Add Telugu Sentiment Classification Example using DistilBERT
Author: Sai-Lakshmi-Bala-Mounika-Gandikota
Pull request: yes

# What does this PR do?

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## What does this PR do?
This PR introduces a new example under `examples/te_sentiment` for **Telugu Sentiment Analysis** using the `transformers` library.  
It fine-tunes the multilingual model `distilbert-base-multilingual-cased` on a small Telugu dataset to classify sentiment as Positive, Negative, or Neutral.

## Why is this needed?
Currently, there are no examples in the repository demonstrating sentiment analysis for **Telugu**, one of the major Indian languages.  
This contribution expands multilingual support and showcases how Hugging Face models can be fine-tuned effectively on low-resource languages.

## Key Features
- `run_te_sentiment.py`: Training script using Hugging Face `Trainer` API.
- `README.md`: Explains dataset preparation, training commands, and evaluation metrics.
- Uses `datasets` library for seamless data loading and preprocessing.
- Lightweight configuration to allow users to fine-tune models on small datasets.

## Test Plan
- Verified that the example runs successfully on a local environment with GPU.
- Ensured model fine-tuning completes and evaluation metrics (accuracy, F1) are printed.
- Checked README instructions for clarity and correctness.

## Related Issues
None.

## Checklist
- [x] I have read and followed the Contributing Guidelines.
- [x] The code runs without errors using the provided dataset.
- [x] Documentation and comments are included.
- [x] No existing functionality is broken.

@stevhliu @ArthurZucker @CyrilVallez — Please review this PR.
This adds a new Telugu sentiment classification example under `examples/te_sentiment`. 😊


Comments:
Hi @stevhliu @ArthurZucker @CyrilVallez 👋
I’ve added a new example for Telugu Sentiment Classification under examples/te_sentiment.
It demonstrates fine-tuning distilbert-base-multilingual-cased on a small Telugu dataset.
Please review this PR when you get a chance — thank you! 😊

Hi 👋, I’m participating in Hacktoberfest 2025!
Could you please add the hacktoberfest-accepted label if this PR qualifies?
Thank you for reviewing and supporting contributors 🌸

What are you trying to do? 🤔

Hi @vjaykrsna 👋
I’m contributing a new example script called te_sentiment, which demonstrates Telugu Sentiment Classification using distilbert-base-multilingual-cased.
The goal is to add multilingual coverage for Indian languages in the examples section.
It includes:

run_te_sentiment.py — for fine-tuning on a small Telugu dataset

README.md — explaining dataset, training steps, and results

I wanted to make this contribution as part of Hacktoberfest 2025 while also adding real value to the repository. 😊

I see,
Keep up the good work 
I was afraid to see two empty files at first 😅