Datasets:
Modalities:
Text
Formats:
csv
Size:
10K - 100K
Tags:
sentiment-analysis
text-classification
bhojpuri
devanagari
low-resource-nlp
cross-lingual-transfer
License:
| language: | |
| - bho | |
| - en | |
| - bh | |
| license: apache-2.0 | |
| tags: | |
| - sentiment-analysis | |
| - text-classification | |
| - bhojpuri | |
| - devanagari | |
| - low-resource-nlp | |
| - cross-lingual-transfer | |
| # 🚀 Bhojpuri Behavioral Corpus (Phase 2: Engineered Refinement) | |
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| **⚠️ NOTICE: Phase 2 Refinement** | |
| *This repository contains the Phase 2 Engineered Refinement. This Phase 2 is automatically refined specifically to prevent class collapse during fine-tuning.* | |
| ## 📌 Executive Summary | |
| The **Bhojpuri Behavioral Corpus (Phase 2)** is a 68,822-row, rigidly balanced dataset engineered to solve the inherent instability of low-resource language fine-tuning. Moving beyond noisy web-scraped corpora, this dataset provides a sterile, perfectly stratified environment (1:1:1 ratio) to teach Large Language Models precise pragmatic boundaries and cross-lingual semantic alignment across diverse domains (Science, Agriculture, Environment, General). | |
| ## 🧠 Architectural Innovations | |
| ### 1. Mathematical Stratification (1:1:1) | |
| To prevent the majority-class collapse common in naturalistic datasets, this corpus is artificially balanced to an exact 1:1:1 ratio: | |
| * **Positive:** 22,940 samples | |
| * **Negative:** 22,940 samples | |
| * **Neutral:** 22,942 samples | |
| This ensures the model's loss function penalizes misclassification equally across all sentiment vectors. | |
| ### 2. Cross-Lingual Alignment via Anchored Translation | |
| A subset of the corpus utilizes an **Anchored Translation** format. Complex technical terms (e.g., *'आइसोटोप'* / Isotope) are paired with their bracketed English equivalents directly within the Bhojpuri string. This is a deliberate architectural choice to provide an explicit semantic alignment signal, bridging the gap between high-resource English representations and low-resource Bhojpuri vernacular. | |
| ### 3. Contemporary Lexical Borrowing | |
| The dataset intentionally preserves English loanwords and technical transliterations within the Devanagari script (e.g., *'मशीन'* / Machine). Rather than artificially sanitizing the corpus to an archaic standard, this reflects authentic, contemporary Bhojpuri morphology. | |
| ## 📊 Dataset Schema | |
| * `id`: Unique identifier. | |
| * `text`: The Bhojpuri utterance (Devanagari script). | |
| * `english_tr`: High-fidelity semantic English translation. | |
| * `label`: Primary sentiment (positive, negative, neutral). | |
| * `domain` / `sub_domain`: Context of the utterance (e.g., agriculture, science). | |
| ## ⚙️ Intended Use & Limitations | |
| * **Best For:** Parameter-efficient fine-tuning (LoRA/QLoRA), Teacher-model initialization, and cross-lingual representation alignment. | |
| * **Limitations:** Because the sentiment distribution is artificially balanced (33% per class), models trained exclusively on this dataset may over-predict positive/negative sentiments in real-world, highly neutral environments without threshold calibration or Adaptive Knowledge Distillation (AdaptKD). | |
| ## 📝 Citation | |
| If you use this dataset in your research, please cite the accompanying paper: | |
| ```bibtex | |
| @article{prasad2026bhojpuri, | |
| title={abhiprd20/Bhojpuri-Behavioral-Corpus-8K}, | |
| author={Prasad, Abhimanyu}, | |
| year={2026}, | |
| ``` |