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#!/bin/bash |
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set -x -e |
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source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0 |
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export HF_DATASETS_OFFLINE=1 |
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export TRANSFORMERS_OFFLINE=1 |
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conda activate muennighoffmodelconv |
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CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13f-6B3-ml-t0/checkpoints/tasky |
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CKPTS=( |
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global_step250 |
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global_step500 |
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global_step750 |
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global_step1000 |
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global_step1250 |
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) |
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EXAMPLE_CKPT=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6b3-ml-t0-lmtoks341b-t0toks13b-xp3capmixnewcodelonglossseq |
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DUMP_PATH=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/6b3t0 |
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OUT_PREFIX=tasky_ |
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TP=1 |
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for i in {0..6}; do |
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CKPT=${CKPTS[$i]} |
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echo "$i" |
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echo "Running $CKPT" |
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OUTPUTCKPT=$DUMP_PATH/"$OUT_PREFIX$CKPT" |
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python $six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/transformers_clone/src/transformers/models/bloom/convert_bloom_original_checkpoint_to_pytorch.py --pytorch_dump_folder_path $OUTPUTCKPT --bloom_checkpoint_path $CKPT_PATH/$CKPT --pretraining_tp $TP --bloom_config_file $EXAMPLE_CKPT/config.json |
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cp -r $EXAMPLE_CKPT/*.json $OUTPUTCKPT/ |
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eval_script="./eval_$i.slurm" |
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cat <<EOT > $eval_script |
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#!/bin/bash |
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#SBATCH --job-name=evaluate_t0 |
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#SBATCH --nodes=1 |
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#SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node! |
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#SBATCH --cpus-per-task=8 # number of cores per tasks |
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#SBATCH --hint=nomultithread # we get physical cores not logical |
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#SBATCH --gres=gpu:1 # number of gpus |
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#SBATCH --constraint=a100 |
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#SBATCH --time 5:00:00 # maximum execution time (HH:MM:SS) |
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#SBATCH --output=%x-%j.out # output file name |
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#SBATCH --account=ajs@a100 |
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#SBATCH --array=0-168 |
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set -x -e |
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source $six_ALL_CCFRWORK/start-py38-pt111 |
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conda activate thomas_t_zero_evaluation |
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CHECKPOINT_PATH=$OUTPUTCKPT |
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WORKDIR=/gpfswork/rech/six/commun/code/tr13f-6B3-ml-t0 |
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pushd "\$WORKDIR" |
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OUTPUT_DIR="\$CHECKPOINT_PATH/evaluation" |
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mkdir -p "\$OUTPUT_DIR" |
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# Validation |
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DATASETS_AND_CONFIGS_VAL=( |
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head_qa,en,en,"multiple_choice_q_and_a_index_with_context_en",validation |
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head_qa,en,en,"multiple_choice_q_and_a_en",validation |
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head_qa,en,en,"multiple_choice_q_and_a_index_en",validation |
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head_qa,en,en,"multiple_choice_a_and_q_with_context_en",validation |
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head_qa,en,en,"multiple_choice_a_and_q_en",validation |
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head_qa,es,en,"multiple_choice_q_and_a_index_with_context_en",validation |
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head_qa,es,en,"multiple_choice_q_and_a_en",validation |
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head_qa,es,en,"multiple_choice_q_and_a_index_en",validation |
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head_qa,es,en,"multiple_choice_a_and_q_with_context_en",validation |
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head_qa,es,en,"multiple_choice_a_and_q_en",validation |
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climate_fever,None,None,"first_evidence_and_claim_itemization",test |
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climate_fever,None,None,"claim_and_all_supporting_evidences",test |
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climate_fever,None,None,"fifth_evidence_and_claim_itemization",test |
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climate_fever,None,None,"third_evidence_claim_pair",test |
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climate_fever,None,None,"second_evidence_and_claim_itemization",test |
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codah,codah,None,"interrogative_instruction_after_sentence_and_choices",train |
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codah,codah,None,"affirmative_instruction_before_sentence_and_choices",train |
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codah,codah,None,"affirmative_instruction_after_sentence_and_choices",train |
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aqua_rat,raw,None,"select_the_best_option",validation |
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aqua_rat,raw,None,"answer_quiz",validation |
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aqua_rat,raw,None,"Answer questions from options",validation |
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commonsense_qa,None,None,"answer_given_question_without_options",validation |
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commonsense_qa,None,None,"question_answering",validation |
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commonsense_qa,None,None,"most_suitable_answer",validation |
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amazon_reviews_multi,en,en,"prompt_title_to_star",validation |
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amazon_reviews_multi,en,en,"prompt_review_to_star",validation |
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amazon_reviews_multi,en,en,"prompt_body_title_to_star",validation |
