https://github.com/selcia25/text-generation-using-lstm
💬This project is a research work on text generation using LSTM (Long Short-Term Memory) neural networks.
https://github.com/selcia25/text-generation-using-lstm
lstm-neural-networks tensorflow text-to-speech textgeneration
Last synced: 3 months ago
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💬This project is a research work on text generation using LSTM (Long Short-Term Memory) neural networks.
- Host: GitHub
- URL: https://github.com/selcia25/text-generation-using-lstm
- Owner: selcia25
- License: mit
- Created: 2024-03-19T16:35:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-22T11:37:16.000Z (about 1 year ago)
- Last Synced: 2025-01-02T08:14:35.352Z (5 months ago)
- Topics: lstm-neural-networks, tensorflow, text-to-speech, textgeneration
- Language: Jupyter Notebook
- Homepage:
- Size: 634 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Text Generation using LSTM
This project explores text generation using LSTM (Long Short-Term Memory) neural networks. It trains an LSTM model on a dataset of Medium article titles to predict the next word in a sequence, allowing for the generation of new text based on a seed text.
## Installation
1. Clone the repository:
```sh
git clone https://github.com/selcia25/text-generation-using-lstm.git
```
2. Install the required libraries:
```sh
pip install pandas numpy tensorflow matplotlib
```
## Dataset
The dataset used for training the model is a collection of Medium article titles. The dataset (`medium_data.csv`) contains a multiple columns including `title` with the titles of the articles.
## Model Training
1. Preprocess the data: Clean the text by removing unnecessary characters and tokenize the text using the `Tokenizer` class from Keras.
2. Generate input sequences: Create input sequences of varying lengths to train the model.
3. Pad sequences: Pad the input sequences to ensure uniform length.
4. Build the LSTM model: Construct a Sequential model with an Embedding layer, a Bidirectional LSTM layer, and a Dense output layer with a softmax activation.
5. Compile the model: Compile the model using the Adam optimizer and categorical crossentropy loss function.
6. Train the model: Train the model on the input sequences and corresponding labels.
## Usage
1. Run the script to train the model.
2. Use the trained model to generate text by providing a seed text and specifying the number of words to generate.
## Examples
Here are some examples of generating text using the trained model:
- Seed text: "implementation of"
- Generated text: "implementation of rnn lstm"
## Contributing
Contributions are welcome! Please fork the repository and create a pull request with your changes.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.