Text Classification
Transformers
Safetensors
Persian
deberta-v2
sentiment-analysis
persian
product-reviews
mdeberta
text-embeddings-inference
Instructions to use Anahii/persian-product-sentiment-mdeberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anahii/persian-product-sentiment-mdeberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Anahii/persian-product-sentiment-mdeberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Anahii/persian-product-sentiment-mdeberta") model = AutoModelForSequenceClassification.from_pretrained("Anahii/persian-product-sentiment-mdeberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Persian Product Review Sentiment Analysis (mDeBERTa)
Binary sentiment classification (positive/negative) fine-tuned on Persian e-commerce product reviews.
Evaluation Results
| Metric | Score |
|---|---|
| Accuracy | 87.54% |
| Macro F1-Score | 87.54% |
| Macro Precision | 87.55% |
| Macro Recall | 87.58% |
Model Description
This model fine-tunes mDeBERTa on Persian product reviews to classify user sentiment into Positive and Negative categories.
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