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amazon_reviews_multi,zh,en,"prompt_title_to_star",validation |
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amazon_reviews_multi,zh,en,"prompt_review_to_star",validation |
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amazon_reviews_multi,zh,en,"prompt_body_title_to_star",validation |
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amazon_reviews_multi,fr,en,"prompt_title_to_star",validation |
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amazon_reviews_multi,fr,en,"prompt_review_to_star",validation |
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amazon_reviews_multi,fr,en,"prompt_body_title_to_star",validation |
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amazon_reviews_multi,es,en,"prompt_title_to_star",validation |
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amazon_reviews_multi,es,en,"prompt_review_to_star",validation |
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amazon_reviews_multi,es,en,"prompt_body_title_to_star",validation |
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art,None,None,"choose_hypothesis_options",validation |
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art,None,None,"choose_hypothesis_believable",validation |
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art,None,None,"choose_hypothesis",validation |
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art,None,None,"choose_hypothesis_desc",validation |
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art,None,None,"choose_hypothesis_likely",validation |
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banking77,None,None,"help_page_topic",test |
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banking77,None,None,"direct_to_which_department",test |
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banking77,None,None,"rephrase_as_banking_term",test |
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blbooksgenre,title_genre_classifiction,None,"multi-choice",train |
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blbooksgenre,title_genre_classifiction,None,"premise_context_first",train |
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blbooksgenre,title_genre_classifiction,None,"classify",train |
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blimp,adjunct_island,None,"grammatical_between_1_2",train |
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blimp,adjunct_island,None,"grammatical_between_A_B",train |
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blimp,adjunct_island,None,"grammatical_which_one_1_2",train |
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blimp,adjunct_island,None,"single_sentence_bad_yes_no",train |
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blimp,adjunct_island,None,"single_sentence_good_yes_no",train |
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conv_ai_3,None,None,"clarification_needed",validation |
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conv_ai_3,None,None,"score_give_number",validation |
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conv_ai_3,None,None,"ambiguous",validation |
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conv_ai_3,None,None,"directly_answer",validation |
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conv_ai_3,None,None,"score_how_much",validation |
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craigslist_bargains,None,None,"good deal for seller no list price implicit",validation |
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craigslist_bargains,None,None,"good deal for seller no list price",validation |
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craigslist_bargains,None,None,"good deal for seller",validation |
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craigslist_bargains,None,None,"best deal",validation |
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ecthr_cases,alleged-violation-prediction,None,"implicit_advice_number",validation |
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ecthr_cases,alleged-violation-prediction,None,"ecthr_alleged_articles_declaration_at_end",validation |
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ecthr_cases,alleged-violation-prediction,None,"ecthr_alleged_articles_question_at_start",validation |
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ecthr_cases,alleged-violation-prediction,None,"implicit_judgment_paragraph",validation |
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ecthr_cases,alleged-violation-prediction,None,"confirm number of violated articles",validation |
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emo,None,None,"persons_describe",validation |
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emo,None,None,"final_message",validation |
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emo,None,None,"what_emotion_do_you_think",validation |
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emo,None,None,"emotional_state",validation |
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emo,None,None,"dialogue_between",validation |
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emotion,None,None,"choose_the_best_emotion_label",test |
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emotion,None,None,"reply_with_emoation_label",test |
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emotion,None,None,"answer_with_class_label",test |
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emotion,None,None,"answer_question_with_emotion_label",test |
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financial_phrasebank,sentences_allagree,None,"share_price_option",train |
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financial_phrasebank,sentences_allagree,None,"sentiment",train |
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financial_phrasebank,sentences_allagree,None,"word_comes_to_mind",train |
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financial_phrasebank,sentences_allagree,None,"complementary_industries",train |
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financial_phrasebank,sentences_allagree,None,"bullish_neutral_bearish",train |
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glue,cola,None,"Make sense yes no",validation |
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glue,cola,None,"is_this_correct",validation |
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glue,cola,None,"editing",validation |
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glue,cola,None,"Following sentence acceptable",validation |
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glue,cola,None,"Previous sentence acceptable",validation |
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glue,sst2,None,"positive negative after",validation |
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glue,sst2,None,"review",validation |
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glue,sst2,None,"said",validation |
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glue,sst2,None,"following positive negative",validation |
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glue,sst2,None,"happy or mad",validation |
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health_fact,None,None,"claim_veracity_classification_after_reading_I_believe",validation |
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health_fact,None,None,"claim_explanation_classification",validation |
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health_fact,None,None,"claim_veracity_classification_tell_me",validation |
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hlgd,None,None,"is_same_event_with_time_interrogative_related",validation |
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hlgd,None,None,"is_same_event_interrogative_talk",validation |
