metadata
language: en
tags:
- bert
- sst2
- glue
- torchdistill
license: apache-2.0
datasets:
- sst2
metrics:
- accuracy
bert-large-uncased fine-tuned on SST-2 dataset, using torchdistill and Google Colab.
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available here.
I submitted prediction files to the GLUE leaderboard, and the overall GLUE score was 80.2.
Yoshitomo Matsubara: "torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP" at EMNLP 2023 Workshop for Natural Language Processing Open Source Software (NLP-OSS)
[OpenReview] [Preprint]
@article{matsubara2023torchdistill,
title={{torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP}},
author={Matsubara, Yoshitomo},
journal={arXiv preprint arXiv:2310.17644},
year={2023}
}