datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
sequence | task_categories
sequence | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
sequence |
|---|---|---|---|---|---|---|---|---|---|
habanoz/openassistant-guanaco-chat-format | habanoz | 2023-12-14T08:47:14Z | 6 | 0 | [
"region:us"
] | null | 2023-12-13T20:12:05Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 15469605
num_examples: 9846
- name: test
num_bytes: 818818
num_examples: 518
download_size: 9432751
dataset_size: 16288423
---
# Dataset Card for "openassistant-guanaco-chat-format"
Copy of [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). Modified to use with hugging face chat tempaltes. | [
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michaelginn/latent-trees-agreement-GENX | michaelginn | 2023-12-14T05:57:04Z | 6 | 0 | [
"region:us"
] | null | 2023-12-14T02:45:30Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: labels
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: depth
dtype: int64
splits:
- name: train
num_bytes: 105894.0
num_examples: 2400
- name: eval
num_bytes: 35298.0
num_examples: 800
- name: test
num_bytes: 210470
num_examples: 800
download_size: 112444
dataset_size: 351662.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
- split: test
path: data/test-*
---
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ryan7653/Jim | ryan7653 | 2023-12-14T03:19:07Z | 6 | 0 | [
"license:wtfpl",
"region:us"
] | null | 2023-12-14T03:18:39Z | ---
license: wtfpl
---
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Seongill/TQA_5_missing_adv_test | Seongill | 2023-12-14T07:38:24Z | 6 | 0 | [
"region:us"
] | null | 2023-12-14T07:38:22Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: status
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2318048
num_examples: 450
download_size: 1334729
dataset_size: 2318048
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] |
kowndinya23/flan2021-submix-llama-2-13b-2048 | kowndinya23 | 2023-12-14T09:57:59Z | 6 | 0 | [
"region:us"
] | null | 2023-12-14T09:51:09Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype:
class_label:
names:
'0': aeslc:1.0.0
'1': ag_news_subset:1.0.0
'2': ai2_arc/ARC-Challenge:1.0.0
'3': ai2_arc/ARC-Easy:1.0.0
'4': anli/r1:0.1.0
'5': anli/r2:0.1.0
'6': anli/r3:0.1.0
'7': bool_q:1.0.0
'8': cnn_dailymail:3.4.0
'9': coqa:1.0.0
'10': cosmos_qa:1.0.0
'11': definite_pronoun_resolution:1.1.0
'12': drop:2.0.0
'13': fix_punct
'14': gem/common_gen:1.1.0
'15': gem/dart:1.1.0
'16': gem/e2e_nlg:1.1.0
'17': gem/web_nlg_en:1.1.0
'18': gem/wiki_lingua_english_en:1.1.0
'19': gigaword:1.2.0
'20': glue/cola:2.0.0
'21': glue/mnli:2.0.0
'22': glue/mrpc:2.0.0
'23': glue/qnli:2.0.0
'24': glue/qqp:2.0.0
'25': glue/sst2:2.0.0
'26': glue/stsb:2.0.0
'27': glue/wnli:2.0.0
'28': hellaswag:1.1.0
'29': huggingface:xsum
'30': imdb_reviews/plain_text:1.0.0
'31': lambada:1.0.0
'32': math_dataset/algebra__linear_1d:1.0.0
'33': multi_news:1.0.0
'34': natural_questions_open:1.0.0
'35': newsroom:1.0.0
'36': openbookqa:0.1.0
'37': opinion_abstracts_idebate
'38': opinion_abstracts_rotten_tomatoes
'39': para_crawl_enes
'40': paws_wiki:1.1.0
'41': piqa:1.0.0
'42': quac:1.0.0
'43': samsum:1.0.0
'44': sentiment140:1.0.0
'45': snli:1.1.0
'46': squad/v1.1:3.0.0
'47': squad/v2.0:3.0.0
'48': story_cloze/2016:1.0.0
'49': super_glue/cb:1.0.2
'50': super_glue/copa:1.0.2
'51': super_glue/multirc:1.0.2
'52': super_glue/record:1.0.2
'53': super_glue/rte:1.0.2
'54': super_glue/wic:1.0.2
'55': super_glue/wsc.fixed:1.0.2
'56': trec:1.0.0
'57': trivia_qa/rc:1.1.0
'58': true_case
'59': unified_qa_science_inst
'60': winogrande:1.1.0
'61': wmt14_translate/fr-en:1.0.0
'62': wmt16_translate/cs-en:1.0.0
'63': wmt16_translate/de-en:1.0.0
'64': wmt16_translate/fi-en:1.0.0
'65': wmt16_translate/ro-en:1.0.0
'66': wmt16_translate/ru-en:1.0.0
'67': wmt16_translate/tr-en:1.0.0
'68': word_segment
'69': yelp_polarity_reviews:0.2.0
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 7668970199.786371
num_examples: 5213501
- name: validation
num_bytes: 77464895.21362898
num_examples: 52662
download_size: 4804405562
dataset_size: 7746435095.0
---
# Dataset Card for "flan2021-submix-llama-2-13b-2048"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ChatRDM/rdmkit-biotools | ChatRDM | 2023-12-14T23:11:14Z | 6 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-14T16:47:07Z | ---
license: mit
---
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kanishka/counterfactual_babylm_prototypical_only | kanishka | 2023-12-14T18:10:43Z | 6 | 0 | [
"region:us"
] | null | 2023-12-14T18:07:18Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 581827546
num_examples: 11633845
- name: validation
num_bytes: 56120230
num_examples: 1026747
download_size: 421677407
dataset_size: 637947776
---
# Dataset Card for "counterfactual_babylm_prototypical_only"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kanishka/counterfactual_babylm_without_prototypical | kanishka | 2023-12-14T18:11:10Z | 6 | 0 | [
"region:us"
] | null | 2023-12-14T18:07:57Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 581830133
num_examples: 11633299
- name: validation
num_bytes: 56120230
num_examples: 1026747
download_size: 421630845
dataset_size: 637950363
---
# Dataset Card for "counterfactual_babylm_without_prototypical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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0x7194633/violet-v1 | 0x7194633 | 2023-12-15T10:09:33Z | 6 | 0 | [
"region:us"
] | null | 2023-12-15T10:08:21Z | ---
dataset_info:
features:
- name: texts
dtype: string
splits:
- name: train
num_bytes: 221386895
num_examples: 129712
download_size: 103291349
dataset_size: 221386895
---
# Dataset Card for "violet-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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bowphs/cc-100-01-percent-untokenized-small | bowphs | 2023-12-15T11:53:47Z | 6 | 0 | [
"region:us"
] | null | 2023-12-15T11:48:12Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2925774117.749719
num_examples: 14718261
download_size: 2420078829
dataset_size: 2925774117.749719
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cc-100-01-percent-untokenized-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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beratcmn/rephrased-instruction-turkish-poems | beratcmn | 2023-12-16T12:38:57Z | 6 | 1 | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:tr",
"license:apache-2.0",
"poem",
"region:us"
] | [
"text-generation"
] | 2023-12-15T23:23:22Z | ---
license: apache-2.0
task_categories:
- text-generation
language:
- tr
tags:
- poem
size_categories:
- 1K<n<10K
---
# This is a rephrased version of my previous dataset `beratcmn/instruction-turkish-poems`. I used the same instructions but I rephrased them to be more clear and understandable also added more variety to the format. | [
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Ram07/em-dialog | Ram07 | 2023-12-16T09:13:35Z | 6 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-16T07:38:40Z | ---
license: mit
---
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] |
kowndinya23/t0-submix-4096 | kowndinya23 | 2023-12-16T10:39:10Z | 6 | 0 | [
"region:us"
] | null | 2023-12-16T10:36:09Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype:
class_label:
names:
'0': adversarial_qa_dbert_answer_the_following_q
'1': adversarial_qa_dbert_based_on
'2': adversarial_qa_dbert_generate_question
'3': adversarial_qa_dbert_question_context_answer
'4': adversarial_qa_dbert_tell_what_it_is
'5': adversarial_qa_dbidaf_answer_the_following_q
'6': adversarial_qa_dbidaf_based_on
'7': adversarial_qa_dbidaf_generate_question
'8': adversarial_qa_dbidaf_question_context_answer
'9': adversarial_qa_dbidaf_tell_what_it_is
'10': adversarial_qa_droberta_answer_the_following_q
'11': adversarial_qa_droberta_based_on
'12': adversarial_qa_droberta_generate_question
'13': adversarial_qa_droberta_question_context_answer
'14': adversarial_qa_droberta_tell_what_it_is
'15': amazon_polarity_Is_this_product_review_positive
'16': amazon_polarity_Is_this_review
'17': amazon_polarity_Is_this_review_negative
'18': amazon_polarity_User_recommend_this_product
'19': amazon_polarity_convey_negative_or_positive_sentiment
'20': amazon_polarity_flattering_or_not
'21': amazon_polarity_negative_or_positive_tone
'22': amazon_polarity_user_satisfied
'23': amazon_polarity_would_you_buy
'24': app_reviews_categorize_rating_using_review
'25': app_reviews_convert_to_rating
'26': app_reviews_convert_to_star_rating
'27': app_reviews_generate_review
'28': cos_e_v1.11_aligned_with_common_sense
'29': cos_e_v1.11_description_question_option_id
'30': cos_e_v1.11_description_question_option_text
'31': cos_e_v1.11_explain_why_human
'32': cos_e_v1.11_generate_explanation_given_text
'33': cos_e_v1.11_i_think
'34': cos_e_v1.11_question_description_option_id
'35': cos_e_v1.11_question_description_option_text
'36': cos_e_v1.11_question_option_description_id
'37': cos_e_v1.11_question_option_description_text
'38': cos_e_v1.11_rationale
'39': dbpedia_14_given_a_choice_of_categories_
'40': dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to
'41': dbpedia_14_given_list_what_category_does_the_paragraph_belong_to
'42': dbpedia_14_pick_one_category_for_the_following_text
'43': dream_answer_to_dialogue
'44': dream_baseline
'45': dream_generate_first_utterance
'46': dream_generate_last_utterance
'47': dream_read_the_following_conversation_and_answer_the_question
'48': duorc_ParaphraseRC_answer_question
'49': duorc_ParaphraseRC_build_story_around_qa
'50': duorc_ParaphraseRC_decide_worth_it
'51': duorc_ParaphraseRC_extract_answer
'52': duorc_ParaphraseRC_generate_question
'53': duorc_ParaphraseRC_generate_question_by_answer
'54': duorc_ParaphraseRC_movie_director
'55': duorc_ParaphraseRC_question_answering
'56': duorc_ParaphraseRC_title_generation
'57': duorc_SelfRC_answer_question
'58': duorc_SelfRC_build_story_around_qa
'59': duorc_SelfRC_decide_worth_it
'60': duorc_SelfRC_extract_answer
'61': duorc_SelfRC_generate_question
'62': duorc_SelfRC_generate_question_by_answer
'63': duorc_SelfRC_movie_director
'64': duorc_SelfRC_question_answering
'65': duorc_SelfRC_title_generation
'66': kilt_tasks_hotpotqa_combining_facts
'67': kilt_tasks_hotpotqa_complex_question
'68': kilt_tasks_hotpotqa_final_exam
'69': kilt_tasks_hotpotqa_formulate
'70': kilt_tasks_hotpotqa_straighforward_qa
'71': qasc_is_correct_1
'72': qasc_is_correct_2
'73': qasc_qa_with_combined_facts_1
'74': qasc_qa_with_separated_facts_1
'75': qasc_qa_with_separated_facts_2
'76': qasc_qa_with_separated_facts_3
'77': qasc_qa_with_separated_facts_4
'78': qasc_qa_with_separated_facts_5
'79': quail_context_description_question_answer_id
'80': quail_context_description_question_answer_text
'81': quail_context_description_question_text
'82': quail_context_question_answer_description_id
'83': quail_context_question_answer_description_text
'84': quail_context_question_description_answer_id
'85': quail_context_question_description_answer_text
'86': quail_context_question_description_text
'87': quail_description_context_question_answer_id
'88': quail_description_context_question_answer_text
'89': quail_description_context_question_text
'90': quail_no_prompt_id
'91': quail_no_prompt_text
'92': quarel_choose_between
'93': quarel_do_not_use
'94': quarel_heres_a_story
'95': quarel_logic_test
'96': quarel_testing_students
'97': quartz_answer_question_based_on
'98': quartz_answer_question_below
'99': quartz_given_the_fact_answer_the_q
'100': quartz_having_read_above_passage
'101': quartz_paragraph_question_plain_concat
'102': quartz_read_passage_below_choose
'103': quartz_use_info_from_paragraph_question
'104': quartz_use_info_from_question_paragraph
'105': quoref_Answer_Friend_Question
'106': quoref_Answer_Question_Given_Context
'107': quoref_Answer_Test
'108': quoref_Context_Contains_Answer
'109': quoref_Find_Answer
'110': quoref_Found_Context_Online
'111': quoref_Given_Context_Answer_Question
'112': quoref_Guess_Answer
'113': quoref_Guess_Title_For_Context
'114': quoref_Read_And_Extract_
'115': quoref_What_Is_The_Answer
'116': race_high_Is_this_the_right_answer
'117': race_high_Read_the_article_and_answer_the_question_no_option_
'118': race_high_Select_the_best_answer
'119': race_high_Select_the_best_answer_generate_span_
'120': race_high_Select_the_best_answer_no_instructions_
'121': race_high_Taking_a_test
'122': race_high_Write_a_multi_choice_question_for_the_following_article
'123': race_high_Write_a_multi_choice_question_options_given_
'124': race_middle_Is_this_the_right_answer
'125': race_middle_Read_the_article_and_answer_the_question_no_option_
'126': race_middle_Select_the_best_answer
'127': race_middle_Select_the_best_answer_generate_span_
'128': race_middle_Select_the_best_answer_no_instructions_
'129': race_middle_Taking_a_test
'130': race_middle_Write_a_multi_choice_question_for_the_following_article
'131': race_middle_Write_a_multi_choice_question_options_given_
'132': ropes_background_new_situation_answer
'133': ropes_background_situation_middle
'134': ropes_given_background_situation
'135': ropes_new_situation_background_answer
'136': ropes_plain_background_situation
'137': ropes_plain_bottom_hint
'138': ropes_plain_no_background
'139': ropes_prompt_beginning
'140': ropes_prompt_bottom_hint_beginning
'141': ropes_prompt_bottom_no_hint
'142': ropes_prompt_mix
'143': ropes_read_background_situation
'144': sciq_Direct_Question
'145': sciq_Direct_Question_Closed_Book_
'146': sciq_Multiple_Choice
'147': sciq_Multiple_Choice_Closed_Book_
'148': sciq_Multiple_Choice_Question_First
'149': social_i_qa_Check_if_a_random_answer_is_valid_or_not
'150': social_i_qa_Generate_answer
'151': social_i_qa_Generate_the_question_from_the_answer
'152': social_i_qa_I_was_wondering
'153': social_i_qa_Show_choices_and_generate_answer
'154': social_i_qa_Show_choices_and_generate_index
'155': web_questions_get_the_answer
'156': web_questions_potential_correct_answer
'157': web_questions_question_answer
'158': web_questions_short_general_knowledge_q
'159': web_questions_whats_the_answer
'160': wiki_bio_comprehension
'161': wiki_bio_guess_person
'162': wiki_bio_key_content
'163': wiki_bio_what_content
'164': wiki_bio_who
'165': wiki_hop_original_choose_best_object_affirmative_1
'166': wiki_hop_original_choose_best_object_affirmative_2
'167': wiki_hop_original_choose_best_object_affirmative_3
'168': wiki_hop_original_choose_best_object_interrogative_1
'169': wiki_hop_original_choose_best_object_interrogative_2
'170': wiki_hop_original_explain_relation
'171': wiki_hop_original_generate_object
'172': wiki_hop_original_generate_subject
'173': wiki_hop_original_generate_subject_and_object
'174': wiki_qa_Decide_good_answer
'175': wiki_qa_Direct_Answer_to_Question
'176': wiki_qa_Generate_Question_from_Topic
'177': wiki_qa_Is_This_True_
'178': wiki_qa_Jeopardy_style
'179': wiki_qa_Topic_Prediction_Answer_Only
'180': wiki_qa_Topic_Prediction_Question_Only
'181': wiki_qa_Topic_Prediction_Question_and_Answer_Pair
'182': wiki_qa_automatic_system
'183': wiki_qa_exercise
'184': wiki_qa_found_on_google
'185': wiqa_does_the_supposed_perturbation_have_an_effect
'186': wiqa_effect_with_label_answer
'187': wiqa_effect_with_string_answer
'188': wiqa_what_is_the_final_step_of_the_following_process
'189': wiqa_what_is_the_missing_first_step
'190': wiqa_what_might_be_the_first_step_of_the_process
'191': wiqa_what_might_be_the_last_step_of_the_process
'192': wiqa_which_of_the_following_is_the_supposed_perturbation
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 4434328372.540715
num_examples: 1630423
- name: validation
num_bytes: 44791415.45928452
num_examples: 16469
download_size: 2706129779
dataset_size: 4479119788.0
---
# Dataset Card for "t0-submix-4096"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
kowndinya23/niv2-submix-4096 | kowndinya23 | 2023-12-16T11:06:53Z | 6 | 0 | [
"region:us"
] | null | 2023-12-16T10:54:49Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype:
class_label:
names:
'0': task001_quoref_question_generation
'1': task002_quoref_answer_generation
'2': task003_mctaco_question_generation_event_duration
'3': task004_mctaco_answer_generation_event_duration
'4': task005_mctaco_wrong_answer_generation_event_duration
'5': task006_mctaco_question_generation_transient_stationary
'6': task007_mctaco_answer_generation_transient_stationary
'7': task008_mctaco_wrong_answer_generation_transient_stationary
'8': task009_mctaco_question_generation_event_ordering
'9': task010_mctaco_answer_generation_event_ordering
'10': task011_mctaco_wrong_answer_generation_event_ordering
'11': task012_mctaco_question_generation_absolute_timepoint
'12': task013_mctaco_answer_generation_absolute_timepoint
'13': task014_mctaco_wrong_answer_generation_absolute_timepoint
'14': task015_mctaco_question_generation_frequency
'15': task016_mctaco_answer_generation_frequency
'16': task017_mctaco_wrong_answer_generation_frequency
'17': task018_mctaco_temporal_reasoning_presence
'18': task019_mctaco_temporal_reasoning_category
'19': task020_mctaco_span_based_question
'20': task021_mctaco_grammatical_logical
'21': task022_cosmosqa_passage_inappropriate_binary
'22': task023_cosmosqa_question_generation
'23': task024_cosmosqa_answer_generation
'24': task025_cosmosqa_incorrect_answer_generation
'25': task026_drop_question_generation
'26': task027_drop_answer_type_generation
'27': task028_drop_answer_generation
'28': task029_winogrande_full_object
'29': task030_winogrande_full_person
'30': task031_winogrande_question_generation_object
'31': task032_winogrande_question_generation_person
'32': task033_winogrande_answer_generation
'33': task034_winogrande_question_modification_object
'34': task035_winogrande_question_modification_person
'35': task036_qasc_topic_word_to_generate_related_fact
'36': task037_qasc_generate_related_fact
'37': task038_qasc_combined_fact
'38': task039_qasc_find_overlapping_words
'39': task040_qasc_question_generation
'40': task041_qasc_answer_generation
'41': task042_qasc_incorrect_option_generation
'42': task043_essential_terms_answering_incomplete_questions
'43': task044_essential_terms_identifying_essential_words
'44': task045_miscellaneous_sentence_paraphrasing
'45': task046_miscellaneous_question_typing
'46': task047_miscellaneous_answering_science_questions
'47': task048_multirc_question_generation
'48': task049_multirc_questions_needed_to_answer
'49': task050_multirc_answerability
'50': task051_multirc_correct_answer_single_sentence
'51': task052_multirc_identify_bad_question
'52': task053_multirc_correct_bad_question
'53': task054_multirc_write_correct_answer
'54': task055_multirc_write_incorrect_answer
'55': task056_multirc_classify_correct_answer
'56': task057_multirc_classify_incorrect_answer
'57': task058_multirc_question_answering
'58': task059_ropes_story_generation
'59': task060_ropes_question_generation
'60': task061_ropes_answer_generation
'61': task062_bigbench_repeat_copy_logic
'62': task063_first_i_elements
'63': task064_all_elements_except_first_i
'64': task065_timetravel_consistent_sentence_classification
'65': task066_timetravel_binary_consistency_classification
'66': task067_abductivenli_answer_generation
'67': task068_abductivenli_incorrect_answer_generation
'68': task069_abductivenli_classification
'69': task070_abductivenli_incorrect_classification
'70': task071_abductivenli_answer_generation
'71': task072_abductivenli_answer_generation
'72': task073_commonsenseqa_answer_generation
'73': task074_squad1.1_question_generation
'74': task075_squad1.1_answer_generation
'75': task076_splash_correcting_sql_mistake
'76': task077_splash_explanation_to_sql
'77': task078_all_elements_except_last_i
'78': task079_conala_concat_strings
'79': task080_piqa_answer_generation
'80': task081_piqa_wrong_answer_generation
'81': task082_babi_t1_single_supporting_fact_question_generation
'82': task083_babi_t1_single_supporting_fact_answer_generation
'83': task084_babi_t1_single_supporting_fact_identify_relevant_fact
'84': task085_unnatural_addsub_arithmetic
'85': task086_translated_symbol_arithmetic
'86': task087_new_operator_addsub_arithmetic
'87': task088_identify_typo_verification
'88': task089_swap_words_verification
'89': task090_equation_learner_algebra
'90': task091_all_elements_from_index_i_to_j
'91': task092_check_prime_classification
'92': task093_conala_normalize_lists
'93': task094_conala_calculate_mean
'94': task095_conala_max_absolute_value
'95': task096_conala_list_index_subtraction
'96': task097_conala_remove_duplicates
'97': task098_conala_list_intersection
'98': task099_reverse_elements_between_index_i_and_j
'99': task1000_pib_translation_tamil_malayalam
'100': task1001_pib_translation_gujarati_urdu
'101': task1002_pib_translation_urdu_gujarati
'102': task1003_pib_translation_bengali_malayalam
'103': task1004_pib_translation_malayalam_bengali
'104': task1005_pib_translation_malayalam_punjabi
'105': task1006_pib_translation_punjabi_malayalam
'106': task1007_pib_translation_english_punjabi
'107': task1008_pib_translation_punjabi_english
'108': task1009_pib_translation_bengali_hindi
'109': task100_concatenate_all_elements_from_index_i_to_j
'110': task1010_pib_translation_hindi_bengali
'111': task1011_pib_translation_hindi_punjabi
'112': task1012_pib_translation_punjabi_hindi
'113': task1013_pib_translation_gujarati_telugu
'114': task1014_pib_translation_telugu_gujarati
'115': task1015_pib_translation_punjabi_tamil
'116': task1016_pib_translation_tamil_punjabi
'117': task1017_pib_translation_hindi_malayalam
'118': task1018_pib_translation_malayalam_hindi
'119': task1019_pib_translation_oriya_telugu
'120': task101_reverse_and_concatenate_all_elements_from_index_i_to_j
'121': task1020_pib_translation_telugu_oriya
'122': task1021_pib_translation_english_malayalam
'123': task1022_pib_translation_malayalam_english
'124': task1023_pib_translation_english_hindi
'125': task1024_pib_translation_hindi_english
'126': task1025_pib_translation_bengali_punjabi
'127': task1026_pib_translation_punjabi_bengali
'128': task1027_pib_translation_marathi_telugu
'129': task1028_pib_translation_telugu_marathi
'130': task1029_pib_translation_marathi_punjabi
'131': task102_commongen_sentence_generation
'132': task1030_pib_translation_punjabi_marathi
'133': task1031_pib_translation_bengali_telugu
'134': task1032_pib_translation_telugu_bengali
'135': task1033_pib_translation_gujarati_hindi
'136': task1034_pib_translation_hindi_gujarati
'137': task1035_pib_translation_tamil_urdu
'138': task1036_pib_translation_urdu_tamil
'139': task1037_pib_translation_telugu_urdu
'140': task1038_pib_translation_urdu_telugu
'141': task1039_pib_translation_oriya_punjabi
'142': task103_facts2story_long_text_generation
'143': task1040_pib_translation_punjabi_oriya
'144': task1041_pib_translation_gujarati_malayalam
'145': task1042_pib_translation_malayalam_gujarati
'146': task1043_pib_translation_gujarati_punjabi
'147': task1044_pib_translation_punjabi_gujarati
'148': task1045_pib_translation_hindi_telugu
'149': task1046_pib_translation_telugu_hindi
'150': task1047_pib_translation_english_telugu
'151': task1048_pib_translation_telugu_english
'152': task1049_pib_translation_malayalam_telugu
'153': task104_semeval_2019_task10_closed_vocabulary_mathematical_answer_generation
'154': task1050_pib_translation_telugu_malayalam
'155': task1051_pib_translation_punjabi_urdu
'156': task1052_pib_translation_urdu_punjabi
'157': task1053_pib_translation_hindi_urdu
'158': task1054_pib_translation_urdu_hindi
'159': task1055_pib_translation_marathi_oriya
'160': task1056_pib_translation_oriya_marathi
'161': task1057_pib_translation_english_urdu
'162': task1058_pib_translation_urdu_english
'163': task1059_pib_translation_malayalam_urdu
'164': task105_story_cloze-rocstories_sentence_generation
'165': task1060_pib_translation_urdu_malayalam
'166': task1061_pib_translation_bengali_marathi
'167': task1062_pib_translation_marathi_bengali
'168': task1063_pib_translation_gujarati_tamil
'169': task1064_pib_translation_tamil_gujarati
'170': task1065_pib_translation_punjabi_telugu
'171': task1066_pib_translation_telugu_punjabi
'172': task1067_pib_translation_bengali_gujarati
'173': task1068_pib_translation_gujarati_bengali
'174': task1069_pib_translation_bengali_urdu
'175': task106_scruples_ethical_judgment
'176': task1070_pib_translation_urdu_bengali
'177': task1071_pib_translation_malayalam_marathi
'178': task1072_pib_translation_marathi_malayalam
'179': task1073_pib_translation_oriya_tamil
'180': task1074_pib_translation_tamil_oriya
'181': task1075_pib_translation_tamil_telugu
'182': task1076_pib_translation_telugu_tamil
'183': task1077_pib_translation_gujarati_oriya
'184': task1078_pib_translation_oriya_gujarati
'185': task1079_pib_translation_english_gujarati
'186': task107_splash_question_to_sql
'187': task1080_pib_translation_gujarati_english
'188': task1081_pib_translation_hindi_marathi
'189': task1082_pib_translation_marathi_hindi
'190': task1083_pib_translation_marathi_tamil
'191': task1084_pib_translation_tamil_marathi
'192': task1085_pib_translation_english_marathi
'193': task1086_pib_translation_marathi_english
'194': task1087_two_number_sum
'195': task1088_array_of_products
'196': task1089_check_monotonic_array
'197': task108_contextualabusedetection_classification
'198': task1090_ted_translation_en_gl
'199': task1091_ted_translation_en_it
'200': task1092_ted_translation_en_pl
'201': task1093_ted_translation_en_fa
'202': task1094_ted_translation_en_pt
'203': task1095_ted_translation_ja_gl
'204': task1096_ted_translation_ja_it
'205': task1097_ted_translation_ja_pl
'206': task1098_ted_translation_ja_fa
'207': task1099_ted_translation_ja_pt
'208': task109_smsspamcollection_spamsmsdetection
'209': task1100_ted_translation_es_gl
'210': task1101_ted_translation_es_it
'211': task1102_ted_translation_es_pl
'212': task1103_ted_translation_es_fa
'213': task1104_ted_translation_es_pt
'214': task1105_ted_translation_ar_gl
'215': task1106_ted_translation_ar_it
'216': task1107_ted_translation_ar_pl
'217': task1108_ted_translation_ar_fa
'218': task1109_ted_translation_ar_pt
'219': task110_logic2text_sentence_generation
'220': task1110_ted_translation_he_gl
'221': task1111_ted_translation_he_it
'222': task1112_ted_translation_he_pl
'223': task1113_ted_translation_he_fa
'224': task1114_ted_translation_he_pt
'225': task1115_alt_ja_id_translation
'226': task1116_alt_id_ja_translation
'227': task1117_alt_ja_id_answer_generation
'228': task1118_alt_ja_fil_translation
'229': task1119_alt_fil_ja_translation
'230': task111_asset_sentence_simplification
'231': task1120_alt_ja_fil_answer_generation
'232': task1121_alt_ja_khm_translation
'233': task1122_alt_khm_ja_translation
'234': task1123_alt_ja_khm_answer_generation
'235': task1124_alt_ja_lo_translation
'236': task1125_alt_lo_ja_translation
'237': task1126_alt_ja_lo_answer_generation
'238': task1127_alt_ja_th_translation
'239': task1128_alt_th_ja_translation
'240': task1129_alt_ja_th_answer_generation
'241': task112_asset_simple_sentence_identification
'242': task1130_xcsr_vi_commonsense_mc_classification
'243': task1131_xcsr_es_commonsense_mc_classification
'244': task1132_xcsr_ur_commonsense_mc_classification
'245': task1133_xcsr_nl_commonsense_mc_classification
'246': task1134_xcsr_hi_commonsense_mc_classification
'247': task1135_xcsr_en_commonsense_mc_classification
'248': task1136_xcsr_fr_commonsense_mc_classification
'249': task1137_xcsr_pt_commonsense_mc_classification
'250': task1138_xcsr_de_commonsense_mc_classification
'251': task1139_xcsr_ru_commonsense_mc_classification
'252': task113_count_frequency_of_letter
'253': task1140_xcsr_pl_commonsense_mc_classification
'254': task1141_xcsr_zh_commonsense_mc_classification
'255': task1142_xcsr_ar_commonsense_mc_classification
'256': task1143_xcsr_it_commonsense_mc_classification
'257': task1144_xcsr_sw_commonsense_mc_classification
'258': task1145_xcsr_jap_commonsense_mc_classification
'259': task1146_country_capital
'260': task1147_country_currency
'261': task1148_maximum_ascii_value
'262': task1149_item_check_edible
'263': task114_is_the_given_word_longest
'264': task1150_delete_max_min
'265': task1151_swap_max_min
'266': task1152_bard_analogical_reasoning_causation
'267': task1153_bard_analogical_reasoning_affordance
'268': task1154_bard_analogical_reasoning_travel
'269': task1155_bard_analogical_reasoning_trash_or_treasure
'270': task1156_bard_analogical_reasoning_tools
'271': task1157_bard_analogical_reasoning_rooms_for_containers
'272': task1158_bard_analogical_reasoning_manipulating_items
'273': task1159_bard_analogical_reasoning_containers
'274': task115_help_advice_classification
'275': task1161_coda19_title_generation
'276': task1162_coda19_title_classification
'277': task1163_coda19_section_classification
'278': task1164_coda19_section_correction_classification
'279': task1167_penn_treebank_coarse_pos_tagging
'280': task1168_brown_coarse_pos_tagging
'281': task1168_xcopa_commonsense_reasoning_ht
'282': task1169_xcopa_commonsense_cause_effect_ht
'283': task116_com2sense_commonsense_reasoning
'284': task1170_xcopa_commonsense_reasoning_id
'285': task1171_xcopa_commonsense_cause_effect_id
'286': task1172_xcopa_commonsense_reasoning_it
'287': task1173_xcopa_commonsense_cause_effect_it
'288': task1174_xcopa_commonsense_reasoning_sw
'289': task1175_xcopa_commonsense_cause_effect_sw
'290': task1176_xcopa_commonsense_reasoning_ta
'291': task1177_xcopa_commonsense_cause_effect_ta
'292': task1178_xcopa_commonsense_reasoning_th
'293': task1179_xcopa_commonsense_cause_effect_th
'294': task117_spl_translation_en_de
'295': task1180_xcopa_commonsense_reasoning_tr
'296': task1181_xcopa_commonsense_cause_effect_tr
'297': task1182_xcopa_commonsense_reasoning_vi
'298': task1183_xcopa_commonsense_cause_effect_vi
'299': task1184_xcopa_commonsense_reasoning_zh
'300': task1185_xcopa_commonsense_cause_effect_zh
'301': task1186_nne_hrngo_classification
'302': task1187_politifact_classification
'303': task1188_count_max_freq_char
'304': task1189_check_char_in_string
'305': task118_semeval_2019_task10_open_vocabulary_mathematical_answer_generation
'306': task1190_add_integer_to_list
'307': task1191_food_veg_nonveg
'308': task1192_food_flavor_profile
'309': task1193_food_course_classification
'310': task1194_kth_largest_element
'311': task1195_disflqa_disfluent_to_fluent_conversion
'312': task1196_atomic_classification_oeffect
'313': task1197_atomic_classification_oreact
'314': task1198_atomic_classification_owant
'315': task1199_atomic_classification_xattr
'316': task119_semeval_2019_task10_geometric_mathematical_answer_generation
'317': task1200_atomic_classification_xeffect
'318': task1201_atomic_classification_xintent
'319': task1202_atomic_classification_xneed
'320': task1203_atomic_classification_xreact
'321': task1204_atomic_classification_hinderedby
'322': task1205_atomic_classification_isafter
'323': task1206_atomic_classification_isbefore
'324': task1207_atomic_classification_atlocation
'325': task1208_atomic_classification_xreason
'326': task1209_atomic_classification_objectuse
'327': task120_zest_text_modification
'328': task1210_atomic_classification_madeupof
'329': task1211_atomic_classification_hassubevent
'330': task1212_atomic_classification_hasproperty
'331': task1213_atomic_classification_desires
'332': task1214_atomic_classification_xwant
'333': task1215_atomic_classification_capableof
'334': task1216_atomic_classification_causes
'335': task1217_atomic_answer_generation
'336': task1218_ted_translation_en_ja
'337': task1219_ted_translation_en_es
'338': task121_zest_text_modification
'339': task1220_ted_translation_en_ar
'340': task1221_ted_translation_en_he
'341': task1222_ted_translation_ja_en
'342': task1223_ted_translation_ja_es
'343': task1224_ted_translation_ja_ar
'344': task1225_ted_translation_ja_he
'345': task1226_ted_translation_es_en
'346': task1227_ted_translation_es_ja
'347': task1228_ted_translation_es_ar
'348': task1229_ted_translation_es_he
'349': task122_conala_list_index_addition
'350': task1230_ted_translation_ar_en
'351': task1231_ted_translation_ar_ja
'352': task1232_ted_translation_ar_es
'353': task1233_ted_translation_ar_he
'354': task1234_ted_translation_he_en
'355': task1235_ted_translation_he_ja
'356': task1236_ted_translation_he_es
'357': task1237_ted_translation_he_ar
'358': task1238_ted_translation_gl_en
'359': task1239_ted_translation_gl_ja
'360': task123_conala_sort_dictionary
