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, 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
- 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.
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