LSTM Text Generation Model
This repository contains an implementation of an LSTM-based text generation model built with PyTorch. The model is trained on a given text dataset to predict the next word in a sequence, allowing for the generation of coherent text based on a seed phrase.
Table of Contents
Installation
Usage
Training
Evaluation
Model Generation
License
Installation
Clone this repository:
Certainly! Below is a sample README file that you can use as a guide for a project utilizing Hugging Face with your LSTM text generation model. Feel free to modify it based on your specific requirements and project details.
LSTM Text Generation Model
This repository contains an implementation of an LSTM-based text generation model built with PyTorch. The model is trained on a given text dataset to predict the next word in a sequence, allowing for the generation of coherent text based on a seed phrase.
Table of Contents
Installation
Usage
Training
Evaluation
Model Generation
License
Installation
Clone this repository:
bash
Copy code
git clone <repository_url>
cd <repository_directory>
Install the required libraries:
bash
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pip install torch torchvision torchaudio matplotlib pandas
Ensure you have the Hugging Face transformers library installed:
bash
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pip install transformers
Usage
Preparing Your Data
Place your text data in a file named input1.txt in the content directory or adjust the file path in the code as needed.
Training the Model
Make sure to adjust hyperparameters in the code as needed, including batch_size, block_size, embedding_dim, and others.
Loading a Pre-Trained Model
To load a pre-trained model, use the load_model function:
Generating Text
To generate text using the trained model, use the generate_text function:
Training
During training, the model saves checkpoints every 200 iterations. Training progress is printed to the console, including training and validation losses.
The training losses and validation losses are saved in training_and_validation_losses.csv, and a plot of the losses is saved as training_loss_and_validation_loss_curve.png.
Example Output
You can view the progress of the training as follows:
Evaluation
To evaluate the model's performance, you can calculate the perplexity using the calculate_perplexity function, which takes the validation data and block size as arguments.
