README.md

Arms deals classification

This is Fredrik Hernqvist's thesis project.

Annotations

I'm annotating about 800 texts. The unprocessed text files are stored in raw_data. As I annotate texts, these get a matching json file in annotated_data. The json files are all compiled into data.json, which is what is used for training.

To start the editor, simply run python3 editor.py. Things might have the wrong scales depending on your resolution. data.json is automatically updated upon saving.

Training

Training is done using training_lightning.py. It has a bunch of arguments which are mostly self explanatory, they can be viewed by running python3 training_lightning.py --help.

To run a basic example, run

python3 training_lightning.py data.json --max_epochs 5 --small_data --print

This will train the BERT model for 5 epochs and print the classification of the trained model. The --small_data argument crops the entire dataset to 20 texts. To view the metrics (this can be done live during training), run tensorboard --logdir lightning/lightning_logs/ and open http://localhost:6006/ in the browser.

We can also change the arguments to use ALBERT instead of BERT using the --classifier argument.

Sequence classification

When setting --task sequence we are trying to predict wether the entire text contains an arms deal or not. It outputs either 0 or 1 for each text.

Token classification

When setting --task token we are trying to predict whether each token is a certain attribute of the arms deal. For each text, it outputs an n*8 matrix. The value at index (i, j) in this matrix is an 1 if token i is an instance of attribute j, otherwise 0. The attributes are Weapon, Buyer, Buyer Country, Seller, Seller Country, Quantity, Price, Date, in that order.

Text Extractor

text_extractor.py was used to get the raw text from the pdf files. It looks at the pdf file, finds the url at the bottom, and uses the newspaper module to download the text from its source. This is not needed anymore.

Tests

Here are the commands for running the tests in the report and the results.

Sequence classification with exactly 400 character chunks of text

Command: python3 training_lightning.py --gpu --classifier albert --task sequence --max_epochs 100 --train_portion 0.8 --lr 0.00001 --batch_size 4 --max_tokens 128 --split fixed --test data.json

Result:

{'TestAccuracy': 0.7857142686843872,
 'TestF1': 0.8167938590049744,
 'TestLoss': 0.5804754495620728,
 'TestPrecision': 0.8045112490653992,
 'TestRecall': 0.8294573426246643}

Token classification with exactly 400 character chunks of text

Command: python3 training_lightning.py --gpu --classifier albert --task token --max_epochs 200 --train_portion 0.8 --lr 0.00001 --batch_size 8 --max_tokens 128 --split fixed --test data.json

Result:

{'TestAccuracy': 0.9911141395568848,
 'TestF1': 0.6636696457862854,
 'TestLoss': 0.06833155453205109,
 'TestPrecision': 0.7458832263946533,
 'TestRecall': 0.5977804660797119}

Sequence classification with variable chunks of text

Command: python3 training_lightning.py --gpu --classifier albert --task sequence --max_epochs 100 --train_portion 0.8 --lr 0.00001 --batch_size 4 --max_tokens 128 --split chunks --test data.json

Result:

{'TestAccuracy': 0.8706896305084229,
 'TestF1': 0.8110831379890442,
 'TestLoss': 0.44118180871009827,
 'TestPrecision': 0.8429319262504578,
 'TestRecall': 0.7815533876419067}

Token classification with variable chunks of text

Command: python3 training_lightning.py --gpu --classifier albert --task token --max_epochs 200 --train_portion 0.8 --lr 0.00001 --batch_size 8 --max_tokens 128 --split chunks --test data.json

Result:

{'TestAccuracy': 0.9966796636581421,
 'TestF1': 0.7691947221755981,
 'TestLoss': 0.018630821257829666,
 'TestPrecision': 0.8495346307754517,
 'TestRecall': 0.7027373909950256}