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license: cc-by-4.0 |
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tags: |
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- sentiment-classification |
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- telugu |
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- xlm-r |
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- multilingual |
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- baseline |
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language: te |
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datasets: |
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- DSL-13-SRMAP/TeSent_Benchmark-Dataset |
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model_name: XLM-R_WOR |
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--- |
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# XLM-R_WOR: XLM-RoBERTa Telugu Sentiment Classification Model (Without Rationale) |
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## Model Overview |
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**XLM-R_WOR** is a Telugu sentiment classification model based on **XLM-RoBERTa (XLM-R)**, a general-purpose multilingual transformer developed by Facebook AI. |
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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**. |
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## Model Details |
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- **Architecture:** XLM-RoBERTa (transformer-based, multilingual) |
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- **Pretraining Data:** 2.5TB of filtered Common Crawl data across 100+ languages, including Telugu |
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- **Pretraining Objective:** Masked Language Modeling (MLM), no Next Sentence Prediction (NSP) |
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- **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 |
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- **Task:** Sentence-level sentiment classification (3-way) |
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- **Rationale Usage:** **Not used** during training or inference ("WOR" = Without Rationale) |
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## Intended Use |
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- **Primary Use:** Benchmarking Telugu sentiment classification on the TeSent_Benchmark-Dataset, especially as a **baseline** for models trained without rationales |
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- **Research Setting:** Suitable for cross-lingual and multilingual NLP research, as well as explainable AI in low-resource settings |
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## Why XLM-R? |
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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. |
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However, Telugu-specific models like MuRIL or L3Cube-Telugu-BERT may offer better cultural and linguistic alignment for purely Telugu tasks. |
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## Performance and Limitations |
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**Strengths:** |
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- Strong transfer learning and contextual modeling for multilingual NLP |
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- Good performance for Telugu sentiment analysis when fine-tuned with local data |
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- Useful as a cross-lingual and multilingual baseline |
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**Limitations:** |
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- May be outperformed by Telugu-specific models for culturally nuanced tasks |
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- Requires sufficient labeled Telugu data for best performance |
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- Since rationales are not used, the model cannot provide explicit explanations for its predictions |
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## Training Data |
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- **Dataset:** [TeSent_Benchmark-Dataset](https://huggingface.co/datasets/dsl-13-srmap/tesent_benchmark-dataset) |
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- **Data Used:** Only the **Content** (Telugu sentence) and **Label** (sentiment label) columns; **rationale** annotations are ignored for XLM-R_WOR training |
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## Language Coverage |
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- **Language:** Telugu (`te`) |
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- **Model Scope:** This implementation and evaluation focus strictly on Telugu sentiment classification |
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## Citation and More Details |
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For detailed experimental setup, evaluation metrics, and comparisons with rationale-based models, **please refer to our paper**. |
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## License |
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Released under [CC BY 4.0](LICENSE). |
