---
license: cc-by-4.0
datasets:
- DSL-13-SRMAP/Telugu-Dataset
language:
- te
tags:
- sentiment-analysis
- text-classification
- telugu
- indic-languages
- muril
- rationale-supervision
- explainable-ai
base_model: google/muril-base-cased
pipeline_tag: text-classification
metrics:
- accuracy
- f1
- auroc
---

# MuRIL_WR

## Model Description

**MuRIL_WR** is a Telugu sentiment classification model built on **MuRIL (Multilingual Representations for Indian Languages)**, a Transformer-based BERT model specifically designed for **Indian languages**, including Telugu and English.

MuRIL is pretrained on a **large and diverse corpus of Indian language text**, including web data, religious scriptures, and news content. In contrast to general multilingual models such as mBERT and XLM-R, MuRIL is better suited to capture **Telugu morphology, syntax, and linguistic structure**.

The suffix **WR** denotes **With Rationale supervision**. This model is fine-tuned using both **sentiment labels and human-annotated rationales**, enabling improved alignment between model predictions and human-identified evidence.

---

## Pretraining Details

- **Pretraining corpus:** Indian language text from web sources, religious texts, and news data
- **Training objectives:**
  - Masked Language Modeling (MLM)
  - Translation Language Modeling (TLM)
- **Language coverage:** 17+ Indian languages, including Telugu and English

---

## Training Data

- **Fine-tuning dataset:** Telugu-Dataset
- **Task:** Sentiment classification
- **Supervision type:** Label + rationale supervision
- **Rationales:** Token-level human-annotated evidence spans

---

## Rationale Supervision

During fine-tuning, **human-provided rationales** guide model learning. Alongside the standard classification loss, an **auxiliary rationale loss** encourages the model’s attention or explanation scores to align with annotated rationale tokens.

This approach improves:

- Interpretability of sentiment predictions
- Alignment between model explanations and human judgment
- Plausibility of generated explanations

---

## Intended Use

This model is intended for:

- Explainable Telugu sentiment classification
- Rationale-supervised learning experiments
- Indian-language explainability research
- Comparative evaluation against label-only (WOR) baselines

MuRIL_WR is particularly effective for **informal, conversational, and social media Telugu text**, where rationale supervision further enhances explanation quality.

---

## Performance Characteristics

Compared to label-only training, rationale supervision typically improves **explanation plausibility** while maintaining competitive sentiment classification performance.

### Strengths

- Strong Telugu-specific linguistic modeling
- Human-aligned explanations via rationale supervision
- Suitable for explainable AI benchmarking in Indian languages

### Limitations

- Requires human-annotated rationales, increasing annotation effort
- Pretraining data bias toward informal text may affect formal Telugu tasks
- Classification gains over WOR may be modest

---

## Use in Explainability Evaluation

**MuRIL_WR** is well-suited for evaluation with explanation frameworks such as FERRET, enabling:

- **Faithfulness evaluation:** How well explanations support model predictions
- **Plausibility evaluation:** How closely explanations align with human rationales

---

## References

- Khanuja et al., 2021  
- Joshi, 2022  
- Das et al., 2022  
- Rajalakshmi et al., 2023