'361': task1240_ted_translation_gl_es
'362': task1241_ted_translation_gl_ar
'363': task1242_ted_translation_gl_he
'364': task1243_ted_translation_gl_it
'365': task1244_ted_translation_gl_pl
'366': task1245_ted_translation_gl_fa
'367': task1246_ted_translation_gl_pt
'368': task1247_ted_translation_it_en
'369': task1248_ted_translation_it_ja
'370': task1249_ted_translation_it_es
'371': task124_conala_pair_averages
'372': task1250_ted_translation_it_ar
'373': task1251_ted_translation_it_he
'374': task1252_ted_translation_it_gl
'375': task1253_ted_translation_it_pl
'376': task1254_ted_translation_it_fa
'377': task1255_ted_translation_it_pt
'378': task1256_ted_translation_pl_en
'379': task1257_ted_translation_pl_ja
'380': task1258_ted_translation_pl_es
'381': task1259_ted_translation_pl_ar
'382': task125_conala_pair_differences
'383': task1260_ted_translation_pl_he
'384': task1261_ted_translation_pl_gl
'385': task1262_ted_translation_pl_it
'386': task1263_ted_translation_pl_fa
'387': task1264_ted_translation_pl_pt
'388': task1265_ted_translation_fa_en
'389': task1266_ted_translation_fa_ja
'390': task1267_ted_translation_fa_es
'391': task1268_ted_translation_fa_ar
'392': task1269_ted_translation_fa_he
'393': task126_scan_structured_text_generation_command_action_all
'394': task1270_ted_translation_fa_gl
'395': task1271_ted_translation_fa_it
'396': task1272_ted_translation_fa_pl
'397': task1273_ted_translation_fa_pt
'398': task1274_ted_translation_pt_en
'399': task1275_ted_translation_pt_ja
'400': task1276_ted_translation_pt_es
'401': task1277_ted_translation_pt_ar
'402': task1278_ted_translation_pt_he
'403': task1279_ted_translation_pt_gl
'404': task127_scan_long_text_generation_action_command_all
'405': task1280_ted_translation_pt_it
'406': task1281_ted_translation_pt_pl
'407': task1282_ted_translation_pt_fa
'408': task1283_hrngo_quality_classification
'409': task1284_hrngo_informativeness_classification
'410': task1285_kpa_keypoint_matching
'411': task1286_openbookqa_question_answering
'412': task1287_glue_qqp_paraphrasing
'413': task1288_glue_mrpc_paraphrasing
'414': task1289_trec_classification
'415': task128_scan_structured_text_generation_command_action_short
'416': task1290_xsum_summarization
'417': task1291_multi_news_summarization
'418': task1292_yelp_review_full_text_categorization
'419': task1293_kilt_tasks_hotpotqa_question_answering
'420': task1294_wiki_qa_answer_verification
'421': task1295_adversarial_qa_question_answering
'422': task1296_wiki_hop_question_answering
'423': task1297_qasc_question_answering
'424': task129_scan_long_text_generation_action_command_short
'425': task1308_amazonreview_category_classification
'426': task1309_amazonreview_summary_classification
'427': task130_scan_structured_text_generation_command_action_long
'428': task1310_amazonreview_rating_classification
'429': task1311_amazonreview_rating_classification
'430': task1312_amazonreview_polarity_classification
'431': task1313_amazonreview_polarity_classification
'432': task1314_country_abbreviation
'433': task1315_find_range_array
'434': task1316_remove_duplicates_string
'435': task1317_country_calling_code
'436': task1318_country_national_dish
'437': task1319_country_by_barcode_prefix
'438': task131_scan_long_text_generation_action_command_long
'439': task1320_country_domain_tld
'440': task1321_country_continent
'441': task1322_country_government_type
'442': task1323_open_subtitles_hi_en_translation
'443': task1324_open_subtitles_te_en_translation
'444': task1325_qa_zre_question_generation_on_subject_relation
'445': task1326_qa_zre_question_generation_from_answer
'446': task1327_qa_zre_answer_generation_from_question
'447': task1328_qa_zre_relation_generation_from_question
'448': task1329_open_subtitles_en_hi_translation
'449': task132_dais_text_modification
'450': task1330_open_subtitles_en_te_translation
'451': task1331_reverse_array
'452': task1332_check_leap_year
'453': task1333_check_validity_date_ddmmyyyy
'454': task1334_sqac_answer_generation
'455': task1335_sqac_question_generation
'456': task1336_peixian_equity_evaluation_corpus_gender_classifier
'457': task1338_peixian_equity_evaluation_corpus_sentiment_classifier
'458': task1339_peixian_equity_evaluation_corpus_text_completion
'459': task133_winowhy_reason_plausibility_detection
'460': task1340_msr_text_compression_compression
'461': task1341_msr_text_classification
'462': task1342_amazon_us_reviews_title
'463': task1343_amazon_us_reviews_rating
'464': task1344_glue_entailment_classification
'465': task1345_glue_qqp_question_paraprashing
'466': task1346_glue_cola_grammatical_correctness_classification
'467': task1347_glue_sts-b_similarity_classification
'468': task134_winowhy_reason_generation
'469': task1350_opus100_translation_en_gu
'470': task1351_opus100_translation_gu_en
'471': task1352_hind_encorp_translation_hi_en
'472': task1353_hind_encorp_translation_en_hi
'473': task1354_sent_comp_classification
'474': task1355_sent_comp_summarization
'475': task1356_xlsum_title_generation
'476': task1357_xlsum_summary_generation
'477': task1358_xlsum_title_generation
'478': task1359_numer_sense_answer_generation
'479': task135_winowhy_wrong_reason_generation
'480': task1360_numer_sense_multiple_choice_qa_generation
'481': task1361_movierationales_classification
'482': task1364_hans_answer_generation
'483': task1365_opustedtalks_translation
'484': task1366_healthfact_classification
'485': task1367_opustedtalks_translation
'486': task1368_healthfact_sentence_generation
'487': task1369_healthfact_sentence_generation
'488': task136_winowhy_knowledge_categorization
'489': task1370_newscomm_classification
'490': task1371_newscomm_translation
'491': task1373_newscomm_translation
'492': task1374_newscomm_translation
'493': task1375_newscomm_translation
'494': task1376_newscomm_translation
'495': task1377_newscomm_translation
'496': task1378_quarel_correct_answer_generation
'497': task1379_quarel_incorrect_answer_generation
'498': task137_detoxifying-lms_classification_toxicity
'499': task1380_quarel_correct_option_generation
'500': task1381_quarel_incorrect_option_generation
'501': task1382_quarel_write_correct_answer
'502': task1383_quarel_write_incorrect_answer
'503': task1384_deal_or_no_dialog_classification
'504': task1385_anli_r1_entailment
'505': task1386_anli_r2_entailment
'506': task1387_anli_r3_entailment
'507': task1388_cb_entailment
'508': task1389_hellaswag_completion
'509': task138_detoxifying-lms_classification_fluency
'510': task1390_wscfixed_coreference
'511': task1391_winogrande_easy_answer_generation
'512': task1392_superglue_multirc_answer_verification
'513': task1393_superglue_copa_text_completion
'514': task1394_meta_woz_task_classification
'515': task1395_europa_ecdc_tm_en_sv_translation
'516': task1396_europa_ecdc_tm_en_de_translation
'517': task1397_europa_ecdc_tm_fr_en_translation
'518': task1398_obqa_question_generation
'519': task1399_obqa_answer_generation
'520': task139_detoxifying-lms_classification_topicality
'521': task1400_obqa_incorrect_answer_generation
'522': task1401_obqa_sentence_generation
'523': task1402_clue_question_generation
'524': task1403_check_validity_date_mmddyyyy
'525': task1404_date_conversion
'526': task1405_find_median
'527': task1406_kth_smallest_element
'528': task1407_dart_question_generation
'529': task1408_dart_similarity_classification
'530': task1409_dart_text_generation
'531': task140_detoxifying-lms_classification_style
'532': task1410_dart_relationship_extraction
'533': task1411_dart_subject_identification
'534': task1412_web_questions_question_answering
'535': task1413_dart_object_identification
'536': task1414_ajgt_twitter_ar_classification
'537': task1415_youtube_caption_corrections_grammar_correction
'538': task1416_youtube_caption_corrections_incorrect_grammar_classification
'539': task1418_bless_semantic_relation_classification
'540': task1419_mathqa_gain
'541': task141_odd-man-out_classification_category
'542': task1420_mathqa_general
'543': task1421_mathqa_other
'544': task1422_mathqa_physics
'545': task1423_mathqa_geometry
'546': task1424_mathqa_probability
'547': task1425_country_iso_numeric
'548': task1426_country_independence_year
'549': task1427_country_region_in_world
'550': task1428_country_surface_area
'551': task1429_evalution_semantic_relation_classification
'552': task142_odd-man-out_classification_no_category
'553': task1431_head_qa_answer_generation
'554': task1432_head_qa_language_translation_en_to_es
'555': task1433_head_qa_language_translation_es_to_en
'556': task1434_head_qa_classification
'557': task1435_ro_sts_parallel_language_translation_ro_to_en
'558': task1436_ro_sts_parallel_language_translation_en_to_ro
'559': task1437_doqa_cooking_question_generation
'560': task1438_doqa_cooking_answer_generation
'561': task1439_doqa_cooking_isanswerable
'562': task143_odd-man-out_classification_generate_category
'563': task1440_doqa_movies_question_generation
'564': task1441_doqa_movies_answer_generation
'565': task1442_doqa_movies_isanswerable
'566': task1443_string_to_number
'567': task1444_round_power_of_two
'568': task1445_closest_integers
'569': task1446_farthest_integers
'570': task1447_drug_extraction_ade
'571': task1448_disease_entity_extraction_ncbi_dataset
'572': task1449_disease_entity_extraction_bc5cdr_dataset
'573': task144_subjqa_question_answering
'574': task1451_drug_dose_extraction
'575': task1452_location_entity_extraction_btc_corpus
'576': task1453_person_entity_extraction_btc_corpus
'577': task145_afs_argument_similarity_death_penalty
'578': task146_afs_argument_similarity_gun_control
'579': task1479_organization_entity_extraction_btc_corpus
'580': task147_afs_argument_similarity_gay_marriage
'581': task1480_gene_extraction_jnlpba_dataset
'582': task1481_gene_extraction_bc2gm_dataset
'583': task1482_gene_extraction_chemprot_dataset
'584': task1483_chemical_extraction_chemprot_dataset
'585': task1484_gene_extraction_linnaeus_dataset
'586': task1485_organ_extraction_anem_dataset
'587': task1486_cell_extraction_anem_dataset
'588': task1487_organism_substance_extraction_anem_dataset
'589': task1488_sarcasmdetection_headline_classification
'590': task1489_sarcasmdetection_tweet_classification
'591': task148_afs_argument_quality_gay_marriage
'592': task1490_bengali_personal_hate_speech_binary_classification
'593': task1491_bengali_political_hate_speech_binary_classification
'594': task1492_bengali_religious_hate_speech_binary_classification
'595': task1493_bengali_geopolitical_hate_speech_binary_classification
'596': task1494_bengali_hate_speech_classification
'597': task1495_adverse_drug_event_classification
'598': task1496_bengali_reviews_sentiment_classification
'599': task1497_bengali_book_reviews_sentiment_classification
'600': task1498_24hour_to_12hour_clock
'601': task1499_dstc3_summarization
'602': task149_afs_argument_quality_death_penalty
'603': task1500_dstc3_classification
'604': task1501_dstc3_answer_generation
'605': task1502_hatexplain_classification
'606': task1503_hatexplain_classification
'607': task1504_hatexplain_answer_generation
'608': task1505_root09_semantic_relation_classification
'609': task1506_celebrity_minimal_dob_span
'610': task1507_boolean_temporal_reasoning
'611': task1508_wordnet_antonyms
'612': task1509_evalution_antonyms
'613': task150_afs_argument_quality_gun_control
'614': task1510_evalution_relation_extraction
'615': task1514_flores_translation_entone
'616': task1515_imppres_longtextgeneration
'617': task1516_imppres_naturallanguageinference
'618': task1517_limit_classfication
'619': task1518_limit_answer_generation
'620': task1519_qa_srl_question_generation
'621': task151_tomqa_find_location_easy_clean
'622': task1520_qa_srl_answer_generation
'623': task1529_scitail1.1_classification
'624': task152_tomqa_find_location_easy_noise
'625': task1530_scitail1.1_sentence_generation
'626': task1531_daily_dialog_type_classification
'627': task1532_daily_dialog_emotion_classification
'628': task1533_daily_dialog_formal_classification
'629': task1534_daily_dialog_question_classification
'630': task1535_daily_dialog_uniqueness_classification
'631': task1536_daily_dialog_happiness_classification
'632': task1537_tamil_offenseval_dravidian_classification
'633': task1538_malayalam_offenseval_dravidian_classification
'634': task1539_kannada_offenseval_dravidian_classification
'635': task153_tomqa_find_location_hard_clean
'636': task1540_parsed_pdfs_summarization
'637': task1541_agnews_classification
'638': task1542_every_ith_element_from_starting
'639': task1543_conll2002_parts_of_speech_tagging_answer_generation
'640': task1544_conll2002_named_entity_recognition_answer_generation
'641': task1545_conll2002_person_name_extraction_answer_generation
'642': task1546_conll2002_location_name_extraction_answer_generation
'643': task1548_wiqa_binary_classification
'644': task1549_wiqa_answer_generation_missing_step
'645': task154_tomqa_find_location_hard_noise
'646': task1551_every_ith_element_from_kth_element
'647': task1552_scitail_question_generation
'648': task1553_cnn_dailymail_summarization
'649': task1554_scitail_classification
'650': task1555_scitail_answer_generation
'651': task1556_scitail_passage_generation
'652': task1557_jfleg_answer_generation
'653': task1558_jfleg_incorrect_answer_generation
'654': task1559_blimp_binary_classification
'655': task155_count_nouns_verbs
'656': task1560_blimp_binary_classification
'657': task1561_clickbait_new_bg_summarization
'658': task1562_zest_text_modification
'659': task1564_triviaqa_answer_generation
'660': task1565_triviaqa_classification
'661': task1566_propara_structured_text_generation
'662': task1567_propara_question_generation
'663': task1568_propara_classification
'664': task1569_cmrc2018_question_generation
'665': task156_codah_classification_adversarial
'666': task1570_cmrc2018_answer_generation
'667': task1571_cmrc2018_answer_generation_starting_index
'668': task1572_samsum_summary
'669': task1573_samsum_classification
'670': task1574_amazon_reviews_multi_language_identification
'671': task1575_amazon_reviews_multi_sentiment_classification
'672': task1576_amazon_reviews_multi_english_language_classification
'673': task1577_amazon_reviews_multi_japanese_language_classification
'674': task1579_gigaword_incorrect_summarization
'675': task157_count_vowels_and_consonants
'676': task1580_eqasc-perturbed_question_generation
'677': task1581_eqasc-perturbed_answer_generation
'678': task1582_bless_hypernym_generation
'679': task1583_bless_meronym_classification
'680': task1584_evalution_meronym_classification
'681': task1585_root09_hypernym_generation
'682': task1586_scifact_title_generation
'683': task1587_scifact_classification
'684': task1588_tecla_classification
'685': task1589_scifact_classification
'686': task158_count_frequency_of_words
'687': task1590_diplomacy_text_generation
'688': task1591_allocine_classification
'689': task1592_yahoo_answers_topics_classfication
'690': task1593_yahoo_answers_topics_classification
'691': task1594_yahoo_answers_topics_question_generation
'692': task1595_event2mind_text_generation_1
'693': task1596_event2mind_text_generation_2
'694': task1597_nyc_slot_filling
'695': task1598_nyc_long_text_generation
'696': task1599_smcalflow_classification
'697': task159_check_frequency_of_words_in_sentence_pair
'698': task1600_smcalflow_sentence_generation
'699': task1601_webquestions_answer_generation
'700': task1602_webquestion_question_genreation
'701': task1603_smcalflow_sentence_generation
'702': task1604_ethos_text_classification
'703': task1605_ethos_text_classification
'704': task1606_ethos_text_classification
'705': task1607_ethos_text_classification
'706': task1608_xquad_en_answer_generation
'707': task1609_xquad_en_question_generation
'708': task160_replace_letter_in_a_sentence
'709': task1610_xquad_es_answer_generation
'710': task1611_xquad_es_question_generation
'711': task1612_sick_label_classification
'712': task1613_sick_given_category_generate_sentence
'713': task1614_sick_text_modify
'714': task1615_sick_tclassify_b_relation_a
'715': task1616_cc_alligned_translate_eng_tel
'716': task1617_cc_alligned_translate_tel_eng
'717': task1618_cc_alligned_classify_tel_eng
'718': task1619_menyo20k-mt_en_yo_translation
'719': task161_count_words_containing_letter
'720': task1620_menyo20k-mt_yo_en_translation
'721': task1621_menyo20k-mt_en_yo_language_identification
'722': task1622_disfl_qa_text_modication
'723': task1623_disfl_qa_disfluent_question_classification
'724': task1624_disfl_qa_question_yesno_classification
'725': task1625_disfl_qa_asnwer_generation
'726': task1626_copa_hr_question_answering
'727': task1627_copa_hr_classification
'728': task1628_copa_hr_question_answering
'729': task1629_copa_hr_classification
'730': task162_count_words_starting_with_letter
'731': task1630_openpi_classification
'732': task1631_openpi_answer_generation
'733': task1637_doqa2.1_cooking_text_summarization
'734': task1638_doqa2.1_movies_text_summarization
'735': task1639_doqa2.1_travel_text_summarization
'736': task163_count_words_ending_with_letter
'737': task1640_aqa1.0_answerable_unanswerable_question_classification
'738': task1645_medical_question_pair_dataset_text_classification
'739': task1646_dataset_card_for_catalonia_independence_corpus_text_classification
'740': task1647_opus_books_en-pt_translation
'741': task1648_opus_books_en-sv_translation
'742': task1649_opus_books_en-no_translation
'743': task164_mcscript_question_answering_text
'744': task1650_opus_books_en-fi_translation
'745': task1651_opus_books_en-es__translation
'746': task1652_opus_books_ca-en_translation
'747': task1654_mkb_translation
'748': task1655_mkb_translation
'749': task1656_gooaq_answer_generation
'750': task1657_gooaq_question_generation
'751': task1658_billsum_summarization
'752': task1659_title_generation
'753': task165_mcscript_question_answering_commonsense
'754': task1660_super_glue_question_generation
'755': task1661_super_glue_classification
'756': task1662_cedr_ru_classification
'757': task1663_cedr_ru_incorrect_classification
'758': task1664_winobias_text_generation
'759': task1665_trainglecopa_question_generation
'760': task1666_cail2018_answer_generation
'761': task1667_cail2018_answer_generation
'762': task1669_md_gender_bias_text_modification
'763': task166_clariq_sentence_generation
'764': task1670_md_gender_bias_text_modification
'765': task1676_xquad-ca_translation
'766': task1677_xquad-ca_translation
'767': task1678_mathqa_answer_selection
'768': task167_strategyqa_question_generation
'769': task1685_menyo20k_translation
'770': task1686_menyo20k_translation
'771': task1689_qed_amara_translation
'772': task168_strategyqa_question_decomposition
'773': task1690_qed_amara_translation
'774': task1691_qed_amara_translation
'775': task1692_qed_amara_translation
'776': task169_strategyqa_sentence_generation
'777': task1703_ljspeech_textmodification
'778': task1704_ljspeech_textmodification
'779': task1705_ljspeech_classification
'780': task1706_ljspeech_classification
'781': task170_hotpotqa_answer_generation
'782': task1711_poki_text_generation
'783': task1712_poki_classification
'784': task1713_convai3_sentence_generation
'785': task1714_convai3_sentence_generation
'786': task171_spl_translation_en_es
'787': task1720_civil_comments_toxicity_classification
'788': task1721_civil_comments_obscenity_classification
'789': task1722_civil_comments_threat_classification
'790': task1723_civil_comments_sexuallyexplicit_classification
'791': task1724_civil_comments_insult_classification
'792': task1725_civil_comments_severtoxicity_classification
'793': task1726_mathqa_correct_answer_generation
'794': task1727_wiqa_what_is_the_effect
'795': task1728_web_nlg_data_to_text
'796': task1729_personachat_generate_next
'797': task172_spl_translation_en_fa
'798': task1730_personachat_choose_next
'799': task1731_quartz_question_answering
'800': task173_spl_translation_en_it
'801': task174_spl_translation_en_ja
'802': task175_spl_translation_en_pl
'803': task176_break_decompose_questions
'804': task177_para-nmt_paraphrasing
'805': task178_quartz_question_answering
'806': task179_participant_extraction
'807': task180_intervention_extraction
'808': task181_outcome_extraction
'809': task182_duorc_question_generation
'810': task183_rhyme_generation
'811': task184_break_generate_question
'812': task184_snli_entailment_to_neutral_text_modification
'813': task185_snli_contradiction_to_neutral_text_modification
'814': task186_snli_contradiction_to_entailment_text_modification
'815': task187_snli_entailment_to_contradiction_text_modification
'816': task188_snli_neutral_to_entailment_text_modification
'817': task189_snli_neutral_to_contradiction_text_modification
'818': task190_snli_classification
'819': task191_hotpotqa_question_generation
'820': task192_hotpotqa_sentence_generation
'821': task193_duorc_question_generation
'822': task194_duorc_answer_generation
'823': task195_sentiment140_classification
'824': task196_sentiment140_answer_generation
'825': task197_mnli_domain_answer_generation
'826': task198_mnli_domain_classification
'827': task199_mnli_classification
'828': task200_mnli_entailment_classification
'829': task201_mnli_neutral_classification
'830': task202_mnli_contradiction_classification
'831': task203_mnli_sentence_generation
'832': task204_mnli_same_genre_classification
'833': task205_remove_even_elements
'834': task206_collatz_conjecture
'835': task207_max_element_lists
'836': task208_combinations_of_list
'837': task209_stancedetection_classification
'838': task210_logic2text_structured_text_generation
'839': task211_logic2text_classification
'840': task212_logic2text_classification
'841': task213_rocstories_correct_ending_classification
'842': task214_rocstories_incorrect_ending_classification
'843': task215_rocstories_incorrect_answer_generation
'844': task216_rocstories_correct_answer_generation
'845': task217_rocstories_ordering_answer_generation
'846': task218_rocstories_swap_order_answer_generation
'847': task219_rocstories_title_answer_generation
'848': task220_rocstories_title_classification
'849': task221_rocstories_two_choice_classification
'850': task222_rocstories_two_chioce_slotting_classification
'851': task223_quartz_explanation_generation
'852': task224_scruples_anecdotes_ethical_judgment
'853': task225_english_language_answer_generation
'854': task226_english_language_answer_relevance_classification
'855': task227_clariq_classification
'856': task228_arc_answer_generation_easy
'857': task229_arc_answer_generation_hard
'858': task230_iirc_passage_classification
'859': task231_iirc_link_classification
'860': task232_iirc_link_number_classification
'861': task233_iirc_link_exists_classification
'862': task234_iirc_passage_line_answer_generation
'863': task235_iirc_question_from_subtext_answer_generation
'864': task236_iirc_question_from_passage_answer_generation
'865': task237_iirc_answer_from_subtext_answer_generation
'866': task238_iirc_answer_from_passage_answer_generation
'867': task239_tweetqa_answer_generation
'868': task240_tweetqa_question_generation
'869': task241_tweetqa_classification
'870': task242_tweetqa_classification
'871': task243_count_elements_in_set_intersection
'872': task244_count_elements_in_set_union
'873': task245_check_presence_in_set_intersection
'874': task246_dream_question_generation
'875': task247_dream_answer_generation
'876': task248_dream_classification
'877': task249_enhanced_wsc_pronoun_disambiguation
'878': task250_spl_translation_en_ar
'879': task251_spl_translation_en_fi
'880': task252_spl_translation_en_tr
'881': task253_spl_translation_en_zh
'882': task254_spl_translation_fi_en
'883': task255_spl_translation_it_en
'884': task256_spl_translation_de_en
'885': task257_spl_translation_ar_en
'886': task258_spl_translation_fa_en
'887': task259_spl_translation_tr_en
'888': task260_spl_translation_zh_en
'889': task261_spl_translation_es_en
'890': task262_spl_translation_ja_en
'891': task263_spl_translation_pl_en
'892': task264_paper_reviews_accept_or_reject_classification
'893': task265_paper_reviews_language_identification
'894': task266_paper_reviews_reviewer_perspective_classification
'895': task267_concatenate_and_reverse_all_elements_from_index_i_to_j
'896': task268_casehold_legal_answer_generation
'897': task269_csrg_counterfactual_story_generation
'898': task270_csrg_counterfactual_context_generation
'899': task271_europarl_translation
'900': task272_europarl_translation
'901': task273_europarl_classification
'902': task274_overruling_legal_classification
'903': task275_enhanced_wsc_paraphrase_generation
'904': task276_enhanced_wsc_classification
'905': task277_stereoset_sentence_generation_stereotype
'906': task278_stereoset_sentence_generation_antistereotype
'907': task279_stereoset_classification_stereotype
'908': task280_stereoset_classification_stereotype_type
'909': task281_points_of_correspondence
'910': task282_scruples_event_time
'911': task283_dream_incorrect_answer_generation
'912': task284_imdb_classification
'913': task285_imdb_answer_generation
'914': task286_olid_offense_judgment
'915': task287_casehold_legal_incorrect_answer_generation
'916': task288_gigaword_summarization
'917': task289_gigaword_summarization
'918': task290_tellmewhy_question_answerability
'919': task291_semeval_2020_task4_commonsense_validation
'920': task292_storycommonsense_character_text_generation
'921': task293_storycommonsense_emotion_text_generation
'922': task294_storycommonsense_motiv_text_generation
'923': task295_semeval_2020_task4_commonsense_reasoning
'924': task296_storycloze_correct_end_classification
'925': task297_storycloze_incorrect_end_classification
'926': task298_storycloze_correct_end_classification
'927': task299_storycloze_sentence_generation
'928': task300_storycloze_order_generation
'929': task301_record_question_generation
'930': task302_record_classification
'931': task303_record_incorrect_answer_generation
'932': task304_numeric_fused_head_resolution
'933': task305_jeopardy_answer_generation_normal
'934': task306_jeopardy_answer_generation_double
'935': task307_jeopardy_answer_generation_final
'936': task308_jeopardy_answer_generation_all
'937': task309_race_answer_generation
'938': task310_race_classification
'939': task311_race_question_generation
'940': task312_europarl_sv_en_translation
'941': task313_europarl_en_sv_translation
'942': task314_europarl_sv-en_classification
'943': task315_europarl_sv-en_language_identification
'944': task316_crows-pairs_classification_stereotype
'945': task317_crows-pairs_classification_stereotype_type
'946': task318_stereoset_classification_gender
'947': task319_stereoset_classification_profession
'948': task320_stereoset_classification_race
'949': task321_stereoset_classification_religion
'950': task322_jigsaw_classification_threat
'951': task323_jigsaw_classification_sexually_explicit
'952': task324_jigsaw_classification_disagree
'953': task325_jigsaw_classification_identity_attack
'954': task326_jigsaw_classification_obscene
'955': task327_jigsaw_classification_toxic
'956': task328_jigsaw_classification_insult
'957': task329_gap_classification
'958': task330_gap_answer_generation
'959': task331_gap_incorrect_answer_generation
'960': task332_tellmewhy_answer_generation
'961': task333_hateeval_classification_hate_en
'962': task334_hateeval_classification_hate_es
'963': task335_hateeval_classification_aggresive_en
'964': task336_hateeval_classification_aggresive_es
'965': task337_hateeval_classification_individual_en
'966': task338_hateeval_classification_individual_es
'967': task339_record_answer_generation
'968': task340_winomt_classification_gender_pro
'969': task341_winomt_classification_gender_anti
'970': task342_winomt_classification_profession_pro
'971': task343_winomt_classification_profession_anti
'972': task344_hybridqa_answer_generation
'973': task345_hybridqa_answer_generation
'974': task346_hybridqa_classification
'975': task347_hybridqa_incorrect_answer_generation
'976': task348_squad2.0_unanswerable_question_generation
'977': task349_squad2.0_answerable_unanswerable_question_classification
'978': task350_winomt_classification_gender_identifiability_pro
'979': task351_winomt_classification_gender_identifiability_anti
'980': task352_coda-19_classification
'981': task353_casino_classification_negotiation_elicit_pref
'982': task354_casino_classification_negotiation_no_need
'983': task355_casino_classification_negotiation_other_need
'984': task356_casino_classification_negotiation_self_need
'985': task357_casino_classification_negotiation_small_talk
'986': task358_casino_classification_negotiation_uv_part
'987': task359_casino_classification_negotiation_vouch_fair
'988': task360_spolin_yesand_response_generation
'989': task361_spolin_yesand_prompt_response_classification
'990': task362_spolin_yesand_prompt_response_sub_classification
'991': task363_sst2_polarity_classification
'992': task364_regard_social_impact_classification
'993': task365_synthetic_remove_vowels
'994': task366_synthetic_return_primes
'995': task367_synthetic_remove_floats
'996': task368_synthetic_even_or_odd_calculation
'997': task369_synthetic_remove_odds
'998': task370_synthetic_remove_divisible_by_3
'999': task371_synthetic_product_of_list
'1000': task372_synthetic_palindrome_numbers
'1001': task373_synthetic_round_tens_place
'1002': task374_synthetic_pos_or_neg_calculation
'1003': task375_classify_type_of_sentence_in_debate
'1004': task376_reverse_order_of_words
'1005': task377_remove_words_of_given_length
'1006': task378_reverse_words_of_given_length
'1007': task379_agnews_topic_classification
'1008': task380_boolq_yes_no_question
'1009': task381_boolq_question_generation
'1010': task382_hybridqa_answer_generation
'1011': task383_matres_classification
'1012': task384_socialiqa_question_classification
'1013': task385_socialiqa_incorrect_answer_generation
'1014': task386_semeval_2018_task3_irony_detection
'1015': task387_semeval_2018_task3_irony_classification
'1016': task388_torque_token_classification
'1017': task389_torque_generate_temporal_question
'1018': task390_torque_text_span_selection
'1019': task391_causal_relationship
'1020': task392_inverse_causal_relationship
'1021': task393_plausible_result_generation
'1022': task394_persianqa_question_generation
'1023': task395_persianqa_answer_generation
'1024': task396_persianqa_classification
'1025': task397_semeval_2018_task1_tweet_anger_detection
'1026': task398_semeval_2018_task1_tweet_joy_detection
'1027': task399_semeval_2018_task1_tweet_sadness_detection
'1028': task400_paws_paraphrase_classification
'1029': task401_numeric_fused_head_reference
'1030': task402_grailqa_paraphrase_generation
'1031': task403_creak_commonsense_inference
'1032': task404_grailqa_paraphrase_validation
'1033': task405_narrativeqa_question_generation
'1034': task406_mickey_fr_sentence_perturbation_generation
'1035': task407_mickey_hi_sentence_perturbation_generation
'1036': task408_mickey_it_sentence_perturbation_generation
'1037': task409_mickey_nl_sentence_perturbation_generation
'1038': task410_mickey_ru_sentence_perturbation_generation
'1039': task411_mickey_vi_sentence_perturbation_generation
'1040': task412_mickey_zh_sentence_perturbation_generation
'1041': task413_mickey_en_sentence_perturbation_generation
'1042': task414_mickey_ar_sentence_perturbation_generation
'1043': task415_mickey_bg_sentence_perturbation_generation
'1044': task416_mickey_de_sentence_perturbation_generation
'1045': task417_mickey_es_sentence_perturbation_generation
'1046': task418_persent_title_generation
'1047': task419_persent_answer_generation
'1048': task420_persent_document_sentiment_classification
'1049': task421_persent_sentence_sentiment_classification
'1050': task422_persent_sentence_sentiment_verification
'1051': task423_persent_document_sentiment_verification
'1052': task424_hindienglish_corpora_hi_en_translation
'1053': task425_hindienglish_corpora_en_hi_translation
'1054': task426_hindienglish_corpora_hi-en_classification
'1055': task427_hindienglish_corpora_hi-en_language_identification
'1056': task428_senteval_inversion
'1057': task429_senteval_tense
'1058': task430_senteval_subject_count
'1059': task431_senteval_object_count
'1060': task432_alt_en_hi_translation
'1061': task433_alt_hi_en_translation
'1062': task434_alt_en_hi_answer_generation
'1063': task435_alt_en_ja_translation
'1064': task436_alt_ja_en_translation
'1065': task437_alt_en_ja_answer_generation
'1066': task438_eng_guj_parallel_corpus_en_gu_translation
