BERT-ENN

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Uncertainty-Aware Reliable Text Classification (KDD 2021)

BERT-ENN 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# BERT-ENN

This repository contains the essential code for the paper [Uncertainty-Aware Reliable Text Classification (KDD 2021)](https://dl.acm.org/doi/10.1145/3447548.3467382).

The paper is to combine evidential uncertainty and BERT for OOD detection tasks in text classification.

You may also check our previous work [Multidimensional Uncertainty-Aware Evidential Neural Networks (AAAI 2021)](https://github.com/snowood1/wenn) in image classification.

## Prerequisites
The code is written by Python 3.6 in Linux system. The cuda version is 10.2. 
The necessary packages include:

torch==1.7.1 
	torchvision==0.8.2 
	torchtext==0.8.1
	transformers==4.1.1 
	tensorflow-gpu==1.15.0 
	tqdm==4.62.2 
	matplotlib==3.3.4 
	numpy==1.19.2 
	scikit-learn==0.24.2
	scipy==1.4.1 
	pandas==1.1.5 
	keras==2.3.0

## Quick Start

**1.** Create folders 'dataset' and 'model_save' to save downloaded datasets and output results.
	
We follow the same datasets in [Outlier Exposure](https://github.com/hendrycks/outlier-exposure/tree/master/NLP_classification). 
You can download the preprocessed datasets and the saved results [from here](https://drive.google.com/drive/folders/1--2NY_z_JfgpvsOsmmOU_Q2zOtcVauI3?usp=sharing).
The preprocessing can be reproduced by: 
	
		python prepare_data.py

**2.**  To reproduce results of Table 3 and 4 using the saved checkpoints, run the code below: (You can change sst to 20news or trec.  )

Baselines
	
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model base --dataset sst --save_path saved_result --index 0		# maximum softmax scores
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model mc-dropout --dataset sst --save_path saved_result  --index 0		# MC-dropout
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model temperature --dataset sst --save_path saved_result  --index 0	# temperature scaling
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model manifold-smoothing --dataset sst --save_path saved_result  --index 0	# Manifold smoothing
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model oe --dataset sst --save_path saved_result  --index 0			# Outlier Explosure
	
	
 ENN
	
	CUDA_VISIBLE_DEVICES=0 python test_bert_enn.py --dataset sst --path ./saved_result/sst/ENN_ori/9.pt	# Vanilla ENN
	CUDA_VISIBLE_DEVICES=0 python test_bert_enn.py --dataset sst --path ./saved_result/sst/ENN_OE/9.pt	# ENN with Outlier Explosure
	CUDA_VISIBLE_DEVICES=0 python test_bert_enn.py --dataset sst --path ./saved_result/sst/ENN_AD/9.pt	# ENN with off-manifold adversial examples
	CUDA_VISIBLE_DEVICES=0 python test_bert_enn.py --dataset sst --path ./saved_result/sst/ENN_MIX/9.pt	# ENN with Mixture Regularizers

**3.**  To train ENN models from scratch:

Baselines
	
	CUDA_VISIBLE_DEVICES=0 python bert.py --dataset sst --seed 0			# vanilla BERT for maximum softmax scores, MC-dropout and temperature scaling
	CUDA_VISIBLE_DEVICES=0 python manifold-smoothing.py --dataset sst --seed 0 	# Manifold smoothing
	CUDA_VISIBLE_DEVICES=0 python bert_oe.py --dataset sst --seed 0			# Outlier Explosure
	
	
 ENN.  Below we use the Hyper-parameters in Table 5. For vanilla ENN, all the betas are set to 0.
 
 	CUDA_VISIBLE_DEVICES=0 python train_bert_enn.py --dataset 20news --beta_in 0 --beta_oe 1 --beta_off 0.1
	CUDA_VISIBLE_DEVICES=0 python train_bert_enn.py --dataset trec --beta_in 0 --beta_oe 1 --beta_off 0.1
	CUDA_VISIBLE_DEVICES=0 python train_bert_enn.py --dataset sst --beta_in 0.01 --beta_oe 1 --beta_off 0.1

**4.**  To evaluate your trained models, you can follow Step 2 but replace the input checkpoints paths. For example:

Baselines
	
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model base --dataset sst --save_path model_save --index 0			# maximum softmax scores
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model mc-dropout --dataset sst --save_path model_save --index 0		# MC-dropout
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model temperature --dataset sst --save_path model_save --index 0		# temperature scaling
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model manifold-smoothing --dataset sst  --save_path model_save --index 0 	# Manifold smoothing
	CUDA_VISIBLE_DEVICES=0 python test_bert.py --model oe -dataset sst --save_path model_save --index 0			# Outlier Explosure
	
	
 ENN
 
 	CUDA_VISIBLE_DEVICES=0 python test_bert_enn.py --dataset 20news --path ./model_save/20news/BERT-ENN-w2adv-0-on-0.0-oe-1.0-off-0.1/9.pt
	CUDA_VISIBLE_DEVICES=0 python test_bert_enn.py --dataset trec --path ./model_save/trec/BERT-ENN-w2adv-0-on-0.0-oe-1.0-off-0.1/9.pt
	CUDA_VISIBLE_DEVICES=0 python test_bert_enn.py --dataset sst --path ./model_save/sst/BERT-ENN-w2adv-0-on-0.01-oe-1.0-off-0.1/9.pt

**5.** We also provide an [example](https://github.com/snowood1/BERT-ENN/blob/main/demo%20result%20figures-final.ipynb) of plotting Figure 3 and Figure 4.

## Reference

The implementation of baselines are modified from:

[1] Outlier Exposure. https://github.com/hendrycks/outlier-exposure/tree/master/NLP_classification

[2] Manifold Calibration. https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning

## Citation

If you find this repo useful in your research, please consider citing:

@inproceedings{hu2021uncertainty,
	  title={Uncertainty-Aware Reliable Text Classification},
	  author={Hu, Yibo and Khan, Latifur},
	  booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
	  pages={628--636},
	  year={2021}
	}

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