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from functools import partial |
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import os |
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import multiprocessing |
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from datasets import load_dataset, load_from_disk |
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import jsonlines |
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"""Get task list: |
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!git clone https://github.com/bigscience-workshop/t-zero.git |
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%cd t-zero |
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!pip install -e .[seqio_tasks] |
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!pip install -q py7zr |
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import t0.seqio_tasks |
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import seqio |
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tasks = [task.name for task in seqio.MixtureRegistry.get('t0_train').tasks] |
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print(tasks) |
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After running the script, merge train & validation jsonls separately into two big files: |
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cat folder_with_all_jsonl/*.jsonl > merged_file.jsonl |
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""" |
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TZERO_TASK_LIST = [ |
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'adversarial_qa_dbert_answer_the_following_q', |
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'adversarial_qa_dbert_based_on', |
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'adversarial_qa_dbert_generate_question', |
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'adversarial_qa_dbert_question_context_answer', |
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'adversarial_qa_dbert_tell_what_it_is', |
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'adversarial_qa_dbidaf_answer_the_following_q', |
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'adversarial_qa_dbidaf_based_on', |
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'adversarial_qa_dbidaf_generate_question', |
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'adversarial_qa_dbidaf_question_context_answer', |
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'adversarial_qa_dbidaf_tell_what_it_is', |
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'adversarial_qa_droberta_answer_the_following_q', |
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'adversarial_qa_droberta_based_on', |
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'adversarial_qa_droberta_generate_question', |
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'adversarial_qa_droberta_question_context_answer', |
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'adversarial_qa_droberta_tell_what_it_is', |
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'ag_news_classify', |
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'ag_news_classify_question_first', |
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'ag_news_classify_with_choices', |
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'ag_news_classify_with_choices_question_first', |
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'ag_news_recommend', |
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'ag_news_which_section', |
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'ag_news_which_section_choices', |
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'amazon_polarity_Is_this_product_review_positive', |
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'amazon_polarity_Is_this_review', |
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'amazon_polarity_Is_this_review_negative', |
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'amazon_polarity_User_recommend_this_product', |
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'amazon_polarity_convey_negative_or_positive_sentiment', |
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'amazon_polarity_flattering_or_not', |
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'amazon_polarity_negative_or_positive_tone', |
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'amazon_polarity_user_satisfied', |
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'amazon_polarity_would_you_buy', |
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'app_reviews_categorize_rating_using_review', |
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'app_reviews_convert_to_rating', |
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'app_reviews_convert_to_star_rating', |
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'app_reviews_generate_review', |
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'cnn_dailymail_3.0.0_2_or_3_sentences', |
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'cnn_dailymail_3.0.0_generate_story', |
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'cnn_dailymail_3.0.0_news_card_view', |
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'cnn_dailymail_3.0.0_news_stock', |
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'cnn_dailymail_3.0.0_news_summary', |
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'cnn_dailymail_3.0.0_spice_up_story', |
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'cnn_dailymail_3.0.0_sum_in_brief', |
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'cnn_dailymail_3.0.0_tldr_summary', |
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'cnn_dailymail_3.0.0_write_an_outline', |
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'common_gen_Example_prompt', |
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'common_gen_Given_concepts_type_1', |
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'common_gen_Given_concepts_type_2', |
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'common_gen_Put_together', |
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'common_gen_choice_in_concept_centric_sentence_generation', |
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'common_gen_random_task_template_prompt', |
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'common_gen_sentence_to_concepts', |
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'common_gen_topic_to_sentence', |
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'common_gen_topics_from_the_sentence', |
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'cos_e_v1.11_aligned_with_common_sense', |
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'cos_e_v1.11_description_question_option_id', |
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'cos_e_v1.11_description_question_option_text', |
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'cos_e_v1.11_explain_why_human', |
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'cos_e_v1.11_generate_explanation_given_text', |
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'cos_e_v1.11_i_think', |
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'cos_e_v1.11_question_description_option_id', |
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'cos_e_v1.11_question_description_option_text', |
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'cos_e_v1.11_question_option_description_id', |
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'cos_e_v1.11_question_option_description_text', |
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'cos_e_v1.11_rationale', |
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'cosmos_qa_context_answer_to_question', |
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'cosmos_qa_context_description_question_answer_id', |
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'cosmos_qa_context_description_question_answer_text', |
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'cosmos_qa_context_description_question_text', |
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'cosmos_qa_context_question_description_answer_id', |
