abhiprd20/Bhojpuri-Behavioral-Corpus-8K

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

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).

---

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%

---

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

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

---

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

---

Dataset

Training data: 50,000 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}
}