spaCy
spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python. spaCy is designed specifically for production use and helps you build applications that process and “understand” large volumes of text. It can be used to build information extraction or natural language understanding systems, or to pre-process text for deep learning.
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spaCy is designed to help you do real work — to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive.It offers following capabilities:
- Blazing fast - spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython.
- Awesome ecosystem - Choose from a variety of plugins, integrate with your machine learning stack and build custom components and workflows.
- Support- Support for 60+ languages
Official Website Link : https://spacy.io/
Links
Documentation
Documentation & Developer Guides
Using SpaCy with Weights & Biases
Track your spaCy model's training metrics as well as save and version your models and datasets with W&B
Reproducible spaCy NLP Experiments with Weights & Biases
How to use Weights & Biases and spaCy to train custom, reproducible NLP pipelines
Hyperparameter Search with spaCy and Weights & Biases
Find the optimal hyperparameters for your spaCy project using W&B Sweeps
Visualizing Prodigy Datasets Using W&B Tables
Use the W&B & Prodigy integration to upload your Prodigy annotated datasets to W&B for easier visualization
Named Entity Recognition with W&B and spaCy
Visualize and explore named entities with spaCy in W&B
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