Urduhack: A Python NLP library for Urdu language

Urduhack is a NLP library for urdu language. It comes with a lot of battery included features to help you process Urdu data in the easiest way possible.

Our Goal

• Academic users Easier experimentation to prove their hypothesis without coding from scratch.
• NLP beginners Learn how to build an NLP project with production level code quality.
• NLP developers Build a production level application within minutes.

🔥 Features Support

• Normalization
• Preprocessing
• Tokenization
• Pipeline Module
• Models
  • Pos tagger
  • Sentimental analysis
  • Sentence classification
  • Documents classification
  • Name entity recognition
  • Image to text
  • Speech to text
• Datasets loader

🛠 Installation

Urduhack officially supports Python 3.6–3.7, and runs great on PyPy.

Installing with tensorflow cpu version.

$ pip install urduhack[tf]

Installing with tensorflow gpu version.

$ pip install urduhack[tf-gpu]

Usage

import urduhack

# Downloading models
urduhack.download()

nlp = urduhack.Pipeline()
text = ""
doc = nlp(text)

for sentence in doc.sentences:
    print(sentence.text)
    for word in sentence.words:
        print(f"{word.text}\t{word.pos}")

    for token in sentence.tokens:
        print(f"{token.text}\t{token.ner}")

🔗 Documentation

Fantastic documentation is available at https://urduhack.readthedocs.io/

How to Contribute

1. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug. There is a Contributor Friendly tag for issues that should be ideal for people who are not very familiar with the codebase yet.
2. Write a test which shows that the bug was fixed or that the feature works as expected.
3. Send a pull request and bug the maintainer until it gets merged and published. :)

👍 Contributors

Special thanks to everyone who contributed to getting the UrduHack to the current state.

Backers

Thank you to all our backers! 🙏 [Become a backer]

Sponsors

Support this project by becoming a sponsor. [Become a sponsor]

📝 Copyright and license

Code released under the MIT License.