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
+- multilingual
+- xlm-roberta
+- baseline
+base_model: xlm-roberta-base
+pipeline_tag: text-classification
+metrics:
+- accuracy
+- f1
+- auroc
+---
+
+# XLM-R_WOR
+
+## Model Description
+
+**XLM-R_WOR** is a Telugu sentiment classification model built on **XLM-RoBERTa (XLM-R)**, a large-scale multilingual Transformer model developed by Facebook AI. XLM-R is designed to enhance cross-lingual understanding by leveraging a substantially larger and more diverse pretraining corpus than mBERT.
+
+The base model is pretrained on approximately **2.5 TB of filtered Common Crawl data** covering **100+ languages**, including Telugu. Unlike mBERT, XLM-R is trained **exclusively with the Masked Language Modeling (MLM) objective**, without using the Next Sentence Prediction (NSP) task. This design choice enables stronger contextual representations and improved transfer learning.
+
+The suffix **WOR** denotes **Without Rationale supervision**. This model is fine-tuned using only sentiment labels, without incorporating human-annotated rationales, and serves as a **label-only baseline**.
+
+---
+
+## Pretraining Details
+
+- **Pretraining corpus:** Filtered Common Crawl (≈2.5 TB, 100+ languages)
+- **Training objective:**
+  - Masked Language Modeling (MLM)
+- **Next Sentence Prediction:** Not used
+- **Language coverage:** Telugu included, but not exclusively targeted
+
+---
+
+## Training Data
+
+- **Fine-tuning dataset:** Telugu-Dataset
+- **Task:** Sentiment classification
+- **Supervision type:** Label-only (no rationale supervision)
+
+---
+
+## Intended Use
+
+This model is intended for:
+
+- Telugu sentiment classification
+- Cross-lingual and multilingual NLP benchmarking
+- Baseline comparisons for explainability and rationale-supervision studies
+- Low-resource Telugu NLP research
+
+Due to its large-scale multilingual pretraining, XLM-R_WOR is particularly effective for transfer learning scenarios where Telugu-specific labeled data is limited.
+
+---
+
+## Performance Characteristics
+
+XLM-R generally provides stronger contextual modeling and improved downstream performance compared to mBERT, owing to its larger and more diverse pretraining corpus and exclusive focus on the MLM objective.
+
+### Strengths
+
+- Strong cross-lingual transfer learning
+- Improved contextual representations over mBERT
+- Reliable baseline for multilingual sentiment analysis
+
+### Limitations
+
+- Not explicitly optimized for Telugu morphology or syntax
+- May underperform compared to Telugu-specialized models such as MuRIL or L3Cube-Telugu-BERT
+- Limited ability to capture fine-grained cultural and regional linguistic nuances
+
+---
+
+## Use as a Baseline
+
+**XLM-R_WOR** serves as a robust and widely accepted baseline for:
+
+- Comparing multilingual models against Telugu-specialized architectures
+- Evaluating the impact of rationale supervision (WOR vs. WR)
+- Benchmarking sentiment classification performance in low-resource Telugu settings
+
+---
+
+## References
+
+- Conneau et al., 2019  
+- Hedderich et al., 2021  
+- Kulkarni et al., 2021  
+- Joshi, 2022  
+- Das et al., 2022  
+- Rajalakshmi et al., 2023