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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 cd Install the required libraries:
bash Copy code pip install torch torchvision torchaudio matplotlib pandas Ensure you have the Hugging Face transformers library installed:
bash Copy code 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.