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Bahar-82/
parsbert-persian-absa

Name: parsbert-persian-absa
Availability: InStock

aspect-based-sentiment-analysis

text-classification

HooshvareLab/bert-fa-base-uncased-sentiment-digikala

finetune

mit

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ParsBERT for Persian Aspect-Based Sentiment Analysis

Fine-tuned for aspect-category sentiment classification (ACSC) on Persian product reviews across four categories: laptops, mobile phones, tablets and smartwatches.

Usage

The model takes an (aspect, text) pair, not text alone:

python

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tok = AutoTokenizer.from_pretrained("Bahar-82/parsbert-persian-absa")
mdl = AutoModelForSequenceClassification.from_pretrained(
    "Bahar-82/parsbert-persian-absa").eval()

text = "صفحه نمایش خوبه ولی باتری ضعیفه"
aspect = "باتری و شارژ"

enc = tok(aspect, text, truncation=True, padding="max_length",
          max_length=96, return_tensors="pt")
with torch.no_grad():
    pred = mdl(**enc).logits.argmax(-1).item()
print(mdl.config.id2label[pred])   # Negative

Passing the full review text matters: feeding a truncated clause instead drops accuracy noticeably, because the model was trained on complete reviews.

Results

Evaluated with 5-fold StratifiedGroupKFold (grouped by review text, so no review appears in both train and test) on 1038 samples:

Metric	Value
Accuracy	89.3% ± 3.1%
F1-macro	0.892

Class	Precision	Recall	F1	Support
Negative	0.873	0.889	0.881	316
Neutral	0.930	0.890	0.910	328
Positive	0.878	0.896	0.887	394

Training

4 epochs, lr 2e-5, batch size 8, max_length 96, weight_decay 0.01, warmup_ratio 0.1, seed 42.

Limitations

Trained on 1038 single-annotator samples without inter-annotator agreement. The base model was already sentiment-tuned on Digikala data, so overlap with the evaluation set cannot be fully ruled out.

Source

https://github.com/Bahar-Gh2003/SMART-ABSA