SimsysDNLP 0.0.4

A deep learning natural language processing service for simulating different systems (normal-parallel-single server).

DNLP Simulation Service

This repository contains the DNLP Simulation Service, a machine learning-based service extraction and simulation framework for natural language processing tasks. The model, built with PyTorch and Transformers, leverages BERT embeddings and multi-head attention for robust service classification and information extraction.

Features

• Service Extraction: Extracts service-related details from unstructured text.
• Simulation: Simulates customer interactions and service response times.
• Customizable NLP Pipeline: Built with flexible NLP components for tailored use cases.
• Cloud-Ready: Compatible with cloud platforms and frameworks like Kaggle and Colab for cloud training and fine-tuning.

Installation

Clone the repository and install dependencies:

git clone https://github.com/Mohamed-Hamdey/DNLP-Simulation-Service.git
cd DNLP-Simulation-Service
pip install -r requirements.txt

Alternatively, install the package directly:

pip install DNLP-Simulation-Service

Usage

Training

To train the model, prepare a dataset in JSON format and run:

from DNLP_Simulation_Service.trainer import NLPService
from DNLP_Simulation_Service.config import ModelConfig

config = ModelConfig(...)
service = NLPService(config=config)
service.train(train_data="path/to/dataset.json")

Prediction

Once trained, make predictions using:

text = "Example service description text."
prediction = service.predict(text)
print(prediction)

Cloud Training

To train on Kaggle or Colab:

1. Upload the repository files to the environment.
2. Use the provided trainer.py script to start training on cloud GPUs.

API Reference

Configuration

The ModelConfig class allows adjustments to:

• embedding_dim
• hidden_dim
• num_heads
• dropout

Data Preparation

Use DataProcessor for preprocessing data and preparing labels.

Contributing

1. Fork the repo
2. Create a branch (git checkout -b feature-branch)
3. Commit changes (git commit -m 'Add feature')
4. Push the branch (git push origin feature-branch)
5. Open a pull request

License

This project is licensed under the MIT License.