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sant1x/
uzbek-sentiment-xlm-roberta

Name: uzbek-sentiment-xlm-roberta
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xlm-roberta

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uzbek-sentiment-xlm-roberta

A binary sentiment classifier for Uzbek-language text, fine-tuned from xlm-roberta-base on a self-collected dataset of product reviews scraped from Uzum Market (uzbekistan's largest e-commerce platform).

Model Details

Base model: xlm-roberta-base
Task: Binary sentiment classification (positive / negative)
Language: Uzbek (uz)
Fine-tuned by: Samandar Norboev

Dataset

Source: Uzum Market product reviews (uzum.uz)
Size: 1,172 reviews (930 positive, 242 negative)
Labeling method: Distant supervision — 5-star reviews labeled positive, 1-star labeled negative
Splits: 80% train / 10% validation / 10% test

Training Details

Epochs: 3
Batch size: 16
Max sequence length: 128
Loss function: Class-weighted cross-entropy (to handle class imbalance)
Hardware: Google Colab T4 GPU

Results (Test Set)

Class	Precision	Recall	F1-Score	Support
Negative	0.7586	0.8800	0.8148	25
Positive	0.9663	0.9247	0.9451	93
Weighted avg	0.9223	0.9153	0.9175	118

Test Accuracy: 91.53% | Weighted F1: 91.75%

Usage

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="sant1x/uzbek-sentiment-xlm-roberta"
)

classifier("Mahsulot juda yaxshi, sifati a'lo!")
# [{'label': 'pos', 'score': 0.97}]

Limitations

Trained on e-commerce reviews only — may not generalize well to other domains (news, social media)
Negative class is underrepresented in training data, which affects recall on negative examples
Does not handle code-switching between Uzbek and Russian well
No coverage of Uzbek dialects

Future Work

Expand dataset with more diverse sources (social media, news comments)
Add neutral class for 3-class classification
Evaluate on out-of-domain Uzbek text

Citation

If you use this model, please cite:

Norboev, S. (2025). Uzbek Sentiment Analysis using XLM-RoBERTa. 
Hugging Face. https://huggingface.co/sant1x/uzbek-sentiment-xlm-roberta