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'cosmos_qa_context_question_description_answer_text', |
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'cosmos_qa_context_question_description_text', |
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'cosmos_qa_description_context_question_answer_id', |
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'cosmos_qa_description_context_question_answer_text', |
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'cosmos_qa_description_context_question_text', |
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'cosmos_qa_no_prompt_id', |
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'cosmos_qa_no_prompt_text', |
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'cosmos_qa_only_question_answer', |
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'dbpedia_14_given_a_choice_of_categories_', |
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'dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to', |
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'dbpedia_14_given_list_what_category_does_the_paragraph_belong_to', |
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'dbpedia_14_pick_one_category_for_the_following_text', |
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'dream_answer_to_dialogue', |
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'dream_baseline', |
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'dream_generate_first_utterance', |
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'dream_generate_last_utterance', |
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'dream_read_the_following_conversation_and_answer_the_question', |
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'duorc_ParaphraseRC_answer_question', |
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'duorc_ParaphraseRC_build_story_around_qa', |
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'duorc_ParaphraseRC_decide_worth_it', |
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'duorc_ParaphraseRC_extract_answer', |
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'duorc_ParaphraseRC_generate_question', |
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'duorc_ParaphraseRC_generate_question_by_answer', |
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'duorc_ParaphraseRC_movie_director', |
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'duorc_ParaphraseRC_question_answering', |
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'duorc_ParaphraseRC_title_generation', |
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'duorc_SelfRC_answer_question', |
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'duorc_SelfRC_build_story_around_qa', |
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'duorc_SelfRC_decide_worth_it', |
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'duorc_SelfRC_extract_answer', |
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'duorc_SelfRC_generate_question', |
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'duorc_SelfRC_generate_question_by_answer', |
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'duorc_SelfRC_movie_director', |
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'duorc_SelfRC_question_answering', |
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'duorc_SelfRC_title_generation', |
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'gigaword_TLDR', |
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'gigaword_first_sentence_title', |
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'gigaword_generate_summary_for_this', |
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'gigaword_in_a_nutshell', |
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'gigaword_make_a_title', |
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'gigaword_reverse_writing', |
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'gigaword_write_a_title_for_this_sentence', |
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'gigaword_write_an_article', |
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'gigaword_write_its_sentence', |
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'glue_mrpc_equivalent', |
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'glue_mrpc_generate_paraphrase', |
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'glue_mrpc_generate_sentence', |
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'glue_mrpc_paraphrase', |
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'glue_mrpc_replace', |
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'glue_mrpc_same_thing', |
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'glue_mrpc_want_to_know', |
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'glue_qqp_answer', |
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'glue_qqp_duplicate', |
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'glue_qqp_duplicate_or_not', |
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'glue_qqp_meaning', |
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'glue_qqp_quora', |
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'glue_qqp_same_thing', |
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'imdb_Movie_Expressed_Sentiment', |
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'imdb_Movie_Expressed_Sentiment_2', |
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'imdb_Negation_template_for_positive_and_negative', |
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'imdb_Reviewer_Enjoyment', |
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'imdb_Reviewer_Enjoyment_Yes_No', |
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'imdb_Reviewer_Expressed_Sentiment', |
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'imdb_Reviewer_Opinion_bad_good_choices', |
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'imdb_Reviewer_Sentiment_Feeling', |
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'imdb_Sentiment_with_choices_', |
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'imdb_Text_Expressed_Sentiment', |
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'imdb_Writer_Expressed_Sentiment', |
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'kilt_tasks_hotpotqa_combining_facts', |
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'kilt_tasks_hotpotqa_complex_question', |
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'kilt_tasks_hotpotqa_final_exam', |
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'kilt_tasks_hotpotqa_formulate', |
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'kilt_tasks_hotpotqa_straighforward_qa', |
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'multi_news_distill', |
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'multi_news_expand_reverse_task_', |
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'multi_news_summarize', |
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'multi_news_summary_scenario', |
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'multi_news_synthesize', |
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'multi_news_what_are_the_key_points', |
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'paws_labeled_final_Concatenation', |
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'paws_labeled_final_Concatenation_no_label', |
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'paws_labeled_final_Meaning', |
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'paws_labeled_final_Meaning_no_label', |
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'paws_labeled_final_PAWS_ANLI_GPT3', |
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'paws_labeled_final_PAWS_ANLI_GPT3_no_label', |
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'paws_labeled_final_Rewrite', |
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'paws_labeled_final_Rewrite_no_label', |
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'paws_labeled_final_context_question', |
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'paws_labeled_final_context_question_no_label', |
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'paws_labeled_final_paraphrase_task', |
