Overview

Given an essay, this project uses machine learning and token classification to separate the essay into distinct argumentative elements (eg. Claim, Evidence, Counterclaim, etc.)

This is a token classification (NER) approach. A longformer (Long-Document transformer) is trained for this task using the HuggingFace Python library.

Dataset

The dataset can be downloaded from Kaggle - https://www.kaggle.com/c/feedback-prize-2021/data. Save the dataset in a directory called "dataset". The code accesses the data in that directory.

Running the Code

• Install the required Python packages using pip install -r requirements.txt
• Create tokens and labels by running create_tokens_training.py. This generates two files - text.txt and labels.txt. These will be used to train the model.
• Train the model (GPU recommended) using the token_classification_training.py. Model will be saved in "/saved_model" directory.
• Use the trained model to predict on new data by running argumentative_element_identifier.py

Training Time

With a GPU (recommended), the model takes about 5 hours to run. Without a GPU, it takes much longer.