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experiment.sh
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#!/bin/bash
# BERT and GPT-2 experiments. Section 7 of the paper. See the README
# experiments/m100/n100/bert/bert_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n100_bert_1 \
--dataset_names \
yahoo_answers_topics \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n100/bert/bert_2.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n100_bert_2 \
--dataset_names \
SetFit/enron_spam \
yelp_review_full \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n100/bert/bert_3.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n100_bert_3 \
--dataset_names \
classla/FRENK-hate-en \
blog_authorship_corpus \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n100/bert/bert_4.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n100_bert_4 \
--dataset_names \
aladar/craigslist_bargains \
movie_rationales \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n100/bert/bert_5.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n100_bert_5 \
--dataset_names \
ag_news \
SetFit/amazon_counterfactual_en \
app_reviews \
christinacdl/clickbait_notclickbait_dataset \
climate_fever \
disaster_response_messages \
aladar/emo \
dair-ai/emotion \
financial_phrasebank \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m100/n100/bert/bert_6.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n100_bert_6 \
--dataset_names \
hyperpartisan_news_detection \
limit \
AmazonScience/massive \
mteb/mtop_domain \
ccdv/patent-classification \
rotten_tomatoes \
silicone \
trec \
tweets_hate_speech_detection \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m100/n100/gpt2/gpt2_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n100_gpt2_1 \
--dataset_names \
yahoo_answers_topics \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 16 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n100/gpt2/gpt2_2.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n100_gpt2_2 \
--dataset_names \
blog_authorship_corpus \
aladar/craigslist_bargains \
SetFit/enron_spam \
movie_rationales \
yelp_review_full \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n100/gpt2/gpt2_3.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n100_gpt2_3 \
--dataset_names \
classla/FRENK-hate-en \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n100/gpt2/gpt2_4.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n100_gpt2_4 \
--dataset_names \
ag_news \
SetFit/amazon_counterfactual_en \
christinacdl/clickbait_notclickbait_dataset \
climate_fever \
disaster_response_messages \
aladar/emo \
dair-ai/emotion \
financial_phrasebank \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n100/gpt2/gpt2_5.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n100_gpt2_5 \
--dataset_names \
hyperpartisan_news_detection \
limit \
AmazonScience/massive \
mteb/mtop_domain \
ccdv/patent-classification \
rotten_tomatoes \
silicone \
trec \
tweets_hate_speech_detection \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n100/gpt2/gpt2_6.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n100_gpt2_6 \
--dataset_names \
app_reviews \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n200/bert/bert_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n200_bert_1 \
--dataset_names \
yahoo_answers_topics \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 8 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n200/bert/bert_2.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n200_bert_2 \
--dataset_names \
SetFit/enron_spam \
yelp_review_full \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n200/bert/bert_3.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n200_bert_3 \
--dataset_names \
classla/FRENK-hate-en \
blog_authorship_corpus \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n200/bert/bert_4.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n200_bert_4 \
--dataset_names \
aladar/craigslist_bargains \
movie_rationales \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n200/bert/bert_5.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n200_bert_5 \
--dataset_names \
ag_news \
SetFit/amazon_counterfactual_en \
app_reviews \
christinacdl/clickbait_notclickbait_dataset \
climate_fever \
disaster_response_messages \
aladar/emo \
dair-ai/emotion \
financial_phrasebank \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m100/n200/bert/bert_6.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n200_bert_6 \
--dataset_names \
hyperpartisan_news_detection \
limit \
AmazonScience/massive \
mteb/mtop_domain \
ccdv/patent-classification \
rotten_tomatoes \
silicone \
trec \
tweets_hate_speech_detection \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m100/n200/gpt2/gpt2_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n200_gpt2_1 \
--dataset_names \
