This repository explores two cutting-edge approaches to headline generation using neural networks: Long Short-Term Memory (LSTM) and Transformers. Each approach leverages different strengths of deep learning to tackle the challenge of generating coherent and contextually relevant headlines.
🙏 I would like to extend my heartfelt gratitude to Santiago Hernández, an expert in Cybersecurity and Artificial Intelligence. His incredible course on Deep Learning, available at Udemy, was instrumental in shaping the development of this project. The insights and techniques learned from his course were crucial in crafting the neural network architecture used in this classifier.
This project has been developed exclusively for educational and learning purposes. It aims to explore and compare different deep learning architectures, specifically LSTM and Transformer models, in the context of headline generation. The objective is to gain hands-on experience in implementing, training, and evaluating neural networks for natural language processing (NLP) tasks.
If you found this project intriguing, I invite you to check out my other AI and machine learning initiatives, where I tackle real-world challenges across various domains:
-
🌍 Advanced Classification of Disaster-Related Tweets Using Deep Learning 🚨
Uncover how social media responds to crises in real time using deep learning to classify tweets related to disasters. -
📰 Fighting Misinformation: Source-Based Fake News Classification 🕵️♂️
Combat misinformation by classifying news articles as real or fake based on their source using machine learning techniques. -
🛡️ IoT Network Malware Classifier with Deep Learning Neural Network Architecture 🚀
Detect malware in IoT network traffic using Deep Learning Neural Networks, offering proactive cybersecurity solutions. -
📧 Spam Email Classification using LSTM 🤖
Classify emails as spam or legitimate using a Bi-directional LSTM model, implementing NLP techniques like tokenization and stopword removal. -
💳 Fraud Detection Model with Deep Neural Networks (DNN)
Detect fraudulent transactions in financial data with Deep Neural Networks, addressing imbalanced datasets and offering scalable solutions. -
🧠🚀 AI-Powered Brain Tumor Classification
Classify brain tumors from MRI scans using Deep Learning, CNNs, and Transfer Learning for fast and accurate diagnostics. -
📊💉 Predicting Diabetes Diagnosis Using Machine Learning
Create a machine learning model to predict the likelihood of diabetes using medical data, helping with early diagnosis. -
🚀🔍 LLM Fine-Tuning and Evaluation
Fine-tune large language models like FLAN-T5, TinyLLAMA, and Aguila7B for various NLP tasks, including summarization and question answering. -
📰 Headline Generation Models: LSTM vs. Transformers
Compare LSTM and Transformer models for generating contextually relevant headlines, leveraging their strengths in sequence modeling. -
🩺💻 Breast Cancer Diagnosis with MLP
Automate breast cancer diagnosis using a Multi-Layer Perceptron (MLP) model to classify tumors as benign or malignant based on biopsy data. -
Deep Learning for Safer Roads 🚗 Exploring CNN-Based and YOLOv11 Driver Drowsiness Detection 💤 Comparing driver drowsiness detection with CNN + MobileNetV2 vs YOLOv11 for real-time accuracy and efficiency 🧠🚗. Exploring both deep learning models to prevent fatigue-related accidents 😴💡.
Long Short-Term Memory (LSTM) networks are a specialized type of Recurrent Neural Network (RNN) designed to capture long-term dependencies in sequential data. They are known for their ability to remember information over long sequences and maintain context, which is crucial for tasks like text generation.
Key Features of LSTMs:
- Memory Cells: LSTMs include memory cells that store information across sequences, which helps in retaining past contexts.
- Gating Mechanisms: They utilize input, output, and forget gates to regulate the flow of information, effectively managing long-term dependencies.
- Sequential Processing: LSTMs process input data one step at a time, evolving their internal state based on new inputs.
Advantages in Headline Generation:
- Contextual Awareness: LSTMs excel at maintaining context over longer sequences, which is essential for generating headlines that are coherent and contextually relevant.
- Temporal Relationships: They are effective in scenarios where the order and timing of words are important, such as generating text where prior words influence the subsequent ones.
The Transformer model, introduced in the paper "Attention is All You Need," represents a significant advancement in sequence modeling. Transformers leverage self-attention mechanisms to handle long-range dependencies and process sequences in parallel.
Key Features of Transformers:
- Self-Attention Mechanism: This mechanism enables the model to weigh the relevance of different words in a sequence, regardless of their position, allowing for a more comprehensive understanding of context.
- Positional Encoding: Transformers incorporate positional information into the input embeddings to maintain the order of words.
- Parallel Processing: Unlike LSTMs, Transformers process entire sequences simultaneously, leading to more efficient training and faster development.
Advantages in Headline Generation:
- Global Context Understanding: Transformers can capture complex relationships between words across the entire sequence, leading to more nuanced and contextually accurate headlines.
- Efficient Training: The ability to process sequences in parallel reduces training times, making Transformers more efficient for large datasets and quicker iterations.
| Feature | LSTM | Transformer |
|---|---|---|
| Architecture | Sequential, uses gates and memory cells | Parallel, uses self-attention mechanisms |
| Context Handling | Maintains long-term dependencies through memory | Captures global context with self-attention |
| Training Efficiency | Slower due to sequential processing | Faster due to parallel processing |
| Complexity | Simpler in terms of architecture | More complex with multiple layers and attention mechanisms |
| Use Case Suitability | Effective for tasks with strong temporal dependencies | Superior for tasks requiring understanding of complex relationships across the entire sequence |
By comparing these two approaches, this project highlights their respective strengths and trade-offs in the context of headline generation. Whether you are interested in the sequential memory capabilities of LSTMs or the advanced attention mechanisms of Transformers, this repository offers a comprehensive guide to implementing and evaluating both methods.
