Natural Language Processing for the next decade. Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Syntactic & Semantic Dependency Parsing, Document Classification
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Apr 16, 2024 - Python
Natural Language Processing for the next decade. Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Syntactic & Semantic Dependency Parsing, Document Classification
💫 Industrial-strength Natural Language Processing (NLP) in Python
大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP
all kinds of text classification models and more with deep learning
Natural Language Processing Best Practices & Examples
CNN-RNN中文文本分类,基于TensorFlow
Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
搜索所有中文NLP数据集,附常用英文NLP数据集
Snips Python library to extract meaning from text
State of the Art Natural Language Processing
Accelerated deep learning R&D
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.
The Python Code Tutorials
⭐️ NLP Algorithms with transformers lib. Supporting Text-Classification, Text-Generation, Information-Extraction, Text-Matching, RLHF, SFT etc.
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
Text Classification Algorithms: A Survey
[ACL 2023] One Embedder, Any Task: Instruction-Finetuned Text Embeddings
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
Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.
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