Netl Automatic Topic Labelling
Generating labels for topics automatically using neural embeddings
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Nlp Journey | 1,563 | 5 months ago | 3 | April 29, 2020 | apache-2.0 | Python | ||||
| Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc. All codes are implemented intensorflow 2.0. | ||||||||||
| Sentiment Analysis | 296 | 6 years ago | 13 | Python | ||||||
| Chinese Shopping Reviews sentiment analysis | ||||||||||
| Ml Projects | 243 | 3 years ago | n,ull | |||||||
| ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python | ||||||||||
| Textclf | 217 | 10 months ago | 8 | mit | Python | |||||
| TextClf ：基于Pytorch/Sklearn的文本分类框架，包括逻辑回归、SVM、TextCNN、TextRNN、TextRCNN、DRNN、DPCNN、Bert等多种模型，通过简单配置即可完成数据处理、模型训练、测试等过程。 | ||||||||||
| Netl Automatic Topic Labelling | 184 | 9 months ago | 10 | apache-2.0 | Python | |||||
| Generating labels for topics automatically using neural embeddings | ||||||||||
| Hyperfoods | 56 | 4 years ago | n,ull | mit | HTML | |||||
| It was built a web application as a step forward in building a 3D recommendation system. | ||||||||||
| Word2vec Sentiment | 50 | 6 years ago | Python | |||||||
| 基于Word2Vec+SVM对电商的评论数据进行情感分析 | ||||||||||
| Text Classification Cn | 33 | 3 years ago | 1 | Python | ||||||
| 中文文本分类实践，基于搜狗新闻语料库，采用传统机器学习方法以及预训练模型等方法 | ||||||||||
| Ml Spam Sms Classification | 23 | 3 years ago | n,ull | Jupyter Notebook | ||||||
| Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification | ||||||||||
| Sentimentanalysis | 21 | 4 years ago | n,ull | Jupyter Notebook | ||||||
| (BOW, TF-IDF, Word2Vec, BERT) Word Embeddings + (SVM, Naive Bayes, Decision Tree, Random Forest) Base Classifiers + Pre-trained BERT on Tensorflow Hub + 1-D CNN and Bi-Directional LSTM on IMDB Movie Reviews Dataset | ||||||||||
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