'1067': task439_eng_guj_parallel_corpus_gu_en_translation
'1068': task440_eng_guj_parallel_corpus_gu-en_classification
'1069': task441_eng_guj_parallel_corpus_gu-en_language_identification
'1070': task442_com_qa_paraphrase_question_generation
'1071': task443_com_qa_ans_question_generation
'1072': task444_com_qa_question_paraphrases_answer_generation
'1073': task446_opus_paracrawl_en_so_translation
'1074': task447_opus_paracrawl_classification
'1075': task448_opus_paracrawl_en_tl_translation
'1076': task449_opus_paracrawl_ig_en_translation
'1077': task450_opus_paracrawl_so_en_translation
'1078': task451_opus_paracrawl_tl_en_translation
'1079': task452_opus_paracrawl_en_ig_translation
'1080': task453_swag_answer_generation
'1081': task454_swag_incorrect_answer_generation
'1082': task455_swag_context_generation
'1083': task456_matres_intention_classification
'1084': task457_matres_conditional_classification
'1085': task458_matres_negation_classification
'1086': task459_matres_static_classification
'1087': task460_qasper_answer_generation
'1088': task461_qasper_question_generation
'1089': task462_qasper_classification
'1090': task463_parsinlu_entailment_classification
'1091': task464_parsinlu_entailment_sentence_generation
'1092': task465_parsinlu_qqp_classification
'1093': task466_parsinlu_qqp_text_modification
'1094': task467_parsinlu_rc_answer_generation
'1095': task468_parsinlu_rc_question_generation
'1096': task469_mrqa_answer_generation
'1097': task470_mrqa_question_generation
'1098': task471_haspart_answer_generation
'1099': task472_haspart_classification
'1100': task473_parsinlu_mc_classification
'1101': task474_parsinlu_mc_classification
'1102': task475_yelp_polarity_classification
'1103': task476_cls_english_books_classification
'1104': task477_cls_english_dvd_classification
'1105': task478_cls_english_music_classification
'1106': task479_cls_german_books_classification
'1107': task480_cls_german_dvd_classification
'1108': task481_cls_german_music_classification
'1109': task482_cls_french_books_classification
'1110': task483_cls_french_dvd_classification
'1111': task484_cls_french_music_classification
'1112': task485_cls_japanese_books_classification
'1113': task486_cls_japanese_dvd_classification
'1114': task487_cls_japanese_music_classification
'1115': task488_extract_all_alphabetical_elements_from_list_in_order
'1116': task489_mwsc_question_generation
'1117': task490_mwsc_options_generation
'1118': task491_mwsc_answer_generation
'1119': task492_mwsc_incorrect_answer_generation
'1120': task493_review_polarity_classification
'1121': task494_review_polarity_answer_generation
'1122': task495_semeval_headline_classification
'1123': task496_semeval_answer_generation
'1124': task497_extract_all_numbers_from_list_in_order
'1125': task498_scruples_anecdotes_whoiswrong_classification
'1126': task499_extract_and_add_all_numbers_from_list
'1127': task500_scruples_anecdotes_title_generation
'1128': task501_scruples_anecdotes_post_type_verification
'1129': task502_scruples_anecdotes_whoiswrong_verification
'1130': task503_scruples_anecdotes_isanswerable
'1131': task504_count_all_alphabetical_elements_in_list
'1132': task505_count_all_numerical_elements_in_list
'1133': task506_position_of_all_alphabetical_elements_in_list
'1134': task507_position_of_all_numerical_elements_in_list
'1135': task508_scruples_dilemmas_more_ethical_isidentifiable
'1136': task509_collate_of_all_alphabetical_and_numerical_elements_in_list_separately
'1137': task510_reddit_tifu_title_summarization
'1138': task511_reddit_tifu_long_text_summarization
'1139': task512_twitter_emotion_classification
'1140': task513_argument_stance_classification
'1141': task514_argument_consequence_classification
'1142': task515_senteval_odd_word_out
'1143': task516_senteval_conjoints_inversion
'1144': task517_emo_classify_emotion_of_dialogue
'1145': task518_emo_different_dialogue_emotions
'1146': task519_aquamuse_question_generation
'1147': task520_aquamuse_answer_given_in_passage
'1148': task521_trivia_question_classification
'1149': task522_news_editorial_summary
'1150': task523_find_if_numbers_or_alphabets_are_more_in_list
'1151': task524_parsinlu_food_aspect_classification
'1152': task525_parsinlu_movie_aspect_classification
'1153': task526_parsinlu_movie_overal_classification
'1154': task527_parsinlu_food_overal_classification
'1155': task528_parsinlu_movie_aspect_detection
'1156': task529_parsinlu_food_aspect_detection
'1157': task530_europarl_en_es_translation
'1158': task531_europarl_es_en_translation
'1159': task532_europarl_en-es_classification
'1160': task533_europarl_es-en_language_identification
'1161': task534_farstail_entailment
'1162': task535_alt_translation_ch_en
'1163': task536_alt_translation_vi_en
'1164': task537_alt_translation_th_en
'1165': task538_alt_translation_bu_en
'1166': task539_alt_translation_ma_en
'1167': task540_alt_translation_la_en
'1168': task541_alt_translation_kh_en
'1169': task542_alt_translation_ja_en
'1170': task543_alt_translation_bh_en
'1171': task544_alt_translation_hi_en
'1172': task545_alt_translation_fi_en
'1173': task546_alt_translation_bg_en
'1174': task547_alt_translation_entk_en
'1175': task548_alt_translation_en_ch
'1176': task549_alt_translation_en_vi
'1177': task550_discofuse_sentence_generation
'1178': task551_alt_translation_en_th
'1179': task552_alt_translation_en_bu
'1180': task553_alt_translation_en_ma
'1181': task554_alt_translation_en_la
'1182': task555_alt_translation_en_kh
'1183': task556_alt_translation_en_ja
'1184': task557_alt_translation_en_ba
'1185': task558_alt_translation_en_hi
'1186': task559_alt_translation_en_fi
'1187': task560_alt_translation_en_entk
'1188': task561_alt_translation_en_bg
'1189': task562_alt_language_identification
'1190': task563_discofuse_answer_generation
'1191': task564_discofuse_classification
'1192': task565_circa_answer_generation
'1193': task566_circa_classification
'1194': task567_circa_text_generation
'1195': task568_circa_question_generation
'1196': task569_recipe_nlg_text_generation
'1197': task570_recipe_nlg_ner_generation
'1198': task571_recipe_nlg_ner_generation
'1199': task572_recipe_nlg_text_generation
'1200': task573_air_dialogue_classification
'1201': task574_air_dialogue_sentence_generation
'1202': task575_air_dialogue_classification
'1203': task576_curiosity_dialogs_answer_generation
'1204': task577_curiosity_dialogs_classification
'1205': task578_curiosity_dialogs_answer_generation
'1206': task579_socialiqa_classification
'1207': task580_socialiqa_answer_generation
'1208': task581_socialiqa_question_generation
'1209': task582_naturalquestion_answer_generation
'1210': task583_udeps_eng_coarse_pos_tagging
'1211': task584_udeps_eng_fine_pos_tagging
'1212': task585_preposition_classification
'1213': task586_amazonfood_polarity_classification
'1214': task587_amazonfood_polarity_correction_classification
'1215': task588_amazonfood_rating_classification
'1216': task589_amazonfood_summary_text_generation
'1217': task590_amazonfood_summary_correction_classification
'1218': task591_sciq_answer_generation
'1219': task592_sciq_incorrect_answer_generation
'1220': task593_sciq_explanation_generation
'1221': task594_sciq_question_generation
'1222': task595_mocha_answer_generation
'1223': task596_mocha_question_generation
'1224': task597_cuad_answer_generation
'1225': task598_cuad_answer_generation
'1226': task599_cuad_question_generation
'1227': task600_find_the_longest_common_substring_in_two_strings
'1228': task601_flores_translation_sntoen
'1229': task602_wikitext-103_answer_generation
'1230': task603_wikitext-103_fill_in_the_blank
'1231': task604_flores_translation_entosn
'1232': task605_find_the_longest_common_subsequence_in_two_lists
'1233': task606_sum_of_all_numbers_in_list_between_positions_i_and_j
'1234': task607_sbic_intentional_offense_binary_classification
'1235': task608_sbic_sexual_offense_binary_classification
'1236': task609_sbic_potentially_offense_binary_classification
'1237': task610_conllpp_ner
'1238': task611_mutual_multi_turn_dialogue
'1239': task612_yorubabbc_classification
'1240': task613_politifact_text_generation
'1241': task614_glucose_cause_event_detection
'1242': task615_moviesqa_answer_generation
'1243': task616_cola_classification
'1244': task617_amazonreview_category_text_generation
'1245': task618_amazonreview_summary_text_generation
'1246': task619_ohsumed_abstract_title_generation
'1247': task620_ohsumed_medical_subject_headings_answer_generation
'1248': task621_ohsumed_yes_no_numerical_answer_generation
'1249': task622_replace_alphabets_in_a_list_by_their_position_in_english_alphabet
'1250': task623_ohsumed_yes_no_answer_generation
'1251': task624_ohsumed_question_answering
'1252': task625_xlwic_true_or_false_answer_generation
'1253': task626_xlwic_sentence_based_on_given_word_sentence_generation
'1254': task627_xlwic_word_with_same_meaning_sentence_generation
'1255': task628_xlwic_word_with_different_meaning_sentence_generation
'1256': task629_dbpedia_14_classification
'1257': task630_dbpedia_14_classification
'1258': task631_dbpedia_14_incorrect_answer_generation
'1259': task632_dbpedia_14_classification
'1260': task633_dbpedia_14_answer_generation
'1261': task634_allegro_reviews_classification
'1262': task635_allegro_reviews_answer_generation
'1263': task636_extract_and_sort_unique_alphabets_in_a_list
'1264': task637_extract_and_sort_unique_digits_in_a_list
'1265': task638_multi_woz_classification
'1266': task639_multi_woz_user_utterance_generation
'1267': task640_esnli_classification
'1268': task641_esnli_classification
'1269': task642_esnli_classification
'1270': task643_refresd_classification
'1271': task644_refresd_translation
'1272': task645_summarization
'1273': task646_answer_generation
'1274': task647_answer_generation
'1275': task648_answer_generation
'1276': task649_race_blank_question_generation
'1277': task650_opus100_ar_en_translation
'1278': task651_opus100_en_ar_translation
'1279': task652_parsinlu_en_fa_translation
'1280': task653_parsinlu_fa_en_translation
'1281': task654_bible_fa_en_translation
'1282': task655_bible_en_fa_translation
'1283': task656_quran_en_fa_translation
'1284': task657_quran_fa_en_translation
'1285': task658_tep_en_fa_translation
'1286': task659_tep_fa_en_translation
'1287': task660_mizan_fa_en_translation
'1288': task661_mizan_en_fa_translation
'1289': task662_global_voices_fa_en_translation
'1290': task663_global_voices_en_fa_translation
'1291': task668_extreme_abstract_summarization
'1292': task669_ambigqa_answer_generation
'1293': task670_ambigqa_question_generation
'1294': task671_ambigqa_text_generation
'1295': task672_amazon_and_yelp_summarization_dataset_summarization
'1296': task672_nummersense
'1297': task673_google_wellformed_query_classification
'1298': task674_google_wellformed_query_sentence_generation
'1299': task675_google_wellformed_query_sentence_generation
'1300': task676_ollie_relationship_answer_generation
'1301': task677_ollie_sentence_answer_generation
'1302': task678_ollie_actual_relationship_answer_generation
'1303': task679_hope_edi_english_text_classification
'1304': task680_hope_edi_tamil_text_classification
'1305': task681_hope_edi_malayalam_text_classification
'1306': task682_online_privacy_policy_text_classification
'1307': task683_online_privacy_policy_text_purpose_answer_generation
'1308': task684_online_privacy_policy_text_information_type_generation
'1309': task738_perspectrum_classification
'1310': task739_lhoestq_question_generation
'1311': task740_lhoestq_answer_generation_quantity
'1312': task741_lhoestq_answer_generation_place
'1313': task742_lhoestq_answer_generation_frequency
'1314': task743_eurlex_summarization
'1315': task744_eurlex_classification
'1316': task745_ai2_arithmetic_questions_arithmetic
'1317': task746_yelp_restaurant_review_classification
'1318': task747_glucose_cause_emotion_detection
'1319': task748_glucose_reverse_cause_event_detection
'1320': task749_glucose_reverse_cause_emotion_detection
'1321': task750_aqua_multiple_choice_answering
'1322': task751_svamp_subtraction_question_answering
'1323': task752_svamp_multiplication_question_answering
'1324': task753_svamp_addition_question_answering
'1325': task754_svamp_common-division_question_answering
'1326': task755_find_longest_substring_and_replace_its_sorted_lowercase_version_in_both_lists
'1327': task756_find_longert_substring_and_return_all_unique_alphabets_in_it
'1328': task757_msr_sqa_question_generation
'1329': task758_msr_sqa_question_answer_generation
'1330': task759_msr_sqa_incorrect_answer_generation
'1331': task760_msr_sqa_long_text_generation
'1332': task761_app_review_classification
'1333': task762_emea_fr_sk_translation
'1334': task763_emea_es_lt_translation
'1335': task764_emea_bg_el_classification
'1336': task765_emea_bg_el_translation
'1337': task766_craigslist_bargains_classification
'1338': task767_craigslist_bargains_classification
'1339': task768_qed_text_span_selection
'1340': task769_qed_summarization
'1341': task770_pawsx_english_text_modification
'1342': task771_pawsx_korean_text_modification
'1343': task772_pawsx_french_text_modification
'1344': task773_pawsx_spanish_text_modification
'1345': task774_pawsx_german_text_modification
'1346': task775_pawsx_chinese_text_modification
'1347': task776_pawsx_japanese_text_modification
'1348': task777_pawsx_english_korean_translation
'1349': task778_pawsx_english_french_translation
'1350': task779_pawsx_english_spanish_translation
'1351': task780_pawsx_english_german_translation
'1352': task781_pawsx_english_chinese_translation
'1353': task782_pawsx_english_japanese_translation
'1354': task783_pawsx_korean_english_translation
'1355': task784_pawsx_korean_french_translation
'1356': task785_pawsx_korean_spanish_translation
'1357': task786_pawsx_korean_german_translation
'1358': task787_pawsx_korean_chinese_translation
'1359': task788_pawsx_korean_japanese_translation
'1360': task789_pawsx_french_english_translation
'1361': task790_pawsx_french_korean_translation
'1362': task791_pawsx_french_spanish_translation
'1363': task792_pawsx_french_german_translation
'1364': task793_pawsx_french_chinese_translation
'1365': task794_pawsx_french_japanese_translation
'1366': task795_pawsx_spanish_english_translation
'1367': task796_pawsx_spanish_korean_translation
'1368': task797_pawsx_spanish_french_translation
'1369': task798_pawsx_spanish_german_translation
'1370': task799_pawsx_spanish_chinese_translation
'1371': task800_pawsx_spanish_japanese_translation
'1372': task801_pawsx_german_english_translation
'1373': task802_pawsx_german_korean_translation
'1374': task803_pawsx_german_french_translation
'1375': task804_pawsx_german_spanish_translation
'1376': task805_pawsx_german_chinese_translation
'1377': task806_pawsx_german_japanese_translation
'1378': task807_pawsx_chinese_english_translation
'1379': task808_pawsx_chinese_korean_translation
'1380': task809_pawsx_chinese_french_translation
'1381': task810_pawsx_chinese_spanish_translation
'1382': task811_pawsx_chinese_german_translation
'1383': task812_pawsx_chinese_japanese_translation
'1384': task813_pawsx_japanese_english_translation
'1385': task814_pawsx_japanese_korean_translation
'1386': task815_pawsx_japanese_french_translation
'1387': task816_pawsx_japanese_spanish_translation
'1388': task817_pawsx_japanese_german_translation
'1389': task818_pawsx_japanese_chinese_translation
'1390': task819_pec_sentiment_classification
'1391': task820_protoqa_answer_generation
'1392': task821_protoqa_question_generation
'1393': task823_peixian-rtgender_sentiment_analysis
'1394': task827_copa_commonsense_reasoning
'1395': task828_copa_commonsense_cause_effect
'1396': task829_giga_fren_translation
'1397': task830_poleval2019_mt_translation
'1398': task831_giga_fren_classification
'1399': task832_poleval2019_mt_classification
'1400': task833_poem_sentiment_classification
'1401': task834_mathdataset_classification
'1402': task835_mathdataset_answer_generation
'1403': task836_viquiquad_question_generation
'1404': task837_viquiquad_answer_generation
'1405': task838_cdt_classification
'1406': task839_cdt_classification
'1407': task840_para_pdt_en_es_translation
'1408': task841_para_pdt_de_en_translation
'1409': task842_para_pdt_cs_en_translation
'1410': task843_financial_phrasebank_classification
'1411': task844_financial_phrasebank_classification
'1412': task845_pubmedqa_question_generation
'1413': task846_pubmedqa_classification
'1414': task847_pubmedqa_question_generation
'1415': task848_pubmedqa_classification
'1416': task849_pubmedqa_answer_generation
'1417': task850_synthetic_longest_palindrome
'1418': task851_synthetic_multiply_evens
'1419': task852_synthetic_multiply_odds
'1420': task853_hippocorpus_long_text_generation
'1421': task854_hippocorpus_classification
'1422': task855_conv_ai_2_classification
'1423': task856_conv_ai_2_classification
'1424': task857_inquisitive_question_generation
'1425': task858_inquisitive_span_detection
'1426': task859_prost_question_generation
'1427': task860_prost_mcq_generation
'1428': task861_asdiv_addsub_question_answering
'1429': task861_prost_mcq_answers_generation
'1430': task862_asdiv_multidiv_question_answering
'1431': task863_asdiv_multiop_question_answering
'1432': task864_asdiv_singleop_question_answering
'1433': task865_mawps_addsub_question_answering
'1434': task866_mawps_multidiv_question_answering
'1435': task867_mawps_multiop_question_answering
'1436': task868_cfq_mcd1_explanation_to_sql
'1437': task868_mawps_singleop_question_answering
'1438': task869_cfq_mcd1_sql_to_explanation
'1439': task870_msmarco_answer_generation
'1440': task871_msmarco_question_generation
'1441': task872_opus_xhosanavy_translation_eng_xhosa
'1442': task873_opus_xhosanavy_translation_xhosa_eng
'1443': task874_opus_xhosanavy_sr
'1444': task875_emotion_classification
'1445': task877_kde4_translation
'1446': task878_kde4_translation
'1447': task879_schema_guided_dstc8_classification
'1448': task880_schema_guided_dstc8_classification
'1449': task881_schema_guided_dstc8_classification
'1450': task886_quail_question_generation
'1451': task887_quail_answer_generation
'1452': task888_reviews_classification
'1453': task889_goemotions_classification
'1454': task890_gcwd_classification
'1455': task891_gap_coreference_resolution
'1456': task892_gap_reverse_coreference_resolution
'1457': task893_gap_fill_the_blank_coreference_resolution
'1458': task896_miam_language_classification
'1459': task897_freebase_qa_topic_question_generation
'1460': task898_freebase_qa_answer_generation
'1461': task899_freebase_qa_topic_generation
'1462': task900_freebase_qa_category_classification
'1463': task901_freebase_qa_category_question_generation
'1464': task902_deceptive_opinion_spam_classification
'1465': task903_deceptive_opinion_spam_classification
'1466': task904_hate_speech_offensive_classification
'1467': task905_hate_speech_offensive_classification
'1468': task906_dialogre_identify_names
'1469': task907_dialogre_identify_relationships
'1470': task908_dialogre_identify_familial_relationships
'1471': task909_dialogre_prevalent_speakers
'1472': task910_bianet_classification
'1473': task911_bianet_translation
'1474': task912_bianet_classification
'1475': task913_bianet_translation
'1476': task914_bianet_translation
'1477': task917_coqa_question_generation
'1478': task918_coqa_answer_generation
'1479': task919_coqa_incorrect_answer_generation
'1480': task921_code_x_glue_information_retreival
'1481': task922_event2mind_word_generation
'1482': task923_event2mind_classifier
'1483': task924_event2mind_word_generation
'1484': task925_coached_conv_pref_classifier
'1485': task926_coached_conv_pref_word_generation
'1486': task927_yelp_negative_to_positive_style_transfer
'1487': task928_yelp_positive_to_negative_style_transfer
'1488': task929_products_reviews_classification
'1489': task930_dailydialog_classification
'1490': task931_dailydialog_classification
'1491': task932_dailydialog_classification
'1492': task933_wiki_auto_style_transfer
'1493': task934_turk_simplification
'1494': task935_defeasible_nli_atomic_classification
'1495': task936_defeasible_nli_snli_classification
'1496': task937_defeasible_nli_social_classification
'1497': task938_copa_hi_commonsense_reasoning
'1498': task939_copa_hi_commonsense_cause_effect
'1499': task940_copa_gu_commonsense_reasoning
'1500': task941_copa_gu_commonsense_cause_effect
'1501': task942_copa_mr_commonsense_reasoning
'1502': task943_copa_mr_commonsense_cause_effect
'1503': task944_wiki_cloze_as_multiple_choice_question_answering
'1504': task945_wiki_cloze_bn_multiple_choice_question_answering
'1505': task946_wiki_cloze_gu_multiple_choice_question_answering
'1506': task947_wiki_cloze_hi_multiple_choice_question_answering
'1507': task948_wiki_cloze_kn_multiple_choice_question_answering
'1508': task949_wiki_cloze_ml_multiple_choice_question_answering
'1509': task950_wiki_cloze_mr_multiple_choice_question_answering
'1510': task951_wiki_cloze_or_multiple_choice_question_answering
'1511': task952_wiki_cloze_pa_multiple_choice_question_answering
'1512': task953_wiki_cloze_ta_multiple_choice_question_answering
'1513': task954_wiki_cloze_te_multiple_choice_question_answering
'1514': task955_wiki_auto_style_transfer
'1515': task956_leetcode_420_strong_password_check
'1516': task957_e2e_nlg_text_generation_generate
'1517': task958_e2e_nlg_text_generation_parse
'1518': task959_e2e_nlg_text_generation_identify
'1519': task960_ancora-ca-ner_named_entity_recognition
'1520': task961_ancora-ca-ner_text_auto_completion
'1521': task962_ancora-ca-ner_missing_word_prediction
'1522': task963_librispeech_asr_next_word_prediction
'1523': task964_librispeech_asr_text_auto_completion
'1524': task965_librispeech_asr_missing_word_prediction
'1525': task966_ruletaker_fact_checking_based_on_given_context
'1526': task967_ruletaker_incorrect_fact_generation_based_on_given_paragraph
'1527': task968_xcopa_commonsense_reasoning_et
'1528': task969_xcopa_commonsense_cause_effect_et
'1529': task970_sherliic_causal_relationship
'1530': task974_prachathai67k_sentiment_classification
'1531': task975_prachathai67k_same_genre_classification
'1532': task976_pib_indian_language_identification
'1533': task977_pib_translation_oriya_urdu
'1534': task978_pib_translation_urdu_oriya
'1535': task979_pib_translation_malayalam_oriya
'1536': task980_pib_translation_oriya_malayalam
'1537': task981_pib_translation_bengali_tamil
'1538': task982_pib_translation_tamil_bengali
'1539': task983_pib_translation_gujarati_marathi
'1540': task984_pib_translation_marathi_gujarati
'1541': task985_pib_translation_hindi_oriya
'1542': task986_pib_translation_oriya_hindi
'1543': task987_pib_translation_english_oriya
'1544': task988_pib_translation_oriya_english
'1545': task989_pib_translation_marathi_urdu
'1546': task990_pib_translation_urdu_marathi
'1547': task991_pib_translation_english_tamil
'1548': task992_pib_translation_tamil_english
'1549': task993_pib_translation_hindi_tamil
'1550': task994_pib_translation_tamil_hindi
'1551': task995_pib_translation_bengali_english
'1552': task996_pib_translation_english_bengali
'1553': task997_pib_translation_bengali_oriya
'1554': task998_pib_translation_oriya_bengali
'1555': task999_pib_translation_malayalam_tamil
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 11747069492.39624
num_examples: 9935372
- name: validation
num_bytes: 118658103.60376057
num_examples: 100358
download_size: 7039758885
dataset_size: 11865727596.0
---
# Dataset Card for "niv2-submix-4096"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kowndinya23/cot-submix-4096 | kowndinya23 | 2023-12-16T11:07:41Z | 6 | 0 | [
"region:us"
] | null | 2023-12-16T11:07:27Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype:
class_label:
names:
'0': cot_creak
'1': cot_creak_ii
'2': cot_ecqa
'3': cot_ecqa_ii
'4': cot_esnli
'5': cot_esnli_ii
'6': cot_gsm8k
'7': cot_gsm8k_ii
'8': cot_qasc
'9': cot_qasc_ii
'10': cot_sensemaking
'11': cot_sensemaking_ii
'12': cot_strategyqa
'13': cot_strategyqa_ii
'14': stream_aqua
'15': stream_aqua_ii
'16': stream_qed
'17': stream_qed_ii
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 205652661.2399643
num_examples: 182005
- name: validation
num_bytes: 2077938.7600356825
num_examples: 1839
download_size: 100330523
dataset_size: 207730600.0
---
# Dataset Card for "cot-submix-4096"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kowndinya23/dialog-submix-4096 | kowndinya23 | 2023-12-16T11:09:22Z | 6 | 0 | [
"region:us"
] | null | 2023-12-16T11:08:33Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype:
class_label:
names:
'0': qrecc
'1': qrecc_ii
'2': wiki_dialog
'3': wiki_dialog_ii
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 1010204960.5976143
num_examples: 548330
- name: validation
num_bytes: 10204667.402385762
num_examples: 5539
download_size: 585112307
dataset_size: 1020409628.0
---
# Dataset Card for "dialog-submix-4096"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-20-sparsity | open-llm-leaderboard | 2023-12-16T13:59:08Z | 6 | 0 | [
"region:us"
] | null | 2023-12-16T13:58:27Z | ---
pretty_name: Evaluation run of wang7776/Llama-2-7b-chat-hf-20-sparsity
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [wang7776/Llama-2-7b-chat-hf-20-sparsity](https://huggingface.co/wang7776/Llama-2-7b-chat-hf-20-sparsity)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-20-sparsity\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-16T13:55:29.096442](https://huggingface.co/datasets/open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-20-sparsity/blob/main/results_2023-12-16T13-55-29.096442.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.47402608498324605,\n\
\ \"acc_stderr\": 0.03438253640771256,\n \"acc_norm\": 0.4787691452187974,\n\
\ \"acc_norm_stderr\": 0.03513920510566655,\n \"mc1\": 0.2974296205630355,\n\
\ \"mc1_stderr\": 0.016002651487361002,\n \"mc2\": 0.45875295870930416,\n\
\ \"mc2_stderr\": 0.01569027399570652\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.014611390804670088,\n \
\ \"acc_norm\": 0.5247440273037542,\n \"acc_norm_stderr\": 0.01459348769493774\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5867357100179247,\n\
\ \"acc_stderr\": 0.004914130855431776,\n \"acc_norm\": 0.7791276638119896,\n\
\ \"acc_norm_stderr\": 0.0041398679751162995\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4148148148148148,\n\
\ \"acc_stderr\": 0.042561937679014075,\n \"acc_norm\": 0.4148148148148148,\n\
\ \"acc_norm_stderr\": 0.042561937679014075\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04068942293855797,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04068942293855797\n },\n\
\ \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n \
\ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5283018867924528,\n \"acc_stderr\": 0.030723535249006107,\n\
\ \"acc_norm\": 0.5283018867924528,\n \"acc_norm_stderr\": 0.030723535249006107\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04181210050035455,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04181210050035455\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n\
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3872832369942196,\n\
\ \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.3872832369942196,\n\
\ \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617747,\n\
\ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617747\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\
acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3872340425531915,\n \"acc_stderr\": 0.03184389265339526,\n\
\ \"acc_norm\": 0.3872340425531915,\n \"acc_norm_stderr\": 0.03184389265339526\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.35964912280701755,\n\
\ \"acc_stderr\": 0.04514496132873634,\n \"acc_norm\": 0.35964912280701755,\n\
\ \"acc_norm_stderr\": 0.04514496132873634\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.04164188720169377,\n\
\ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.04164188720169377\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2751322751322751,\n \"acc_stderr\": 0.023000086859068652,\n \"\
acc_norm\": 0.2751322751322751,\n \"acc_norm_stderr\": 0.023000086859068652\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.25396825396825395,\n\
\ \"acc_stderr\": 0.038932596106046734,\n \"acc_norm\": 0.25396825396825395,\n\
\ \"acc_norm_stderr\": 0.038932596106046734\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5258064516129032,\n\
\ \"acc_stderr\": 0.02840609505765332,\n \"acc_norm\": 0.5258064516129032,\n\
\ \"acc_norm_stderr\": 0.02840609505765332\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3497536945812808,\n \"acc_stderr\": 0.03355400904969566,\n\
\ \"acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.03355400904969566\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\
: 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.5818181818181818,\n \"acc_stderr\": 0.03851716319398395,\n\
\ \"acc_norm\": 0.5818181818181818,\n \"acc_norm_stderr\": 0.03851716319398395\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5909090909090909,\n \"acc_stderr\": 0.03502975799413007,\n \"\
acc_norm\": 0.5909090909090909,\n \"acc_norm_stderr\": 0.03502975799413007\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.6787564766839378,\n \"acc_stderr\": 0.033699508685490674,\n\
\ \"acc_norm\": 0.6787564766839378,\n \"acc_norm_stderr\": 0.033699508685490674\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.39487179487179486,\n \"acc_stderr\": 0.02478431694215637,\n\
\ \"acc_norm\": 0.39487179487179486,\n \"acc_norm_stderr\": 0.02478431694215637\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073835,\n \
\ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073835\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.42016806722689076,\n \"acc_stderr\": 0.03206183783236152,\n\
\ \"acc_norm\": 0.42016806722689076,\n \"acc_norm_stderr\": 0.03206183783236152\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\"\
: 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.6477064220183486,\n\
\ \"acc_stderr\": 0.020480568843998986,\n \"acc_norm\": 0.6477064220183486,\n\
\ \"acc_norm_stderr\": 0.020480568843998986\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.30092592592592593,\n \"acc_stderr\": 0.03128039084329881,\n\
\ \"acc_norm\": 0.30092592592592593,\n \"acc_norm_stderr\": 0.03128039084329881\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.6519607843137255,\n \"acc_stderr\": 0.03343311240488419,\n \"\
acc_norm\": 0.6519607843137255,\n \"acc_norm_stderr\": 0.03343311240488419\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6244725738396625,\n \"acc_stderr\": 0.03152256243091156,\n \
\ \"acc_norm\": 0.6244725738396625,\n \"acc_norm_stderr\": 0.03152256243091156\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5695067264573991,\n\
\ \"acc_stderr\": 0.0332319730294294,\n \"acc_norm\": 0.5695067264573991,\n\
\ \"acc_norm_stderr\": 0.0332319730294294\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5648854961832062,\n \"acc_stderr\": 0.043482080516448585,\n\
\ \"acc_norm\": 0.5648854961832062,\n \"acc_norm_stderr\": 0.043482080516448585\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6115702479338843,\n \"acc_stderr\": 0.04449270350068382,\n \"\
acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.04449270350068382\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5555555555555556,\n\
\ \"acc_stderr\": 0.04803752235190193,\n \"acc_norm\": 0.5555555555555556,\n\
\ \"acc_norm_stderr\": 0.04803752235190193\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.558282208588957,\n \"acc_stderr\": 0.03901591825836184,\n\
\ \"acc_norm\": 0.558282208588957,\n \"acc_norm_stderr\": 0.03901591825836184\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.046897659372781335,\n\
\ \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.046897659372781335\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7136752136752137,\n\
\ \"acc_stderr\": 0.02961432369045665,\n \"acc_norm\": 0.7136752136752137,\n\
\ \"acc_norm_stderr\": 0.02961432369045665\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.01685739124747255,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.01685739124747255\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.49710982658959535,\n \"acc_stderr\": 0.02691864538323901,\n\
\ \"acc_norm\": 0.49710982658959535,\n \"acc_norm_stderr\": 0.02691864538323901\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24804469273743016,\n\
