Remove priority filter (#517)

* templates for tweets_hate_speech

* Update templates.yaml

* Create templates.yaml

* deleting tweets template

* removing priority filter

* quality changes

* Update utils.py

Co-authored-by: Shanya Sharma - s0s0cr3 <[email protected]>

Author: shanyas10

promptsource/app.py

@@ -216,27 +216,9 @@ def get_infos(d_name):
     #
     # Loads dataset information
     #
-    if mode == "Prompted dataset viewer":
-        priority_filter = False
-        priority_max_templates = None
-    else:  # mode = Sourcing
-        priority_filter = st.sidebar.checkbox(
-            "Filter Priority Datasets", help="This filter surfaces datasets with less than X prompts."
-        )
-        if priority_filter:
-            priority_max_templates = st.sidebar.number_input(
-                "Max no of templates per dataset", min_value=1, max_value=50, value=2, step=1
-            )
-        else:
-            # Clear working priority dataset retained in the
-            # priority list with more than priority_max_templates
-            state.working_priority_ds = None
-            priority_max_templates = None
 
     dataset_list = list_datasets(
         template_collection,
-        priority_filter,
-        priority_max_templates,
         state,
     )
 
@@ -251,12 +233,6 @@ def get_infos(d_name):
         help="Select the dataset to work on.",
     )
 
-    if mode == "Sourcing":
-        # On dataset change, clear working priority dataset
-        # retained in the priority list with more than priority_max_templates
-        if dataset_key != state.working_priority_ds:
-            state.working_priority_ds = None
-
     #
     # If a particular dataset is selected, loads dataset and template information
     #
@@ -447,9 +423,6 @@ def get_infos(d_name):
                         dataset_templates.add_template(template)
                         reset_template_state()
                         state.template_name = new_template_name
-                        # Keep the current working dataset in priority list
-                        if priority_filter:
-                            state.working_priority_ds = dataset_key
                 else:
                     state.new_template_name = None
 

promptsource/utils.py

@@ -131,262 +131,8 @@ def filter_english_datasets():
     return sorted(english_datasets)
 
