Create README.md

README.md

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+---
+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