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'paws_labeled_final_task_description_no_label', |
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'qasc_is_correct_1', |
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'qasc_is_correct_2', |
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'qasc_qa_with_combined_facts_1', |
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'qasc_qa_with_separated_facts_1', |
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'qasc_qa_with_separated_facts_2', |
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'qasc_qa_with_separated_facts_3', |
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'qasc_qa_with_separated_facts_4', |
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'qasc_qa_with_separated_facts_5', |
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'quail_context_description_question_answer_id', |
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'quail_context_description_question_answer_text', |
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'quail_context_description_question_text', |
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'quail_context_question_answer_description_id', |
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'quail_context_question_answer_description_text', |
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'quail_context_question_description_answer_id', |
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'quail_context_question_description_answer_text', |
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'quail_context_question_description_text', |
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'quail_description_context_question_answer_id', |
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'quail_description_context_question_answer_text', |
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'quail_description_context_question_text', |
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'quail_no_prompt_id', |
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'quail_no_prompt_text', |
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'quarel_choose_between', |
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'quarel_do_not_use', |
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'quarel_heres_a_story', |
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'quarel_logic_test', |
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'quarel_testing_students', |
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'quartz_answer_question_based_on', |
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'quartz_answer_question_below', |
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'quartz_given_the_fact_answer_the_q', |
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'quartz_having_read_above_passage', |
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'quartz_paragraph_question_plain_concat', |
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'quartz_read_passage_below_choose', |
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'quartz_use_info_from_paragraph_question', |
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'quartz_use_info_from_question_paragraph', |
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'quoref_Answer_Friend_Question', |
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'quoref_Answer_Question_Given_Context', |
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'quoref_Answer_Test', |
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'quoref_Context_Contains_Answer', |
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'quoref_Find_Answer', |
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'quoref_Found_Context_Online', |
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'quoref_Given_Context_Answer_Question', |
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'quoref_Guess_Answer', |
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'quoref_Guess_Title_For_Context', |
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'quoref_Read_And_Extract_', |
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'quoref_What_Is_The_Answer', |
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'ropes_background_new_situation_answer', |
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'ropes_background_situation_middle', |
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'ropes_given_background_situation', |
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'ropes_new_situation_background_answer', |
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'ropes_plain_background_situation', |
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'ropes_plain_bottom_hint', |
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'ropes_plain_no_background', |
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'ropes_prompt_beginning', |
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'ropes_prompt_bottom_hint_beginning', |
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'ropes_prompt_bottom_no_hint', |
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'ropes_prompt_mix', |
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'ropes_read_background_situation', |
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'rotten_tomatoes_Movie_Expressed_Sentiment', |
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'rotten_tomatoes_Movie_Expressed_Sentiment_2', |
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'rotten_tomatoes_Reviewer_Enjoyment', |
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'rotten_tomatoes_Reviewer_Enjoyment_Yes_No', |
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'rotten_tomatoes_Reviewer_Expressed_Sentiment', |
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'rotten_tomatoes_Reviewer_Opinion_bad_good_choices', |
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'rotten_tomatoes_Reviewer_Sentiment_Feeling', |
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'rotten_tomatoes_Sentiment_with_choices_', |
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'rotten_tomatoes_Text_Expressed_Sentiment', |
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'rotten_tomatoes_Writer_Expressed_Sentiment', |
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'samsum_Generate_a_summary_for_this_dialogue', |
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'samsum_Given_the_above_dialogue_write_a_summary', |
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'samsum_Sum_up_the_following_dialogue', |
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'samsum_Summarize_', |
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'samsum_Summarize_this_dialogue_', |
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'samsum_To_sum_up_this_dialog', |
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'samsum_Write_a_dialogue_that_match_this_summary', |
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'sciq_Direct_Question', |
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'sciq_Direct_Question_Closed_Book_', |
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'sciq_Multiple_Choice', |
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'sciq_Multiple_Choice_Closed_Book_', |
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'sciq_Multiple_Choice_Question_First', |
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'social_i_qa_Check_if_a_random_answer_is_valid_or_not', |
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'social_i_qa_Generate_answer', |
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'social_i_qa_Generate_the_question_from_the_answer', |
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'social_i_qa_I_was_wondering', |
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'social_i_qa_Show_choices_and_generate_answer', |
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'social_i_qa_Show_choices_and_generate_index', |
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'trec_fine_grained_ABBR', |
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'trec_fine_grained_ABBR_context_first', |
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'trec_fine_grained_DESC', |