\ \"acc_stderr\": 0.014444157808261467,\n \"acc_norm\": 0.24804469273743016,\n\
\ \"acc_norm_stderr\": 0.014444157808261467\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.4934640522875817,\n \"acc_stderr\": 0.028627470550556054,\n\
\ \"acc_norm\": 0.4934640522875817,\n \"acc_norm_stderr\": 0.028627470550556054\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5627009646302251,\n\
\ \"acc_stderr\": 0.028173917761762892,\n \"acc_norm\": 0.5627009646302251,\n\
\ \"acc_norm_stderr\": 0.028173917761762892\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5432098765432098,\n \"acc_stderr\": 0.02771666165019404,\n\
\ \"acc_norm\": 0.5432098765432098,\n \"acc_norm_stderr\": 0.02771666165019404\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.36879432624113473,\n \"acc_stderr\": 0.028782227561347243,\n \
\ \"acc_norm\": 0.36879432624113473,\n \"acc_norm_stderr\": 0.028782227561347243\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.35723598435462844,\n\
\ \"acc_stderr\": 0.012238615750316508,\n \"acc_norm\": 0.35723598435462844,\n\
\ \"acc_norm_stderr\": 0.012238615750316508\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4007352941176471,\n \"acc_stderr\": 0.029768263528933116,\n\
\ \"acc_norm\": 0.4007352941176471,\n \"acc_norm_stderr\": 0.029768263528933116\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4624183006535948,\n \"acc_stderr\": 0.020170614974969768,\n \
\ \"acc_norm\": 0.4624183006535948,\n \"acc_norm_stderr\": 0.020170614974969768\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5272727272727272,\n\
\ \"acc_stderr\": 0.04782001791380061,\n \"acc_norm\": 0.5272727272727272,\n\
\ \"acc_norm_stderr\": 0.04782001791380061\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.49795918367346936,\n \"acc_stderr\": 0.0320089533497105,\n\
\ \"acc_norm\": 0.49795918367346936,\n \"acc_norm_stderr\": 0.0320089533497105\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6517412935323383,\n\
\ \"acc_stderr\": 0.033687874661154596,\n \"acc_norm\": 0.6517412935323383,\n\
\ \"acc_norm_stderr\": 0.033687874661154596\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6900584795321637,\n \"acc_stderr\": 0.035469769593931624,\n\
\ \"acc_norm\": 0.6900584795321637,\n \"acc_norm_stderr\": 0.035469769593931624\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2974296205630355,\n\
\ \"mc1_stderr\": 0.016002651487361002,\n \"mc2\": 0.45875295870930416,\n\
\ \"mc2_stderr\": 0.01569027399570652\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7071823204419889,\n \"acc_stderr\": 0.012789321118542618\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.17816527672479152,\n \
\ \"acc_stderr\": 0.01054013252754948\n }\n}\n```"
repo_url: https://huggingface.co/wang7776/Llama-2-7b-chat-hf-20-sparsity
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|arc:challenge|25_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|gsm8k|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hellaswag|10_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-55-29.096442.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T13-55-29.096442.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- '**/details_harness|winogrande|5_2023-12-16T13-55-29.096442.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-16T13-55-29.096442.parquet'
- config_name: results
data_files:
- split: 2023_12_16T13_55_29.096442
path:
- results_2023-12-16T13-55-29.096442.parquet
- split: latest
path:
- results_2023-12-16T13-55-29.096442.parquet
---
# Dataset Card for Evaluation run of wang7776/Llama-2-7b-chat-hf-20-sparsity
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [wang7776/Llama-2-7b-chat-hf-20-sparsity](https://huggingface.co/wang7776/Llama-2-7b-chat-hf-20-sparsity) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-20-sparsity",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-16T13:55:29.096442](https://huggingface.co/datasets/open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-20-sparsity/blob/main/results_2023-12-16T13-55-29.096442.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.47402608498324605,
"acc_stderr": 0.03438253640771256,
"acc_norm": 0.4787691452187974,
"acc_norm_stderr": 0.03513920510566655,
"mc1": 0.2974296205630355,
"mc1_stderr": 0.016002651487361002,
"mc2": 0.45875295870930416,
"mc2_stderr": 0.01569027399570652
},
"harness|arc:challenge|25": {
"acc": 0.5,
"acc_stderr": 0.014611390804670088,
"acc_norm": 0.5247440273037542,
"acc_norm_stderr": 0.01459348769493774
},
"harness|hellaswag|10": {
"acc": 0.5867357100179247,
"acc_stderr": 0.004914130855431776,
"acc_norm": 0.7791276638119896,
"acc_norm_stderr": 0.0041398679751162995
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720683,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720683
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4148148148148148,
"acc_stderr": 0.042561937679014075,
"acc_norm": 0.4148148148148148,
"acc_norm_stderr": 0.042561937679014075
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5,
"acc_stderr": 0.04068942293855797,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04068942293855797
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5283018867924528,
"acc_stderr": 0.030723535249006107,
"acc_norm": 0.5283018867924528,
"acc_norm_stderr": 0.030723535249006107
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5,
"acc_stderr": 0.04181210050035455,
"acc_norm": 0.5,
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```
## Dataset Details
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[More Information Needed] | [
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adhisetiawan/pothole-segmentation-v2 | adhisetiawan | 2023-12-17T05:45:06Z | 6 | 0 | [
"region:us"
] | null | 2023-12-17T05:45:02Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 6311570.0
num_examples: 79
- name: validation
num_bytes: 337776.0
num_examples: 5
download_size: 6599847
dataset_size: 6649346.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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wanony/mika | wanony | 2023-12-17T10:17:00Z | 6 | 0 | [
"license:gpl-3.0",
"region:us"
] | null | 2023-12-17T10:13:49Z | ---
license: gpl-3.0
---
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floleuerer/OASST-DE_sharegpt | floleuerer | 2023-12-17T21:19:22Z | 6 | 0 | [
"region:us"
] | null | 2023-12-17T21:15:49Z | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 7964857
num_examples: 3721
download_size: 4326364
dataset_size: 7964857
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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shaaz10/goodsrate | shaaz10 | 2023-12-14T05:11:40Z | 5 | 0 | [
"license:unknown",
"region:us"
] | null | 2023-11-08T12:44:16Z | ---
license: unknown
---
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gabrielmbmb/test-dataset | gabrielmbmb | 2023-12-15T16:59:50Z | 5 | 0 | [
"region:us"
] | null | 2023-11-21T12:07:46Z | ---
dataset_info:
features:
- name: input
dtype: string
- name: generation_model
sequence: string
- name: generation_prompt
dtype: string
- name: raw_generation_responses
sequence: string
- name: generations
sequence: string
- name: labelling_model
dtype: string
- name: labelling_prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_labelling_response
dtype: string
- name: rating
sequence: float64
- name: rationale
sequence: string
splits:
- name: train
num_bytes: 5409749
num_examples: 327
download_size: 2747002
dataset_size: 5409749
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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worldboss/bank-of-ghana-rates | worldboss | 2023-12-15T05:47:34Z | 5 | 0 | [
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:summarization",
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_categories:translation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"ghana",
"news",
"ghana-news",
"bank-of-ghana",
"exchange-rates",
"ghana data",
"region:us"
] | [
"conversational",
"text-generation",
"summarization",
"question-answering",
"text-classification",
"text-retrieval",
"translation"
] | 2023-11-25T01:01:13Z | ---
language:
- en
license: apache-2.0
size_categories:
- 100K<n<1M
task_categories:
- conversational
- text-generation
- summarization
- question-answering
- text-classification
- text-retrieval
- translation
pretty_name: No Robots
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: Date
dtype: string
- name: Currency
dtype: string
- name: Currency_Pair
dtype: string
- name: Buying
dtype: string
- name: Selling
dtype: string
- name: Mid_Rate
dtype: string
splits:
- name: train
num_bytes: 8610567
num_examples: 132259
download_size: 2270427
dataset_size: 8610567
tags:
- ghana
- news
- ghana-news
- bank-of-ghana
- exchange-rates
- ghana data
---
### Description 🙅♂️🤖
Bank of Ghana historical and real-time exchange rates data. [Bank of Ghana](https://www.bog.gov.gh/treasury-and-the-markets/historical-interbank-fx-rates/)
Click Here:[](https://colab.research.google.com/drive/1zZUIyp9zBhwL5CqHS3Ggf5vJCr_yTYw0?usp=sharing)
### Data Format
```shell
{
"date": "...",
"currency": "...",
"currency_pair": "...",
"buying": "...",
"selling": "...",
"mid_rate": "..."
}
```
### Load Dataset
```shell
pip install datasets
```
```python
from datasets import load_dataset
rates = load_dataset("worldboss/bank-of-ghana-rates", split="train")
pd.DataFrame(rates).head()
```
### Author
The data was constructed by Theophilus Siameh ([email protected]). | [
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Dnsibu/serials | Dnsibu | 2023-12-12T21:46:06Z | 5 | 0 | [
"task_categories:token-classification",
"size_categories:n<1K",
"language:en",
"region:us"
] | [
"token-classification"
] | 2023-12-01T19:27:40Z | ---
task_categories:
- token-classification
language:
- en
pretty_name: serials
size_categories:
- n<1K
---
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] |
abulte/datagouvfr-datasets | abulte | 2023-12-14T09:08:32Z | 5 | 0 | [
"license:etalab-2.0",
"region:us"
] | null | 2023-12-02T17:44:36Z | ---
license: etalab-2.0
configs:
- config_name: compact
data_files: "data/datagouvfr-datasets.csv"
- config_name: raw
data_files: "data/datagouvfr-datasets-raw.csv"
---
# datagouvfr-datasets
This dataset is created from [data.gouv.fr's catalog](https://www.data.gouv.fr/en/datasets/catalogue-des-donnees-de-data-gouv-fr/), i.e. the French official open data platform list of datasets.
data.gouv.fr's datasets with `archived` status or with no associated ressource are excluded.
This has two configs, described below.
## compact
This config exposes 3 features:
- `id`
- `content`
- `tags`
`content` is the concatenation of the dataset's title and its description, stripped from markdown (markdown to text).
## raw
All columns from the original catalog, with the preprocess filter described above. Description is still in markdown.
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nirantk/dbpedia-entities-google-palm-gemini-embedding-001-100K | nirantk | 2023-12-16T15:57:45Z | 5 | 2 | [
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | [
"feature-extraction"
] | 2023-12-08T12:51:46Z | ---
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: embedding
sequence: float64
splits:
- name: train
num_bytes: 653564666
num_examples: 100000
download_size: 671003094
dataset_size: 653564666
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
pretty_name: 'DBPedia 100K: Gemini Google Embedding Model 001'
size_categories:
- 10K<n<100K
---
# Dataset Card for DBPedia 100K: Gemini Google Embedding Model 001
100K vectors from DBPedia!
Embedding Model: Google's latest Embedding Model 001 -- the successor to the Gecko Models!
## Dataset Details
### Dataset Description
100K Google Embeddings -- 768 dimensions
Created: December 2023
Text used for Embedding: title (string) + text (string)
Embedding Model: Google's `models/embedding-001`
- **Curated by:** [Nirant Kasliwal](https://nirantk.com/about)
- **Funded by:** [Qdrant Gmbh](https://qdrant.tech)
- **Language(s) (NLP):** English
- **License:** Apache License 2.0
## Uses
This dataset is useful for benchmarking the embedding performance, testing the vectors on an existing dataset. E.g. you can compare Google and OpenAI for the same text using this dataset.
## Dataset Creation
Unlike the OpenAI Embedding, this creation used "title" and "content" attribute of the embedding model along with `task_type="retrieval_document"`
```python
result = genai.embed_content(
model="models/embedding-001",
content="Qdrant is the best vector search engine to use with Gemini",
task_type="retrieval_document",
title="Qdrant x Gemini",
)
```
## Source Data
This dataset is a slice of the earlier work from @KShivendu_: https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M/
The 1M dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity
From those 1M, I selected 100K at random and created embedding from them.
### Recommendations
The dataset is released as is, I'm not aware of biases, limitations or other risks arising from the use of embedding models and datasets.
Embedding models are cryptographically secure and should not be used for security use cases.
## Dataset Card Authors
- [Nirant Kasliwal](https://nirantk.com/about/)
## Dataset Card Contact
Write to nirant [dot] kasliwal [at] qdrant.com if you have questions! | [
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MarkGu/math_rephrased_processed_dataset | MarkGu | 2023-12-11T03:41:40Z | 5 | 1 | [
"region:us"
] | null | 2023-12-11T03:41:37Z | ---
dataset_info:
features:
- name: Instruction
dtype: string
- name: Response
dtype: string
splits:
- name: train
num_bytes: 36659433
num_examples: 50000
download_size: 14466166
dataset_size: 36659433
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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tyzhu/ds2_names_10_lora_merge | tyzhu | 2023-12-11T07:54:56Z | 5 | 0 | [
"region:us"
] | null | 2023-12-11T07:53:43Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 44101.0
num_examples: 300
- name: validation
num_bytes: 12243.333333333334
num_examples: 100
download_size: 0
dataset_size: 56344.333333333336
---
# Dataset Card for "ds2_names_10_lora_merge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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aav-ds/Israel-HAMAS_war_news | aav-ds | 2023-12-11T19:24:26Z | 5 | 0 | [
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | [
"text-classification",
"text-generation"
] | 2023-12-11T11:54:53Z | ---
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- text-classification
- text-generation
pretty_name: Israel-HAMAS war news
dataset_info:
features:
- name: url
dtype: string
- name: datetime
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: provider
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 14269346
num_examples: 13103
download_size: 6081837
dataset_size: 14269346
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Israel-HAMAS war news
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Dataset Description
**Point of Contact:** [Alexander Akhterov](mailto:[email protected])
### Dataset Summary
The "Israel-HAMAS war news" dataset is an English-language dataset of news about Israel war against the terrorist organization -
HAMAS that happened after "black Saturday" - massive murders of civilian Israeli people on the 7th of October 2023.
We've accumulated news from the following sources:
- BBC (live news) - from 2023-11-05 to 2023-11-18. Total: 805
- The Times of Israel (live news) - from 2023-10-07 to 2023-11-18. Total: 6581
- Al Jazeera (live news) - from 2023-11-04 to 2023-11-25. Total: 3297
- Al Mayadeen (articles from the site) - from 2023-10-08 to 2023-11-24. Total: 74
- WAFA "News" Agency (articles from the site section "Occupation")- from 2023-09-28 to 2023-11-26. Total: 1020
- CNN live news - from 2023-10-26 to 2023-11-26. Total: 1428
All news is collected using Beautiful Soup Python library. In the case of the BBC site, the WAFA site, and the Al Mayadeen site
it was enough to use simple GET requests and in the other cases we used Silenium.
### Supported Tasks and Leaderboards
`sentiment-classification`, `semantic-similarity-classification`: The idea behind the dataset is to fine-tune one of the LLMs
to make a news sentiment analysis regarding the pro- and anti-Israel attitudes (but the collected dataset can be used also
for other NLP tasks). One of the main issue is data labeling. To overcome it we suppose that almost all news from the Palestinian
"news" agency "WAFA" and Lebanese agency "Al Mayadeen" has anti-Israel position. Most of the Al Jazeera news also tends to be against
Israel. In return news by "The Times of Israel" is mostly pro-Israel.
For example, the following piece of news carries an anti-Israel pattern:
> KHAN YUNIS, Sunday, December 10, 2023 (WAFA) - At least 10 civilians were killed, mostly children, and dozens more were wounded
> early this morning as Israeli warplanes bombed a residential house in Khan Yunis, south of the Gaza Strip, as the Israeli
> aggression on the enclave enters its 65th day in a row. (WAFA "news" agency).
On the contrary, the following information from "The Times of Israel" is pro-Israel:
> Several thousand people demonstrate against antisemitism in Berlin as Germany grapples with a large increase in anti-Jewish
> incidents following Hamas’s assault on Israel two months ago. Police estimate that around 3,200 people gathered in the rain
> in the German capital, while organizers put the figure at 10,000, German news agency dpa reports. Participants in the protest,
> titled “Never again is now,” march to the Brandenburg Gate.
### Languages
The text in the dataset is in English since most news is in English. The associated BCP-47 code is en.
## Dataset Structure
### Data Instances
```
{'url': 'https://www.timesofisrael.com/liveblog_entry/man-arrested-in-death-of-jewish-protester-during-dueling-california-rallies-over-war/',
'datetime': '2023-11-16T18:36:15',
'title': 'Man arrested in death of Jewish protester during dueling California rallies over war',
'text': 'California authorities say they have arrested a man in connection with the death of a Jewish protester during demonstrations over the Israel-Hamas war.\nThe Ventura County Sheriff’s Office says the 50-year-old suspect was arrested today and will be booked into jail in the investigation of involuntary manslaughter — the unintentional killing of another person. The district attorney will decide whether there is enough evidence to bring a formal charge.\nPaul Kessler, 69, died early November 6 at a hospital following a November 5 confrontation with a pro-Palestinian demonstrator in Thousand Oaks, a suburb northwest of Los Angeles.\nSheriff Jim Fryhoff said subsequently that deputies determined Kessler had fallen backward and struck his head on the ground. The pro-Palestinian demonstrator stayed at the scene and told deputies he had called 911, Fryhoff said.',
'provider': 'The Times of Israel',
'source': 'site-live-news'}
```
### Data Fields
- "url" - link to the piece of news;
- "datetime" - news date and time (YYYY-mm-ddTHH:MM:SS);
- "title" - news title;
- "text" - news text;
- "provider" - news providers, can be 'BBC', 'The Times of Israel', 'Al Jazeera', 'Al Mayadeen', 'WAFA News Agency', and 'CNN';
- "source" - where news was collected.
## Dataset Creation
### Curation Rationale
The "Israel-HAMAS war news" dataset was built as a solution to the data annotation problem for the sentiment analysis task
regarding the pro- and anti-Israel attitudes. We've supposed that almost all news from the Palestinian "news" agency "WAFA",
Lebanese agency "Al Mayadeen", and Qatari "Al Jazeera" has anti-Israel position. In return news by "The Times of Israel"
is mostly pro-Israel.
### Source Data
The official sites of the news agencies are the sources of the dataset:
- The BBC - https://www.bbc.com/
- The Times of Israel - https://www.timesofisrael.com/
- Al Jazeera - https://www.aljazeera.com/
- Al Mayadeen - https://english.almayadeen.net/
- WAFA News Agency - https://english.wafa.ps/
- CNN - https://edition.cnn.com/
-
#### Initial Data Collection and Normalization
The data was obtained by web scraping of the appropriate sections of the news agencies' official sites.
### Annotations
The dataset does not contain any additional annotations.
### Personal and Sensitive Information
The news authors were removed.
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kanishka/counterfactual_babylm_aann_indef_anan | kanishka | 2023-12-13T01:24:54Z | 5 | 0 | [
"region:us"
] | null | 2023-12-11T22:27:59Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 581833637
num_examples: 11632617
- name: validation
num_bytes: 56120230
num_examples: 1026747
download_size: 0
dataset_size: 637953867
---
# Dataset Card for "counterfactual_babylm_aann_indef_anan"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
atmallen/quirky_popqa_pythia-410m_bob_easy | atmallen | 2023-12-12T06:54:24Z | 5 | 0 | [
"region:us"
] | null | 2023-12-12T01:13:02Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
- name: popularity
dtype: int64
- name: difficulty
dtype: float64
- name: statement
dtype: string
- name: character
dtype: string
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: bob_log_odds
dtype: float64
splits:
- name: train
num_bytes: 956505.0212765958
num_examples: 6132
- name: validation
num_bytes: 72149.154
num_examples: 462
- name: test
num_bytes: 76436.57
num_examples: 490
download_size: 401584
dataset_size: 1105090.7452765957
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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yunus-emre/eval_history_tf | yunus-emre | 2023-12-12T07:40:21Z | 5 | 0 | [
"region:us"
] | null | 2023-12-12T07:39:11Z | ---
dataset_info:
features:
- name: answer
dtype: bool
- name: text
dtype: string
splits:
- name: test
num_bytes: 6270
num_examples: 75
download_size: 5257
dataset_size: 6270
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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kristmh/test_high_vs_med_low_priority | kristmh | 2023-12-12T10:51:37Z | 5 | 0 | [
"region:us"
] | null | 2023-12-12T10:51:32Z | ---
configs:
- config_name: default
data_files:
- split: test_separate
path: data/test_separate-*
dataset_info:
features:
- name: text_clean
dtype: string
- name: labels
dtype: int64
- name: class
dtype: string
splits:
- name: test_separate
num_bytes: 15564885
num_examples: 22097
download_size: 7787624
dataset_size: 15564885
---
# Dataset Card for "test_high_vs_med_low_priority"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ShreyansJain04/adobe_csm_finetune | ShreyansJain04 | 2023-12-13T16:34:37Z | 5 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-13T16:34:10Z | ---
license: mit
---
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sproos/summeval-tr | sproos | 2023-12-13T22:40:50Z | 5 | 0 | [
"region:us"
] | null | 2023-12-13T17:47:14Z | ---
dataset_info:
features:
- name: machine_summaries
sequence: string
- name: human_summaries
sequence: string
- name: relevance
sequence: float64
- name: coherence
sequence: float64
- name: fluency
sequence: float64
- name: consistency
sequence: float64
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1173088
num_examples: 100
download_size: 506357
dataset_size: 1173088
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "summeval-tr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Erynan/eval_just_100 | Erynan | 2023-12-13T22:04:43Z | 5 | 0 | [
"region:us"
] | null | 2023-12-13T22:04:42Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_reasonable
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 29403
num_examples: 100
download_size: 16405
dataset_size: 29403
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] |
open-llm-leaderboard/details_jondurbin__bagel-7b-v0.1 | open-llm-leaderboard | 2023-12-13T22:24:57Z | 5 | 0 | [
"region:us"
] | null | 2023-12-13T22:24:10Z | ---
pretty_name: Evaluation run of jondurbin/bagel-7b-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/bagel-7b-v0.1](https://huggingface.co/jondurbin/bagel-7b-v0.1) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jondurbin__bagel-7b-v0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-13T22:21:19.370455](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__bagel-7b-v0.1/blob/main/results_2023-12-13T22-21-19.370455.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6444628923452076,\n\
\ \"acc_stderr\": 0.03207947307494766,\n \"acc_norm\": 0.6486636077964215,\n\
\ \"acc_norm_stderr\": 0.03272162055597949,\n \"mc1\": 0.36474908200734396,\n\
\ \"mc1_stderr\": 0.016850961061720113,\n \"mc2\": 0.5265273331495968,\n\
\ \"mc2_stderr\": 0.015203565029897394\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5947098976109215,\n \"acc_stderr\": 0.014346869060229321,\n\
\ \"acc_norm\": 0.6390784982935154,\n \"acc_norm_stderr\": 0.014034761386175452\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6303525194184425,\n\
\ \"acc_stderr\": 0.0048172272922402835,\n \"acc_norm\": 0.8314080860386377,\n\
\ \"acc_norm_stderr\": 0.0037362592995204874\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\
\ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\
\ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544064,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544064\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\"\
: 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\
\ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n\
\ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305527,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305527\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.023415293433568525,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.023415293433568525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8181818181818182,\n \"acc_stderr\": 0.027479603010538804,\n \"\
acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.027479603010538804\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.02399150050031304,\n \
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.02399150050031304\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34814814814814815,\n \"acc_stderr\": 0.02904560029061626,\n \
\ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.02904560029061626\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.029837962388291932,\n\
\ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.029837962388291932\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"\
acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5601851851851852,\n \"acc_stderr\": 0.0338517797604481,\n \"acc_norm\"\
: 0.5601851851851852,\n \"acc_norm_stderr\": 0.0338517797604481\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.026156867523931045,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.026156867523931045\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621126,\n\
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621126\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7085201793721974,\n\
\ \"acc_stderr\": 0.030500283176545847,\n \"acc_norm\": 0.7085201793721974,\n\
\ \"acc_norm_stderr\": 0.030500283176545847\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7520661157024794,\n \"acc_stderr\": 0.039418975265163046,\n \"\
acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.039418975265163046\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742179,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742179\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5267857142857143,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281376\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\
\ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\
\ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7023121387283237,\n \"acc_stderr\": 0.024617055388677003,\n\
\ \"acc_norm\": 0.7023121387283237,\n \"acc_norm_stderr\": 0.024617055388677003\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2860335195530726,\n\
\ \"acc_stderr\": 0.015113972129062138,\n \"acc_norm\": 0.2860335195530726,\n\
\ \"acc_norm_stderr\": 0.015113972129062138\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.0239291555173513,\n\
\ \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.0239291555173513\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.024922001168886335,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.024922001168886335\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \
\ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46479791395045633,\n\
\ \"acc_stderr\": 0.012738547371303957,\n \"acc_norm\": 0.46479791395045633,\n\
\ \"acc_norm_stderr\": 0.012738547371303957\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6862745098039216,\n \"acc_stderr\": 0.018771683893528176,\n \
\ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.018771683893528176\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.763265306122449,\n \"acc_stderr\": 0.02721283588407316,\n\
\ \"acc_norm\": 0.763265306122449,\n \"acc_norm_stderr\": 0.02721283588407316\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.02553843336857833,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.02553843336857833\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536934,\n \
\ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536934\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.36474908200734396,\n\
\ \"mc1_stderr\": 0.016850961061720113,\n \"mc2\": 0.5265273331495968,\n\
\ \"mc2_stderr\": 0.015203565029897394\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8058405682715075,\n \"acc_stderr\": 0.011116983392392654\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4806671721000758,\n \
\ \"acc_stderr\": 0.01376218570985135\n }\n}\n```"
repo_url: https://huggingface.co/jondurbin/bagel-7b-v0.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|arc:challenge|25_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|gsm8k|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hellaswag|10_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T22-21-19.370455.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T22-21-19.370455.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- '**/details_harness|winogrande|5_2023-12-13T22-21-19.370455.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-13T22-21-19.370455.parquet'
- config_name: results
data_files:
- split: 2023_12_13T22_21_19.370455
path:
- results_2023-12-13T22-21-19.370455.parquet
- split: latest
path:
- results_2023-12-13T22-21-19.370455.parquet
---
# Dataset Card for Evaluation run of jondurbin/bagel-7b-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jondurbin/bagel-7b-v0.1](https://huggingface.co/jondurbin/bagel-7b-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_jondurbin__bagel-7b-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-13T22:21:19.370455](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__bagel-7b-v0.1/blob/main/results_2023-12-13T22-21-19.370455.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6444628923452076,
"acc_stderr": 0.03207947307494766,
"acc_norm": 0.6486636077964215,
"acc_norm_stderr": 0.03272162055597949,
"mc1": 0.36474908200734396,
"mc1_stderr": 0.016850961061720113,
"mc2": 0.5265273331495968,
"mc2_stderr": 0.015203565029897394
},
"harness|arc:challenge|25": {
"acc": 0.5947098976109215,
"acc_stderr": 0.014346869060229321,
"acc_norm": 0.6390784982935154,
"acc_norm_stderr": 0.014034761386175452
},
"harness|hellaswag|10": {
"acc": 0.6303525194184425,
"acc_stderr": 0.0048172272922402835,
"acc_norm": 0.8314080860386377,
"acc_norm_stderr": 0.0037362592995204874
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544064,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544064
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7430555555555556,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.7430555555555556,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.55,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6473988439306358,
"acc_stderr": 0.036430371689585475,
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```
## Dataset Details
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[More Information Needed] | [
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TurtleLiu/Psy_Test | TurtleLiu | 2023-12-14T01:53:47Z | 5 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T01:52:47Z | ---
license: apache-2.0
---
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davidgaofc/shadow_inout_bal | davidgaofc | 2023-12-14T03:33:01Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T03:28:35Z | ---
