albert-base-v2-persian-sentiment-snapfood

ALBERT Persian Sentiment (Snappfood)

Binary sentiment classifier for Persian, fine-tuned from shekar-ai/albert-base-v2-persian-zwnj-naab-mlm on the Snappfood corpus of Persian food-delivery reviews.

Usage

Shekar

from shekar import SentimentClassifier

classifier = SentimentClassifier()
print(classifier("غذا عالی بود و خیلی سریع رسید."))
print(classifier("کیفیت غذا افتضاح بود و سرد به دستم رسید."))

('positive', 0.9934062957763672)
('negative', 0.9793806672096252)

Transformers

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="shekar-ai/albert-base-v2-persian-sentiment-snapfood",
)

for prediction in classifier([
    "غذا عالی بود و خیلی سریع رسید.",
    "کیفیت غذا افتضاح بود و سرد به دستم رسید.",
]):
    print(prediction["label"], round(prediction["score"], 4))

POSITIVE 0.9957
NEGATIVE 0.9768

Training

Data. The Snappfood sentiment dataset — user reviews of a Persian food-delivery service, labeled HAPPY / SAD and mapped to POSITIVE / NEGATIVE. Roughly 56.7k reviews for training and 6.3k held out for validation.

Evaluation

On the held-out validation split (~6.3k reviews):

<details> <summary>Validation during training</summary> </details>

Citation

@article{Amirivojdan2025Shekar,
  author  = {Amirivojdan, Ahmad},
  title   = {{Shekar: A Python Toolkit for Persian Natural Language Processing}},
  journal = {Journal of Open Source Software},
  volume  = {10},
  number  = {114},
  pages   = {9128},
  year    = {2025},
  doi     = {10.21105/joss.09128},
  url     = {https://joss.theoj.org/papers/10.21105/joss.09128}
}