Stars
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Extract Replies to a Specific Tweet using Python and Tweepy - Scrape Any Tweet Reply
Python script to pull replies to a specific Tweet and extract user mentions
Sentiment Analysis using Emoji ranking and practical POS scoring approach
This project allows you to get a complete list of twitter thread replies and be notified new twitter threads replies
📘 dict subclass with keylist/keypath support, built-in I/O operations (base64, csv, html, ini, json, pickle, plist, query-string, toml, xls, xml, yaml), s3 support and many utilities.
Pure python implementation of product quantization for nearest neighbor search
A library for efficient similarity search and clustering of dense vectors.
Limiter, compressor, convolver, equalizer and auto volume and many other plugins for PipeWire applications
Quickly and accurately render even the largest data.
Makes Interactive Chart Widget, Cleans raw data, Runs baseline models, Interactive hyperparameter tuning & tracking
State of the Art Natural Language Processing
Library for translating between 200 languages. Built on 🤗 transformers.
Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Snips Python library to extract meaning from text
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI )
PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks…
Tools, wrappers, etc... for data science with a concentration on text processing
Basic Utilities for PyTorch Natural Language Processing (NLP)
An open-source NLP research library, built on PyTorch.
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
Library for fast text representation and classification.
Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet
Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning