hugginglearners/ml-news-classify-fastai

Malayalam (മലയാളം) Classifier using fastai (Working in Progress)

🥳 This model is my attempt to use machine learning using Malayalam Language. Huge inspiration from Malayalam Text Classifier. Courtesy to @waydegilliam for blurr

🌈 മലയാളത്തിൽ മെഷീൻ ലീർണിങ് പഠിക്കാനും പിന്നേ പരിചയപ്പെടാനും, to be continued...

How its built ? & How to use ?

Please find the notebook used for training the model

Usage:

First, install the utilities to load the model as well as blurr, which was used to train this model.

!pip install huggingface_hub[fastai]
!git clone https://github.com/ohmeow/blurr.git && cd blurr && pip install -e ".[dev]"

from huggingface_hub import from_pretrained_fastai
learner = from_pretrained_fastai("rajeshradhakrishnan/ml-news-classify-fastai")


sentences = ["ഓഹരി വിപണി തകരുമ്പോള്‍ നിക്ഷേപം എങ്ങനെ സുരക്ഷിതമാക്കാം",
             "വാര്‍ണറുടെ ഒറ്റക്കയ്യന്‍ ക്യാച്ചില്‍ അമ്പരന്ന് ക്രിക്കറ്റ് ലോകം"]

probs = learner.predict(sentences)
# 'business', 'entertainment', 'sports', 'technology'
for idx in range(len(sentences)):
  print(f"Probability that sentence '{sentences[idx]}' is business is: {100*probs[idx]['probs'][0]:.2f}%")
  print(f"Probability that sentence '{sentences[idx]}' is entertainment is: {100*probs[idx]['probs'][1]:.2f}%")
  print(f"Probability that sentence '{sentences[idx]}' is sports is: {100*probs[idx]['probs'][2]:.2f}%")
  print(f"Probability that sentence '{sentences[idx]}' is technology is: {100*probs[idx]['probs'][3]:.2f}%")

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Model card

Model description

The is a Malayalam classifier model for labels 'business', 'entertainment', 'sports', 'technology'.

Intended uses & limitations

The model can be used to categorize malayalam new sfeed.

Training and evaluation data

Data is from the AI4Bharat-IndicNLP Dataset and wrapper to extract only Malayalam data( HF dataset)!.

Citation

@article{kunchukuttan2020indicnlpcorpus,
    title={AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages},
    author={Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
    year={2020},
    journal={arXiv preprint arXiv:2005.00085},
}