yahoo_answers_topics \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 16 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n200/gpt2/gpt2_2.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n200_gpt2_2 \
--dataset_names \
blog_authorship_corpus \
aladar/craigslist_bargains \
SetFit/enron_spam \
movie_rationales \
yelp_review_full \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n200/gpt2/gpt2_3.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n200_gpt2_3 \
--dataset_names \
classla/FRENK-hate-en \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n200/gpt2/gpt2_4.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n200_gpt2_4 \
--dataset_names \
ag_news \
SetFit/amazon_counterfactual_en \
christinacdl/clickbait_notclickbait_dataset \
climate_fever \
disaster_response_messages \
aladar/emo \
dair-ai/emotion \
financial_phrasebank \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n200/gpt2/gpt2_5.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n200_gpt2_5 \
--dataset_names \
hyperpartisan_news_detection \
limit \
AmazonScience/massive \
mteb/mtop_domain \
ccdv/patent-classification \
rotten_tomatoes \
silicone \
trec \
tweets_hate_speech_detection \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n200/gpt2/gpt2_6.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n200_gpt2_6 \
--dataset_names \
app_reviews \
--num_test 200 \
--num_subsamples 50 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n50/bert/bert_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n50_bert_1 \
--dataset_names \
yahoo_answers_topics \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n50/bert/bert_2.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n50_bert_2 \
--dataset_names \
SetFit/enron_spam \
yelp_review_full \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n50/bert/bert_3.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n50_bert_3 \
--dataset_names \
classla/FRENK-hate-en \
blog_authorship_corpus \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n50/bert/bert_4.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n50_bert_4 \
--dataset_names \
aladar/craigslist_bargains \
movie_rationales \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n50/bert/bert_5.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n50_bert_5 \
--dataset_names \
ag_news \
SetFit/amazon_counterfactual_en \
app_reviews \
christinacdl/clickbait_notclickbait_dataset \
climate_fever \
disaster_response_messages \
aladar/emo \
dair-ai/emotion \
financial_phrasebank \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m100/n50/bert/bert_6.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n50_bert_6 \
--dataset_names \
hyperpartisan_news_detection \
limit \
AmazonScience/massive \
mteb/mtop_domain \
ccdv/patent-classification \
rotten_tomatoes \
silicone \
trec \
tweets_hate_speech_detection \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m100/n50/gpt2/gpt2_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n50_gpt2_1 \
--dataset_names \
yahoo_answers_topics \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 16 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n50/gpt2/gpt2_2.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n50_gpt2_2 \
--dataset_names \
blog_authorship_corpus \
aladar/craigslist_bargains \
SetFit/enron_spam \
movie_rationales \
yelp_review_full \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n50/gpt2/gpt2_3.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n50_gpt2_3 \
--dataset_names \
classla/FRENK-hate-en \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n50/gpt2/gpt2_4.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n50_gpt2_4 \
--dataset_names \
ag_news \
SetFit/amazon_counterfactual_en \
christinacdl/clickbait_notclickbait_dataset \
climate_fever \
disaster_response_messages \
aladar/emo \
dair-ai/emotion \
financial_phrasebank \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n50/gpt2/gpt2_5.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n50_gpt2_5 \
--dataset_names \
hyperpartisan_news_detection \
limit \
AmazonScience/massive \
mteb/mtop_domain \
ccdv/patent-classification \
rotten_tomatoes \
silicone \
trec \
tweets_hate_speech_detection \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n50/gpt2/gpt2_6.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n50_gpt2_6 \
--dataset_names \
app_reviews \
--num_test 50 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n500/bert/bert_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n500_bert_1 \
--dataset_names \
yahoo_answers_topics \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 8 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n500/bert/bert_2.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n500_bert_2 \
--dataset_names \
SetFit/enron_spam \
yelp_review_full \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n500/bert/bert_3.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n500_bert_3 \
--dataset_names \
classla/FRENK-hate-en \
