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.

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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)

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Performance

Model	Accuracy	F1 (Macro)
English BERT (zero-shot)	33.13%	0.1659
XLM-RoBERTa (zero-shot)	76.45%	0.7630
mBERT (fine-tuned)	94.81%	0.9481
XLM-RoBERTa (fine-tuned) ← this model	97.60%	0.9761
Out-of-distribution (30 new sentences)	70.00%	0.6777

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

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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.

Language	English Zero-Shot	XLM Zero-Shot	Fine-tuned
Maithili	33.33%	69.86%	85.63%
Bhojpuri	33.13%	76.45%	97.60%

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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%)

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Labels

Label	Integer	Meaning
negative	0	Negative sentiment
neutral	1	Neutral / factual
positive	2	Positive sentiment

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Training Details

Parameter	Value
Base model	cardiffnlp/twitter-xlm-roberta-base-sentiment
Epochs	3
Batch size	16
Max sequence length	128
Warmup steps	200
Weight decay	0.01
Mixed precision	fp16
Best model metric	F1 macro

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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.

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Related Models

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

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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}
}

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📊 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

Model	English	Hindi	Maithili	Bhojpuri
English model	79.5% ✓	34.0%	33.3%	33.0%
Hindi model	60.0%	68.0% ✓	63.3%	61.7%
Maithili model	63.0%	59.0%	90.3% ✓	75.0%
⭐ Bhojpuri model (this model)	59.0%	47.3%	47.3%	98.0% ✓

F1 Matrix (macro)

Model	English	Hindi	Maithili	Bhojpuri
English model	0.5424 ✓	0.1912	0.1667	0.1654
Hindi model	0.4362	0.6778 ✓	0.6319	0.6042
Maithili model	0.4443	0.5757	0.9035 ✓	0.7458
⭐ Bhojpuri model (this model)	0.4250	0.4166	0.4114	0.9801 ✓

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