dataset_info:
features:
- name: Text
dtype: string
- name: Label
dtype: int64
splits:
- name: train
num_bytes: 438384.1768292683
num_examples: 910
download_size: 191225
dataset_size: 438384.1768292683
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Seongill/NQ_5_missing_adv | Seongill | 2023-12-14T06:28:14Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T06:28:12Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: status
dtype: string
splits:
- name: train
num_bytes: 14863743
num_examples: 3610
download_size: 8082600
dataset_size: 14863743
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Tippawan/test2-data-semi-p1 | Tippawan | 2023-12-14T06:35:45Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T06:35:42Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
- name: prob
sequence: float64
- name: ifpass
sequence: int64
- name: pred
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 10279348
num_examples: 6601
download_size: 1455542
dataset_size: 10279348
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] |
kowndinya23/t0-submix-llama-2-13b-2048 | kowndinya23 | 2023-12-14T10:04:10Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T10:01:01Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype:
class_label:
names:
'0': adversarial_qa_dbert_answer_the_following_q
'1': adversarial_qa_dbert_based_on
'2': adversarial_qa_dbert_generate_question
'3': adversarial_qa_dbert_question_context_answer
'4': adversarial_qa_dbert_tell_what_it_is
'5': adversarial_qa_dbidaf_answer_the_following_q
'6': adversarial_qa_dbidaf_based_on
'7': adversarial_qa_dbidaf_generate_question
'8': adversarial_qa_dbidaf_question_context_answer
'9': adversarial_qa_dbidaf_tell_what_it_is
'10': adversarial_qa_droberta_answer_the_following_q
'11': adversarial_qa_droberta_based_on
'12': adversarial_qa_droberta_generate_question
'13': adversarial_qa_droberta_question_context_answer
'14': adversarial_qa_droberta_tell_what_it_is
'15': amazon_polarity_Is_this_product_review_positive
'16': amazon_polarity_Is_this_review
'17': amazon_polarity_Is_this_review_negative
'18': amazon_polarity_User_recommend_this_product
'19': amazon_polarity_convey_negative_or_positive_sentiment
'20': amazon_polarity_flattering_or_not
'21': amazon_polarity_negative_or_positive_tone
'22': amazon_polarity_user_satisfied
'23': amazon_polarity_would_you_buy
'24': app_reviews_categorize_rating_using_review
'25': app_reviews_convert_to_rating
'26': app_reviews_convert_to_star_rating
'27': app_reviews_generate_review
'28': cos_e_v1.11_aligned_with_common_sense
'29': cos_e_v1.11_description_question_option_id
'30': cos_e_v1.11_description_question_option_text
'31': cos_e_v1.11_explain_why_human
'32': cos_e_v1.11_generate_explanation_given_text
'33': cos_e_v1.11_i_think
'34': cos_e_v1.11_question_description_option_id
'35': cos_e_v1.11_question_description_option_text
'36': cos_e_v1.11_question_option_description_id
'37': cos_e_v1.11_question_option_description_text
'38': cos_e_v1.11_rationale
'39': dbpedia_14_given_a_choice_of_categories_
'40': dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to
'41': dbpedia_14_given_list_what_category_does_the_paragraph_belong_to
'42': dbpedia_14_pick_one_category_for_the_following_text
'43': dream_answer_to_dialogue
'44': dream_baseline
'45': dream_generate_first_utterance
'46': dream_generate_last_utterance
'47': dream_read_the_following_conversation_and_answer_the_question
'48': duorc_ParaphraseRC_answer_question
'49': duorc_ParaphraseRC_build_story_around_qa
'50': duorc_ParaphraseRC_decide_worth_it
'51': duorc_ParaphraseRC_extract_answer
'52': duorc_ParaphraseRC_generate_question
'53': duorc_ParaphraseRC_generate_question_by_answer
'54': duorc_ParaphraseRC_movie_director
'55': duorc_ParaphraseRC_question_answering
'56': duorc_ParaphraseRC_title_generation
'57': duorc_SelfRC_answer_question
'58': duorc_SelfRC_build_story_around_qa
'59': duorc_SelfRC_decide_worth_it
'60': duorc_SelfRC_extract_answer
'61': duorc_SelfRC_generate_question
'62': duorc_SelfRC_generate_question_by_answer
'63': duorc_SelfRC_movie_director
'64': duorc_SelfRC_question_answering
'65': duorc_SelfRC_title_generation
'66': kilt_tasks_hotpotqa_combining_facts
'67': kilt_tasks_hotpotqa_complex_question
'68': kilt_tasks_hotpotqa_final_exam
'69': kilt_tasks_hotpotqa_formulate
'70': kilt_tasks_hotpotqa_straighforward_qa
'71': qasc_is_correct_1
'72': qasc_is_correct_2
'73': qasc_qa_with_combined_facts_1
'74': qasc_qa_with_separated_facts_1
'75': qasc_qa_with_separated_facts_2
'76': qasc_qa_with_separated_facts_3
'77': qasc_qa_with_separated_facts_4
'78': qasc_qa_with_separated_facts_5
'79': quail_context_description_question_answer_id
'80': quail_context_description_question_answer_text
'81': quail_context_description_question_text
'82': quail_context_question_answer_description_id
'83': quail_context_question_answer_description_text
'84': quail_context_question_description_answer_id
'85': quail_context_question_description_answer_text
'86': quail_context_question_description_text
'87': quail_description_context_question_answer_id
'88': quail_description_context_question_answer_text
'89': quail_description_context_question_text
'90': quail_no_prompt_id
'91': quail_no_prompt_text
'92': quarel_choose_between
'93': quarel_do_not_use
'94': quarel_heres_a_story
'95': quarel_logic_test
'96': quarel_testing_students
'97': quartz_answer_question_based_on
'98': quartz_answer_question_below
'99': quartz_given_the_fact_answer_the_q
'100': quartz_having_read_above_passage
'101': quartz_paragraph_question_plain_concat
'102': quartz_read_passage_below_choose
'103': quartz_use_info_from_paragraph_question
'104': quartz_use_info_from_question_paragraph
'105': quoref_Answer_Friend_Question
'106': quoref_Answer_Question_Given_Context
'107': quoref_Answer_Test
'108': quoref_Context_Contains_Answer
'109': quoref_Find_Answer
'110': quoref_Found_Context_Online
'111': quoref_Given_Context_Answer_Question
'112': quoref_Guess_Answer
'113': quoref_Guess_Title_For_Context
'114': quoref_Read_And_Extract_
'115': quoref_What_Is_The_Answer
'116': race_high_Is_this_the_right_answer
'117': race_high_Read_the_article_and_answer_the_question_no_option_
'118': race_high_Select_the_best_answer
'119': race_high_Select_the_best_answer_generate_span_
'120': race_high_Select_the_best_answer_no_instructions_
'121': race_high_Taking_a_test
'122': race_high_Write_a_multi_choice_question_for_the_following_article
'123': race_high_Write_a_multi_choice_question_options_given_
'124': race_middle_Is_this_the_right_answer
'125': race_middle_Read_the_article_and_answer_the_question_no_option_
'126': race_middle_Select_the_best_answer
'127': race_middle_Select_the_best_answer_generate_span_
'128': race_middle_Select_the_best_answer_no_instructions_
'129': race_middle_Taking_a_test
'130': race_middle_Write_a_multi_choice_question_for_the_following_article
'131': race_middle_Write_a_multi_choice_question_options_given_
'132': ropes_background_new_situation_answer
'133': ropes_background_situation_middle
'134': ropes_given_background_situation
'135': ropes_new_situation_background_answer
'136': ropes_plain_background_situation
'137': ropes_plain_bottom_hint
'138': ropes_plain_no_background
'139': ropes_prompt_beginning
'140': ropes_prompt_bottom_hint_beginning
'141': ropes_prompt_bottom_no_hint
'142': ropes_prompt_mix
'143': ropes_read_background_situation
'144': sciq_Direct_Question
'145': sciq_Direct_Question_Closed_Book_
'146': sciq_Multiple_Choice
'147': sciq_Multiple_Choice_Closed_Book_
'148': sciq_Multiple_Choice_Question_First
'149': social_i_qa_Check_if_a_random_answer_is_valid_or_not
'150': social_i_qa_Generate_answer
'151': social_i_qa_Generate_the_question_from_the_answer
'152': social_i_qa_I_was_wondering
'153': social_i_qa_Show_choices_and_generate_answer
'154': social_i_qa_Show_choices_and_generate_index
'155': web_questions_get_the_answer
'156': web_questions_potential_correct_answer
'157': web_questions_question_answer
'158': web_questions_short_general_knowledge_q
'159': web_questions_whats_the_answer
'160': wiki_bio_comprehension
'161': wiki_bio_guess_person
'162': wiki_bio_key_content
'163': wiki_bio_what_content
'164': wiki_bio_who
'165': wiki_hop_original_choose_best_object_affirmative_1
'166': wiki_hop_original_choose_best_object_affirmative_2
'167': wiki_hop_original_choose_best_object_affirmative_3
'168': wiki_hop_original_choose_best_object_interrogative_1
'169': wiki_hop_original_choose_best_object_interrogative_2
'170': wiki_hop_original_explain_relation
'171': wiki_hop_original_generate_object
'172': wiki_hop_original_generate_subject
'173': wiki_hop_original_generate_subject_and_object
'174': wiki_qa_Decide_good_answer
'175': wiki_qa_Direct_Answer_to_Question
'176': wiki_qa_Generate_Question_from_Topic
'177': wiki_qa_Is_This_True_
'178': wiki_qa_Jeopardy_style
'179': wiki_qa_Topic_Prediction_Answer_Only
'180': wiki_qa_Topic_Prediction_Question_Only
'181': wiki_qa_Topic_Prediction_Question_and_Answer_Pair
'182': wiki_qa_automatic_system
'183': wiki_qa_exercise
'184': wiki_qa_found_on_google
'185': wiqa_does_the_supposed_perturbation_have_an_effect
'186': wiqa_effect_with_label_answer
'187': wiqa_effect_with_string_answer
'188': wiqa_what_is_the_final_step_of_the_following_process
'189': wiqa_what_is_the_missing_first_step
'190': wiqa_what_might_be_the_first_step_of_the_process
'191': wiqa_what_might_be_the_last_step_of_the_process
'192': wiqa_which_of_the_following_is_the_supposed_perturbation
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 4188821256.374292
num_examples: 1606162
- name: validation
num_bytes: 42311694.62570806
num_examples: 16224
download_size: 2556388383
dataset_size: 4231132951.0
---
# Dataset Card for "t0-submix-llama-2-13b-2048"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
kowndinya23/niv2-submix-llama-2-13b-2048 | kowndinya23 | 2023-12-14T10:33:44Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T10:20:51Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype:
class_label:
names:
'0': task001_quoref_question_generation
'1': task002_quoref_answer_generation
'2': task003_mctaco_question_generation_event_duration
'3': task004_mctaco_answer_generation_event_duration
'4': task005_mctaco_wrong_answer_generation_event_duration
'5': task006_mctaco_question_generation_transient_stationary
'6': task007_mctaco_answer_generation_transient_stationary
'7': task008_mctaco_wrong_answer_generation_transient_stationary
'8': task009_mctaco_question_generation_event_ordering
'9': task010_mctaco_answer_generation_event_ordering
'10': task011_mctaco_wrong_answer_generation_event_ordering
'11': task012_mctaco_question_generation_absolute_timepoint
'12': task013_mctaco_answer_generation_absolute_timepoint
'13': task014_mctaco_wrong_answer_generation_absolute_timepoint
'14': task015_mctaco_question_generation_frequency
'15': task016_mctaco_answer_generation_frequency
'16': task017_mctaco_wrong_answer_generation_frequency
'17': task018_mctaco_temporal_reasoning_presence
'18': task019_mctaco_temporal_reasoning_category
'19': task020_mctaco_span_based_question
'20': task021_mctaco_grammatical_logical
'21': task022_cosmosqa_passage_inappropriate_binary
'22': task023_cosmosqa_question_generation
'23': task024_cosmosqa_answer_generation
'24': task025_cosmosqa_incorrect_answer_generation
'25': task026_drop_question_generation
'26': task027_drop_answer_type_generation
'27': task028_drop_answer_generation
'28': task029_winogrande_full_object
'29': task030_winogrande_full_person
'30': task031_winogrande_question_generation_object
'31': task032_winogrande_question_generation_person
'32': task033_winogrande_answer_generation
'33': task034_winogrande_question_modification_object
'34': task035_winogrande_question_modification_person
'35': task036_qasc_topic_word_to_generate_related_fact
'36': task037_qasc_generate_related_fact
'37': task038_qasc_combined_fact
'38': task039_qasc_find_overlapping_words
'39': task040_qasc_question_generation
'40': task041_qasc_answer_generation
'41': task042_qasc_incorrect_option_generation
'42': task043_essential_terms_answering_incomplete_questions
'43': task044_essential_terms_identifying_essential_words
'44': task045_miscellaneous_sentence_paraphrasing
'45': task046_miscellaneous_question_typing
'46': task047_miscellaneous_answering_science_questions
'47': task048_multirc_question_generation
'48': task049_multirc_questions_needed_to_answer
'49': task050_multirc_answerability
'50': task051_multirc_correct_answer_single_sentence
'51': task052_multirc_identify_bad_question
'52': task053_multirc_correct_bad_question
'53': task054_multirc_write_correct_answer
'54': task055_multirc_write_incorrect_answer
'55': task056_multirc_classify_correct_answer
'56': task057_multirc_classify_incorrect_answer
'57': task058_multirc_question_answering
'58': task059_ropes_story_generation
'59': task060_ropes_question_generation
'60': task061_ropes_answer_generation
'61': task062_bigbench_repeat_copy_logic
'62': task063_first_i_elements
'63': task064_all_elements_except_first_i
'64': task065_timetravel_consistent_sentence_classification
'65': task066_timetravel_binary_consistency_classification
'66': task067_abductivenli_answer_generation
'67': task068_abductivenli_incorrect_answer_generation
'68': task069_abductivenli_classification
'69': task070_abductivenli_incorrect_classification
'70': task071_abductivenli_answer_generation
'71': task072_abductivenli_answer_generation
'72': task073_commonsenseqa_answer_generation
'73': task074_squad1.1_question_generation
'74': task075_squad1.1_answer_generation
'75': task076_splash_correcting_sql_mistake
'76': task077_splash_explanation_to_sql
'77': task078_all_elements_except_last_i
'78': task079_conala_concat_strings
'79': task080_piqa_answer_generation
'80': task081_piqa_wrong_answer_generation
'81': task082_babi_t1_single_supporting_fact_question_generation
'82': task083_babi_t1_single_supporting_fact_answer_generation
'83': task084_babi_t1_single_supporting_fact_identify_relevant_fact
'84': task085_unnatural_addsub_arithmetic
'85': task086_translated_symbol_arithmetic
'86': task087_new_operator_addsub_arithmetic
'87': task088_identify_typo_verification
'88': task089_swap_words_verification
'89': task090_equation_learner_algebra
'90': task091_all_elements_from_index_i_to_j
'91': task092_check_prime_classification
'92': task093_conala_normalize_lists
'93': task094_conala_calculate_mean
'94': task095_conala_max_absolute_value
'95': task096_conala_list_index_subtraction
'96': task097_conala_remove_duplicates
'97': task098_conala_list_intersection
'98': task099_reverse_elements_between_index_i_and_j
'99': task1000_pib_translation_tamil_malayalam
'100': task1001_pib_translation_gujarati_urdu
'101': task1002_pib_translation_urdu_gujarati
'102': task1003_pib_translation_bengali_malayalam
'103': task1004_pib_translation_malayalam_bengali
'104': task1005_pib_translation_malayalam_punjabi
'105': task1006_pib_translation_punjabi_malayalam
'106': task1007_pib_translation_english_punjabi
'107': task1008_pib_translation_punjabi_english
'108': task1009_pib_translation_bengali_hindi
'109': task100_concatenate_all_elements_from_index_i_to_j
'110': task1010_pib_translation_hindi_bengali
'111': task1011_pib_translation_hindi_punjabi
'112': task1012_pib_translation_punjabi_hindi
'113': task1013_pib_translation_gujarati_telugu
'114': task1014_pib_translation_telugu_gujarati
'115': task1015_pib_translation_punjabi_tamil
'116': task1016_pib_translation_tamil_punjabi
'117': task1017_pib_translation_hindi_malayalam
'118': task1018_pib_translation_malayalam_hindi
'119': task1019_pib_translation_oriya_telugu
'120': task101_reverse_and_concatenate_all_elements_from_index_i_to_j
'121': task1020_pib_translation_telugu_oriya
'122': task1021_pib_translation_english_malayalam
'123': task1022_pib_translation_malayalam_english
'124': task1023_pib_translation_english_hindi
'125': task1024_pib_translation_hindi_english
'126': task1025_pib_translation_bengali_punjabi
'127': task1026_pib_translation_punjabi_bengali
'128': task1027_pib_translation_marathi_telugu
'129': task1028_pib_translation_telugu_marathi
'130': task1029_pib_translation_marathi_punjabi
'131': task102_commongen_sentence_generation
'132': task1030_pib_translation_punjabi_marathi
'133': task1031_pib_translation_bengali_telugu
'134': task1032_pib_translation_telugu_bengali
'135': task1033_pib_translation_gujarati_hindi
'136': task1034_pib_translation_hindi_gujarati
'137': task1035_pib_translation_tamil_urdu
'138': task1036_pib_translation_urdu_tamil
'139': task1037_pib_translation_telugu_urdu
'140': task1038_pib_translation_urdu_telugu
'141': task1039_pib_translation_oriya_punjabi
'142': task103_facts2story_long_text_generation
'143': task1040_pib_translation_punjabi_oriya
'144': task1041_pib_translation_gujarati_malayalam
'145': task1042_pib_translation_malayalam_gujarati
'146': task1043_pib_translation_gujarati_punjabi
'147': task1044_pib_translation_punjabi_gujarati
'148': task1045_pib_translation_hindi_telugu
'149': task1046_pib_translation_telugu_hindi
'150': task1047_pib_translation_english_telugu
'151': task1048_pib_translation_telugu_english
'152': task1049_pib_translation_malayalam_telugu
'153': task104_semeval_2019_task10_closed_vocabulary_mathematical_answer_generation
'154': task1050_pib_translation_telugu_malayalam
'155': task1051_pib_translation_punjabi_urdu
'156': task1052_pib_translation_urdu_punjabi
'157': task1053_pib_translation_hindi_urdu
'158': task1054_pib_translation_urdu_hindi
'159': task1055_pib_translation_marathi_oriya
'160': task1056_pib_translation_oriya_marathi
'161': task1057_pib_translation_english_urdu
'162': task1058_pib_translation_urdu_english
'163': task1059_pib_translation_malayalam_urdu
'164': task105_story_cloze-rocstories_sentence_generation
'165': task1060_pib_translation_urdu_malayalam
'166': task1061_pib_translation_bengali_marathi
'167': task1062_pib_translation_marathi_bengali
'168': task1063_pib_translation_gujarati_tamil
'169': task1064_pib_translation_tamil_gujarati
'170': task1065_pib_translation_punjabi_telugu
'171': task1066_pib_translation_telugu_punjabi
'172': task1067_pib_translation_bengali_gujarati
'173': task1068_pib_translation_gujarati_bengali
'174': task1069_pib_translation_bengali_urdu
'175': task106_scruples_ethical_judgment
'176': task1070_pib_translation_urdu_bengali
'177': task1071_pib_translation_malayalam_marathi
'178': task1072_pib_translation_marathi_malayalam
'179': task1073_pib_translation_oriya_tamil
'180': task1074_pib_translation_tamil_oriya
'181': task1075_pib_translation_tamil_telugu
'182': task1076_pib_translation_telugu_tamil
'183': task1077_pib_translation_gujarati_oriya
'184': task1078_pib_translation_oriya_gujarati
'185': task1079_pib_translation_english_gujarati
'186': task107_splash_question_to_sql
'187': task1080_pib_translation_gujarati_english
'188': task1081_pib_translation_hindi_marathi
'189': task1082_pib_translation_marathi_hindi
'190': task1083_pib_translation_marathi_tamil
'191': task1084_pib_translation_tamil_marathi
'192': task1085_pib_translation_english_marathi
'193': task1086_pib_translation_marathi_english
'194': task1087_two_number_sum
'195': task1088_array_of_products
'196': task1089_check_monotonic_array
'197': task108_contextualabusedetection_classification
'198': task1090_ted_translation_en_gl
'199': task1091_ted_translation_en_it
'200': task1092_ted_translation_en_pl
'201': task1093_ted_translation_en_fa
'202': task1094_ted_translation_en_pt
'203': task1095_ted_translation_ja_gl
'204': task1096_ted_translation_ja_it
'205': task1097_ted_translation_ja_pl
'206': task1098_ted_translation_ja_fa
'207': task1099_ted_translation_ja_pt
'208': task109_smsspamcollection_spamsmsdetection
'209': task1100_ted_translation_es_gl
'210': task1101_ted_translation_es_it
'211': task1102_ted_translation_es_pl
'212': task1103_ted_translation_es_fa
'213': task1104_ted_translation_es_pt
'214': task1105_ted_translation_ar_gl
'215': task1106_ted_translation_ar_it
'216': task1107_ted_translation_ar_pl
'217': task1108_ted_translation_ar_fa
'218': task1109_ted_translation_ar_pt
'219': task110_logic2text_sentence_generation
'220': task1110_ted_translation_he_gl
'221': task1111_ted_translation_he_it
'222': task1112_ted_translation_he_pl
'223': task1113_ted_translation_he_fa
'224': task1114_ted_translation_he_pt
'225': task1115_alt_ja_id_translation
'226': task1116_alt_id_ja_translation
'227': task1117_alt_ja_id_answer_generation
'228': task1118_alt_ja_fil_translation
'229': task1119_alt_fil_ja_translation
'230': task111_asset_sentence_simplification
'231': task1120_alt_ja_fil_answer_generation
'232': task1121_alt_ja_khm_translation
'233': task1122_alt_khm_ja_translation
'234': task1123_alt_ja_khm_answer_generation
'235': task1124_alt_ja_lo_translation
'236': task1125_alt_lo_ja_translation
'237': task1126_alt_ja_lo_answer_generation
'238': task1127_alt_ja_th_translation
'239': task1128_alt_th_ja_translation
'240': task1129_alt_ja_th_answer_generation
'241': task112_asset_simple_sentence_identification
'242': task1130_xcsr_vi_commonsense_mc_classification
'243': task1131_xcsr_es_commonsense_mc_classification
'244': task1132_xcsr_ur_commonsense_mc_classification
'245': task1133_xcsr_nl_commonsense_mc_classification
'246': task1134_xcsr_hi_commonsense_mc_classification
'247': task1135_xcsr_en_commonsense_mc_classification
'248': task1136_xcsr_fr_commonsense_mc_classification
'249': task1137_xcsr_pt_commonsense_mc_classification
'250': task1138_xcsr_de_commonsense_mc_classification
'251': task1139_xcsr_ru_commonsense_mc_classification
'252': task113_count_frequency_of_letter
'253': task1140_xcsr_pl_commonsense_mc_classification
'254': task1141_xcsr_zh_commonsense_mc_classification
'255': task1142_xcsr_ar_commonsense_mc_classification
'256': task1143_xcsr_it_commonsense_mc_classification
'257': task1144_xcsr_sw_commonsense_mc_classification
'258': task1145_xcsr_jap_commonsense_mc_classification
'259': task1146_country_capital
'260': task1147_country_currency
'261': task1148_maximum_ascii_value
'262': task1149_item_check_edible
'263': task114_is_the_given_word_longest
'264': task1150_delete_max_min
'265': task1151_swap_max_min
'266': task1152_bard_analogical_reasoning_causation
'267': task1153_bard_analogical_reasoning_affordance
'268': task1154_bard_analogical_reasoning_travel
'269': task1155_bard_analogical_reasoning_trash_or_treasure
'270': task1156_bard_analogical_reasoning_tools
'271': task1157_bard_analogical_reasoning_rooms_for_containers
'272': task1158_bard_analogical_reasoning_manipulating_items
'273': task1159_bard_analogical_reasoning_containers
'274': task115_help_advice_classification
'275': task1161_coda19_title_generation
'276': task1162_coda19_title_classification
'277': task1163_coda19_section_classification
'278': task1164_coda19_section_correction_classification
'279': task1167_penn_treebank_coarse_pos_tagging
'280': task1168_brown_coarse_pos_tagging
'281': task1168_xcopa_commonsense_reasoning_ht
'282': task1169_xcopa_commonsense_cause_effect_ht
'283': task116_com2sense_commonsense_reasoning
'284': task1170_xcopa_commonsense_reasoning_id
'285': task1171_xcopa_commonsense_cause_effect_id
'286': task1172_xcopa_commonsense_reasoning_it
'287': task1173_xcopa_commonsense_cause_effect_it
'288': task1174_xcopa_commonsense_reasoning_sw
'289': task1175_xcopa_commonsense_cause_effect_sw
'290': task1176_xcopa_commonsense_reasoning_ta
'291': task1177_xcopa_commonsense_cause_effect_ta
'292': task1178_xcopa_commonsense_reasoning_th
'293': task1179_xcopa_commonsense_cause_effect_th
'294': task117_spl_translation_en_de
'295': task1180_xcopa_commonsense_reasoning_tr
'296': task1181_xcopa_commonsense_cause_effect_tr
'297': task1182_xcopa_commonsense_reasoning_vi
'298': task1183_xcopa_commonsense_cause_effect_vi
'299': task1184_xcopa_commonsense_reasoning_zh
'300': task1185_xcopa_commonsense_cause_effect_zh
'301': task1186_nne_hrngo_classification
'302': task1187_politifact_classification
'303': task1188_count_max_freq_char
'304': task1189_check_char_in_string
'305': task118_semeval_2019_task10_open_vocabulary_mathematical_answer_generation
'306': task1190_add_integer_to_list
'307': task1191_food_veg_nonveg
'308': task1192_food_flavor_profile
'309': task1193_food_course_classification
'310': task1194_kth_largest_element
'311': task1195_disflqa_disfluent_to_fluent_conversion
'312': task1196_atomic_classification_oeffect
'313': task1197_atomic_classification_oreact
'314': task1198_atomic_classification_owant
'315': task1199_atomic_classification_xattr
'316': task119_semeval_2019_task10_geometric_mathematical_answer_generation
'317': task1200_atomic_classification_xeffect
'318': task1201_atomic_classification_xintent
'319': task1202_atomic_classification_xneed
'320': task1203_atomic_classification_xreact
'321': task1204_atomic_classification_hinderedby
'322': task1205_atomic_classification_isafter
'323': task1206_atomic_classification_isbefore
'324': task1207_atomic_classification_atlocation
'325': task1208_atomic_classification_xreason
'326': task1209_atomic_classification_objectuse
'327': task120_zest_text_modification
'328': task1210_atomic_classification_madeupof
'329': task1211_atomic_classification_hassubevent
'330': task1212_atomic_classification_hasproperty
'331': task1213_atomic_classification_desires
'332': task1214_atomic_classification_xwant
'333': task1215_atomic_classification_capableof
'334': task1216_atomic_classification_causes
'335': task1217_atomic_answer_generation
'336': task1218_ted_translation_en_ja
'337': task1219_ted_translation_en_es
'338': task121_zest_text_modification
'339': task1220_ted_translation_en_ar
'340': task1221_ted_translation_en_he
'341': task1222_ted_translation_ja_en
'342': task1223_ted_translation_ja_es
'343': task1224_ted_translation_ja_ar
'344': task1225_ted_translation_ja_he
'345': task1226_ted_translation_es_en
'346': task1227_ted_translation_es_ja
'347': task1228_ted_translation_es_ar
'348': task1229_ted_translation_es_he
'349': task122_conala_list_index_addition
'350': task1230_ted_translation_ar_en
'351': task1231_ted_translation_ar_ja
'352': task1232_ted_translation_ar_es
'353': task1233_ted_translation_ar_he
'354': task1234_ted_translation_he_en
'355': task1235_ted_translation_he_ja
'356': task1236_ted_translation_he_es
'357': task1237_ted_translation_he_ar
'358': task1238_ted_translation_gl_en
'359': task1239_ted_translation_gl_ja
'360': task123_conala_sort_dictionary
'361': task1240_ted_translation_gl_es
'362': task1241_ted_translation_gl_ar
'363': task1242_ted_translation_gl_he
'364': task1243_ted_translation_gl_it
'365': task1244_ted_translation_gl_pl
'366': task1245_ted_translation_gl_fa
'367': task1246_ted_translation_gl_pt
'368': task1247_ted_translation_it_en
'369': task1248_ted_translation_it_ja
'370': task1249_ted_translation_it_es
'371': task124_conala_pair_averages
'372': task1250_ted_translation_it_ar
'373': task1251_ted_translation_it_he
'374': task1252_ted_translation_it_gl
'375': task1253_ted_translation_it_pl
'376': task1254_ted_translation_it_fa
'377': task1255_ted_translation_it_pt
'378': task1256_ted_translation_pl_en
'379': task1257_ted_translation_pl_ja
'380': task1258_ted_translation_pl_es
'381': task1259_ted_translation_pl_ar
'382': task125_conala_pair_differences
'383': task1260_ted_translation_pl_he
'384': task1261_ted_translation_pl_gl
'385': task1262_ted_translation_pl_it
'386': task1263_ted_translation_pl_fa
'387': task1264_ted_translation_pl_pt
'388': task1265_ted_translation_fa_en
'389': task1266_ted_translation_fa_ja
'390': task1267_ted_translation_fa_es
'391': task1268_ted_translation_fa_ar
'392': task1269_ted_translation_fa_he
'393': task126_scan_structured_text_generation_command_action_all
'394': task1270_ted_translation_fa_gl
'395': task1271_ted_translation_fa_it
'396': task1272_ted_translation_fa_pl
'397': task1273_ted_translation_fa_pt
'398': task1274_ted_translation_pt_en
'399': task1275_ted_translation_pt_ja
'400': task1276_ted_translation_pt_es
'401': task1277_ted_translation_pt_ar
'402': task1278_ted_translation_pt_he
'403': task1279_ted_translation_pt_gl
'404': task127_scan_long_text_generation_action_command_all
'405': task1280_ted_translation_pt_it
'406': task1281_ted_translation_pt_pl
'407': task1282_ted_translation_pt_fa
'408': task1283_hrngo_quality_classification
'409': task1284_hrngo_informativeness_classification
'410': task1285_kpa_keypoint_matching
'411': task1286_openbookqa_question_answering
'412': task1287_glue_qqp_paraphrasing
'413': task1288_glue_mrpc_paraphrasing
'414': task1289_trec_classification
'415': task128_scan_structured_text_generation_command_action_short
'416': task1290_xsum_summarization
'417': task1291_multi_news_summarization
'418': task1292_yelp_review_full_text_categorization
'419': task1293_kilt_tasks_hotpotqa_question_answering
'420': task1294_wiki_qa_answer_verification
'421': task1295_adversarial_qa_question_answering
'422': task1296_wiki_hop_question_answering
'423': task1297_qasc_question_answering
'424': task129_scan_long_text_generation_action_command_short
'425': task1308_amazonreview_category_classification
'426': task1309_amazonreview_summary_classification
'427': task130_scan_structured_text_generation_command_action_long
'428': task1310_amazonreview_rating_classification
'429': task1311_amazonreview_rating_classification
'430': task1312_amazonreview_polarity_classification
'431': task1313_amazonreview_polarity_classification
'432': task1314_country_abbreviation
'433': task1315_find_range_array
'434': task1316_remove_duplicates_string
'435': task1317_country_calling_code
'436': task1318_country_national_dish
'437': task1319_country_by_barcode_prefix
'438': task131_scan_long_text_generation_action_command_long
'439': task1320_country_domain_tld
'440': task1321_country_continent
'441': task1322_country_government_type
'442': task1323_open_subtitles_hi_en_translation
'443': task1324_open_subtitles_te_en_translation
'444': task1325_qa_zre_question_generation_on_subject_relation
'445': task1326_qa_zre_question_generation_from_answer
'446': task1327_qa_zre_answer_generation_from_question
'447': task1328_qa_zre_relation_generation_from_question
'448': task1329_open_subtitles_en_hi_translation
'449': task132_dais_text_modification
'450': task1330_open_subtitles_en_te_translation
'451': task1331_reverse_array
'452': task1332_check_leap_year
'453': task1333_check_validity_date_ddmmyyyy
'454': task1334_sqac_answer_generation
'455': task1335_sqac_question_generation
'456': task1336_peixian_equity_evaluation_corpus_gender_classifier
'457': task1338_peixian_equity_evaluation_corpus_sentiment_classifier
'458': task1339_peixian_equity_evaluation_corpus_text_completion
'459': task133_winowhy_reason_plausibility_detection
'460': task1340_msr_text_compression_compression
'461': task1341_msr_text_classification
'462': task1342_amazon_us_reviews_title
'463': task1343_amazon_us_reviews_rating
'464': task1344_glue_entailment_classification
'465': task1345_glue_qqp_question_paraprashing
'466': task1346_glue_cola_grammatical_correctness_classification
'467': task1347_glue_sts-b_similarity_classification
'468': task134_winowhy_reason_generation
'469': task1350_opus100_translation_en_gu
'470': task1351_opus100_translation_gu_en
'471': task1352_hind_encorp_translation_hi_en
'472': task1353_hind_encorp_translation_en_hi
'473': task1354_sent_comp_classification
'474': task1355_sent_comp_summarization
'475': task1356_xlsum_title_generation
'476': task1357_xlsum_summary_generation
'477': task1358_xlsum_title_generation
'478': task1359_numer_sense_answer_generation
'479': task135_winowhy_wrong_reason_generation
'480': task1360_numer_sense_multiple_choice_qa_generation
'481': task1361_movierationales_classification
'482': task1364_hans_answer_generation
'483': task1365_opustedtalks_translation
'484': task1366_healthfact_classification
'485': task1367_opustedtalks_translation
'486': task1368_healthfact_sentence_generation
'487': task1369_healthfact_sentence_generation
'488': task136_winowhy_knowledge_categorization
'489': task1370_newscomm_classification
'490': task1371_newscomm_translation
'491': task1373_newscomm_translation
'492': task1374_newscomm_translation
'493': task1375_newscomm_translation
'494': task1376_newscomm_translation
'495': task1377_newscomm_translation
'496': task1378_quarel_correct_answer_generation
'497': task1379_quarel_incorrect_answer_generation
'498': task137_detoxifying-lms_classification_toxicity
