Awesome
Source codes for ``Efficient Classification of Long Documents Using Transformers''
Please refer to our paper for more details and cite our paper if you find this repo useful:
@inproceedings{park-etal-2022-efficient,
title = "Efficient Classification of Long Documents Using Transformers",
author = "Park, Hyunji and
Vyas, Yogarshi and
Shah, Kashif",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.79",
doi = "10.18653/v1/2022.acl-short.79",
pages = "702--709",
}
Instructions
1. Install required libraries
pip install -r requirements.txt
python -m spacy download en_core_web_sm
2. Prepare the datasets
Hyperpartisan News Detection
mkdir data/hyperpartisan
wget -P data/hyperpartisan/ https://zenodo.org/record/1489920/files/articles-training-byarticle-20181122.zip
wget -P data/hyperpartisan/ https://zenodo.org/record/1489920/files/ground-truth-training-byarticle-20181122.zip
unzip data/hyperpartisan/articles-training-byarticle-20181122.zip -d data/hyperpartisan
unzip data/hyperpartisan/ground-truth-training-byarticle-20181122.zip -d data/hyperpartisan
rm data/hyperpartisan/*zip
20NewsGroups
EURLEX-57K
mkdir data/EURLEX57K
wget -O data/EURLEX57K/datasets.zip http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/datasets.zip
unzip data/EURLEX57K/datasets.zip -d data/EURLEX57K
rm data/EURLEX57K/datasets.zip
rm -rf data/EURLEX57K/__MACOSX
mv data/EURLEX57K/dataset/* data/EURLEX57K
rm -rf data/EURLEX57K/dataset
wget -O data/EURLEX57K/EURLEX57K.json http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/eurovoc_en.json
- Running
train.py
with the --data eurlex
flag reads and prepares the data from data/EURLEX57K/{train, dev, test}/*.json
files
- Running
train.py
with the --data eurlex --inverted
flag creates Inverted EURLEX data by inverting the order of the sections
data/EURLEX57K/EURLEX57K.json
contains label information.
CMU Book Summary Dataset
wget -P data/ http://www.cs.cmu.edu/~dbamman/data/booksummaries.tar.gz
tar -xf data/booksummaries.tar.gz -C data
- Running
train.py
with the --data books
flag reads and prepares the data from data/booksummaries/booksummaries.txt
- Running
train.py
with the --data books --pairs
flag creates Paired Book Summary by combining pairs of summaries and their labels
3. Run the models
e.g. python train.py --model_name bertplusrandom --data books --pairs --batch_size 8 --epochs 20 --lr 3e-05
cf. Note that we use the source code for the CogLTX model: https://github.com/Sleepychord/CogLTX
Hyperparameters used
Hyperpartisan
Parameter | BERT | BERT+TextRank | BERT+Random | Longformer | ToBERT |
---|
Batch size | 8 | 8 | 8 | 16 | 8 |
Epochs | 20 | 20 | 20 | 20 | 20 |
LR | 3e-05 | 3e-05 | 5e-05 | 5e-05 | 5e-05 |
Scheduler | NA | NA | NA | warmup | NA |
20NewsGroups, Book Summary, Paired Book Summary
Parameter | BERT | BERT+TextRank | BERT+Random | Longformer | ToBERT |
---|
Batch size | 8 | 8 | 8 | 16 | 8 |
Epochs | 20 | 20 | 20 | 20 | 20 |
LR | 3e-05 | 3e-05 | 3e-05 | 0.005 | 3e-05 |
Scheduler | NA | NA | NA | warmup | NA |
EURLEX, Inverted EURLEX
Parameter | BERT | BERT+TextRank | BERT+Random | Longformer | ToBERT |
---|
Batch size | 8 | 8 | 8 | 16 | 8 |
Epochs | 20 | 20 | 20 | 20 | 20 |
LR | 5e-05 | 5e-05 | 5e-05 | 0.005 | 5e-05 |
Scheduler | NA | NA | NA | warmup | NA |