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https://github.com/mahdizynali/deep-analysis-persian-news

Deep context explicit and text analysis of Persian news with deep NLP and using Google Trax and jax and (seprated pytorch branch)
https://github.com/mahdizynali/deep-analysis-persian-news

deep-mind machine-learning natural-language-processing nlp persian-nlp pytorch tensorflow text-analysis trax

Last synced: 4 months ago
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Deep context explicit and text analysis of Persian news with deep NLP and using Google Trax and jax and (seprated pytorch branch)

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README

          

# Deep Analysis of Persian News 📰🧠🇮🇷

This project performs deep natural language analysis on Persian news articles using modern neural network architectures. It leverages an LSTM-based model built with [Trax](https://github.com/google/trax) and [JAX](https://github.com/google/jax) to understand and classify the explicit content of Persian news text. Also i have provided seprated Pytorch branch for those whom prefers traditional way. Final model has been traiend on over 6 Millions parameters.

## 🔍 Project Goals

- Perform **context-aware analysis** on Persian news headlines or full articles
- Leverage **LSTM-based deep learning models** for semantic understanding
- Achieve strong performance benchmarks on large-scale Persian datasets

## 🚀 Key Features

- 🧠 **Deep Neural Network**: LSTM-based model implemented using Trax and JAX
- 📰 **Persian Language Support**: Tailored preprocessing and custom tokenization for Persian Lang
- 📊 **Large Dataset**: Trained on over **200,000 news articles**
- ✅ **High Accuracy**: Achieves **80% classification accuracy** on validation set

## Benchmark
On first try with a simple Trax network.
| Dataset | Loss | Accuracy | Parameters |
|------------|------|----------|------------|
| Train | 0.60223989 | 0.84375000 | 6 Millions |
| Validation | 0.65386836 | 0.80412000 | 6 Millions |

---

## 📦 Try It

1. Clone the repository:
```bash
git clone https://github.com/mahdizynali/Deep-Analysis-Persian-News.git
cd Deep-Analysis-Persian-News
```
```
pip3 install -r requirements.txt
```
For train a new model :
```
python3 main.py train
```
Try prediction:
```
python3 main.py predict
```