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'trec_fine_grained_DESC_context_first', |
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'trec_fine_grained_ENTY', |
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'trec_fine_grained_HUM', |
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'trec_fine_grained_HUM_context_first', |
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'trec_fine_grained_LOC', |
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'trec_fine_grained_LOC_context_first', |
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'trec_fine_grained_NUM', |
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'trec_fine_grained_NUM_context_first', |
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'trec_fine_grained_open', |
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'trec_fine_grained_open_context_first', |
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'trec_pick_the_best_descriptor', |
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'trec_trec1', |
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'trec_trec2', |
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'trec_what_category_best_describe', |
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'trec_which_category_best_describes', |
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'wiki_bio_comprehension', |
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'wiki_bio_guess_person', |
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'wiki_bio_key_content', |
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'wiki_bio_what_content', |
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'wiki_bio_who', |
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'wiki_hop_original_choose_best_object_affirmative_1', |
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'wiki_hop_original_choose_best_object_affirmative_2', |
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'wiki_hop_original_choose_best_object_affirmative_3', |
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'wiki_hop_original_choose_best_object_interrogative_1', |
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'wiki_hop_original_choose_best_object_interrogative_2', |
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'wiki_hop_original_explain_relation', |
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'wiki_hop_original_generate_object', |
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'wiki_hop_original_generate_subject', |
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'wiki_hop_original_generate_subject_and_object', |
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'wiki_qa_Decide_good_answer', |
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'wiki_qa_Direct_Answer_to_Question', |
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'wiki_qa_Generate_Question_from_Topic', |
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'wiki_qa_Is_This_True_', |
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'wiki_qa_Jeopardy_style', |
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'wiki_qa_Topic_Prediction_Answer_Only', |
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'wiki_qa_Topic_Prediction_Question_Only', |
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'wiki_qa_Topic_Prediction_Question_and_Answer_Pair', |
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'wiki_qa_automatic_system', |
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'wiki_qa_exercise', |
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'wiki_qa_found_on_google', |
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'wiqa_does_the_supposed_perturbation_have_an_effect', |
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'wiqa_effect_with_label_answer', |
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'wiqa_effect_with_string_answer', |
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'wiqa_what_is_the_final_step_of_the_following_process', |
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'wiqa_what_is_the_missing_first_step', |
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'wiqa_what_might_be_the_first_step_of_the_process', |
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'wiqa_what_might_be_the_last_step_of_the_process', |
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'wiqa_which_of_the_following_is_the_supposed_perturbation', |
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'xsum_DOC_boils_down_to_simple_idea_that', |
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'xsum_DOC_given_above_write_one_sentence', |
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'xsum_DOC_how_would_you_rephrase_few_words', |
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'xsum_DOC_tldr', |
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'xsum_DOC_write_summary_of_above', |
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'xsum_article_DOC_summary', |
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'xsum_college_roommate_asked_DOC_so_I_recap', |
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'xsum_read_below_DOC_write_abstract', |
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'xsum_summarize_DOC', |
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'xsum_summarize_this_DOC_summary', |
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'yelp_review_full_based_on_that', |
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'yelp_review_full_format_rating', |
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'yelp_review_full_format_score', |
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'yelp_review_full_format_star', |
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'yelp_review_full_on_a_scale', |
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'yelp_review_full_so_i_would', |
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'yelp_review_full_this_place' |
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] |
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def write_to_jsonl_hub(task_name, split): |
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ds = load_dataset("bigscience/P3", task_name) |
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if split in ds: |
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with jsonlines.open(f'p3_{task_name}_{split}.jsonl', mode='w') as writer: |
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for example in ds[split].select(range(len(ds[split]))): |
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writer.write({ |
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"inputs": example["inputs_pretokenized"], |
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"targets": example["targets_pretokenized"] |
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}) |
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def write_to_jsonl_disk(task_name, split): |
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ds = load_from_disk(f"{os.environ['six_ALL_CCFRSCRATCH']}/datasets/p3/{task_name}") |
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if split in ds: |
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with jsonlines.open(f'p3_{task_name}_{split}.jsonl', mode='w') as writer: |
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for example in ds[split].select(range(len(ds[split]))): |
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writer.write({ |
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"inputs": example["inputs_pretokenized"], |
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"targets": example["targets_pretokenized"] |
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}) |
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with multiprocessing.Pool(num_proc=multiprocessing.cpu_count()) as pool: |
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pool.map(partial(write_to_jsonl_disk, split="train"), TZERO_TASK_LIST) |
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pool.map(partial(write_to_jsonl_disk, split="validation"), TZERO_TASK_LIST) |
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