Text-de-shuffling

Finetuning GPT2 to descramble sentences.

Sentence Reconstruction with Finetuned GPT-2

This repository contains the code and resources for finetuning a GPT-2 model to perform sentence reconstruction, converting scrambled sentences back to their original grammatical form using the same words.

Contents

• training.py: The main script for finetuning the GPT-2 model on the sentence reconstruction task.
• inference.py: A script for using the finetuned model to reconstruct sentences from scrambled input.
• notebook.ipynb: A Jupyter Notebook demonstrating the training and inference process.

Getting Started

Prerequisites

• Python 3.7 or higher
• PyTorch
• Transformers (Hugging Face)
• Numpy
• Pandas

Installation

1. Clone the repository:

Training

To train the GPT-2 model on the sentence reconstruction task, run the training.py script:

This script will run training in a modal nvidia A10 GPU. Run:

modal run training.py

1. Load the GPT-2 model and tokenizer.
2. Prepare the training data by scrambling sentences and creating input-output pairs.
3. Finetune the GPT-2 model on the sentence reconstruction task.
4. Save the finetuned model.

Inference

To use the finetuned model for sentence reconstruction, run the inference.py script:

The script will output the reconstructed sentence.

Jupyter Notebook

The notebook.ipynb Jupyter Notebook provides a step-by-step guide through the training and inference process. You can run the notebook to explore the code and the model's performance.

Results

The finetuned GPT-2 model achieved an impressive performance on the sentence reconstruction task. The model was able to handle a variety of sentence structures and word arrangements, but struggled with some more complex or unusual sentence constructions.