'499': task1380_quarel_correct_option_generation
'500': task1381_quarel_incorrect_option_generation
'501': task1382_quarel_write_correct_answer
'502': task1383_quarel_write_incorrect_answer
'503': task1384_deal_or_no_dialog_classification
'504': task1385_anli_r1_entailment
'505': task1386_anli_r2_entailment
'506': task1387_anli_r3_entailment
'507': task1388_cb_entailment
'508': task1389_hellaswag_completion
'509': task138_detoxifying-lms_classification_fluency
'510': task1390_wscfixed_coreference
'511': task1391_winogrande_easy_answer_generation
'512': task1392_superglue_multirc_answer_verification
'513': task1393_superglue_copa_text_completion
'514': task1394_meta_woz_task_classification
'515': task1395_europa_ecdc_tm_en_sv_translation
'516': task1396_europa_ecdc_tm_en_de_translation
'517': task1397_europa_ecdc_tm_fr_en_translation
'518': task1398_obqa_question_generation
'519': task1399_obqa_answer_generation
'520': task139_detoxifying-lms_classification_topicality
'521': task1400_obqa_incorrect_answer_generation
'522': task1401_obqa_sentence_generation
'523': task1402_clue_question_generation
'524': task1403_check_validity_date_mmddyyyy
'525': task1404_date_conversion
'526': task1405_find_median
'527': task1406_kth_smallest_element
'528': task1407_dart_question_generation
'529': task1408_dart_similarity_classification
'530': task1409_dart_text_generation
'531': task140_detoxifying-lms_classification_style
'532': task1410_dart_relationship_extraction
'533': task1411_dart_subject_identification
'534': task1412_web_questions_question_answering
'535': task1413_dart_object_identification
'536': task1414_ajgt_twitter_ar_classification
'537': task1415_youtube_caption_corrections_grammar_correction
'538': task1416_youtube_caption_corrections_incorrect_grammar_classification
'539': task1418_bless_semantic_relation_classification
'540': task1419_mathqa_gain
'541': task141_odd-man-out_classification_category
'542': task1420_mathqa_general
'543': task1421_mathqa_other
'544': task1422_mathqa_physics
'545': task1423_mathqa_geometry
'546': task1424_mathqa_probability
'547': task1425_country_iso_numeric
'548': task1426_country_independence_year
'549': task1427_country_region_in_world
'550': task1428_country_surface_area
'551': task1429_evalution_semantic_relation_classification
'552': task142_odd-man-out_classification_no_category
'553': task1431_head_qa_answer_generation
'554': task1432_head_qa_language_translation_en_to_es
'555': task1433_head_qa_language_translation_es_to_en
'556': task1434_head_qa_classification
'557': task1435_ro_sts_parallel_language_translation_ro_to_en
'558': task1436_ro_sts_parallel_language_translation_en_to_ro
'559': task1437_doqa_cooking_question_generation
'560': task1438_doqa_cooking_answer_generation
'561': task1439_doqa_cooking_isanswerable
'562': task143_odd-man-out_classification_generate_category
'563': task1440_doqa_movies_question_generation
'564': task1441_doqa_movies_answer_generation
'565': task1442_doqa_movies_isanswerable
'566': task1443_string_to_number
'567': task1444_round_power_of_two
'568': task1445_closest_integers
'569': task1446_farthest_integers
'570': task1447_drug_extraction_ade
'571': task1448_disease_entity_extraction_ncbi_dataset
'572': task1449_disease_entity_extraction_bc5cdr_dataset
'573': task144_subjqa_question_answering
'574': task1451_drug_dose_extraction
'575': task1452_location_entity_extraction_btc_corpus
'576': task1453_person_entity_extraction_btc_corpus
'577': task145_afs_argument_similarity_death_penalty
'578': task146_afs_argument_similarity_gun_control
'579': task1479_organization_entity_extraction_btc_corpus
'580': task147_afs_argument_similarity_gay_marriage
'581': task1480_gene_extraction_jnlpba_dataset
'582': task1481_gene_extraction_bc2gm_dataset
'583': task1482_gene_extraction_chemprot_dataset
'584': task1483_chemical_extraction_chemprot_dataset
'585': task1484_gene_extraction_linnaeus_dataset
'586': task1485_organ_extraction_anem_dataset
'587': task1486_cell_extraction_anem_dataset
'588': task1487_organism_substance_extraction_anem_dataset
'589': task1488_sarcasmdetection_headline_classification
'590': task1489_sarcasmdetection_tweet_classification
'591': task148_afs_argument_quality_gay_marriage
'592': task1490_bengali_personal_hate_speech_binary_classification
'593': task1491_bengali_political_hate_speech_binary_classification
'594': task1492_bengali_religious_hate_speech_binary_classification
'595': task1493_bengali_geopolitical_hate_speech_binary_classification
'596': task1494_bengali_hate_speech_classification
'597': task1495_adverse_drug_event_classification
'598': task1496_bengali_reviews_sentiment_classification
'599': task1497_bengali_book_reviews_sentiment_classification
'600': task1498_24hour_to_12hour_clock
'601': task1499_dstc3_summarization
'602': task149_afs_argument_quality_death_penalty
'603': task1500_dstc3_classification
'604': task1501_dstc3_answer_generation
'605': task1502_hatexplain_classification
'606': task1503_hatexplain_classification
'607': task1504_hatexplain_answer_generation
'608': task1505_root09_semantic_relation_classification
'609': task1506_celebrity_minimal_dob_span
'610': task1507_boolean_temporal_reasoning
'611': task1508_wordnet_antonyms
'612': task1509_evalution_antonyms
'613': task150_afs_argument_quality_gun_control
'614': task1510_evalution_relation_extraction
'615': task1514_flores_translation_entone
'616': task1515_imppres_longtextgeneration
'617': task1516_imppres_naturallanguageinference
'618': task1517_limit_classfication
'619': task1518_limit_answer_generation
'620': task1519_qa_srl_question_generation
'621': task151_tomqa_find_location_easy_clean
'622': task1520_qa_srl_answer_generation
'623': task1529_scitail1.1_classification
'624': task152_tomqa_find_location_easy_noise
'625': task1530_scitail1.1_sentence_generation
'626': task1531_daily_dialog_type_classification
'627': task1532_daily_dialog_emotion_classification
'628': task1533_daily_dialog_formal_classification
'629': task1534_daily_dialog_question_classification
'630': task1535_daily_dialog_uniqueness_classification
'631': task1536_daily_dialog_happiness_classification
'632': task1537_tamil_offenseval_dravidian_classification
'633': task1538_malayalam_offenseval_dravidian_classification
'634': task1539_kannada_offenseval_dravidian_classification
'635': task153_tomqa_find_location_hard_clean
'636': task1540_parsed_pdfs_summarization
'637': task1541_agnews_classification
'638': task1542_every_ith_element_from_starting
'639': task1543_conll2002_parts_of_speech_tagging_answer_generation
'640': task1544_conll2002_named_entity_recognition_answer_generation
'641': task1545_conll2002_person_name_extraction_answer_generation
'642': task1546_conll2002_location_name_extraction_answer_generation
'643': task1548_wiqa_binary_classification
'644': task1549_wiqa_answer_generation_missing_step
'645': task154_tomqa_find_location_hard_noise
'646': task1551_every_ith_element_from_kth_element
'647': task1552_scitail_question_generation
'648': task1553_cnn_dailymail_summarization
'649': task1554_scitail_classification
'650': task1555_scitail_answer_generation
'651': task1556_scitail_passage_generation
'652': task1557_jfleg_answer_generation
'653': task1558_jfleg_incorrect_answer_generation
'654': task1559_blimp_binary_classification
'655': task155_count_nouns_verbs
'656': task1560_blimp_binary_classification
'657': task1561_clickbait_new_bg_summarization
'658': task1562_zest_text_modification
'659': task1564_triviaqa_answer_generation
'660': task1565_triviaqa_classification
'661': task1566_propara_structured_text_generation
'662': task1567_propara_question_generation
'663': task1568_propara_classification
'664': task1569_cmrc2018_question_generation
'665': task156_codah_classification_adversarial
'666': task1570_cmrc2018_answer_generation
'667': task1571_cmrc2018_answer_generation_starting_index
'668': task1572_samsum_summary
'669': task1573_samsum_classification
'670': task1574_amazon_reviews_multi_language_identification
'671': task1575_amazon_reviews_multi_sentiment_classification
'672': task1576_amazon_reviews_multi_english_language_classification
'673': task1577_amazon_reviews_multi_japanese_language_classification
'674': task1579_gigaword_incorrect_summarization
'675': task157_count_vowels_and_consonants
'676': task1580_eqasc-perturbed_question_generation
'677': task1581_eqasc-perturbed_answer_generation
'678': task1582_bless_hypernym_generation
'679': task1583_bless_meronym_classification
'680': task1584_evalution_meronym_classification
'681': task1585_root09_hypernym_generation
'682': task1586_scifact_title_generation
'683': task1587_scifact_classification
'684': task1588_tecla_classification
'685': task1589_scifact_classification
'686': task158_count_frequency_of_words
'687': task1590_diplomacy_text_generation
'688': task1591_allocine_classification
'689': task1592_yahoo_answers_topics_classfication
'690': task1593_yahoo_answers_topics_classification
'691': task1594_yahoo_answers_topics_question_generation
'692': task1595_event2mind_text_generation_1
'693': task1596_event2mind_text_generation_2
'694': task1597_nyc_slot_filling
'695': task1598_nyc_long_text_generation
'696': task1599_smcalflow_classification
'697': task159_check_frequency_of_words_in_sentence_pair
'698': task1600_smcalflow_sentence_generation
'699': task1601_webquestions_answer_generation
'700': task1602_webquestion_question_genreation
'701': task1603_smcalflow_sentence_generation
'702': task1604_ethos_text_classification
'703': task1605_ethos_text_classification
'704': task1606_ethos_text_classification
'705': task1607_ethos_text_classification
'706': task1608_xquad_en_answer_generation
'707': task1609_xquad_en_question_generation
'708': task160_replace_letter_in_a_sentence
'709': task1610_xquad_es_answer_generation
'710': task1611_xquad_es_question_generation
'711': task1612_sick_label_classification
'712': task1613_sick_given_category_generate_sentence
'713': task1614_sick_text_modify
'714': task1615_sick_tclassify_b_relation_a
'715': task1616_cc_alligned_translate_eng_tel
'716': task1617_cc_alligned_translate_tel_eng
'717': task1618_cc_alligned_classify_tel_eng
'718': task1619_menyo20k-mt_en_yo_translation
'719': task161_count_words_containing_letter
'720': task1620_menyo20k-mt_yo_en_translation
'721': task1621_menyo20k-mt_en_yo_language_identification
'722': task1622_disfl_qa_text_modication
'723': task1623_disfl_qa_disfluent_question_classification
'724': task1624_disfl_qa_question_yesno_classification
'725': task1625_disfl_qa_asnwer_generation
'726': task1626_copa_hr_question_answering
'727': task1627_copa_hr_classification
'728': task1628_copa_hr_question_answering
'729': task1629_copa_hr_classification
'730': task162_count_words_starting_with_letter
'731': task1630_openpi_classification
'732': task1631_openpi_answer_generation
'733': task1637_doqa2.1_cooking_text_summarization
'734': task1638_doqa2.1_movies_text_summarization
'735': task1639_doqa2.1_travel_text_summarization
'736': task163_count_words_ending_with_letter
'737': task1640_aqa1.0_answerable_unanswerable_question_classification
'738': task1645_medical_question_pair_dataset_text_classification
'739': task1646_dataset_card_for_catalonia_independence_corpus_text_classification
'740': task1647_opus_books_en-pt_translation
'741': task1648_opus_books_en-sv_translation
'742': task1649_opus_books_en-no_translation
'743': task164_mcscript_question_answering_text
'744': task1650_opus_books_en-fi_translation
'745': task1651_opus_books_en-es__translation
'746': task1652_opus_books_ca-en_translation
'747': task1654_mkb_translation
'748': task1655_mkb_translation
'749': task1656_gooaq_answer_generation
'750': task1657_gooaq_question_generation
'751': task1658_billsum_summarization
'752': task1659_title_generation
'753': task165_mcscript_question_answering_commonsense
'754': task1660_super_glue_question_generation
'755': task1661_super_glue_classification
'756': task1662_cedr_ru_classification
'757': task1663_cedr_ru_incorrect_classification
'758': task1664_winobias_text_generation
'759': task1665_trainglecopa_question_generation
'760': task1666_cail2018_answer_generation
'761': task1667_cail2018_answer_generation
'762': task1669_md_gender_bias_text_modification
'763': task166_clariq_sentence_generation
'764': task1670_md_gender_bias_text_modification
'765': task1676_xquad-ca_translation
'766': task1677_xquad-ca_translation
'767': task1678_mathqa_answer_selection
'768': task167_strategyqa_question_generation
'769': task1685_menyo20k_translation
'770': task1686_menyo20k_translation
'771': task1689_qed_amara_translation
'772': task168_strategyqa_question_decomposition
'773': task1690_qed_amara_translation
'774': task1691_qed_amara_translation
'775': task1692_qed_amara_translation
'776': task169_strategyqa_sentence_generation
'777': task1703_ljspeech_textmodification
'778': task1704_ljspeech_textmodification
'779': task1705_ljspeech_classification
'780': task1706_ljspeech_classification
'781': task170_hotpotqa_answer_generation
'782': task1711_poki_text_generation
'783': task1712_poki_classification
'784': task1713_convai3_sentence_generation
'785': task1714_convai3_sentence_generation
'786': task171_spl_translation_en_es
'787': task1720_civil_comments_toxicity_classification
'788': task1721_civil_comments_obscenity_classification
'789': task1722_civil_comments_threat_classification
'790': task1723_civil_comments_sexuallyexplicit_classification
'791': task1724_civil_comments_insult_classification
'792': task1725_civil_comments_severtoxicity_classification
'793': task1726_mathqa_correct_answer_generation
'794': task1727_wiqa_what_is_the_effect
'795': task1728_web_nlg_data_to_text
'796': task1729_personachat_generate_next
'797': task172_spl_translation_en_fa
'798': task1730_personachat_choose_next
'799': task1731_quartz_question_answering
'800': task173_spl_translation_en_it
'801': task174_spl_translation_en_ja
'802': task175_spl_translation_en_pl
'803': task176_break_decompose_questions
'804': task177_para-nmt_paraphrasing
'805': task178_quartz_question_answering
'806': task179_participant_extraction
'807': task180_intervention_extraction
'808': task181_outcome_extraction
'809': task182_duorc_question_generation
'810': task183_rhyme_generation
'811': task184_break_generate_question
'812': task184_snli_entailment_to_neutral_text_modification
'813': task185_snli_contradiction_to_neutral_text_modification
'814': task186_snli_contradiction_to_entailment_text_modification
'815': task187_snli_entailment_to_contradiction_text_modification
'816': task188_snli_neutral_to_entailment_text_modification
'817': task189_snli_neutral_to_contradiction_text_modification
'818': task190_snli_classification
'819': task191_hotpotqa_question_generation
'820': task192_hotpotqa_sentence_generation
'821': task193_duorc_question_generation
'822': task194_duorc_answer_generation
'823': task195_sentiment140_classification
'824': task196_sentiment140_answer_generation
'825': task197_mnli_domain_answer_generation
'826': task198_mnli_domain_classification
'827': task199_mnli_classification
'828': task200_mnli_entailment_classification
'829': task201_mnli_neutral_classification
'830': task202_mnli_contradiction_classification
'831': task203_mnli_sentence_generation
'832': task204_mnli_same_genre_classification
'833': task205_remove_even_elements
'834': task206_collatz_conjecture
'835': task207_max_element_lists
'836': task208_combinations_of_list
'837': task209_stancedetection_classification
'838': task210_logic2text_structured_text_generation
'839': task211_logic2text_classification
'840': task212_logic2text_classification
'841': task213_rocstories_correct_ending_classification
'842': task214_rocstories_incorrect_ending_classification
'843': task215_rocstories_incorrect_answer_generation
'844': task216_rocstories_correct_answer_generation
'845': task217_rocstories_ordering_answer_generation
'846': task218_rocstories_swap_order_answer_generation
'847': task219_rocstories_title_answer_generation
'848': task220_rocstories_title_classification
'849': task221_rocstories_two_choice_classification
'850': task222_rocstories_two_chioce_slotting_classification
'851': task223_quartz_explanation_generation
'852': task224_scruples_anecdotes_ethical_judgment
'853': task225_english_language_answer_generation
'854': task226_english_language_answer_relevance_classification
'855': task227_clariq_classification
'856': task228_arc_answer_generation_easy
'857': task229_arc_answer_generation_hard
'858': task230_iirc_passage_classification
'859': task231_iirc_link_classification
'860': task232_iirc_link_number_classification
'861': task233_iirc_link_exists_classification
'862': task234_iirc_passage_line_answer_generation
'863': task235_iirc_question_from_subtext_answer_generation
'864': task236_iirc_question_from_passage_answer_generation
'865': task237_iirc_answer_from_subtext_answer_generation
'866': task238_iirc_answer_from_passage_answer_generation
'867': task239_tweetqa_answer_generation
'868': task240_tweetqa_question_generation
'869': task241_tweetqa_classification
'870': task242_tweetqa_classification
'871': task243_count_elements_in_set_intersection
'872': task244_count_elements_in_set_union
'873': task245_check_presence_in_set_intersection
'874': task246_dream_question_generation
'875': task247_dream_answer_generation
'876': task248_dream_classification
'877': task249_enhanced_wsc_pronoun_disambiguation
'878': task250_spl_translation_en_ar
'879': task251_spl_translation_en_fi
'880': task252_spl_translation_en_tr
'881': task253_spl_translation_en_zh
'882': task254_spl_translation_fi_en
'883': task255_spl_translation_it_en
'884': task256_spl_translation_de_en
'885': task257_spl_translation_ar_en
'886': task258_spl_translation_fa_en
'887': task259_spl_translation_tr_en
'888': task260_spl_translation_zh_en
'889': task261_spl_translation_es_en
'890': task262_spl_translation_ja_en
'891': task263_spl_translation_pl_en
'892': task264_paper_reviews_accept_or_reject_classification
'893': task265_paper_reviews_language_identification
'894': task266_paper_reviews_reviewer_perspective_classification
'895': task267_concatenate_and_reverse_all_elements_from_index_i_to_j
'896': task268_casehold_legal_answer_generation
'897': task269_csrg_counterfactual_story_generation
'898': task270_csrg_counterfactual_context_generation
'899': task271_europarl_translation
'900': task272_europarl_translation
'901': task273_europarl_classification
'902': task274_overruling_legal_classification
'903': task275_enhanced_wsc_paraphrase_generation
'904': task276_enhanced_wsc_classification
'905': task277_stereoset_sentence_generation_stereotype
'906': task278_stereoset_sentence_generation_antistereotype
'907': task279_stereoset_classification_stereotype
'908': task280_stereoset_classification_stereotype_type
'909': task281_points_of_correspondence
'910': task282_scruples_event_time
'911': task283_dream_incorrect_answer_generation
'912': task284_imdb_classification
'913': task285_imdb_answer_generation
'914': task286_olid_offense_judgment
'915': task287_casehold_legal_incorrect_answer_generation
'916': task288_gigaword_summarization
'917': task289_gigaword_summarization
'918': task290_tellmewhy_question_answerability
'919': task291_semeval_2020_task4_commonsense_validation
'920': task292_storycommonsense_character_text_generation
'921': task293_storycommonsense_emotion_text_generation
'922': task294_storycommonsense_motiv_text_generation
'923': task295_semeval_2020_task4_commonsense_reasoning
'924': task296_storycloze_correct_end_classification
'925': task297_storycloze_incorrect_end_classification
'926': task298_storycloze_correct_end_classification
'927': task299_storycloze_sentence_generation
'928': task300_storycloze_order_generation
'929': task301_record_question_generation
'930': task302_record_classification
'931': task303_record_incorrect_answer_generation
'932': task304_numeric_fused_head_resolution
'933': task305_jeopardy_answer_generation_normal
'934': task306_jeopardy_answer_generation_double
'935': task307_jeopardy_answer_generation_final
'936': task308_jeopardy_answer_generation_all
'937': task309_race_answer_generation
'938': task310_race_classification
'939': task311_race_question_generation
'940': task312_europarl_sv_en_translation
'941': task313_europarl_en_sv_translation
'942': task314_europarl_sv-en_classification
'943': task315_europarl_sv-en_language_identification
'944': task316_crows-pairs_classification_stereotype
'945': task317_crows-pairs_classification_stereotype_type
'946': task318_stereoset_classification_gender
'947': task319_stereoset_classification_profession
'948': task320_stereoset_classification_race
'949': task321_stereoset_classification_religion
'950': task322_jigsaw_classification_threat
'951': task323_jigsaw_classification_sexually_explicit
'952': task324_jigsaw_classification_disagree
'953': task325_jigsaw_classification_identity_attack
'954': task326_jigsaw_classification_obscene
'955': task327_jigsaw_classification_toxic
'956': task328_jigsaw_classification_insult
'957': task329_gap_classification
'958': task330_gap_answer_generation
'959': task331_gap_incorrect_answer_generation
'960': task332_tellmewhy_answer_generation
'961': task333_hateeval_classification_hate_en
'962': task334_hateeval_classification_hate_es
'963': task335_hateeval_classification_aggresive_en
'964': task336_hateeval_classification_aggresive_es
'965': task337_hateeval_classification_individual_en
'966': task338_hateeval_classification_individual_es
'967': task339_record_answer_generation
'968': task340_winomt_classification_gender_pro
'969': task341_winomt_classification_gender_anti
'970': task342_winomt_classification_profession_pro
'971': task343_winomt_classification_profession_anti
'972': task344_hybridqa_answer_generation
'973': task345_hybridqa_answer_generation
'974': task346_hybridqa_classification
'975': task347_hybridqa_incorrect_answer_generation
'976': task348_squad2.0_unanswerable_question_generation
'977': task349_squad2.0_answerable_unanswerable_question_classification
'978': task350_winomt_classification_gender_identifiability_pro
'979': task351_winomt_classification_gender_identifiability_anti
'980': task352_coda-19_classification
'981': task353_casino_classification_negotiation_elicit_pref
'982': task354_casino_classification_negotiation_no_need
'983': task355_casino_classification_negotiation_other_need
'984': task356_casino_classification_negotiation_self_need
'985': task357_casino_classification_negotiation_small_talk
'986': task358_casino_classification_negotiation_uv_part
'987': task359_casino_classification_negotiation_vouch_fair
'988': task360_spolin_yesand_response_generation
'989': task361_spolin_yesand_prompt_response_classification
'990': task362_spolin_yesand_prompt_response_sub_classification
'991': task363_sst2_polarity_classification
'992': task364_regard_social_impact_classification
'993': task365_synthetic_remove_vowels
'994': task366_synthetic_return_primes
'995': task367_synthetic_remove_floats
'996': task368_synthetic_even_or_odd_calculation
'997': task369_synthetic_remove_odds
'998': task370_synthetic_remove_divisible_by_3
'999': task371_synthetic_product_of_list
'1000': task372_synthetic_palindrome_numbers
'1001': task373_synthetic_round_tens_place
'1002': task374_synthetic_pos_or_neg_calculation
'1003': task375_classify_type_of_sentence_in_debate
'1004': task376_reverse_order_of_words
'1005': task377_remove_words_of_given_length
'1006': task378_reverse_words_of_given_length
'1007': task379_agnews_topic_classification
'1008': task380_boolq_yes_no_question
'1009': task381_boolq_question_generation
'1010': task382_hybridqa_answer_generation
'1011': task383_matres_classification
'1012': task384_socialiqa_question_classification
'1013': task385_socialiqa_incorrect_answer_generation
'1014': task386_semeval_2018_task3_irony_detection
'1015': task387_semeval_2018_task3_irony_classification
'1016': task388_torque_token_classification
'1017': task389_torque_generate_temporal_question
'1018': task390_torque_text_span_selection
'1019': task391_causal_relationship
'1020': task392_inverse_causal_relationship
'1021': task393_plausible_result_generation
'1022': task394_persianqa_question_generation
'1023': task395_persianqa_answer_generation
'1024': task396_persianqa_classification
'1025': task397_semeval_2018_task1_tweet_anger_detection
'1026': task398_semeval_2018_task1_tweet_joy_detection
'1027': task399_semeval_2018_task1_tweet_sadness_detection
'1028': task400_paws_paraphrase_classification
'1029': task401_numeric_fused_head_reference
'1030': task402_grailqa_paraphrase_generation
'1031': task403_creak_commonsense_inference
'1032': task404_grailqa_paraphrase_validation
'1033': task405_narrativeqa_question_generation
'1034': task406_mickey_fr_sentence_perturbation_generation
'1035': task407_mickey_hi_sentence_perturbation_generation
'1036': task408_mickey_it_sentence_perturbation_generation
'1037': task409_mickey_nl_sentence_perturbation_generation
'1038': task410_mickey_ru_sentence_perturbation_generation
'1039': task411_mickey_vi_sentence_perturbation_generation
'1040': task412_mickey_zh_sentence_perturbation_generation
'1041': task413_mickey_en_sentence_perturbation_generation
'1042': task414_mickey_ar_sentence_perturbation_generation
'1043': task415_mickey_bg_sentence_perturbation_generation
'1044': task416_mickey_de_sentence_perturbation_generation
'1045': task417_mickey_es_sentence_perturbation_generation
'1046': task418_persent_title_generation
'1047': task419_persent_answer_generation
'1048': task420_persent_document_sentiment_classification
'1049': task421_persent_sentence_sentiment_classification
'1050': task422_persent_sentence_sentiment_verification
'1051': task423_persent_document_sentiment_verification
'1052': task424_hindienglish_corpora_hi_en_translation
'1053': task425_hindienglish_corpora_en_hi_translation
'1054': task426_hindienglish_corpora_hi-en_classification
'1055': task427_hindienglish_corpora_hi-en_language_identification
'1056': task428_senteval_inversion
'1057': task429_senteval_tense
'1058': task430_senteval_subject_count
'1059': task431_senteval_object_count
'1060': task432_alt_en_hi_translation
'1061': task433_alt_hi_en_translation
'1062': task434_alt_en_hi_answer_generation
'1063': task435_alt_en_ja_translation
'1064': task436_alt_ja_en_translation
'1065': task437_alt_en_ja_answer_generation
'1066': task438_eng_guj_parallel_corpus_en_gu_translation
'1067': task439_eng_guj_parallel_corpus_gu_en_translation
'1068': task440_eng_guj_parallel_corpus_gu-en_classification
'1069': task441_eng_guj_parallel_corpus_gu-en_language_identification
'1070': task442_com_qa_paraphrase_question_generation
'1071': task443_com_qa_ans_question_generation
'1072': task444_com_qa_question_paraphrases_answer_generation
'1073': task446_opus_paracrawl_en_so_translation
'1074': task447_opus_paracrawl_classification
'1075': task448_opus_paracrawl_en_tl_translation
'1076': task449_opus_paracrawl_ig_en_translation
'1077': task450_opus_paracrawl_so_en_translation
'1078': task451_opus_paracrawl_tl_en_translation
'1079': task452_opus_paracrawl_en_ig_translation
'1080': task453_swag_answer_generation
'1081': task454_swag_incorrect_answer_generation
'1082': task455_swag_context_generation
'1083': task456_matres_intention_classification
'1084': task457_matres_conditional_classification
'1085': task458_matres_negation_classification
'1086': task459_matres_static_classification
'1087': task460_qasper_answer_generation
'1088': task461_qasper_question_generation
'1089': task462_qasper_classification
'1090': task463_parsinlu_entailment_classification
'1091': task464_parsinlu_entailment_sentence_generation
'1092': task465_parsinlu_qqp_classification
'1093': task466_parsinlu_qqp_text_modification
'1094': task467_parsinlu_rc_answer_generation
'1095': task468_parsinlu_rc_question_generation
'1096': task469_mrqa_answer_generation
'1097': task470_mrqa_question_generation
'1098': task471_haspart_answer_generation
'1099': task472_haspart_classification
'1100': task473_parsinlu_mc_classification
'1101': task474_parsinlu_mc_classification
'1102': task475_yelp_polarity_classification
'1103': task476_cls_english_books_classification
'1104': task477_cls_english_dvd_classification
'1105': task478_cls_english_music_classification
'1106': task479_cls_german_books_classification
'1107': task480_cls_german_dvd_classification
'1108': task481_cls_german_music_classification
'1109': task482_cls_french_books_classification
'1110': task483_cls_french_dvd_classification
'1111': task484_cls_french_music_classification
'1112': task485_cls_japanese_books_classification
'1113': task486_cls_japanese_dvd_classification
'1114': task487_cls_japanese_music_classification
'1115': task488_extract_all_alphabetical_elements_from_list_in_order
'1116': task489_mwsc_question_generation
'1117': task490_mwsc_options_generation
'1118': task491_mwsc_answer_generation
'1119': task492_mwsc_incorrect_answer_generation
'1120': task493_review_polarity_classification
'1121': task494_review_polarity_answer_generation
'1122': task495_semeval_headline_classification
'1123': task496_semeval_answer_generation
'1124': task497_extract_all_numbers_from_list_in_order
'1125': task498_scruples_anecdotes_whoiswrong_classification
'1126': task499_extract_and_add_all_numbers_from_list
'1127': task500_scruples_anecdotes_title_generation
'1128': task501_scruples_anecdotes_post_type_verification
'1129': task502_scruples_anecdotes_whoiswrong_verification
'1130': task503_scruples_anecdotes_isanswerable
'1131': task504_count_all_alphabetical_elements_in_list
'1132': task505_count_all_numerical_elements_in_list
'1133': task506_position_of_all_alphabetical_elements_in_list
'1134': task507_position_of_all_numerical_elements_in_list
'1135': task508_scruples_dilemmas_more_ethical_isidentifiable
'1136': task509_collate_of_all_alphabetical_and_numerical_elements_in_list_separately
'1137': task510_reddit_tifu_title_summarization
'1138': task511_reddit_tifu_long_text_summarization
'1139': task512_twitter_emotion_classification
'1140': task513_argument_stance_classification
'1141': task514_argument_consequence_classification
'1142': task515_senteval_odd_word_out
'1143': task516_senteval_conjoints_inversion
'1144': task517_emo_classify_emotion_of_dialogue
'1145': task518_emo_different_dialogue_emotions
'1146': task519_aquamuse_question_generation
'1147': task520_aquamuse_answer_given_in_passage
'1148': task521_trivia_question_classification
'1149': task522_news_editorial_summary
'1150': task523_find_if_numbers_or_alphabets_are_more_in_list
'1151': task524_parsinlu_food_aspect_classification
'1152': task525_parsinlu_movie_aspect_classification
'1153': task526_parsinlu_movie_overal_classification
'1154': task527_parsinlu_food_overal_classification
'1155': task528_parsinlu_movie_aspect_detection
'1156': task529_parsinlu_food_aspect_detection
'1157': task530_europarl_en_es_translation
'1158': task531_europarl_es_en_translation
'1159': task532_europarl_en-es_classification
'1160': task533_europarl_es-en_language_identification
'1161': task534_farstail_entailment
'1162': task535_alt_translation_ch_en
'1163': task536_alt_translation_vi_en
'1164': task537_alt_translation_th_en
'1165': task538_alt_translation_bu_en
'1166': task539_alt_translation_ma_en
'1167': task540_alt_translation_la_en
'1168': task541_alt_translation_kh_en
'1169': task542_alt_translation_ja_en
'1170': task543_alt_translation_bh_en
'1171': task544_alt_translation_hi_en
'1172': task545_alt_translation_fi_en
'1173': task546_alt_translation_bg_en
'1174': task547_alt_translation_entk_en
'1175': task548_alt_translation_en_ch
'1176': task549_alt_translation_en_vi
'1177': task550_discofuse_sentence_generation
'1178': task551_alt_translation_en_th
'1179': task552_alt_translation_en_bu
'1180': task553_alt_translation_en_ma
'1181': task554_alt_translation_en_la
'1182': task555_alt_translation_en_kh
'1183': task556_alt_translation_en_ja
'1184': task557_alt_translation_en_ba
'1185': task558_alt_translation_en_hi
'1186': task559_alt_translation_en_fi
'1187': task560_alt_translation_en_entk
'1188': task561_alt_translation_en_bg
'1189': task562_alt_language_identification
'1190': task563_discofuse_answer_generation
'1191': task564_discofuse_classification
'1192': task565_circa_answer_generation
'1193': task566_circa_classification
'1194': task567_circa_text_generation
'1195': task568_circa_question_generation
