abhiprd2000/bhojpuri-sentiment-model

Bhojpuri Sentiment Analysis Model

Author: Abhimanyu Prasad | @abhiprd20

Fine-tuned XLM-RoBERTa model for 3-class sentiment analysis on Bhojpuri text in Devanagari script. This is the first publicly available sentiment model for the Bhojpuri language.

Model Description

This model is part of a cross-lingual transfer study comparing sentiment analysis across English, Hindi, Maithili, and Bhojpuri — four languages spanning high-resource to extremely low-resource.

Base model: cardiffnlp/twitter-xlm-roberta-base-sentiment

Task: 3-class sentiment classification — Positive, Negative, Neutral

Language: Bhojpuri (भोजपुरी) — Devanagari script

Training data: 18,049 unique Bhojpuri sentences (balanced across 3 classes)

Performance

Evaluated on a fixed balanced test set of 501 sentences (167 per class).

Cross-Lingual Findings

The zero-shot results reveal a clear pattern: English BERT fails on all three Indic languages at nearly identical rates (~33%), while multilingual models recover significantly, with Bhojpuri showing the strongest zero-shot transfer (76.45%) — likely due to its closer lexical proximity to Hindi compared to Maithili.

Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="abhiprd20/bhojpuri-sentiment-model"
)

# Example Bhojpuri sentences
texts = [
    "ई खाना बहुत स्वादिष्ट बा।",       # positive
    "आज बहुत थकान लागत बा।",            # negative
    "हम कल पटना जाइब।",                 # neutral
]

for text in texts:
    result = classifier(text)[0]
    print(f"{text}")
    print(f"  → {result['label']} ({result['score']*100:.1f}%)\n")

Output:

ई खाना बहुत स्वादिष्ट बा।
  → positive (97.2%)

आज बहुत थकान लागत बा।
  → negative (95.8%)

हम कल पटना जाइब।
  → neutral (91.4%)

Labels

Training Details

Dataset

Training data: 18,049 unique Bhojpuri sentences in Devanagari script with balanced 3-class sentiment labels. Note: Dataset contains translated content from English, acknowledged as a limitation.

Test set: Fixed balanced set of 501 sentences (167 per class), held out before training with zero leakage verified.

Related Models

• —`abhiprd20/nlp-sentiment-model` — English baseline
• —`abhiprd20/maithili-sentiment-model` — Maithili
• —`abhiprd20/hindi-sentiment-model` — Hindi

Citation

If you use this model, please cite:

@misc{prasad2026bhojpuri,
  author    = {Abhimanyu Prasad},
  title     = {Bhojpuri Sentiment Analysis: Cross-Lingual Transfer Study},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/abhiprd20/bhojpuri-sentiment-model}
}

📊 Cross-Language Evaluation

Each model was evaluated on all 4 languages (300 sentences per language, 100 per class). This shows how well models trained on one language transfer to others.

Accuracy Matrix

F1 Matrix (macro)

Key Findings

• —Excellent in-language performance (98%) but weak cross-lingual transfer.
• —Bhojpuri → Maithili transfer is only 47.3%, worse than the reverse direction (Maithili → Bhojpuri: 75%).
• —Asymmetric transfer between Maithili and Bhojpuri is a key finding of this research — despite linguistic similarity, transfer is not bidirectional.

Full paper: This cross-evaluation is part of a research study on cross-lingual transfer for low-resource Bihari languages. See the companion datasets and models: Maithili | Bhojpuri | Hindi | English