https://github.com/mjahmadee/intent_classification

Intent Classification
https://github.com/mjahmadee/intent_classification

intent-classification intent-detection nlp question-answering

Last synced: about 1 year ago
JSON representation

Intent Classification

• Host: GitHub
• URL: https://github.com/mjahmadee/intent_classification
• Owner: MJAHMADEE
• License: mit
• Created: 2023-07-15T05:24:54.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-03-16T12:48:04.000Z (about 2 years ago)
• Last Synced: 2025-01-11T08:51:38.737Z (over 1 year ago)
• Topics: intent-classification, intent-detection, nlp, question-answering
• Language: Jupyter Notebook
• Homepage:
• Size: 7.13 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Intent Classification using LSTM 🤖

![Python](https://img.shields.io/badge/Python-3.8-blue.svg)

![TensorFlow](https://img.shields.io/badge/TensorFlow-2.4-orange.svg)

![NLP](https://img.shields.io/badge/NLP-GloVe-green.svg)

Intent Classification with Neural Networks is an NLP project that uses Long Short-Term Memory (LSTM) networks to classify user queries into predefined categories.

## Features 🌟

- Utilizes GloVe embeddings for high-quality word representations.

- Employs LSTM networks to capture long-term dependencies in text data.

- Offers a detailed pipeline from text preprocessing to model evaluation.

- Includes multiple model configurations to explore the impact of hyperparameters.

## Setup and Installation 🛠️

1. Clone the repository.

2. Install the required Python libraries.

3. Download and set up the GloVe embeddings.

4. Prepare the dataset by running the preprocessing scripts.

## Dataset 📁

The project is tested on a publicly available intent classification dataset, structured with text inputs and intent labels.

## Model Training and Evaluation 🚀

- The model training process involves multiple steps including data preprocessing, feature extraction, and training LSTM models.

- Various configurations with different hyperparameters (like hidden dimensions) are tested to find the best performing model.

- Evaluation metrics such as accuracy, precision, recall, and F1-score are calculated to assess the model performance.

## Results and Discussion 📊

- The project includes detailed analysis of the model performance, showcasing the effectiveness of LSTM models in handling text classification tasks.

- Visualizations like confusion matrices are provided to give insights into model predictions.

## License 📜

The project is open-sourced under the MIT License.

## Acknowledgements 🙌

- Thanks to the Stanford NLP Group for providing the GloVe embeddings.

- The intent classification dataset contributors for providing a rich dataset for analysis.

For more details, visit the [GitHub repository](https://github.com/MJAHMADEE/Intent_Classification).