Popular GitHub Repositories for Count Vectorizer

Top 200 deep learning Github repositories sorted by the number of stars.

Developer friendly Natural Language Processing ?

Starter code to solve real world text data problems. Includes: Gensim Word2Vec, phrase embeddings, Text Classification with Logistic Regression, word count with pyspark, simple text preprocessing, pre-trained embeddings and more.

A Pytorch implementation of Sparsely-Gated Mixture of Experts, for massively increasing the parameter count of language models

Top 100 trending deep learning repositories sorted by the number of stars gained on a specific day.

Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx

中文情感分析库(Chinese Sentiment))可对文本进行情绪分析、正负情感分析。Text analysis, supporting multiple methods including word count, readability, document similarity, sentiment analysis, Word2Vec .

文本分析包，支持字数统计、可读性、文档相似度、情感分析在内的多种文本分析方法。Text analysis, supporting multiple methods including word count, readability, document similarity, sentiment analysis, Word2Vec/GloVe, and Large Language Models (LLMs).

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Run a Jupyter Notebook to detect, track, and count cars in a video using Maximo Visual Insights (formerly PowerAI Vision) and OpenCV

RETVec is an efficient, multilingual, and adversarially-robust text vectorizer.

This project aims to count every vehicle (motorcycle, bus, car, cycle, truck, train) detected in the input video using YOLOv3 object-detection algorithm.

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Count number of parameters / MACs / FLOPS for ONNX models.

A tensorflow based Faster RCNN inception v2 python model to detect and count humans in real time images, videos & camera.

[ICIP'24 Lecture Presentation] Official implementation of "CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer".

Document Search Engine project with TF-IDF abd Google universal sentence encoder model

This project employs emotion detection in textual data, specifically trained on Twitter data comprising tweets labeled with corresponding emotions. It seamlessly takes text inputs and provides the most fitting emotion assigned to it.

Movie Recommendation Chatbot provides information about a movie like plot, genre, revenue, budget, imdb rating, imdb links, etc. The model was trained with Kaggle’s movies metadata dataset. To give a recommendation of similar movies, Cosine Similarity and TFID vectorizer were used. Slack API was used to provide a Front End for the chatbot. IBM Watson was used to link the Python code for Natural Language Processing with the front end hosted on Slack API. Libraries like nltk, sklearn, pandas and nlp were used to perform Natural Language Processing and cater to user queries and responses.

NLP based Classification Model that predicts a person's personality type as one of the 16 Myers Briggs personality types. Extremely challenging project dealing with correlation between human psychology and casual writing styles and handling heavily imbalanced classes. Check the app here - https://mb-predictor-motetuzs5q-uc.a.run.app/