blog_authorship_corpus \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n500/bert/bert_4.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n500_bert_4 \
--dataset_names \
aladar/craigslist_bargains \
movie_rationales \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m100/n500/bert/bert_5.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n500_bert_5 \
--dataset_names \
ag_news \
SetFit/amazon_counterfactual_en \
app_reviews \
christinacdl/clickbait_notclickbait_dataset \
climate_fever \
disaster_response_messages \
aladar/emo \
dair-ai/emotion \
financial_phrasebank \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m100/n500/bert/bert_6.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m100_n500_bert_6 \
--dataset_names \
hyperpartisan_news_detection \
limit \
AmazonScience/massive \
mteb/mtop_domain \
ccdv/patent-classification \
rotten_tomatoes \
silicone \
trec \
tweets_hate_speech_detection \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m100/n500/gpt2/gpt2_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n500_gpt2_1 \
--dataset_names \
yahoo_answers_topics \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 16 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n500/gpt2/gpt2_2.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n500_gpt2_2 \
--dataset_names \
blog_authorship_corpus \
aladar/craigslist_bargains \
SetFit/enron_spam \
movie_rationales \
yelp_review_full \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n500/gpt2/gpt2_3.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n500_gpt2_3 \
--dataset_names \
classla/FRENK-hate-en \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m100/n500/gpt2/gpt2_4.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n500_gpt2_4 \
--dataset_names \
ag_news \
SetFit/amazon_counterfactual_en \
christinacdl/clickbait_notclickbait_dataset \
climate_fever \
disaster_response_messages \
aladar/emo \
dair-ai/emotion \
financial_phrasebank \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n500/gpt2/gpt2_5.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n500_gpt2_5 \
--dataset_names \
hyperpartisan_news_detection \
limit \
AmazonScience/massive \
mteb/mtop_domain \
ccdv/patent-classification \
rotten_tomatoes \
silicone \
trec \
tweets_hate_speech_detection \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m100/n500/gpt2/gpt2_6.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m100_n500_gpt2_6 \
--dataset_names \
app_reviews \
--num_test 500 \
--num_subsamples 20 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 1
# experiments/m50/n100/bert/bert_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m50_n100_bert_1 \
--dataset_names \
yahoo_answers_topics \
--num_train 50 \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m50/n100/bert/bert_2.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m50_n100_bert_2 \
--dataset_names \
SetFit/enron_spam \
yelp_review_full \
--num_train 50 \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m50/n100/bert/bert_3.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m50_n100_bert_3 \
--dataset_names \
classla/FRENK-hate-en \
blog_authorship_corpus \
--num_train 50 \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m50/n100/bert/bert_4.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m50_n100_bert_4 \
--dataset_names \
aladar/craigslist_bargains \
movie_rationales \
--num_train 50 \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 8 \
--per_device_eval_batch_size_classification 32 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 2
# experiments/m50/n100/bert/bert_5.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m50_n100_bert_5 \
--dataset_names \
ag_news \
SetFit/amazon_counterfactual_en \
app_reviews \
christinacdl/clickbait_notclickbait_dataset \
climate_fever \
disaster_response_messages \
aladar/emo \
dair-ai/emotion \
financial_phrasebank \
--num_train 50 \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m50/n100/bert/bert_6.sh
TQDM_DISABLE=1 python run.py \
--lm_type bert \
--run_name m50_n100_bert_6 \
--dataset_names \
hyperpartisan_news_detection \
limit \
AmazonScience/massive \
mteb/mtop_domain \
ccdv/patent-classification \
rotten_tomatoes \
silicone \
trec \
tweets_hate_speech_detection \
--num_train 50 \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 16 \
--per_device_train_batch_size_classification 16 \
--per_device_eval_batch_size_classification 64 \
--num_train_epochs_classification 3 \
--num_train_epochs_pretrain 2
# experiments/m50/n100/gpt2/gpt2_1.sh
TQDM_DISABLE=1 python run.py \
--lm_type gpt2 \
--run_name m50_n100_gpt2_1 \
--dataset_names \
yahoo_answers_topics \
--num_train 50 \
--num_test 100 \
--num_subsamples 100 \
--per_device_train_batch_size_pretrain 8 \
--per_device_train_batch_size_classification 4 \
--per_device_eval_batch_size_classification 16 \
--num_train_epochs_classification 1 \
--num_train_epochs_pretrain 1
# experiments/m50/n100/gpt2/gpt2_2.sh
TQDM_DISABLE=1 python run.py \