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Website and baselines for the Moral Uncertainty task.

Shell 2.86% Python 48.21% Jupyter Notebook 48.93%

moral-uncertainty's Introduction

moral-uncertainty

Dataset and baselines for the Moral Uncertainty benchmark. See the Moral Uncertainty website for details.

Dataset

You can download the dataset files from the links in the table:

Split	Download	# examples
Train	train.csv	13,910
Test	test.csv	2,771

Baselines

The code for training our baseline models is in moral-uncertainty/baselines. Our specific baselines can be reproduced with:

# bert-base-uncased
python tune.py --ngpus 1 --model bert-base-uncased --learning_rate 3e-5 --batch_size 16 --nepochs 4 --gradient_acc_steps 1 --verbose

# bert-large-uncased
python tune.py --ngpus 1 --model bert-large-uncased --learning_rate 5e-6 --batch_size 16 --nepochs 4 --gradient_acc_steps 1 --verbose

# roberta-large
python tune.py --ngpus 1 --model roberta-large --learning_rate 1e-5 --batch_size 16 --nepochs 4 --gradient_acc_steps 1 --verbose

# albert-xxlarge-v2
python tune.py --ngpus 1 --model albert-xxlarge-v2 --learning_rate 3e-5 --batch_size 16 --nepochs 4 --gradient_acc_steps 1 --verbose

# microsoft/deberta-v3-large
python tune.py --ngpus 1 --model microsoft/deberta-v3-large --learning_rate 1e-5 --batch_size 16 --nepochs 4 --gradient_acc_steps 1 --verbose

# microsoft/deberta-v2-xxlarge
python tune.py --ngpus 1 --model microsoft/deberta-v2-xxlarge --learning_rate 1e-6 --batch_size 8 --nepochs 4 --gradient_acc_steps 1 --verbose

For the GPT-3 baseline, see moral-uncertainty/baselines/finetune_gpt3.ipynb.

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