Natural Language Processing Best Practices & Examples
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Updated
Aug 30, 2022 - Python
Natural Language Processing Best Practices & Examples
Facilitating the design, comparison and sharing of deep text matching models.
A synthetic data generator for text recognition
Algorithms for outlier, adversarial and drift detection
🎨 ASCII art library for Python
Python MUD/MUX/MUSH/MU* development system
A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
Interact, analyze and structure massive text, image, embedding, audio and video datasets
A menu for pygame (pygame-ce also supported!). Simple, and easy to use
Facilitating the design, comparison and sharing of deep text matching models.
🗣️ Tool to generate adversarial text examples and test machine learning models against them
A sentence segmenter that actually works!
Specify a github or local repo, github pull request, arXiv or Sci-Hub paper, Youtube transcript or documentation URL on the web and scrape into a text file and clipboard for easier LLM ingestion
Detect and fix skew in images containing text
A framework for cleaning Chinese dialog data
Unofficial implementation of Google's FNet: Mixing Tokens with Fourier Transforms
Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector
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