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hlgd,None,None,"is_same_event_with_time_interrogative_talk",validation |
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hlgd,None,None,"is_same_event_refer",validation |
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hlgd,None,None,"is_same_event_editor_asks",validation |
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hyperpartisan_news_detection,byarticle,None,"consider_does_it_follow_a_hyperpartisan_argumentation",train |
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hyperpartisan_news_detection,byarticle,None,"follows_hyperpartisan_argumentation",train |
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hyperpartisan_news_detection,byarticle,None,"consume_with_caution",train |
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hyperpartisan_news_detection,byarticle,None,"extreme_left_wing_or_right_wing",train |
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hyperpartisan_news_detection,byarticle,None,"consider_it_exhibits_extreme_one_sidedness",train |
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liar,None,None,"Given statement guess category",validation |
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lince,sa_spaeng,None,"original poster expressed sentiment",validation |
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lince,sa_spaeng,None,"sentiment trying to express",validation |
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lince,sa_spaeng,None,"express sentiment",validation |
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lince,sa_spaeng,None,"negation template",validation |
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lince,sa_spaeng,None,"the author seem",validation |
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math_qa,None,None,"choose_correct_og",test |
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math_qa,None,None,"pick_the_correct",test |
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math_qa,None,None,"first_choice_then_problem",test |
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math_qa,None,None,"problem_set_type",test |
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math_qa,None,None,"gre_problem",test |
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movie_rationales,None,None,"Standard binary sentiment analysis",validation |
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movie_rationales,None,None,"Evidences sentiment classification",validation |
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movie_rationales,None,None,"Evidences + review",validation |
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movie_rationales,None,None,"Generate evidences and sentiment",validation |
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mwsc,None,None,"in-the-sentence-question-first",validation |
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mwsc,None,None,"what-think",validation |
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mwsc,None,None,"in-the-sentence",validation |
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mwsc,None,None,"options-or",validation |
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mwsc,None,None,"is-correct",validation |
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poem_sentiment,None,None,"positive_or_negative_sentiment_variation_2",validation |
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poem_sentiment,None,None,"question_answer_format",validation |
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poem_sentiment,None,None,"guess_sentiment_without_options_variation_1",validation |
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poem_sentiment,None,None,"positive_or_negative_sentiment_variation_1",validation |
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poem_sentiment,None,None,"most_appropriate_sentiment",validation |
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onestop_english,None,None,"esl_context",train |
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onestop_english,None,None,"ara_context",train |
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onestop_english,None,None,"determine_reading_level_from_the_first_three_sentences",train |
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onestop_english,None,None,"esl_variation",train |
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onestop_english,None,None,"assess",train |
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pubmed_qa,pqa_labeled,None,"Long Answer to Final Decision",train |
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pubmed_qa,pqa_labeled,None,"Question Answering (Short)",train |
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riddle_sense,None,None,"most_suitable_answer",validation |
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riddle_sense,None,None,"answer_given_question_without_options",validation |
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riddle_sense,None,None,"question_to_answer_index",validation |
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riddle_sense,None,None,"question_answering",validation |
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scicite,None,None,"Classify intent w/section (select choice)",validation |
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scicite,None,None,"Classify intent (choices first)",validation |
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scicite,None,None,"Classify intent (select choice)",validation |
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scicite,None,None,"Classify intent",validation |
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scicite,None,None,"can_describe",validation |
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selqa,answer_selection_analysis,None,"is-he-talking-about",validation |
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selqa,answer_selection_analysis,None,"would-make-sense-qu-rand",validation |
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selqa,answer_selection_analysis,None,"make-sense-rand",validation |
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selqa,answer_selection_analysis,None,"which-answer-1st-vs-random",validation |
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snips_built_in_intents,None,None,"voice_intent",train |
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snips_built_in_intents,None,None,"categorize_query",train |
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snips_built_in_intents,None,None,"intent_query",train |
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snips_built_in_intents,None,None,"categorize_query_brief",train |
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snips_built_in_intents,None,None,"query_intent",train |
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) |
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DATASET_AND_CONFIG="\${DATASETS_AND_CONFIGS_VAL[\$SLURM_ARRAY_TASK_ID]}" |
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echo "\$ARGUMENT" |
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# Run T0 evaluation |
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# For PrefixLM add --prefixlm |
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IFS=',' read dataset_name dataset_config_name template_config_name template_name split <<< "\${DATASET_AND_CONFIG}" |
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python t-zero/evaluation/run_eval.py \ |
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--dataset_name "\$dataset_name" \ |
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--dataset_config_name "\$dataset_config_name" \ |
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--template_config_name "\$template_config_name" \ |
