym)工具包.pytorch-model-to-tensorflow
transformers-model of pytorch1.x to tensorflow2.x, deploy for tf-servingTft-Preprocess
tensorflow-transformer(tft) of pre-processing and post-processing of text-classificationweb-demo
web-demo of http and uiqwen2-sft
Qwen1.5-SFT(阿里, Ali), Qwen_Qwen1.5-2B-Chat/Qwen_Qwen1.5-7B-Chat微调(transformers)/LORA(peft)/推理Love Open Source and this site? Check out how you can help us