 
-def list_datasets(template_collection, _priority_filter, _priority_max_templates, _state):
+def list_datasets(template_collection, _state):
     """Get all the datasets to work with."""
     dataset_list = filter_english_datasets()
-    count_dict = template_collection.get_templates_count()
-    if _priority_filter:
-        dataset_list = list(
-            set(dataset_list)
-            - set(
-                list(
-                    d
-                    for d in count_dict
-                    if count_dict[d] > _priority_max_templates and d != _state.working_priority_ds
-                )
-            )
-        )
-        dataset_list.sort()
-    else:
-        dataset_list.sort(key=lambda x: DATASET_ORDER.get(x, 1000))
+    dataset_list.sort(key=lambda x: x.lower())
     return dataset_list
-
-
-DATASET_ORDER = dict(
-    [
-        ("glue", 0),
-        ("squad", 1),
-        ("bookcorpusopen", 2),
-        ("wikipedia", 3),
-        ("wikitext", 4),
-        ("imdb", 5),
-        ("super_glue", 6),
-        ("cnn_dailymail", 7),
-        ("openwebtext", 8),
-        ("common_voice", 9),
-        ("xsum", 10),
-        ("wmt16", 11),
-        ("conll2003", 12),
-        ("ag_news", 13),
-        ("universal_dependencies", 14),
-        ("wiki_qa", 15),
-        ("bookcorpus", 16),
-        ("wiki40b", 17),
-        ("wiki_dpr", 18),
-        ("xnli", 19),
-        ("squad_kor_v1", 20),
-        ("emotion", 21),
-        ("wikiann", 22),
-        ("amazon_us_reviews", 23),
-        ("squad_v2", 24),
-        ("amazon_reviews_multi", 25),
-        ("librispeech_asr", 26),
-        ("blimp", 27),
-        ("scitail", 28),
-        ("anli", 29),
-        ("samsum", 30),
-        ("lambada", 31),
-        ("multi_nli", 32),
-        ("daily_dialog", 33),
-        ("snli", 34),
-        ("opus_euconst", 35),
-        ("rotten_tomatoes", 36),
-        ("scientific_papers", 37),
-        ("trec", 38),
-        ("reddit_tifu", 39),
-        ("ai2_arc", 40),
-        ("patrickvonplaten", 41),
-        ("gigaword", 42),
-        ("swag", 43),
-        ("timit_asr", 44),
-        ("oscar", 45),
-        ("tweet_eval", 46),
-        ("newsgroup", 47),
-        ("billsum", 48),
-        ("gem", 49),
-        ("blended_skill_talk", 50),
-        ("eli5", 51),
-        ("ade_corpus_v2", 52),
-        ("race", 53),
-        ("wikihow", 54),
-        ("piqa", 55),
-        ("xtreme", 56),
-        ("commonsense_qa", 57),
-        ("wiki_snippets", 58),
-        ("mlsum", 59),
-        ("multi_news", 60),
-        ("wmt14", 61),
-        ("asnq", 62),
-        ("toriving", 63),
-        ("crime_and_punish", 64),
-        ("few_rel", 65),
-        ("code_search_net", 66),
-        ("universal_morphologies", 67),
-        ("ms_marco", 68),
-        ("trivia_qa", 69),
-        ("lama", 70),
-        ("newsroom", 71),
-        ("hellaswag", 72),
-        ("adversarial_qa", 73),
-        ("hatexplain", 74),
-        ("hans", 75),
-        ("kilt_tasks", 76),
-        ("xglue", 77),
-        ("amazon_polarity", 78),
-        ("meta_woz", 79),
-        ("opus_books", 80),
-        ("wmt18", 81),
-        ("covid_qa_deepset", 82),
-        ("emotion\\dataset_infos.json", 83),
-        ("wmt19", 84),
-        ("discofuse", 85),
-        ("mrqa", 86),
-        ("winogrande", 87),
-        ("go_emotions", 88),
-        ("tydiqa", 89),
-        ("yelp_polarity", 90),
-        ("banking77", 91),
-        ("math_dataset", 92),
-        ("pubmed_qa", 93),
-        ("opus_ubuntu", 94),
-        ("acronym_identification", 95),
-        ("math_qa", 96),
-        ("babi_qa", 97),
-        ("dbpedia_14", 98),
-        ("ted_multi", 99),
-        ("allocine", 100),
-        ("hotpot_qa", 101),
-        ("cc_news", 102),
-        ("conll2002", 103),
-        ("cuad", 104),
-        ("mc_taco", 105),
-        ("silicone", 106),
-        ("discovery", 107),
-        ("mt_eng_vietnamese", 108),
-        ("quac", 109),
-        ("conllpp", 110),
-        ("ubuntu_dialogs_corpus", 111),
-        ("esnli", 112),
-        ("doc2dial", 113),
-        ("squad_kor_v2", 114),
-        ("opus_gnome", 115),
-        ("german_legal_entity_recognition", 116),
-        ("openbookqa", 117),
-        ("tapaco", 118),
-        ("xquad_r", 119),
-        ("imdb\\dataset_infos.json", 120),
-        ("opus_wikipedia", 121),
-        ("amr", 122),
-        ("wnut_17", 123),
-        ("empathetic_dialogues", 124),
-        ("cbt", 125),
-        ("opus_rf", 126),
-        ("narrativeqa", 127),
-        ("mnist", 128),
-        ("sick", 129),
-        ("swda", 130),
-        ("aeslc", 131),
-        ("art", 132),
-        ("coqa", 133),
-        ("opus100", 134),
-        ("sst", 135),
-        ("big_patent", 136),
-        ("germeval_14", 137),
-        ("liar", 138),
-        ("un_pc", 139),
-        ("alt", 140),
-        ("circa", 141),
-        ("scan", 142),
-        ("wikisql", 143),
-        ("reddit", 144),
-        ("wino_bias", 145),
-        ("financial_phrasebank", 146),
-        ("social_i_qa", 147),
-        ("newsqa", 148),
-        ("cosmos_qa", 149),
-        ("classla", 150),
-        ("scicite", 151),
-        ("codah", 152),
-        ("ehealth_kd", 153),
-        ("wikicorpus", 154),
-        ("ccaligned_multilingual", 155),
-        ("cos_e", 156),
-        ("thaisum", 157),
-        ("cfq", 158),
-        ("yahoo_answers_topics", 159),
-        ("wmt", 160),
-        ("natural_questions", 161),
-        ("cc100", 162),
-        ("paws", 163),
-        ("boolq", 164),
-        ("break_data", 165),
-        ("pragmeval", 166),
-        ("arabic_speech_corpus", 167),
-        ("text\\dataset_infos.json", 168),
-        ("md_gender_bias", 169),
-        ("mlqa", 170),
-        ("arabic_billion_words", 171),
-        ("dialog_re", 172),
-        ("tweets_hate_speech_detection", 173),
-        ("ecthr_cases", 174),
-        ("json\\dataset_infos.json", 175),
-        ("conv_ai_2", 176),
-        ("dream", 177),
-        ("kor_ner", 178),
-        ("youtube_caption_corrections", 179),
-        ("spider", 180),
-        ("air_dialogue", 181),
-        ("arxiv_dataset", 182),
-        ("data", 183),
-        ("quora", 184),
-        ("docred", 185),
-        ("guardian_authorship", 186),
-        ("quartz", 187),
-        ("yelp_review_full", 188),
-        ("xquad", 189),
-        ("ted_talks_iwslt", 190),
-        ("orange_sum", 191),
-        ("indonlu", 192),
-        ("tweet_qa", 193),
-        ("multi_woz_v22", 194),
-        ("s2orc", 195),
-        ("clarin-pl", 196),
-        ("cord19", 197),
-        ("emo", 198),
-        ("indic_glue", 199),
-        ("ethos", 200),
-        ("persiannlp", 201),
-        ("totto", 202),
-        ("wongnai_reviews", 203),
-        ("bavard", 204),
-        ("europa_ecdc_tm", 205),
-        ("google_wellformed_query", 206),
-        ("paws-x", 207),
-        ("emea", 208),
-        ("fever", 209),
-        ("asset", 210),
-        ("kilt_wikipedia", 211),
-        ("clinc_oos", 212),
-        ("conv_ai_3", 213),
-        ("ncbi_disease", 214),
-        ("sentiment140", 215),
-        ("quarel", 216),
-        ("txt", 217),
-        ("ajgt_twitter_ar", 218),
-        ("ambig_qa", 219),
-        ("ptb_text_only", 220),
-        ("stsb_multi_mt", 221),
-        ("web_questions", 222),
-        ("winograd_wsc", 223),
-        ("eurlex", 224),
-        ("muchocine", 225),
-        ("app_reviews", 226),
-        ("aqua_rat", 227),
-        ("bible_para", 228),
-        ("wiki_auto", 229),
-        ("cifar10", 230),
-        ("eli5\\dataset_infos.json", 231),
-        ("quail", 232),
-        ("hyperpartisan_news_detection", 233),
-    ]
-)