'1196': task569_recipe_nlg_text_generation
'1197': task570_recipe_nlg_ner_generation
'1198': task571_recipe_nlg_ner_generation
'1199': task572_recipe_nlg_text_generation
'1200': task573_air_dialogue_classification
'1201': task574_air_dialogue_sentence_generation
'1202': task575_air_dialogue_classification
'1203': task576_curiosity_dialogs_answer_generation
'1204': task577_curiosity_dialogs_classification
'1205': task578_curiosity_dialogs_answer_generation
'1206': task579_socialiqa_classification
'1207': task580_socialiqa_answer_generation
'1208': task581_socialiqa_question_generation
'1209': task582_naturalquestion_answer_generation
'1210': task583_udeps_eng_coarse_pos_tagging
'1211': task584_udeps_eng_fine_pos_tagging
'1212': task585_preposition_classification
'1213': task586_amazonfood_polarity_classification
'1214': task587_amazonfood_polarity_correction_classification
'1215': task588_amazonfood_rating_classification
'1216': task589_amazonfood_summary_text_generation
'1217': task590_amazonfood_summary_correction_classification
'1218': task591_sciq_answer_generation
'1219': task592_sciq_incorrect_answer_generation
'1220': task593_sciq_explanation_generation
'1221': task594_sciq_question_generation
'1222': task595_mocha_answer_generation
'1223': task596_mocha_question_generation
'1224': task597_cuad_answer_generation
'1225': task598_cuad_answer_generation
'1226': task599_cuad_question_generation
'1227': task600_find_the_longest_common_substring_in_two_strings
'1228': task601_flores_translation_sntoen
'1229': task602_wikitext-103_answer_generation
'1230': task603_wikitext-103_fill_in_the_blank
'1231': task604_flores_translation_entosn
'1232': task605_find_the_longest_common_subsequence_in_two_lists
'1233': task606_sum_of_all_numbers_in_list_between_positions_i_and_j
'1234': task607_sbic_intentional_offense_binary_classification
'1235': task608_sbic_sexual_offense_binary_classification
'1236': task609_sbic_potentially_offense_binary_classification
'1237': task610_conllpp_ner
'1238': task611_mutual_multi_turn_dialogue
'1239': task612_yorubabbc_classification
'1240': task613_politifact_text_generation
'1241': task614_glucose_cause_event_detection
'1242': task615_moviesqa_answer_generation
'1243': task616_cola_classification
'1244': task617_amazonreview_category_text_generation
'1245': task618_amazonreview_summary_text_generation
'1246': task619_ohsumed_abstract_title_generation
'1247': task620_ohsumed_medical_subject_headings_answer_generation
'1248': task621_ohsumed_yes_no_numerical_answer_generation
'1249': task622_replace_alphabets_in_a_list_by_their_position_in_english_alphabet
'1250': task623_ohsumed_yes_no_answer_generation
'1251': task624_ohsumed_question_answering
'1252': task625_xlwic_true_or_false_answer_generation
'1253': task626_xlwic_sentence_based_on_given_word_sentence_generation
'1254': task627_xlwic_word_with_same_meaning_sentence_generation
'1255': task628_xlwic_word_with_different_meaning_sentence_generation
'1256': task629_dbpedia_14_classification
'1257': task630_dbpedia_14_classification
'1258': task631_dbpedia_14_incorrect_answer_generation
'1259': task632_dbpedia_14_classification
'1260': task633_dbpedia_14_answer_generation
'1261': task634_allegro_reviews_classification
'1262': task635_allegro_reviews_answer_generation
'1263': task636_extract_and_sort_unique_alphabets_in_a_list
'1264': task637_extract_and_sort_unique_digits_in_a_list
'1265': task638_multi_woz_classification
'1266': task639_multi_woz_user_utterance_generation
'1267': task640_esnli_classification
'1268': task641_esnli_classification
'1269': task642_esnli_classification
'1270': task643_refresd_classification
'1271': task644_refresd_translation
'1272': task645_summarization
'1273': task646_answer_generation
'1274': task647_answer_generation
'1275': task648_answer_generation
'1276': task649_race_blank_question_generation
'1277': task650_opus100_ar_en_translation
'1278': task651_opus100_en_ar_translation
'1279': task652_parsinlu_en_fa_translation
'1280': task653_parsinlu_fa_en_translation
'1281': task654_bible_fa_en_translation
'1282': task655_bible_en_fa_translation
'1283': task656_quran_en_fa_translation
'1284': task657_quran_fa_en_translation
'1285': task658_tep_en_fa_translation
'1286': task659_tep_fa_en_translation
'1287': task660_mizan_fa_en_translation
'1288': task661_mizan_en_fa_translation
'1289': task662_global_voices_fa_en_translation
'1290': task663_global_voices_en_fa_translation
'1291': task668_extreme_abstract_summarization
'1292': task669_ambigqa_answer_generation
'1293': task670_ambigqa_question_generation
'1294': task671_ambigqa_text_generation
'1295': task672_amazon_and_yelp_summarization_dataset_summarization
'1296': task672_nummersense
'1297': task673_google_wellformed_query_classification
'1298': task674_google_wellformed_query_sentence_generation
'1299': task675_google_wellformed_query_sentence_generation
'1300': task676_ollie_relationship_answer_generation
'1301': task677_ollie_sentence_answer_generation
'1302': task678_ollie_actual_relationship_answer_generation
'1303': task679_hope_edi_english_text_classification
'1304': task680_hope_edi_tamil_text_classification
'1305': task681_hope_edi_malayalam_text_classification
'1306': task682_online_privacy_policy_text_classification
'1307': task683_online_privacy_policy_text_purpose_answer_generation
'1308': task684_online_privacy_policy_text_information_type_generation
'1309': task738_perspectrum_classification
'1310': task739_lhoestq_question_generation
'1311': task740_lhoestq_answer_generation_quantity
'1312': task741_lhoestq_answer_generation_place
'1313': task742_lhoestq_answer_generation_frequency
'1314': task743_eurlex_summarization
'1315': task744_eurlex_classification
'1316': task745_ai2_arithmetic_questions_arithmetic
'1317': task746_yelp_restaurant_review_classification
'1318': task747_glucose_cause_emotion_detection
'1319': task748_glucose_reverse_cause_event_detection
'1320': task749_glucose_reverse_cause_emotion_detection
'1321': task750_aqua_multiple_choice_answering
'1322': task751_svamp_subtraction_question_answering
'1323': task752_svamp_multiplication_question_answering
'1324': task753_svamp_addition_question_answering
'1325': task754_svamp_common-division_question_answering
'1326': task755_find_longest_substring_and_replace_its_sorted_lowercase_version_in_both_lists
'1327': task756_find_longert_substring_and_return_all_unique_alphabets_in_it
'1328': task757_msr_sqa_question_generation
'1329': task758_msr_sqa_question_answer_generation
'1330': task759_msr_sqa_incorrect_answer_generation
'1331': task760_msr_sqa_long_text_generation
'1332': task761_app_review_classification
'1333': task762_emea_fr_sk_translation
'1334': task763_emea_es_lt_translation
'1335': task764_emea_bg_el_classification
'1336': task765_emea_bg_el_translation
'1337': task766_craigslist_bargains_classification
'1338': task767_craigslist_bargains_classification
'1339': task768_qed_text_span_selection
'1340': task769_qed_summarization
'1341': task770_pawsx_english_text_modification
'1342': task771_pawsx_korean_text_modification
'1343': task772_pawsx_french_text_modification
'1344': task773_pawsx_spanish_text_modification
'1345': task774_pawsx_german_text_modification
'1346': task775_pawsx_chinese_text_modification
'1347': task776_pawsx_japanese_text_modification
'1348': task777_pawsx_english_korean_translation
'1349': task778_pawsx_english_french_translation
'1350': task779_pawsx_english_spanish_translation
'1351': task780_pawsx_english_german_translation
'1352': task781_pawsx_english_chinese_translation
'1353': task782_pawsx_english_japanese_translation
'1354': task783_pawsx_korean_english_translation
'1355': task784_pawsx_korean_french_translation
'1356': task785_pawsx_korean_spanish_translation
'1357': task786_pawsx_korean_german_translation
'1358': task787_pawsx_korean_chinese_translation
'1359': task788_pawsx_korean_japanese_translation
'1360': task789_pawsx_french_english_translation
'1361': task790_pawsx_french_korean_translation
'1362': task791_pawsx_french_spanish_translation
'1363': task792_pawsx_french_german_translation
'1364': task793_pawsx_french_chinese_translation
'1365': task794_pawsx_french_japanese_translation
'1366': task795_pawsx_spanish_english_translation
'1367': task796_pawsx_spanish_korean_translation
'1368': task797_pawsx_spanish_french_translation
'1369': task798_pawsx_spanish_german_translation
'1370': task799_pawsx_spanish_chinese_translation
'1371': task800_pawsx_spanish_japanese_translation
'1372': task801_pawsx_german_english_translation
'1373': task802_pawsx_german_korean_translation
'1374': task803_pawsx_german_french_translation
'1375': task804_pawsx_german_spanish_translation
'1376': task805_pawsx_german_chinese_translation
'1377': task806_pawsx_german_japanese_translation
'1378': task807_pawsx_chinese_english_translation
'1379': task808_pawsx_chinese_korean_translation
'1380': task809_pawsx_chinese_french_translation
'1381': task810_pawsx_chinese_spanish_translation
'1382': task811_pawsx_chinese_german_translation
'1383': task812_pawsx_chinese_japanese_translation
'1384': task813_pawsx_japanese_english_translation
'1385': task814_pawsx_japanese_korean_translation
'1386': task815_pawsx_japanese_french_translation
'1387': task816_pawsx_japanese_spanish_translation
'1388': task817_pawsx_japanese_german_translation
'1389': task818_pawsx_japanese_chinese_translation
'1390': task819_pec_sentiment_classification
'1391': task820_protoqa_answer_generation
'1392': task821_protoqa_question_generation
'1393': task823_peixian-rtgender_sentiment_analysis
'1394': task827_copa_commonsense_reasoning
'1395': task828_copa_commonsense_cause_effect
'1396': task829_giga_fren_translation
'1397': task830_poleval2019_mt_translation
'1398': task831_giga_fren_classification
'1399': task832_poleval2019_mt_classification
'1400': task833_poem_sentiment_classification
'1401': task834_mathdataset_classification
'1402': task835_mathdataset_answer_generation
'1403': task836_viquiquad_question_generation
'1404': task837_viquiquad_answer_generation
'1405': task838_cdt_classification
'1406': task839_cdt_classification
'1407': task840_para_pdt_en_es_translation
'1408': task841_para_pdt_de_en_translation
'1409': task842_para_pdt_cs_en_translation
'1410': task843_financial_phrasebank_classification
'1411': task844_financial_phrasebank_classification
'1412': task845_pubmedqa_question_generation
'1413': task846_pubmedqa_classification
'1414': task847_pubmedqa_question_generation
'1415': task848_pubmedqa_classification
'1416': task849_pubmedqa_answer_generation
'1417': task850_synthetic_longest_palindrome
'1418': task851_synthetic_multiply_evens
'1419': task852_synthetic_multiply_odds
'1420': task853_hippocorpus_long_text_generation
'1421': task854_hippocorpus_classification
'1422': task855_conv_ai_2_classification
'1423': task856_conv_ai_2_classification
'1424': task857_inquisitive_question_generation
'1425': task858_inquisitive_span_detection
'1426': task859_prost_question_generation
'1427': task860_prost_mcq_generation
'1428': task861_asdiv_addsub_question_answering
'1429': task861_prost_mcq_answers_generation
'1430': task862_asdiv_multidiv_question_answering
'1431': task863_asdiv_multiop_question_answering
'1432': task864_asdiv_singleop_question_answering
'1433': task865_mawps_addsub_question_answering
'1434': task866_mawps_multidiv_question_answering
'1435': task867_mawps_multiop_question_answering
'1436': task868_cfq_mcd1_explanation_to_sql
'1437': task868_mawps_singleop_question_answering
'1438': task869_cfq_mcd1_sql_to_explanation
'1439': task870_msmarco_answer_generation
'1440': task871_msmarco_question_generation
'1441': task872_opus_xhosanavy_translation_eng_xhosa
'1442': task873_opus_xhosanavy_translation_xhosa_eng
'1443': task874_opus_xhosanavy_sr
'1444': task875_emotion_classification
'1445': task877_kde4_translation
'1446': task878_kde4_translation
'1447': task879_schema_guided_dstc8_classification
'1448': task880_schema_guided_dstc8_classification
'1449': task881_schema_guided_dstc8_classification
'1450': task886_quail_question_generation
'1451': task887_quail_answer_generation
'1452': task888_reviews_classification
'1453': task889_goemotions_classification
'1454': task890_gcwd_classification
'1455': task891_gap_coreference_resolution
'1456': task892_gap_reverse_coreference_resolution
'1457': task893_gap_fill_the_blank_coreference_resolution
'1458': task896_miam_language_classification
'1459': task897_freebase_qa_topic_question_generation
'1460': task898_freebase_qa_answer_generation
'1461': task899_freebase_qa_topic_generation
'1462': task900_freebase_qa_category_classification
'1463': task901_freebase_qa_category_question_generation
'1464': task902_deceptive_opinion_spam_classification
'1465': task903_deceptive_opinion_spam_classification
'1466': task904_hate_speech_offensive_classification
'1467': task905_hate_speech_offensive_classification
'1468': task906_dialogre_identify_names
'1469': task907_dialogre_identify_relationships
'1470': task908_dialogre_identify_familial_relationships
'1471': task909_dialogre_prevalent_speakers
'1472': task910_bianet_classification
'1473': task911_bianet_translation
'1474': task912_bianet_classification
'1475': task913_bianet_translation
'1476': task914_bianet_translation
'1477': task917_coqa_question_generation
'1478': task918_coqa_answer_generation
'1479': task919_coqa_incorrect_answer_generation
'1480': task921_code_x_glue_information_retreival
'1481': task922_event2mind_word_generation
'1482': task923_event2mind_classifier
'1483': task924_event2mind_word_generation
'1484': task925_coached_conv_pref_classifier
'1485': task926_coached_conv_pref_word_generation
'1486': task927_yelp_negative_to_positive_style_transfer
'1487': task928_yelp_positive_to_negative_style_transfer
'1488': task929_products_reviews_classification
'1489': task930_dailydialog_classification
'1490': task931_dailydialog_classification
'1491': task932_dailydialog_classification
'1492': task933_wiki_auto_style_transfer
'1493': task934_turk_simplification
'1494': task935_defeasible_nli_atomic_classification
'1495': task936_defeasible_nli_snli_classification
'1496': task937_defeasible_nli_social_classification
'1497': task938_copa_hi_commonsense_reasoning
'1498': task939_copa_hi_commonsense_cause_effect
'1499': task940_copa_gu_commonsense_reasoning
'1500': task941_copa_gu_commonsense_cause_effect
'1501': task942_copa_mr_commonsense_reasoning
'1502': task943_copa_mr_commonsense_cause_effect
'1503': task944_wiki_cloze_as_multiple_choice_question_answering
'1504': task945_wiki_cloze_bn_multiple_choice_question_answering
'1505': task946_wiki_cloze_gu_multiple_choice_question_answering
'1506': task947_wiki_cloze_hi_multiple_choice_question_answering
'1507': task948_wiki_cloze_kn_multiple_choice_question_answering
'1508': task949_wiki_cloze_ml_multiple_choice_question_answering
'1509': task950_wiki_cloze_mr_multiple_choice_question_answering
'1510': task951_wiki_cloze_or_multiple_choice_question_answering
'1511': task952_wiki_cloze_pa_multiple_choice_question_answering
'1512': task953_wiki_cloze_ta_multiple_choice_question_answering
'1513': task954_wiki_cloze_te_multiple_choice_question_answering
'1514': task955_wiki_auto_style_transfer
'1515': task956_leetcode_420_strong_password_check
'1516': task957_e2e_nlg_text_generation_generate
'1517': task958_e2e_nlg_text_generation_parse
'1518': task959_e2e_nlg_text_generation_identify
'1519': task960_ancora-ca-ner_named_entity_recognition
'1520': task961_ancora-ca-ner_text_auto_completion
'1521': task962_ancora-ca-ner_missing_word_prediction
'1522': task963_librispeech_asr_next_word_prediction
'1523': task964_librispeech_asr_text_auto_completion
'1524': task965_librispeech_asr_missing_word_prediction
'1525': task966_ruletaker_fact_checking_based_on_given_context
'1526': task967_ruletaker_incorrect_fact_generation_based_on_given_paragraph
'1527': task968_xcopa_commonsense_reasoning_et
'1528': task969_xcopa_commonsense_cause_effect_et
'1529': task970_sherliic_causal_relationship
'1530': task974_prachathai67k_sentiment_classification
'1531': task975_prachathai67k_same_genre_classification
'1532': task976_pib_indian_language_identification
'1533': task977_pib_translation_oriya_urdu
'1534': task978_pib_translation_urdu_oriya
'1535': task979_pib_translation_malayalam_oriya
'1536': task980_pib_translation_oriya_malayalam
'1537': task981_pib_translation_bengali_tamil
'1538': task982_pib_translation_tamil_bengali
'1539': task983_pib_translation_gujarati_marathi
'1540': task984_pib_translation_marathi_gujarati
'1541': task985_pib_translation_hindi_oriya
'1542': task986_pib_translation_oriya_hindi
'1543': task987_pib_translation_english_oriya
'1544': task988_pib_translation_oriya_english
'1545': task989_pib_translation_marathi_urdu
'1546': task990_pib_translation_urdu_marathi
'1547': task991_pib_translation_english_tamil
'1548': task992_pib_translation_tamil_english
'1549': task993_pib_translation_hindi_tamil
'1550': task994_pib_translation_tamil_hindi
'1551': task995_pib_translation_bengali_english
'1552': task996_pib_translation_english_bengali
'1553': task997_pib_translation_bengali_oriya
'1554': task998_pib_translation_oriya_bengali
'1555': task999_pib_translation_malayalam_tamil
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 10873953298.721619
num_examples: 9823351
- name: validation
num_bytes: 109838169.27838081
num_examples: 99226
download_size: 6509640182
dataset_size: 10983791468.0
---
# Dataset Card for "niv2-submix-llama-2-13b-2048"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ArasAyen/Zure | ArasAyen | 2023-12-14T13:31:32Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T13:28:47Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2955756339.0
num_examples: 505
download_size: 2873075140
dataset_size: 2955756339.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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tourist800/ORKG_train_data_with_prefix | tourist800 | 2023-12-14T16:45:06Z | 5 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-14T16:42:27Z | ---
license: mit
---
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sproos/twitter-pairclass-de | sproos | 2023-12-14T18:47:19Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T17:14:44Z | ---
dataset_info:
features:
- name: sent1
sequence: string
- name: sent2
sequence: string
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 11674128
num_examples: 1
download_size: 4564592
dataset_size: 11674128
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter-pairclass-de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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sproos/twitter-pairclass-tr | sproos | 2023-12-14T18:47:47Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T17:15:14Z | ---
dataset_info:
features:
- name: sent1
sequence: string
- name: sent2
sequence: string
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 11403288
num_examples: 1
download_size: 4721036
dataset_size: 11403288
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter-pairclass-tr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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davidgaofc/RM_inout_bal_train | davidgaofc | 2023-12-14T17:52:25Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T17:51:53Z | ---
dataset_info:
features:
- name: Text
dtype: string
- name: Label
dtype: int64
splits:
- name: train
num_bytes: 439306.3841463415
num_examples: 910
download_size: 194959
dataset_size: 439306.3841463415
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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sproos/mindsmall-de | sproos | 2023-12-14T20:12:33Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T19:40:48Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: query
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_bytes: 7674611
num_examples: 1419
download_size: 0
dataset_size: 7674611
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mindsmall-de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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sproos/mindsmall-tr | sproos | 2023-12-14T22:59:34Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T22:59:30Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: query
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_bytes: 7251401
num_examples: 1419
download_size: 4248957
dataset_size: 7251401
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mindsmall-tr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kanishka/counterfactual_babylm_naan_non_num | kanishka | 2023-12-14T23:59:06Z | 5 | 0 | [
"region:us"
] | null | 2023-12-14T23:58:47Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 581830660
num_examples: 11632617
- name: validation
num_bytes: 56120230
num_examples: 1026747
download_size: 421690306
dataset_size: 637950890
---
# Dataset Card for "counterfactual_babylm_naan_non_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nairaxo/shikomori-sentiment | nairaxo | 2023-12-15T01:14:05Z | 5 | 0 | [
"region:us"
] | null | 2023-12-15T01:14:04Z | ---
dataset_info:
features:
- name: Text
dtype: string
- name: Polarity
dtype: float64
- name: Sentiment
dtype: string
- name: Dialect
dtype: string
- name: Source
dtype: string
- name: Type
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2275872
num_examples: 17419
download_size: 1041646
dataset_size: 2275872
---
# Dataset Card for "shikomori-sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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We-Want-GPU/orca-alpaca-gpt4-math | We-Want-GPU | 2023-12-15T01:56:17Z | 5 | 0 | [
"region:us"
] | null | 2023-12-15T01:56:03Z | ---
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 132170356
num_examples: 124183
download_size: 69522299
dataset_size: 132170356
---
# Dataset Card for "orca-alpaca-gpt4-math"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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vivekdugale/llama2_amod_mental_health_convs_1k | vivekdugale | 2023-12-15T05:27:44Z | 5 | 0 | [
"region:us"
] | null | 2023-12-15T05:27:23Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1402402
num_examples: 1000
download_size: 799616
dataset_size: 1402402
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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EsakkiSundar/sundarpersonal | EsakkiSundar | 2023-12-15T11:38:22Z | 5 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-15T07:10:33Z | ---
license: mit
---
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MathDG/DocLayNet-base-law | MathDG | 2023-12-15T09:31:07Z | 5 | 0 | [
"license:other",
"region:us"
] | null | 2023-12-15T09:07:37Z | ---
license: other
license_name: other
license_link: LICENSE
dataset_info:
features:
- name: id
dtype: string
- name: texts
sequence: string
- name: bboxes_block
sequence:
sequence: int64
- name: bboxes_line
sequence:
sequence: int64
- name: categories
sequence:
class_label:
names:
'0': Caption
'1': Footnote
'2': Formula
'3': List-item
'4': Page-footer
'5': Page-header
'6': Picture
'7': Section-header
'8': Table
'9': Text
'10': Title
- name: image
dtype: image
- name: page_hash
dtype: string
- name: original_filename
dtype: string
- name: page_no
dtype: int32
- name: num_pages
dtype: int32
- name: original_width
dtype: int32
- name: original_height
dtype: int32
- name: coco_width
dtype: int32
- name: coco_height
dtype: int32
- name: collection
dtype: string
- name: doc_category
dtype: string
splits:
- name: train
num_bytes: 437969372.61070913
num_examples: 1084
- name: validation
num_bytes: 45861372.73611111
num_examples: 117
- name: test
num_bytes: 33038984.336673345
num_examples: 86
download_size: 596745929
dataset_size: 516869729.68349355
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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clarin-knext/kpwr-long | clarin-knext | 2023-12-15T15:01:16Z | 5 | 0 | [
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-12-15T14:53:44Z | ---
license: cc-by-sa-4.0
---
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Adun/isuzu-ds-test | Adun | 2023-12-15T15:18:49Z | 5 | 0 | [
"region:us"
] | null | 2023-12-15T15:03:49Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3104769.0
num_examples: 14
download_size: 3050513
dataset_size: 3104769.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ChunB1/kindle | ChunB1 | 2023-12-15T22:07:27Z | 5 | 0 | [
"region:us"
] | null | 2023-12-15T22:07:26Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1301861
num_examples: 10000
download_size: 715932
dataset_size: 1301861
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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zjguoHF/processed_wikitext103_train_dataset | zjguoHF | 2023-12-16T00:07:43Z | 5 | 0 | [
"region:us"
] | null | 2023-12-16T00:06:32Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 726134340
num_examples: 1801350
download_size: 261058092
dataset_size: 726134340
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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zjguoHF/processed_wikitext103_validation_dataset | zjguoHF | 2023-12-16T00:10:55Z | 5 | 0 | [
"region:us"
] | null | 2023-12-16T00:10:50Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: validation
num_bytes: 1522176
num_examples: 3760
download_size: 542292
dataset_size: 1522176
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
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DragonLine/ksponspeech_04_preprocess | DragonLine | 2023-12-16T12:24:34Z | 5 | 0 | [
"region:us"
] | null | 2023-12-16T11:31:31Z | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 95285083320
num_examples: 99200
- name: test
num_bytes: 11910672176
num_examples: 12400
- name: valid
num_bytes: 11910608544
num_examples: 12400
download_size: 23787011926
dataset_size: 119106364040
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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bn22/albanian-corpora-bert-tokenized-4k-mistral | bn22 | 2023-12-16T15:36:48Z | 5 | 0 | [
"region:us"
] | null | 2023-12-16T15:34:52Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 5306027500
num_examples: 99625
download_size: 1170411361
dataset_size: 5306027500
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Yunij/daig2_tokenized_dataset | Yunij | 2023-12-17T05:03:10Z | 5 | 0 | [
"region:us"
] | null | 2023-12-17T05:03:06Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: prompt_name
dtype: string
- name: source
dtype: string
- name: RDizzl3_seven
dtype: bool
- name: cleaned_text
dtype: string
- name: input_ids
sequence:
sequence: int64
- name: attention_mask
sequence:
sequence: int64
splits:
- name: train
num_bytes: 293764426
num_examples: 44868
download_size: 108904729
dataset_size: 293764426
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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GamblerYu/eth_tx_cls | GamblerYu | 2023-12-17T10:18:10Z | 5 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T10:10:08Z | ---
license: apache-2.0
---
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NguyenVanHieu1605/NER | NguyenVanHieu1605 | 2023-12-17T15:40:46Z | 5 | 0 | [
"region:us"
] | null | 2023-12-17T15:40:26Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: NE_labels
sequence: int64
splits:
- name: train
num_bytes: 3474172
num_examples: 14986
- name: valid
num_bytes: 872991
num_examples: 3465
- name: test
num_bytes: 791856
num_examples: 3683
download_size: 1253151
dataset_size: 5139019
---
# Dataset Card for "NER"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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SayanoAI/RVC-Studio | SayanoAI | 2023-12-12T22:28:04Z | 4 | 2 | [
"license:mit",
"region:us"
] | null | 2023-09-03T00:13:36Z | ---
license: mit
---
Data files used for [RVC Studio](https://github.com/SayanoAI/RVC-Studio) (use the app to download them to the correct location) | [
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ganeshjcs/hindi-headline-article-generation | ganeshjcs | 2023-12-14T16:37:06Z | 4 | 0 | [
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-09-14T02:53:23Z | ---
license: cc-by-sa-4.0
---
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DucHaiten/NSFW | DucHaiten | 2023-12-17T10:37:57Z | 4 | 0 | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-19T15:31:53Z | ---
license: creativeml-openrail-m
---
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Intel/COCO-Counterfactuals | Intel | 2023-12-12T02:20:39Z | 4 | 1 | [
"license:cc-by-4.0",
"counterfactuals",
"Data Augmentation",
"Multimodal Models",
"Transformer",
"Diffusion Models",
"Cross Attention Control",
"Prompt-to-Prompt",
"region:us"
] | null | 2023-09-24T18:32:16Z | ---
license: cc-by-4.0
tags:
- counterfactuals
- Data Augmentation
- Multimodal Models
- Transformer
- Diffusion Models
- Cross Attention Control
- Prompt-to-Prompt
pretty_name: COCO-Counterfactuals
---
# Dataset Card for COCO-Counterfactuals
<!-- Provide a quick summary of the dataset. -->
COCO-Counterfactuals is a high quality synthetic dataset for multimodal vision-language model evaluation and for training data augmentation.
## Dataset Details
### Dataset Description
COCO-Counterfactuals is a high quality synthetic dataset for multimodal vision-language model evaluation and for training data augmentation. Each COCO-Counterfactuals example includes a pair of image-text pairs; one is a counterfactual variation of the other. The two captions are identical to each other except a noun subject. The two corresponding synthetic images differ only in terms of the altered subject in the two captions. In our accompanying paper, we showed that the COCO-Counterfactuals dataset is challenging for existing pre-trained multimodal models and significantly increase the difficulty of the zero-shot image-text retrieval and image-text matching tasks. Our experiments also demonstrate that augmenting training data with COCO-Counterfactuals improves OOD generalization on multiple downstream tasks.
- **License:** CC-BY-4.0
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://huggingface.co/datasets/Intel/COCO-Counterfactuals
- **Paper:** https://openreview.net/forum?id=7AjdHnjIHX
### Data
The captions are located in `data/examples.jsonl` and the images are located in `data/images.zip`. You can load the data as follows:
```python
from datasets import load_dataset
examples = load_dataset('Intel/COCO-Counterfactuals', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
You can get `<YOUR USER ACCESS TOKEN>` by following these steps:
1) log into your Hugging Face account
2) click on your profile picture
3) click "Settings"
4) click "Access Tokens"
5) generate an access token
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Despite the impressive recent improvements in text-to-image generation capabilities, models such as Stable Diffusion have well-known limitations that should be considered when utilizing datasets which
are derived from them. We do not foresee significant risks of security threats or human rights violations in our work. However, the automated nature of our image generation process may introduce the possibility of our COCO-Counterfactuals dataset
containing images that some individuals may consider inappropriate or offensive.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
https://openreview.net/forum?id=7AjdHnjIHX
Tiep Le and Phillip Howard contributed equally.
**BibTeX:**
```
@inproceedings{le2023cococounterfactuals,
author = {Tiep Le and Vasudev Lal and Phillip Howard},
title = {{COCO}-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairs},
booktitle = {Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year = 2023,
url={https://openreview.net/forum?id=7AjdHnjIHX},
}
```
## Dataset Card Authors
Tiep Le and Vasudev Lal and Phillip Howard
## Dataset Card Contact
[email protected]; [email protected]; [email protected] | [
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bene-ges/wiki-en-asr-adapt | bene-ges | 2023-12-14T10:59:19Z | 4 | 0 | [
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2309.17267",
"region:us"
] | null | 2023-09-29T10:23:21Z | ---
license: cc-by-sa-4.0
language:
- en
size_categories:
- 10M<n<100M
---
This is the dataset presented in my [ASRU-2023 paper](https://arxiv.org/abs/2309.17267).
It consists of multiple files:
Keys2Paragraphs.txt (internal name in scripts: yago_wiki.txt):
4.3 million unique words/phrases (English Wikipedia titles or their parts) occurring in 33.8 million English Wikipedia paragraphs.