For comprehensive information about this project, check out this Medium article.
This repository is organized to provide clear and practical examples for implementing and evaluating both LSTM and Transformer-based headline generation models. The structure is designed to facilitate both hands-on experimentation and code reuse.
-
LSTM_Headline_Generator.ipynb: This Jupyter notebook provides a comprehensive walkthrough for implementing and training a headline generation model using the Long Short-Term Memory (LSTM) architecture. It includes detailed sections on:- Data Preprocessing: Preparing and cleaning the dataset for use with the LSTM model.
- Model Creation: Building the LSTM model architecture tailored for headline generation.
- Training: Instructions and code for training the model, including hyperparameter tuning and validation.
- Evaluation: Techniques and metrics for assessing the performance and quality of generated headlines.
-
Transformer_Headline_Generator.ipynb: This Jupyter notebook covers the implementation and training of a headline generation model using Transformer architecture. It features:- Data Preparation: Steps to preprocess and format the data for use with Transformer models.
- Model Design: Building the Transformer model, including attention mechanisms and positional encodings.
- Training: Guidelines for training the Transformer model, with a focus on efficiency and effectiveness.
- Evaluation: Methods for evaluating the model’s performance and quality of generated headlines.
-
LSTMHeadlineGenerator.py: This Python class wraps the trained LSTM model, providing a user-friendly interface for generating headlines. It includes:- Model Loading: Methods for loading pre-trained LSTM models and associated weights.
- Text Generation: Functions to generate coherent headlines from input prompts, with options for customization.
-
TransformersHeadlineGenerator.py: This Python class encapsulates the trained Transformer model, simplifying the process of generating headlines. Features include:- Model Integration: Functions for loading and utilizing the Transformer model, including handling pre-trained weights.
- Text Generation: Tools to generate headlines based on prompts, with options to adjust generation parameters and improve output quality.
By organizing the repository in this manner, users can easily navigate between practical implementations and reusable components, enabling effective exploration and comparison of LSTM and Transformer models for headline generation.
Make sure you have the following installed:
- Python 3.x 🐍
- Jupyter Notebook 📓
- Required libraries (detailed in
requirements.txt)
Install the necessary dependencies with:
pip install -r requirements.txt-
Training the Models:
- Open
LSTM_Headline_Generator.ipynbto explore the LSTM model's data preprocessing, training, and evaluation process. - Open
Transformer_Headline_Generator.ipynbto see the implementation and training of the Transformer model.
- Open
-
Generating Headlines:
- After training, use the wrapper classes to generate headlines. These classes handle everything internally, making it easy to test the models.
- Example usage:
num_words_to_generate = 10 start_prompt = "Blockchain" # Initialize the headline generators lstm_model = LSTMHeadlineGenerator() transformer_model = TransformersHeadlineGenerator() # Generate headlines headline_lstm = lstm_model.generate_text_from_prompt(start_prompt, num_words_to_generate) headline_transformer = transformer_model.generate_text_from_prompt(start_prompt, num_words_to_generate) print("📰 LSTM Headline:", headline_lstm) -> 'Blockchain Technology And Its Impact On The Financial Industry And Opportunities' print("📰 Transformer Headline:", headline_transformer) -> 'blockchain technology in the manufacturing : opportunities and conservation'
This project has been developed exclusively for educational and learning purposes. It aims to explore and compare different deep learning architectures, specifically LSTM and Transformer models, in the context of headline generation. The objective is to gain hands-on experience in implementing, training, and evaluating neural networks for natural language processing (NLP) tasks.
We welcome contributions! If you have ideas for improving the models, adding new features, or enhancing the documentation, feel free to fork the repository and submit a pull request. 🙌
This project is licensed under the MIT License. See the LICENSE file for details.
Special thanks to the authors of the papers and libraries used in this project, including:
- Attention is All You Need - The original Transformer paper.
- Hochreiter & Schmidhuber - The original LSTM paper.
🙏 I would like to extend my heartfelt gratitude to Santiago Hernández, an expert in Cybersecurity and Artificial Intelligence. His incredible course on Deep Learning, available at Udemy, was instrumental in shaping the development of this project. The insights and techniques learned from his course were crucial in crafting the neural network architecture architectures.
This project is licensed under the MIT License, an open-source software license that allows developers to freely use, copy, modify, and distribute the software. 🛠️ This includes use in both personal and commercial projects, with the only requirement being that the original copyright notice is retained. 📄
Please note the following limitations:
- The software is provided "as is", without any warranties, express or implied. 🚫🛡️
- If you distribute the software, whether in original or modified form, you must include the original copyright notice and license. 📑
- The license allows for commercial use, but you cannot claim ownership over the software itself. 🏷️
The goal of this license is to maximize freedom for developers while maintaining recognition for the original creators.
MIT License
Copyright (c) 2024 Dream software - Sergio Sánchez
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.