The CARE Dataset is described in the following paper: [https://arxiv.org/abs/2201.11895]
@article{DBLP:journals/corr/abs-2201-11895,
author = {Jane Dwivedi-Yu and
Alon Y. Halevy},
title = {The CARE Dataset for Affective Response Detection},
journal = {CoRR},
volume = {abs/2201.11895},
year = {2022},
url = {https://arxiv.org/abs/2201.11895},
eprinttype = {arXiv},
eprint = {2201.11895},
timestamp = {Wed, 02 Feb 2022 15:00:01 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-11895.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
conda create -n care -y python=3.7 && conda activate care
pip: -r requirements.txt
Download the post ids and care labels with:
wget dl.fbaipublicfiles.com/care/care_db_ids_and_labels.csv
To download the metadata associated with the post ids, run
python download_posts.py
By default, it uses the following defaults:
python download_posts.py --data_file ./care_db_ids_and_labels.csv --output_file ./data/post_id_metadata.json --chunk_dir ./data/chunks/ --n 10 --cpus 2
This process all post ids from ./data/care_db_ids_and_labels.csv, use 2 cpus to process batches of 10 post ids at a time, write the intermediate outputs to ./data/chunks/ and finally aggregate them into the file ./data/care_db_ids_and_labels.csv
The CARE method can be run to label posts given the comment texts. See care_predict.py for an example.
See care_bert.py for an example of how to load and use CARE-BERT for inference. The model with the command:
wget dl.fbaipublicfiles.com/care/care_bert.pth
All code and data is released under CC-BY-NC licensing. See our LICENSE file for licensing details.
