abhiprd2000/hindi-sentiment-model

Hindi Sentiment Analysis Model

Author: Abhimanyu Prasad | @abhiprd20

Fine-tuned XLM-RoBERTa model for 3-class sentiment analysis on Hindi text in Devanagari script, trained as part of a cross-lingual transfer study across English, Hindi, Maithili, and Bhojpuri.

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Model Description

This model is part of a cross-lingual transfer study examining how well NLP models transfer across languages of varying resource levels — from high-resource English to extremely low-resource Maithili and Bhojpuri.

Hindi serves as the pivot language in this study: it is the highest-resource of the three Indic languages, pre-trained into XLM-RoBERTa, and linguistically related to both Maithili and Bhojpuri. Comparing Hindi results against Maithili and Bhojpuri reveals how linguistic proximity and resource availability interact in cross-lingual transfer.

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

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

Language: Hindi (हिन्दी) — Devanagari script

Training data: 20,000 sentences (balanced, sampled from iam-tsr/hindi-sentiments)

Training dataset citation: iam-tsr/hindi-sentiments — MIT License

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Performance

Model	Accuracy	F1 (Macro)
English BERT (zero-shot)	35.33%	0.2263
XLM-RoBERTa (zero-shot)	63.07%	0.6339
mBERT (fine-tuned)	67.86%	0.6778
XLM-RoBERTa (fine-tuned) ← this model	70.66%	0.7063
Out-of-distribution (30 new sentences)	90.00%	0.9017

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

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Notable Finding — OOD Generalisation

Hindi shows an unusual pattern compared to Maithili and Bhojpuri: lower in-distribution accuracy (70.66%) but significantly higher out-of-distribution accuracy (90.00%). This suggests the model generalises better to naturally written Hindi despite being trained on translated data.

Language	Fine-tuned (in-dist)	OOD (real-world)
Hindi	70.66%	90.00%
Bhojpuri	97.60%	70.00%
Maithili	85.63%	64.00%

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Cross-Lingual Comparison

Language	English Zero-Shot	XLM Zero-Shot	Fine-tuned
Hindi	35.33%	63.07%	70.66%
Maithili	33.33%	69.86%	85.63%
Bhojpuri	33.13%	76.45%	97.60%

English BERT drops to ~33-35% across all three Indic languages, confirming the language barrier. XLM-RoBERTa recovers substantially in all cases due to multilingual pretraining on Devanagari script.

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Usage

from transformers import pipeline

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

# Example Hindi sentences
texts = [
    "यह खाना बहुत स्वादिष्ट है।",        # positive
    "आज बहुत थकान महसूस हो रही है।",      # negative
    "मैं कल दिल्ली जाऊंगा।",             # neutral
]

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

Output:

यह खाना बहुत स्वादिष्ट है।
  → positive (94.3%)

आज बहुत थकान महसूस हो रही है।
  → negative (91.7%)

मैं कल दिल्ली जाऊंगा।
  → neutral (88.5%)

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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
Training samples	20,000 (balanced, ~6,666 per class)
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 sampled from iam-tsr/hindi-sentiments (MIT License) — 127,000 Hindi sentences translated from English social media text with 3-class sentiment labels. 20,000 balanced rows used for training.

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

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Related Models

• abhiprd20/nlp-sentiment-model — English baseline
• abhiprd20/maithili-sentiment-model — Maithili
• abhiprd20/bhojpuri-sentiment-model — Bhojpuri

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Citation

If you use this model, please cite:

@misc{prasad2026hindi,
  author    = {Abhimanyu Prasad},
  title     = {Hindi Sentiment Analysis: Cross-Lingual Transfer Study},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/abhiprd20/hindi-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 (this model)	60.0%	68.0% ✓	63.3%	61.7%
Maithili model	63.0%	59.0%	90.3% ✓	75.0%
Bhojpuri 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 (this model)	0.4362	0.6778 ✓	0.6319	0.6042
Maithili model	0.4443	0.5757	0.9035 ✓	0.7458
Bhojpuri model	0.4250	0.4166	0.4114	0.9801 ✓

Key Findings

• Hindi transfers significantly better than English to both Maithili (63.3%) and Bhojpuri (61.7%), nearly doubling English performance.
• Supports the hypothesis that linguistic proximity (Hindi → Bihari languages) aids cross-lingual transfer.
• Hindi model performs reasonably on English (60%), suggesting partial bidirectional transfer.

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