Keys2Corruptions.txt (internal name in scripts: sub_misspells.txt):
26 million phrase pairs in the corrupted phrase inventory, as recognized by different ASR models
Keys2Related.txt (internal name in scripts: related_phrases.txt):
62.7 million phrase pairs in the related phrase inventory
FalsePositives.txt (internal name in scripts: false_positives.txt):
449 thousand phrase pairs in the false positive phrase inventory
NgramMappings.txt (internal name in scripts: replacement_vocab_filt.txt):
5.5 million character n-gram mappings dictionary
asr
outputs of g2p+tts+asr using 4 different ASR systems (conformer ctc was used twice),
gives pairs of initial phrase and its recognition result.
Does not include .wav files, but these can be reproduced by feeding g2p to tts
giza
raw outputs of GIZA++ alignments for each corpus,
from these we get NgramMappings.txt and Keys2Corruptions.txt
This [example code](https://github.com/bene-ges/nemo_compatible/blob/spellmapper_new_false_positive_sampling/scripts/nlp/en_spellmapper/dataset_preparation/build_training_data_from_wiki_en_asr_adapt.sh) shows how to generate training data from this dataset.
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asgaardlab/GamePhysicsDailyDump | asgaardlab | 2023-12-18T01:12:00Z | 4 | 2 | [
"task_categories:video-classification",
"language:en",
"license:mit",
"game",
"game-physics",
"game-bug",
"video-understanding",
"region:us"
] | [
"video-classification"
] | 2023-10-08T03:05:20Z | ---
license: mit
task_categories:
- video-classification
language:
- en
tags:
- game
- game-physics
- game-bug
- video-understanding
pretty_name: GamePhysics
---
# GamePhysics Dataset (Daily Dump)
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newsmediabias/news-bias-full-data | newsmediabias | 2023-12-13T16:11:44Z | 4 | 1 | [
"task_categories:text-classification",
"task_categories:token-classification",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2312.00168",
"region:us"
] | [
"text-classification",
"token-classification"
] | 2023-10-24T18:51:24Z | ---
license: cc-by-nc-4.0
language:
- en
pretty_name: Navigating News Narratives
task_categories:
- text-classification
- token-classification
---
# Navigating News Narratives: A Media Bias Analysis Dataset
## Introduction
The growing prevalence of bias in news media critically influences public perceptions on various imperative subjects, encompassing areas like political standpoints, health, resource allocation, racial perspectives, age, gender biases, and climate change. In light of this, it's crucial for media outlets to uphold the ethics of providing precise information and heightening public cognizance about these pivotal issues. The necessity for a mechanism that could counteract the spread of fallacious or misleading details is thus underscored, aiming to rejuvenate public confidence in the media.
## Dataset Description
This dataset encompasses multiple dimensions of biases in news media, such as political inclinations, hate speech, toxicity, sexism, ageism, and more, establishing its distinctiveness in the realm of similar datasets. It's noteworthy that the dataset explicitly refrains from including any personally identifiable information (PII).
### Format
The data structure is tabulated as follows:
- **Text**: The main content.
- **Dimension**: Descriptive category of the text.
- **Biased_Words**: A compilation of words regarded as biased.
- **Aspect**: Specific sub-topic within the main content.
- **Label**: Indicates the presence (True) or absence (False) of bias. The label is ternary - highly biased, slightly biased and neutral
- **Toxicity**: Indicates the presence (True) or absence (False) of bias.
- **Identity_mention**: Mention of any identity based on words match.
### Annotation Scheme
The labels and annotations in the dataset are generated through a system of Active Learning, cycling through:
- Manual Labeling
- Semi-Supervised Learning
- Human Verification
The scheme comprises:
- **Bias Label**: Specifies the degree of bias (e.g., no bias, mild, or strong).
- **Words/Phrases Level Biases**: Pinpoints specific biased terms or phrases.
- **Subjective Bias (Aspect)**: Highlights biases pertinent to content dimensions.
*Due to the nuances of semantic match algorithms, certain labels such as 'identity' and 'aspect' may appear distinctively different.*
## Datasets Utilized
For a comprehensive perspective, various news categories like climate crisis summaries, occupational, spiritual/faith, etc., have been curated using RSS. Active learning has been harnessed to classify sentences based on their neutrality or degree of bias and identify biased terms.
Moreover, this compilation incorporates data from several reputable sources:
- **MBIC (media bias)**: [Source](https://zenodo.org/records/4474336)
- **Hyperpartisan news**: [Source](https://huggingface.co/datasets/hyperpartisan_news_detection)
- **Toxic comment classification**: [Kaggle Source](https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge)
- **Jigsaw Unintended Bias**: [Kaggle Source](https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification)
- **Age Bias**: [Harvard Source](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/L4OAKN)
- **Multi-dimensional news (Ukraine)**: [Source](https://zenodo.org/records/3885351#.ZF0KoxHMLtV)
- **Social biases**: [Source](https://maartensap.com/social-bias-frames/)
## Objective
This dataset aims to provide unrestricted, complimentary access, striving to facilitate its adoption among global AI researchers and practitioners. Emphasis is placed on ensuring easy accessibility and usage of the dataset.
https://arxiv.org/abs/2312.00168
## Licensing & Citation
If you leverage this dataset in your work, we kindly request you to cite us:
> Navigating News Narratives: A Media Bias Analysis Dataset © 2023 by Shaina Raza, Vector Institute is licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
For any other datasets utilized in this project, please ensure appropriate attribution as detailed in the aforementioned list.
[Hugging Face Source](https://huggingface.co/datasets/newsmediabias/news-bias-full-data/edit/main/README.md) - Accessed on 10/24/2023.
## Citation
```bibtex
@misc{Raza2023MediaBiasDataset,
title={Navigating News Narratives: A Media Bias Analysis Dataset},
author={Shaina Raza},
year={2023},
note={Licensed under CC BY-NC 4.0},
url={https://creativecommons.org/licenses/by-nc/4.0/}
}
```
```bibtex
@article{raza2023navigating,
title={Navigating News Narratives: A Media Bias Analysis Dataset},
author={Raza, Shaina},
journal={arXiv preprint arXiv:2312.00168},
year={2023}
}
``` | [
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fangchenliu/maskdp_data | fangchenliu | 2023-12-16T04:20:31Z | 4 | 0 | [
"license:mit",
"arxiv:2211.12740",
"region:us"
] | null | 2023-11-14T07:39:51Z | ---
license: mit
---
# Dataset for Masked Autoencoding for Scalable and Generalizable Decision Making
This is the dataset used in paper [Masked Autoencoding for Scalable and Generalizable Decision Making
](https://arxiv.org/pdf/2211.12740.pdf).
```
@inproceedings{liu2022masked,
title={Masked Autoencoding for Scalable and Generalizable Decision Making},
author={Liu, Fangchen and Liu, Hao and Grover, Aditya and Abbeel, Pieter},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}
```
## Dataset format
The dataset is organized in the following format:
```
├── maskdp_train
│ ├── cheetah
│ │ ├── expert # near-expert rollouts from TD3 policy
| | | ├── cheetah_run
| | | | ├── 0.npy
| | | | ├── 1.npy
| | | | ├── ...
| | | ├── cheetah_run_backwards
│ │ ├── sup # supervised data, full experience replay with extrinsic reward
| | | ├── cheetah_run
| | | ├── cheetah_run_backwards
│ │ ├── semi # semi-supervised data, full experience replay with extrinsic + intrinsic reward
| | | ├── cheetah_run
| | | ├── cheetah_run_backwards
│ │ ├── unsup # unsupervised data, full experience replay with intrinsic reward
| | | ├── 0.npy
| | | ├── 1.npy
| | | ├── ...
│ ├── walker
...
│ ├── quadruped
...
├── maskdp_eval
│ ├── expert
│ │ ├── cheetah_run
│ │ ├── cheetah_run_backwards
│ │ ├── ...
│ │ ├── walker_stand
│ │ ├── quadruped_walk
│ │ ├── ...
│ ├── unsup
│ │ ├── cheetah
│ │ ├── walker
│ │ ├── quadruped
```
It contains 4 types of data:
1. unsupervised (training experience using purely intrinsic reward)
2. semi-supervised (training experience using intrinsic reward + extrinsic reward)
3. supervised (training experience using purely extrinsic reward)
4. expert (rollouts from a near-expert TD3 checkpoint) | [
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harpreetsahota/zero_shot_comparison | harpreetsahota | 2023-12-11T16:05:10Z | 4 | 0 | [
"region:us"
] | null | 2023-11-15T13:33:27Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: rationale
dtype: string
- name: task
dtype: string
- name: type
dtype: string
- name: decilm_generation
dtype: string
- name: mistral_generation
dtype: string
splits:
- name: train
num_bytes: 67718
num_examples: 30
download_size: 54407
dataset_size: 67718
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "zero_shot_comparison"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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yangjinlong/yjb | yangjinlong | 2023-12-13T03:59:38Z | 4 | 0 | [
"license:mit",
"region:us"
] | null | 2023-11-15T14:21:18Z | ---
license: mit
---
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agucci/mri-sym2 | agucci | 2023-12-17T09:50:26Z | 4 | 1 | [
"doi:10.57967/hf/1372",
"region:us"
] | null | 2023-11-16T09:36:56Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: line
dtype: string
- name: rad_score
dtype: string
- name: session
dtype: int64
splits:
- name: train
num_bytes: 68961229.076
num_examples: 1476
- name: test
num_bytes: 68472028.992
num_examples: 1674
download_size: 137564710
dataset_size: 137433258.06800002
---
# Dataset Card for mri-sym2
### Dataset Summary
SymBrain, an annotated dataset of brain MRI images designed to advance the field of brain symmetry detection and segmentation.
Our dataset comprises a diverse collection of brain MRI T1w and T2w scans from the [dHCP](https://biomedia.github.io/dHCP-release-notes/download.html) dataset.
Each annotated to highlight the ideal **straight** mid-sagittal plane (MSP), demarcating the brain into two symmetrical hemispheres.
The accurate extraction of the MSP has the potential to greatly enhance segmentation precision.
Researchers and practitioners can utilize this dataset to devise innovative methods for enhanced brain MRI image segmentation.
SymBrain's rich and extensive content empowers the research community to address complex challenges in neuroimaging analysis,
ultimately contributing to advancements in medical diagnostics and treatment planning.
Symmetry analysis plays an important role in medical image processing, particularly in the detection of diseases and malformations.
SymBrain leverages the inherent bilateral symmetry observed in brain MRI images,
making it an invaluable resource for the development and evaluation
of automated algorithms aimed at detecting the symmetry axis within brain MRI data.
## Dataset Structure
The dataset contains 1476 T1w images types and 1674 T2w images.
The differences between the modalities lie in the intensity variations of the different brain areas.
All the images are accessible in the 'train' part of the dataset.
## Dataset Creation
### Loading the data
The dataset contains a 'train' split of 1476 rows, containing the t1 type images, and a 'test' split of 1674 rows, with the t2 type images.
```python
dataset = load_dataset("agucci/mri-sym2")
# first dataset example selection:
dataset['train'][0]
```
**Attributes :**
- *image:* PIL image, shape (290, 290)
- *line:* Straight line annotation coordinates on the image. ({'x':x1, 'y':y1}, {'x':x2, 'y':y2}). Where (x1,y1), (x2,y2) are the starting and end points of the line.
- *radscore:* Radiology score of the volume the image was extracted from. Please refer to [dHCP doc](https://biomedia.github.io/dHCP-release-notes/download.html#metadata) for scores explanation.
- *session:* Session-ID of the original dataset, used for scan retrieval.
### Source Data
[dHCP](https://biomedia.github.io/dHCP-release-notes/download.html) dataset.
Three slices have been extracted from each of the 1050 3D volumes, creating 3150 images.
### Annotations
The authors did Annotations manually with the [V7lab tools](https://www.v7labs.com/).
### Licensing Information
mit
### Citation Information
When using the data please cite :
**authors papers coming soon**
and
**dhcp dataset**
Data were provided by the developing Human Connectome Project, KCL-Imperial-
Oxford Consortium funded by the European Research Council under the Eu-
ropean Union Seventh Framework Programme (FP/2007-2013) / ERC Grant
Agreement no. [319456]. We are grateful to the families who generously sup-
ported this trial. | [
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tfoldi/tesla3_av_rosbags | tfoldi | 2023-12-17T17:27:02Z | 4 | 0 | [
"task_categories:object-detection",
"size_categories:100M<n<1B",
"license:mit",
"rosbag",
"ros2",
"lidar",
"vehicle",
"car",
"canbus",
"autonomous_vehicles",
"region:us"
] | [
"object-detection"
] | 2023-11-20T13:27:20Z | ---
license: mit
task_categories:
- object-detection
tags:
- rosbag
- ros2
- lidar
- vehicle
- car
- canbus
- autonomous_vehicles
pretty_name: tesla3_av_rosbags
size_categories:
- 100M<n<1B
---
# Tesla Model 3 ROS Data Repository
## Overview
This repository contains a collection of ROS bag files (in MCAP format) and video streams from a Tesla Model 3 vehicle. It includes data from multiple runs, featuring a variety of sensors such as VLP-16 LiDAR, CAN bus signals, GPS, a 9-axis IMU, and four camera streams.
## Dataset Description
The data is organized into multiple runs, each containing synchronized streams from the following sensors:
* VLP-16 LiDAR: 3D point cloud data capturing the vehicle's surroundings.
* CAN Bus: Vehicle's internal communication data including speed, steering angle, and more.
* GPS: Geolocation data showing the vehicle's position.
* 9-Axis IMU: Inertial data providing acceleration, orientation, and gyroscope measurements.
* Camera Streams: Four video streams capturing front, rear, and side views.
# License
This project is licensed under the MIT License - see the LICENSE file for details.
# Contact
For any queries or issues, please open an issue in the repository, or contact me directly at [email protected] | [
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Lancelot457357/Napoleon_Bonaparte_voice_data | Lancelot457357 | 2023-12-16T00:25:41Z | 4 | 0 | [
"license:cc",
"region:us"
] | null | 2023-11-22T09:18:49Z | ---
license: cc
---
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BrookBvn/Homelander | BrookBvn | 2023-12-12T04:56:20Z | 4 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-01T18:02:43Z | ---
license: openrail
---
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HelgeKn/SemEval_categories | HelgeKn | 2023-12-14T10:44:12Z | 4 | 0 | [
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2023-12-04T15:31:59Z | ---
license: apache-2.0
language:
- en
size_categories:
- n<1K
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jahb57/bert_embeddings_BATCH_14 | jahb57 | 2023-12-11T14:14:43Z | 4 | 0 | [
"region:us"
] | null | 2023-12-04T17:45:07Z | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: last_hidden_state
sequence:
sequence: float32
- name: pooler_output
sequence: float32
splits:
- name: train
num_bytes: 19669131636
num_examples: 100000
download_size: 19793578186
dataset_size: 19669131636
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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TrustHLT/LaCour | TrustHLT | 2023-12-11T11:34:03Z | 4 | 2 | [
"language_creators:found",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"language:en",
"language:fr",
"language:ru",
"language:es",
"language:hr",
"language:it",
"language:pt",
"language:tr",
"language:pl",
"language:lt",
"language:de",
"language:uk",
"language:hu",
"language:nl",
"language:sq",
"language:ro",
"language:sr",
"license:cc-by-sa-4.0",
"legal",
"hearing",
"oral argument",
"transcript",
"echr",
"dialog",
"region:us"
] | null | 2023-12-07T08:16:31Z | ---
language_creators:
- found
- machine-generated
language:
- en
- fr
- ru
- es
- hr
- it
- pt
- tr
- pl
- lt
- de
- uk
- hu
- nl
- sq
- ro
- sr
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
- n<1K
pretty_name: LaCour!
tags:
- legal
- hearing
- oral argument
- transcript
- echr
- dialog
dataset_info:
- config_name: documents
features:
- name: id
dtype: int32
- name: webcast_id
dtype: string
- name: hearing_title
dtype: string
- name: hearing_date
dtype: string
- name: hearing_type
dtype: string
- name: application_number
sequence: string
- name: case_id
dtype: string
- name: case_name
dtype: string
- name: case_url
dtype: string
- name: ecli
dtype: string
- name: type
dtype: string
- name: document_date
dtype: string
- name: importance
dtype: int32
- name: articles
sequence: string
- name: respondent_government
sequence: string
- name: issue
dtype: string
- name: strasbourg_caselaw
dtype: string
- name: external_sources
dtype: string
- name: conclusion
dtype: string
- name: separate_opinion
dtype: bool
splits:
- name: train
num_bytes: 962951
num_examples: 369
download_size: 1714597
dataset_size: 962951
- config_name: transcripts
features:
- name: id
dtype: int32
- name: webcast_id
dtype: string
- name: segment_id
dtype: int32
- name: speaker_name
dtype: string
- name: speaker_role
dtype: string
- name: data
sequence:
- name: begin
dtype: float32
- name: end
dtype: float32
- name: language
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 13950690
num_examples: 4000
download_size: 5475134
dataset_size: 13950690
viewer: false
---
## Dataset Description
- **Homepage: https://trusthlt.org/lacour**
- **Repository: https://github.com/trusthlt/lacour-corpus**
- **Paper: tbd**
### Dataset Summary
This dataset contains transcribed court hearings sourced from official hearings of the __European Court of Human Rights__ ([https://www.echr.coe.int/webcasts-of-hearings](https://www.echr.coe.int/webcasts-of-hearings)). The hearings are 154 selected webcasts (videos) from 2012-2022 in their original language (no interpretation). With manual annotation for language labels and automatic processing of the extracted audio with [pyannote](https://huggingface.co/pyannote/speaker-diarization) and [whisper-large-v2](https://huggingface.co/openai/whisper-large-v2), the resulting dataset contains 4000 speaker turns and 88920 individual lines. The dataset contains two subsets, the transcripts and the metadata with linked documents. The transcripts are additionally available as .txt or .xml.
### Languages
The largest amounts in the transcripts are:
English, French
A smaller portion also contains the following languages:
Russian, Spanish, Croatian, Italian, Portuguese, Turkish, Polish, Lithuanian, German, Ukrainian, Hungarian, Dutch, Albanian, Romanian, Serbian
The collected metadata is:
English
## Dataset Structure
### Data Instances
Each instance in transcripts represents an entire segment of a transcript, similar to a conversation turn in a dialog.
```
{
'id': 0,
'webcast_id': '1021112_29112017',
'segment_id': 0,
'speaker_name': 'UNK',
'speaker_role': 'Announcer',
'data': {
'begin': [12.479999542236328],
'end': [13.359999656677246],
'language': ['fr'],
'text': ['La Cour!']
}
}
```
Each instance in documents represents a information on a document in hudoc associated with a hearing and the metadata associated with a hearing. The actual document is linked and can also be found in [hudoc](https://hudoc.echr.coe.int) with the case_id. Note: `hearing_type` states the type of the hearing, `type` states the type of the document. If the hearing is a "Grand Chamber hearing", the "CHAMBER" document refers to a different hearing.
```
{
'id': 16,
'webcast_id': '1232311_02102012',
'hearing_title': 'Michaud v. France (nos. 12323/11)',
'hearing_date': '2012-10-02 00:00:00',
'hearing_type': 'Chamber hearing',
'application_number': ['12323/11'],
'case_id': '001-115377',
'case_name': 'CASE OF MICHAUD v. FRANCE',
'case_url': 'https://hudoc.echr.coe.int/eng?i=001-115377',
'ecli': 'ECLI:CE:ECHR:2012:1206JUD001232311',
'type': 'CHAMBER',
'document_date': '2012-12-06 00:00:00',
'importance': 1,
'articles': ['8', '8-1', '8-2', '34', '35'],
'respondent_government': ['FRA'],
'issue': 'Decision of the National Bar Council of 12 July 2007 “adopting regulations on internal procedures for implementing the obligation to combat money laundering and terrorist financing, and an internal supervisory mechanism to guarantee compliance with those procedures” ; Article 21-1 of the Law of 31 December 1971 ; Law no. 2004-130 of 11 February 2004 ; Monetary and Financial Code',
'strasbourg_caselaw': 'André and Other v. France, no 18603/03, 24 July 2008;Bosphorus Hava Yollari Turizm ve Ticaret Anonim Sirketi v. Ireland [GC], no 45036/98, ECHR 2005-VI;[...]',
'external_sources': 'Directive 91/308/EEC, 10 June 1991;Article 6 of the Treaty on European Union;Charter of Fundamental Rights of the European Union;Articles 169, 170, 173, 175, 177, 184 and 189 of the Treaty establishing the European Community;Recommendations 12 and 16 of the financial action task force (“FATF”) on money laundering;Council of Europe Convention on Laundering, Search, Seizure and Confiscation of the Proceeds from Crime and on the Financing of Terrorism (16 May 2005)',
'conclusion': 'Remainder inadmissible;No violation of Article 8 - Right to respect for private and family life (Article 8-1 - Respect for correspondence;Respect for private life)',
'separate_opinion': True
}
```
### Data Fields
transcripts:
* id: the identifier
* webcast_id: the identifier for the hearing
* segment_id: the identifier of the current speaker segment in the current hearing
* speaker_name: the name of the speaker (not given for Applicant, Government or Third Party)
* speaker_role: the role/party the speaker represents (`Announcer` for announcements, `Judge` for judges, `JudgeP` for judge president, `Applicant` for representatives of the applicant, `Government` for representatives of the respondent government, `ThirdParty` for representatives of third party interveners)
* data: sequence of the following fields
* begin: the timestamp for begin of line (in seconds)
* end: the timestamp for end of line (in seconds)
* language: the language spoken (in ISO 639-1)
* text: the spoken line
documents:
* id: the identifier
* webcast_id: the identifier for the hearing (allows linking to transcripts)
* hearing_title: the title of the hearing
* hearing_date: the date of the hearing
* hearing_type: the type of hearing (Grand Chamber, Chamber or Grand Chamber Judgment Hearing)
* application_number: the application numbers which are associated with the hearing and case
* case_id: the id of the case
* case_name: the name of the case
* case_url: the direct link to the document
* ecli: the ECLI (European Case Law Identifier)
* type: the type of the document
* document_date: the date of the document
* importance: the importance score of the case (1 is the highest importance, key case)
* articles: the concerning articles of the Convention of Human Rights
* respondent_government: the code of the respondent government(s) (in ISO-3166 Alpha-3)
* issue: the references to the issue of the case
* strasbourg_caselaw: the list of cases in the ECHR which are relevant to the current case
* external_sources: the relevant references outside of the ECHR
* conclusion: the short textual description of the conclusion
* separate_opinion: the indicator if there is a separate opinion
### Data Splits
The dataset is only split into a train set.
## Dataset Creation
### Curation Rationale
This datasets provides partly corrected transcribed webcasts to enable the processing of hearings in legal NLP. No specific task is given.
### Source Data
#### Data Collection
The data was collected by transcribing the publicly available [webcasts of the ECHR](https://www.echr.coe.int/webcasts-of-hearings) with the help of [pyannote](https://huggingface.co/pyannote/speaker-diarization) and [whisper-large-v2](https://huggingface.co/openai/whisper-large-v2). The documents were sourced from the [ECHR hudoc database](https://hudoc.echr.coe.int).
#### Who are the source producers?
Participants in hearings before the ECHR for the audio and video material. Employees and judges of the ECHR for the documents.
### Annotations
#### Annotation process
**language identification** Spoken languages were manually identified by research assistants. Disagreements were discussed to achieve the final language label.
**transcript correction** All parts spoken by Judge or Judge President are corrected for the languages English and French by research assistants with a high proficiency in the respective language.
#### Personal and Sensitive Information
The dataset contains names of judges and other participants in the hearings. Due to those names being available in the public court material, we did not remove them. The machine-generated transcripts may also contain names, which were neither checked nor removed. In case of sensitive information, we rely on the provided material to provide protection (occasionally bleeping out names which should not have been mentioned in webcasts, appropriate anonymization in the documents).
## Additional Information
Download the transcripts and linked documents:
```
from datasets import load_dataset
lacour_transcripts = load_dataset("TrustHLT/LaCour", "transcripts") # default config
lacour_documents = load_dataset("TrustHLT/LaCour", "documents")
```
Formatted versions of the transcripts in .txt and .xml and more information on the collection and creation can be found on [github](https://github.com/trusthlt/lacour-corpus).
### Citation Information
Please cite this data using:
```
[tbd]
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bdsaglam/web_nlg-erx-sft-sharegpt | bdsaglam | 2023-12-17T20:13:39Z | 4 | 0 | [
"region:us"
] | null | 2023-12-09T09:11:03Z | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 45857180
num_examples: 35426
- name: dev
num_bytes: 5780353
num_examples: 4464
download_size: 11931313
dataset_size: 51637533
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
---
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SantiagoPG/ocr_texts | SantiagoPG | 2023-12-11T13:01:21Z | 4 | 0 | [
"task_categories:question-answering",
"language:en",
"region:us"
] | [
"question-answering"
] | 2023-12-09T22:25:53Z | ---
task_categories:
- question-answering
language:
- en
pretty_name: Doc_QA Dataset
--- | [
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ISTNetworks/open-orca-arabic | ISTNetworks | 2023-12-11T09:59:23Z | 4 | 0 | [
"license:other",
"region:us"
] | null | 2023-12-10T10:19:31Z | ---
license: other
license_name: ist
license_link: LICENSE
---
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nirantk/dbpedia-entities-efficient-splade-100K | nirantk | 2023-12-11T03:19:32Z | 4 | 2 | [
"task_categories:feature-extraction",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | [
"feature-extraction"
] | 2023-12-10T19:22:47Z | ---
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: openai
sequence: float32
- name: splade
sequence: float32
splits:
- name: train
num_bytes: 12862697823
num_examples: 100000
download_size: 901410913
dataset_size: 12862697823
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
pretty_name: 'DBPedia SPLADE + OpenAI: 100,000 Vectors'
size_categories:
- 100K<n<1M
---
# DBPedia SPLADE + OpenAI: 100,000 SPLADE Sparse Vectors + OpenAI Embedding
This dataset has both OpenAI and SPLADE vectors for 100,000 DBPedia entries. This adds SPLADE Vectors to [KShivendu/dbpedia-entities-openai-1M/](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M)
Model id used to make these vectors:
```python
model_id = "naver/efficient-splade-VI-BT-large-doc"
```
For processing the query, use this:
```python
model_id = "naver/efficient-splade-VI-BT-large-query"
```
If you'd like to extract the indices and weights/values from the vectors, you can do so using the following snippet:
```python
import numpy as np
vec = np.array(ds[0]['vec']) # where ds is the dataset
def get_indices_values(vec):
sparse_indices = vec.nonzero()
sparse_values = vec[sparse_indices]
return sparse_indices, sparse_values
``` | [
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joey234/rt_non_affix | joey234 | 2023-12-11T04:48:19Z | 4 | 0 | [
"region:us"
] | null | 2023-12-11T04:48:16Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
splits:
- name: test
num_bytes: 122830.3789868668
num_examples: 963
download_size: 79719
dataset_size: 122830.3789868668
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "rt_non_affix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Yzuygulama/turkishReviews-ds-mini | Yzuygulama | 2023-12-11T17:53:22Z | 4 | 0 | [
"region:us"
] | null | 2023-12-11T11:28:09Z | ---
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 1252876.2642514652
num_examples: 3378
- name: validation
num_bytes: 139455.7357485349
num_examples: 376
download_size: 896649
dataset_size: 1392332.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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pgajo/nick-dataset | pgajo | 2023-12-11T14:12:08Z | 4 | 0 | [
"region:us"
] | null | 2023-12-11T14:06:10Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 1918145060.272
num_examples: 27702
- name: test
num_bytes: 835022060.538
num_examples: 11873
download_size: 2822876508
dataset_size: 2753167120.81
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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kfahn/julia_kaleidescope | kfahn | 2023-12-11T18:03:10Z | 4 | 0 | [
"region:us"
] | null | 2023-12-11T18:03:06Z | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 50110003.0
num_examples: 120
download_size: 32906929
dataset_size: 50110003.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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tollefj/nor-instruct-v2 | tollefj | 2023-12-11T20:29:42Z | 4 | 0 | [
"region:us"
] | null | 2023-12-11T19:37:45Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 56495545
num_examples: 104465
- name: test
num_bytes: 3222227
num_examples: 5726
download_size: 38505086
dataset_size: 59717772
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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ouvic215/Soldering-Data-pix2pix-1209-white-1 | ouvic215 | 2023-12-12T07:34:34Z | 4 | 0 | [
"region:us"
] | null | 2023-12-12T07:29:06Z | ---
dataset_info:
features:
- name: mask_image
dtype: image
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 511555221.25
num_examples: 6799
download_size: 510366317
dataset_size: 511555221.25
---
# Dataset Card for "Soldering-Data-pix2pix-1209-white-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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umarigan/falcon_feedback_instruction | umarigan | 2023-12-13T06:52:08Z | 4 | 1 | [
"region:us"
] | null | 2023-12-12T07:29:35Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 7942624
num_examples: 7401
download_size: 5146500
dataset_size: 7942624
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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textdetox/multilingual_toxic_lexicon | textdetox | 2023-12-12T10:54:10Z | 4 | 0 | [
"language:en",
"language:ru",
"language:uk",
"language:es",
"language:de",
"language:ar",
"language:am",
"language:hi",
"language:zh",
"license:openrail++",
"region:us"
] | null | 2023-12-12T09:26:27Z | ---
language:
- en
- ru
- uk
- es
- de
- ar
- am
- hi
- zh
license: openrail++
dataset_info:
features:
- name: text
dtype: string
splits:
- name: am
num_bytes: 4677
num_examples: 269
- name: es
num_bytes: 14687
num_examples: 1196
- name: ru
num_bytes: 4152959
num_examples: 139491
- name: uk
num_bytes: 149341
num_examples: 7130
- name: en
num_bytes: 33567
num_examples: 2930
- name: zh
num_bytes: 5672
num_examples: 538
- name: ar
num_bytes: 4306
num_examples: 327
- name: hi
num_bytes: 2775
num_examples: 134
- name: de
num_bytes: 723
num_examples: 65
download_size: 1989267
dataset_size: 4368707
configs:
- config_name: default
data_files:
- split: am
path: data/am-*
- split: es
path: data/es-*
- split: ru
path: data/ru-*
- split: uk
path: data/uk-*
- split: en
path: data/en-*
- split: zh
path: data/zh-*
- split: ar
path: data/ar-*
- split: hi
path: data/hi-*
- split: de
path: data/de-*
---
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] |
thanhdath/vietnamese_legal_closed_qa_validation | thanhdath | 2023-12-17T15:59:52Z | 4 | 0 | [
"region:us"
] | null | 2023-12-12T09:44:12Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: selected_contexts
sequence: string
- name: answer
dtype: string
- name: provider
dtype: string
splits:
- name: train
num_bytes: 11584665
num_examples: 2000
download_size: 4122035
dataset_size: 11584665
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
### Evaluation dataset for Vietnamse Legal RAG Chatbot
The dataset is manually filterred and refined by humans. Each sample in the dataset contains a question, K contexts and an answer generated by OpenAI GPT-4. The answer is refined by humans. | [
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huggingface/autotrain-data-hepu-o4zf-ravz-13 | huggingface | 2023-12-12T13:50:39Z | 4 | 0 | [
"region:us"
] | null | 2023-12-12T13:50:37Z | Invalid username or password. | [
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IndicRAGware/hindi-RAG-20k | IndicRAGware | 2023-12-12T21:01:30Z | 4 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-12T21:01:05Z | ---
license: apache-2.0
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 60085060
num_examples: 20508
download_size: 17395917
dataset_size: 60085060
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ericdantas/dataset_test | ericdantas | 2023-12-12T21:07:11Z | 4 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-12T21:03:32Z | ---
license: mit
---
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AfnanTS/TokenizedEnglish | AfnanTS | 2023-12-12T21:52:45Z | 4 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-12T21:50:19Z | ---
license: apache-2.0
dataset_info:
features:
- name: obj_label
dtype: string
- name: masked_sentences
dtype: string
- name: label
sequence: int64
- name: labels
sequence: int64
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 188603660
num_examples: 26796
- name: test
num_bytes: 10480377
num_examples: 1489
- name: valid
num_bytes: 10480917
num_examples: 1489
download_size: 4813627
dataset_size: 209564954
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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