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Artificial Intelligence · Major Project

Text Classification for Indian Languages

Classifies Hindi and Marathi news text with IndicBERT

3-4 weeks build

Overview

Topic classification for Hindi and Marathi news using the public IndicNLP news article dataset from AI4Bharat. A TF-IDF baseline is trained first, then IndicBERT is fine-tuned for each language and a multilingual model is tested across both. The report covers Devanagari normalisation and why tokenisers built for English handle these scripts poorly.

What makes it stand up

  • 01Devanagari text normalisation and cleaning pipeline
  • 02TF-IDF baseline before any transformer is trained
  • 03IndicBERT fine-tuned separately for Hindi and Marathi
  • 04Cross-lingual test: train on one language, evaluate on the other
  • 05Per-class F1 and confusion matrices in the report

Module breakdown

  1. 01

    Data

    IndicNLP news categories, splits and normalisation.

  2. 02

    Baseline

    Character and word TF-IDF with linear classifiers.

  3. 03

    Fine-tuning

    IndicBERT sequence classification per language.

  4. 04

    Cross-lingual

    Zero-shot transfer experiments between languages.

  5. 05

    API

    FastAPI endpoint with a simple bilingual web form.

After this, you will be able to

  • Explain why subword tokenisation matters for Indic scripts
  • Defend the baseline-first experimental design
  • Demo classifying a Hindi or Marathi headline live

Technology stack

  • Python
  • PyTorch
  • Transformers
  • Hugging Face
  • FastAPI

Complete kit

₹7,299₹12,399
-41%
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Delivered in under 48 hours

Level
Major Project
Domain
Artificial Intelligence
Build time
3-4 weeks
Difficulty

You receive

  • Complete, commented source code
  • 60-90 page project report (IEEE format)
  • Editable presentation deck
  • Architecture, ER and UML diagrams
  • Local setup and deployment guide
  • Sample dataset or seed data
  • Viva question bank with answers
  • One 45-minute walkthrough call
  • 30 days of doubt-clearing support
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