Create README.md

README.md

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+---
+license: cc-by-4.0
+tags:
+- sentiment-classification
+- telugu
+- xlm-r
+- multilingual
+- baseline
+language: te
+datasets:
+- DSL-13-SRMAP/TeSent_Benchmark-Dataset
+model_name: XLM-R_WOR
+---
+
+# XLM-R_WOR: XLM-RoBERTa Telugu Sentiment Classification Model (Without Rationale)
+
+## Model Overview
+
+**XLM-R_WOR** is a Telugu sentiment classification model based on **XLM-RoBERTa (XLM-R)**, a general-purpose multilingual transformer developed by Facebook AI.  
+The "WOR" in the model name stands for "**Without Rationale**", indicating that this model is trained only with sentiment labels from the TeSent_Benchmark-Dataset and **does not use human-annotated rationales**.
+
+---
+
+## Model Details
+
+- **Architecture:** XLM-RoBERTa (transformer-based, multilingual)
+- **Pretraining Data:** 2.5TB of filtered Common Crawl data across 100+ languages, including Telugu
+- **Pretraining Objective:** Masked Language Modeling (MLM), no Next Sentence Prediction (NSP)
+- **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, especially as a **baseline** for models trained without rationales
+- **Research Setting:** Suitable for cross-lingual and multilingual NLP research, as well as explainable AI in low-resource settings
+
+---
+
+## Why XLM-R?
+
+XLM-R is designed for cross-lingual understanding and contextual modeling, providing strong transfer learning capabilities and improved downstream performance compared to mBERT. When fine-tuned with local Telugu data, XLM-R delivers solid results for sentiment analysis.  
+However, Telugu-specific models like MuRIL or L3Cube-Telugu-BERT may offer better cultural and linguistic alignment for purely Telugu tasks.
+
+---
+
+## Performance and Limitations
+
+**Strengths:**  
+- Strong transfer learning and contextual modeling for multilingual NLP
+- Good performance for Telugu sentiment analysis when fine-tuned with local data
+- Useful as a cross-lingual and multilingual baseline
+
+**Limitations:**  
+- May be outperformed by Telugu-specific models for culturally nuanced tasks
+- Requires sufficient labeled Telugu data for best performance
+- Since rationales are not used, the model cannot provide explicit explanations for its predictions
+
+---
+
+## 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 XLM-R_WOR training
+
+---
+
+## Language Coverage
+
+- **Language:** Telugu (`te`)
+- **Model Scope:** This implementation and evaluation focus strictly on Telugu sentiment classification
+
+---
+
+## Citation and More Details
+
+For detailed experimental setup, evaluation metrics, and comparisons with rationale-based models, **please refer to our paper**.
+
+
+
+---
+
+## License
+
+Released under [CC BY 4.0](LICENSE).