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---
license: cc-by-4.0
datasets:
- DSL-13-SRMAP/Telugu-Dataset
language:
- te
tags:
- sentiment-analysis
- text-classification
- telugu
- telugu-specific
- bert
- rationale-supervision
- explainable-ai
base_model: l3cube-pune/telugu-bert
pipeline_tag: text-classification
metrics:
- accuracy
- f1
- auroc
---
# Telugu-BERT_WR
## Model Description
**Telugu-BERT_WR** is a Telugu sentiment classification model built on **Telugu-BERT (L3Cube-Telugu-BERT)**, a Transformer-based BERT model pretrained **exclusively on Telugu text** by the L3Cube Pune research group.
The base model is pretrained on **Telugu OSCAR**, **Wikipedia**, and **news corpora** using the **Masked Language Modeling (MLM)** objective. Being tailored specifically for Telugu, Telugu-BERT captures **language-specific vocabulary, syntax, semantics, and idiomatic expressions** more effectively than multilingual models such as mBERT and XLM-R.
The suffix **WR** denotes **With Rationale supervision**. This model is fine-tuned using both **sentiment labels and human-annotated rationales**, enabling stronger alignment between predictions and human-identified evidence.
---
## Pretraining Details
- **Pretraining corpora:**
- Telugu OSCAR
- Telugu Wikipedia
- Telugu news data
- **Training objective:**
- Masked Language Modeling (MLM)
- **Language coverage:** Telugu only
---
## 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** are incorporated alongside sentiment labels. In addition to the standard classification loss, an **auxiliary rationale loss** guides the model to align its attention or explanation scores with annotated rationale tokens.
This supervision 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
- Monolingual Telugu NLP research
- Comparative evaluation against label-only (WOR) baselines
Telugu-BERT_WR is particularly suitable for **pure Telugu text analysis** when sufficient labeled data and human rationales are available.
---
## Performance Characteristics
Rationale supervision enhances **explanation quality and human alignment**, while preserving the strong sentiment classification capability of Telugu-BERT.
### Strengths
- Deep understanding of Telugu vocabulary and syntax
- Superior handling of nuanced and idiomatic sentiment expressions
- Human-aligned explanations through rationale supervision
### Limitations
- Not designed for cross-lingual or multilingual tasks
- Requires annotated rationales, increasing annotation cost
- Performance depends on availability of sufficient Telugu training data
---
## Use in Explainability Evaluation
**Telugu-BERT_WR** is well-suited for evaluation with explanation frameworks such as FERRET, enabling:
- **Faithfulness evaluation:** How well explanations support the model’s predictions
- **Plausibility evaluation:** How closely explanations align with human rationales
---
## References
- Joshi et al. (2022). Telugu-BERT. EMNLP. |