Persian Sentiment Analysis for Delivery Complaints
📌 Introduction
This model is fine-tuned HooshvareLab/bert-fa-base-uncased model to classify Persian comments related to delivery complaints. The model predicts whether a comment is about a negative delivery experience or not.
📊 Dataset
The model was fine-tuned using the ** Basalam comments ** dataset from Kaggle: 📂 Basalam Comments Dataset
This dataset contains Persian comments labeled as:
- 1 (Bad Delivery Complaint): The comment expresses dissatisfaction with delivery service.
- 0 (Not a Delivery Complaint): The comment is unrelated to delivery issues.
🛠 Training Details
- Base Model:
HooshvareLab/bert-fa-base-uncased
- Fine-Tuning Dataset: Basalam comments
- NoteBook
- Evaluation Metrics:
- Accuracy:
91%
- F1-score:
87%
- Accuracy:
📥 How to Use
You can load and use the fine-tuned model as follows:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
def classify_comment(text):
model_name = "alireza-2003/bert_fa_bad_delivery"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits).item()
return "Bad Delivery Complaint" if prediction == 1 else "Not a Complaint"
comment = "بسته من خیلی دیر رسید و اصلا راضی نبودم!"
print(classify_comment(comment))
📌 Next Steps
- Further fine-tuning with more diverse Persian datasets.
- Improve model performance by handling edge cases better.
- Deploy as an API for real-time classification.
📝 Author: [Alireza]
📅 Last Updated: [2/16/2025]
🔗 Dataset: Kaggle Dataset
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Model tree for alireza-2003/bert_fa_bad_delivery
Base model
HooshvareLab/bert-fa-base-uncased