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Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
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
I have read and followed the Contributing Guidelines.
The code runs without errors using the provided dataset.
Documentation and comments are included.
No existing functionality is broken.
@stevhliu@ArthurZucker@Cyrilvallez — Please review this PR.
This adds a new Telugu sentiment classification example under examples/te_sentiment. 😊
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 🌸
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. 😊
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What does this PR do?
Fixes # (issue)
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documentation guidelines, and
here are tips on formatting docstrings.
Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
What does this PR do?
This PR introduces a new example under
examples/te_sentimentfor Telugu Sentiment Analysis using thetransformerslibrary.It fine-tunes the multilingual model
distilbert-base-multilingual-casedon 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 FaceTrainerAPI.README.md: Explains dataset preparation, training commands, and evaluation metrics.datasetslibrary for seamless data loading and preprocessing.Test Plan
Related Issues
None.
Checklist
@stevhliu @ArthurZucker @Cyrilvallez — Please review this PR.
This adds a new Telugu sentiment classification example under
examples/te_sentiment. 😊