Neural Scam Artist






TL;DR
A dataset of scam emails is scraped from an anti-fraud website. The dataset is then deduplicated
using MinHash and LSH. The deduplicated dataset is used for fine-tuning GPT-2.

Comic stolen from Agent-X Comics.

📖 Table of contents

☁️ Project Description

Objective

The goal of this project is create a new dataset of fraudulent emails that can advance the
research on intelligent email assistants.

Web Scraper

Data is scraped from the website https://antifraudintl.org/.
At first, a set of thread urls is collected and stored. Then, each thread is searched for
emails. For each thread, at most one email is kept as the rest are duplicates. Metadata
(Subject, Date etc) is removed. The resultant dataset is stored inside a csv file.

Deduplication

To avoid the quadratic complexity, a cheap alternative is selected: MinHash and LSH using the datasketch library. For each document, this method
efficiently locates its nearest neighbors. Because this leads to a a large amount of false
negatives (i.e. dulpicate documents that are classified as non-duplicates), the approach is
extended by creating a duplicate graph. Nodes in this graph represent documents and are connected
with an edge if their respective documents have been classified as duplicates. To deduplicate the
dataset, connected components of the
graph are located and for each component only a single node is selected. A
readability criterion is used for selection.

GPT-2

A small pretrained GPT-2 model from the
Huggingface library
is fine-tuned on the deduplicated dataset. A collection of cherry-picked randomly selected
generated samples can be found here here.

📁 Shared Files

Resource Size #Samples Link
Full dataset 128.5 MB 85,160 Link
Deduplicated dataset 74.2 MB 58,227 Link
Thread urls 6.4 MB 95,324 Link
GPT-2 Checkpoints ~1.5 GB Link

🧰 Requirements

See requirements.txt.

⚙️ Installation

$ git clone https://github.com/davidsvy/Neural-Scam-Artist
$ cd Neural-Scam-Artist
$ pip install -r requirements.txt

🧻 Usage

To generate dataset (~3 hours on Colab):


$ python create_dataset.py [-c configs/create_dataset.yaml]

To deduplicate dataset (~30 minutes on Colab):

$ python deduplicate_dataset.py [-c configs/deduplicate_dataset.yaml]

To train GPT-2 (~3 hours/epoch on Colab with K80):

$ python gpt2_train.py [-c configs/gpt2_train.yaml]

To generate text with GPT-2:

$ python gpt2_sample.py [-c configs/gpt2_sample.yaml]

GitHub

View Github