Hugging Face Transformers: Fine-tuning DistilBERT for Binary Classification Tasks
Creating high-performing natural language models is as time-consuming as it is expensive, but recent advances in transfer learning as applied to the domain of NLP have made it easy for companies to use pretrained models for their natural language tasks.
In this repository, we propose code to be used as a reference point for fine-tuning pretrained models from the Hugging Face Transformers Library on binary classification tasks using TF 2.0.
Specifically, we will be using:
- Comet.ml as our experimentation framework
- nlpaug for data augmentation
- DistilBERT--a distilled version of BERT
to predict toxic comments on a modified version of the Jigsaw Toxic Comment dataset on Kaggle.
If anything is confusing, please see the accompanying Medium article for an explanation of my methodologies!
As mentioned previously, the datasets used to train our models are based on the Jigsaw Toxic Comment dataset found on Kaggle. This dataset has labels intended for a multi-label classification task (e.g. Toxic, Severe Toxic, Obscene, Threat, Insult, Identity Hate), but we decided against using these labels due to their subjectivity.
Instead, we converted the original dataset to a binary classification task where labels are either classified as toxic ('isToxic == 1) or non-toxic ('isToxic == 0). Toxic comments make up 9.58% of this intermediate dataset.
To get our unbalanced dataset, we undersampled the majority class of this intermediate dataset until toxic comments make up 20.15% of all data.
To get our balanced dataset, we used nlpaug to augment the minority class of the unbalanced dataset
until we reached a 50-50 class distribution. Text augmentation was performed with synonym replacement using BERT embeddings.
Any files or folders with unbalanced or balanced in the name is in relation to these two datasets.
Both models follow the same architecture:
[DistilBERT CLS Token Layer] + [Dense 256] + [Dense 32] + [Single Output Layer]
The only difference is whether they were trained on the balanced dataset with binary_crossentropy loss
or the unbalanced dataset with focal_loss.
The results are as follows:
balanced_model
- Test Accuracy: 0.8801
- Test AUC-ROC: 0.9656
unbalanced_model
- Test Accuracy: 0.9218
- Test AUC-ROC: 0.9691
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
@misc{ma2019nlpaug,
title={NLP Augmentation},
author={Edward Ma},
howpublished={https://github.com/makcedward/nlpaug},
year={2019}
}
@misc{Kaggle,
title={Toxic Comment Classification Challenge},
author={Jigsaw / Conversation AI},
howpublished={https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data},
year={2017}
}
Copyright 2021 Ray William
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.