---
license: cc-by-4.0
tags:
- sentiment-classification
- telugu
- indicbert
- indian-languages
- baseline
language: te
datasets:
- DSL-13-SRMAP/TeSent_Benchmark-Dataset
model_name: IndicBERT_WOR
---

# IndicBERT_WOR: IndicBERT Telugu Sentiment Classification Model (Without Rationale)

## Model Overview

**IndicBERT_WOR** is a Telugu sentiment classification model based on **IndicBERT (ai4bharat/indicBERTv2-MLM-only)**, a multilingual BERT-like transformer developed by AI4Bharat.  
The "WOR" in the model name stands for "**Without Rationale**", meaning this model is trained only with sentiment labels from the TeSent_Benchmark-Dataset and **does not use human-annotated rationales**.

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## Model Details

- **Architecture:** IndicBERT (BERT-like, multilingual for Indian languages)
- **Pretraining Data:** OSCAR and AI4Bharat curated corpora for 12 Indian languages (including Telugu and English)
- **Pretraining Objective:** Masked Language Modeling (MLM)
- **Fine-tuning Data:** [TeSent_Benchmark-Dataset](https://huggingface.co/datasets/dsl-13-srmap/tesent_benchmark-dataset), using only sentence-level sentiment labels (positive, negative, neutral); rationale annotations are disregarded
- **Task:** Sentence-level sentiment classification (3-way)
- **Rationale Usage:** **Not used** during training or inference ("WOR" = Without Rationale)

---

## Intended Use

- **Primary Use:** Benchmarking Telugu sentiment classification on the TeSent_Benchmark-Dataset as a **baseline** for models trained without rationales
- **Research Setting:** Well suited for monolingual Telugu NLP tasks, especially in low-resource and explainable AI research

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## Why IndicBERT?

IndicBERT provides language-aware tokenization, clean embeddings, and faster training for Indian languages.  
It is well suited for monolingual Telugu tasks, but does not support code-mixed data or cross-lingual transfer. For Telugu sentiment classification, IndicBERT delivers efficient and accurate results due to its tailored pretraining.

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## Performance and Limitations

**Strengths:**  
- Language-aware tokenization and embeddings for Telugu
- Faster training and inference compared to larger multilingual models
- Robust baseline for monolingual Telugu sentiment classification

**Limitations:**  
- Not suitable for code-mixed or cross-lingual tasks
- Telugu-specific models may outperform on highly nuanced or domain-specific data
- Since rationales are not used, the model cannot provide explicit explanations for its predictions

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## Training Data

- **Dataset:** [TeSent_Benchmark-Dataset](https://huggingface.co/datasets/dsl-13-srmap/tesent_benchmark-dataset)
- **Data Used:** Only the **Content** (Telugu sentence) and **Label** (sentiment label) columns; **rationale** annotations are ignored for IndicBERT_WOR training

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## Language Coverage

- **Language:** Telugu (`te`)
- **Model Scope:** Strictly monolingual Telugu sentiment classification

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## Citation and More Details

For detailed experimental setup, evaluation metrics, and comparisons with rationale-based models, **please refer to our paper**.



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## License

Released under [CC BY 4.0](LICENSE).