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--template_name "\$template_name" \ |
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--split "\$split" \ |
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--model_name_or_path "\$CHECKPOINT_PATH" \ |
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--output_dir "\$OUTPUT_DIR" \ |
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--per_device_eval_batch_size 4 \ |
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--max_length 2048 \ |
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--dtype float16 |
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EOT |
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sbatch $eval_script |
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lm_eval_script="./lm_eval_$i.slurm" |
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cat <<EOT > $lm_eval_script |
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#!/bin/bash |
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#SBATCH --job-name=lmeval |
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#SBATCH --nodes=1 |
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#SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node! |
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#SBATCH --cpus-per-task=8 # number of cores per tasks |
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#SBATCH --hint=nomultithread # we get physical cores not logical |
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#SBATCH --gres=gpu:1 # number of gpus |
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#SBATCH --constraint=a100 |
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#SBATCH --time 20:00:00 # maximum execution time (HH:MM:SS) |
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#SBATCH --output=%x-%j.out # output file name |
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#SBATCH --account=ajs@a100 |
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#SBATCH --array=0-12 |
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set -x -e |
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source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0 |
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conda activate muennighofflmevalgen |
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echo "START TIME: $(date)" |
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# defining the right environment variables |
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export TRANSFORMERS_CACHE=$six_ALL_CCFRWORK/models |
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export HF_DATASETS_CACHE=$six_ALL_CCFRWORK/datasets |
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export HF_MODULES_CACHE=$six_ALL_CCFRWORK/modules |
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export HF_METRICS_CACHE=$six_ALL_CCFRWORK/metrics |
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export HF_DATASETS_OFFLINE=1 |
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export TRANSFORMERS_OFFLINE=1 |
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export TOKENIZERS_PARALLELISM=false |
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# Converted transformer checkpoint |
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MODEL_CKPT=$OUTPUTCKPT |
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cd /gpfsscratch/rech/six/commun/experiments/muennighoff/lm-evaluation-harness |
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DATASETS_AND_CONFIGS=( |
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wmt14_fr_en,fr-en,"version-en-fr-target" |
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wmt14_fr_en,fr-en,"a_good_translation-en-fr-target" |
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wmt14_fr_en,fr-en,"a_good_translation-en-fr-source+target" |
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wmt14_fr_en,fr-en,"xglm-en-fr-target" |
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wmt14_fr_en,fr-en,"gpt3-en-fr" |
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wmt14_fr_en,fr-en,"version-fr-en-target" |
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wmt14_fr_en,fr-en,"a_good_translation-fr-en-target" |
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wmt14_fr_en,fr-en,"a_good_translation-fr-en-source+target" |
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wmt14_fr_en,fr-en,"xglm-fr-en-target" |
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wmt14_fr_en,fr-en,"gpt3-fr-en" |
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wmt14_hi_en,hi-en,"version-en-hi-target" |
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wmt14_hi_en,hi-en,"a_good_translation-en-hi-target" |
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wmt14_hi_en,hi-en,"a_good_translation-en-hi-source+target" |
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wmt14_hi_en,hi-en,"xglm-en-hi-target" |
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wmt14_hi_en,hi-en,"gpt-3-en-hi-target" |
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wmt14_hi_en,hi-en,"version-hi-en-target" |
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wmt14_hi_en,hi-en,"a_good_translation-hi-en-target" |
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wmt14_hi_en,hi-en,"a_good_translation-hi-en-source+target" |
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wmt14_hi_en,hi-en,"xglm-hi-en-target" |
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wmt14_hi_en,hi-en,"gpt-3-hi-en-target" |
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mlsum_es,"es","layman_summ_es" |
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mlsum_es,"es","palm_prompt" |
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mlsum_es,"es","summarise_this_in_es_few_sentences" |
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) |
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DATASET_AND_CONFIG="\${DATASETS_AND_CONFIGS[\$SLURM_ARRAY_TASK_ID]}" |
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echo "\$ARGUMENT" |
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IFS=',' read dataset_name lang template_name <<< "\${DATASET_AND_CONFIG}" |
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# Use this fork of lm-eval: https://github.com/bigscience-workshop/lm-evaluation-harness/pull/109 |
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python main.py \ |
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--model_api_name 'hf-causal' \ |
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--model_args "pretrained=\$MODEL_CKPT,use_accelerate=True,tokenizer=\$MODEL_CKPT,dtype=float16" \ |
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--device cuda \ |
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--batch_size 16 \ |
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--no_tracking \ |
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--task_name "\$dataset_name" \ |
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--template_names "\$template_name" \ |
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--bootstrap_iters 10 \ |
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--limit 3000 |
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mkdir -p "$OUTPUTCKPT/evaluation/\$dataset_name" |
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mv "outputs/*$CKPT*\$dataset_name*" "$OUTPUTCKPT/evaluation/\$dataset_name/" |
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echo "END TIME: $(date)" |
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EOT |
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sbatch $lm_eval_script |
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done |
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