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.

You can reach out core contributor Mr Ikram Ali @ https://github.com/akkefa

• 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.

• Normalization
• Preprocessing
• Tokenization
• Pipeline Module
• Models
  • Pos tagger
  • Lemmatizer
  • Name entity recognition
  • Sentimental analysis
  • Image to text
  • Question answering system
• Datasets loader

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]

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}")

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

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

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

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

Code released under the MIT License.