Introductory note to this fork

This fork adds simple baselines based on bag-of-word to the repository

Try it:

Calling the original KNN with original gzip distance:

python main_text.py

Calling the KNN with bag-of-words distance:

python main_text.py --bow_knn

Calling a bag-of-words classifier:

python main_text.py --bow_trained

What follows is from the original repo. All options of the original repo should still be possible.

Code for Paper: “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors

Require

See requirements.txt.

Install requirements in a clean environment:

conda create -n npc python=3.7
conda activate npc
pip install -r requirements.txt

Run

python main_text.py

By default, this will only use 100 test and training samples per class as a quick demo. They can be changed by --num_test, --num_train.

--compressor <gzip, lzma, bz2>
--dataset <AG_NEWS, SogouNews, DBpedia, YahooAnswers, 20News, Ohsumed_single, R8, R52, kinnews, kirnews, swahili, filipino> [Note that for small datasets like kinnews, default 100-shot is too big, need to set --num_test and --num_train.]
--num_train <INT>
--num_test <INT>
--data_dir <DIR> [This needs to be specified for R8, R52 and Ohsumed.]
--all_test [This will use the whole test dataset.]
--all_train
--record [This will record the distance matrix in order to save for the future use. It's helpful when you when to run on the whole dataset.]
--test_idx_start <INT>
--test_idx_end <INT> [These two args help us to run on a certain range of test set. Also helpful for calculating the distance matrix on the whole dataset.]
--para [This will use multiprocessing to accelerate.]
--output_dir <DIR> [The output directory to save information of tested indices or distance matrix.]

Calculate Accuracy (Optional)

If we want to calculate accuracy from recorded distance file , use

python main_text.py --record --score --distance_fn <DISTANCE DIR> 

to calculate accuracy. Otherwise, the accuracy will be calculated automatically using the command in the last section.

Use Custom Dataset

You can use your own custom dataset by passing custom to --dataset; pass the data directory that contains train.txt and test.txt to --data_dir; pass the class number to the --class_num.

Both train.txt and test.txt are expected to have the format {label}\t{text} per line.

You can change the delimiter according to you dataset by changing delimiter in load_custom_dataset() in data.py.