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COMPETITION_DESC.md

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-Sample competition description
+## Overview
+Multimodal Foundation Models (MMFMs) have shown unprecedented performance in many computer vision tasks. However, on some very specific tasks like document understanding, their performance is still underwhelming. In order to evaluate and improve multimodal models for the task of document image comprehension, we harness a large amount of publicly available and privately gathered data and propose a challenge. Our challenge is running in two separate phases. For the first phase, we have released a training and a validation set consisting of publicly available data; and for the second phase, an alien test set will be released soon for the final leaderboard.
+
+## Dates
+Phase 1 Submission Due: 15th May 2024  
+Phase 2 Submission Due: 30th May 2024  
+Solution Review: 1th-7th June 2024  
+Announcement of Winners: 8th June 2024  
+
+## Sponsorship
+Our challenge winners prize is awarded in collaboration with:
+![image info](https://i.ibb.co/7Czmpnz/tensorleap-logo.png)
+
+

DATASET_DESC.md

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-Sample dataset description
+## Dataset
+### Phase 1  
+For this phase, we build up a comprehensive data suite comprising publically available datasets including DocVQA, FUNSD, IconQA, InfogrpahicsVQA, Tabfact, TextbookVQA, WebSrc, Wildreceipt, WTQ. All these datasets are aligned with the goal of the challenge of image document understanding in very specific domains like table fact-checking, answering questions about receipts, question-answering about infographics, and visual question-answering about documents, etc. This collection consists of a train and test set and can be downloaded from MMFM Data Collection.
+
+### Phase 2  
+For this phase, an alien test set will be released. The original intent of this dataset is to prevent people from overfitting on the publicly available datasets. This test set will consist of data that is similar to the distribution of the phase 1 dataset collection but will not consist of any publically available data sources. This data will be shared by the organizers to the teams in due time. 

RULES.md

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+### General Rules
+- To be eligible for participation in our challenge, you must register your team via CMT: [https://cmt3.research.microsoft.com/MMFM2024](https://cmt3.research.microsoft.com/MMFM2024).
+    - Please select the `challenge track` while registering.
+
+- The organizing committee reserves the right to verify the results and the code.
+- The winners of the competition will be required to open source their code under MIT or more permissive licence. 
+- For eligibility to win the prize money, the participants are required to open source their code and the model weights. For participants who submit results with closed-source models, the organizers will not be able to consider them for the prize money. This is to ensure that the organizers can verify the results and the code. All submitted results would be documented in a leaderboard, closed-source results will be marked.
+
+Please read the full rules here: Challenge Phases and General Rules.  

SUBMISSION_DESC.md

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-Sample submission description
+### Registration
+Please register for the competition and select the challenge track via the workshop site: MMFM2024 on CMT3.   
+
+### Participation
+The challenge will be running in two phases:  
+#### Phase 1
+For this phase, train and test sets are provided. We encourage the participants to read our GitHub Readme.   
+The submission file is a `.csv` file in the following structure (the headings `id,pred,split` cannot be changed)
+A test submission file can be downloaded from [here](https://drive.google.com/file/d/1o8l1r9jTAYcuBgM95rV4v1lbpv2VRoYu/view?usp=sharing)  
+
+```
+id,pred,split
+docvqa_docvqa_0_4,1000,public
+docvqa_docvqa_5_0,12:00,public
+docvqa_docvqa_12_0,70,public
+docvqa_docvqa_21_2,Balance sheet,public
+docvqa_docvqa_35_1,1969,public
+docvqa_docvqa_46_1,No,public
+docvqa_docvqa_51_1,75,public
+docvqa_docvqa_58_6,Conversion program,public
+docvqa_docvqa_62_0,1400-05,public
+docvqa_docvqa_63_0,Smoke chamber,public
+docvqa_docvqa_83_2,250,public
+```
+where the 3 columns are sample id, prediction and split (public or private track).  
+The sample id format for the 10 public datasets are:
+
+```
+DocVQA: docvqa_docvqa_x_x  
+InfographicsVQA: infographicvqa_infographicvqa_x_x_x  
+WebSrc: websrc_websrc_x_x_x  
+WTQ: wtq_wtq_x_x_x  
+IconQA Fill in the blank: iconqa_fill_in_blank_iconqa_x_x  
+FunSD: funsd_funsd_x_x_x_x_x  
+IconQA Choose text: iconqa_choose_txt_iconqa_x_x  
+WildReceipt: wildreceipt_wildreceipt_x_x_x_x_x  
+TextbookQA: textbookqa_textbookqa_x_x_x_x_x  
+TabFact: tabfact_tabfact_x_x_x
+```
+The sample id consists of 3 components: dataset name, sub dataset name and the original sample id. 
+The sub dataset name is the same as dataset name except for `iconqa_fill_in_blank_iconqa` and `iconqa_choose_txt_iconqa` 
+Here `x_x` is the original sample id given in the `processed_data/dataset_name/converted_output_test.json` file. 
+The test submission file [here](https://drive.google.com/file/d/1o8l1r9jTAYcuBgM95rV4v1lbpv2VRoYu/view?usp=sharing) can be used as a template where you can find all the sample ids   
+
+
+#### Phase 2
+An alien test set will be released after the phase 1 deadline. The participants will be required to submit their results on the alien test set. Again, with the code and the model weights (which should be the same as phase 1).  
+
+Only participants who submitted results for Phase 1 before its deadline will receive access to Phase 2 evaluation data, and only participants who submitted Phase 2 results will be eligible for the competition.  

category_to_sample_ids_dict.json

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+{"docvqa": ["docvqa_docvqa_0_4", "docvqa_docvqa_5_0", "docvqa_docvqa_12_0", "docvqa_docvqa_21_2", "docvqa_docvqa_35_1", "docvqa_docvqa_46_1", "docvqa_docvqa_51_1", "docvqa_docvqa_58_6", "docvqa_docvqa_62_0", "docvqa_docvqa_63_0", "docvqa_docvqa_83_2", "docvqa_docvqa_88_1", "docvqa_docvqa_90_1", "docvqa_docvqa_107_3", "docvqa_docvqa_111_4", "docvqa_docvqa_123_3", "docvqa_docvqa_123_7", "docvqa_docvqa_127_3", "docvqa_docvqa_131_3", "docvqa_docvqa_132_1", "docvqa_docvqa_136_0", "docvqa_docvqa_151_2", "docvqa_docvqa_152_1", "docvqa_docvqa_163_0", "docvqa_docvqa_165_3", "docvqa_docvqa_172_0", "docvqa_docvqa_176_0", "docvqa_docvqa_178_2", "docvqa_docvqa_182_6", "docvqa_docvqa_189_2", "docvqa_docvqa_191_5", "docvqa_docvqa_192_0", "docvqa_docvqa_200_5", "docvqa_docvqa_206_3", "docvqa_docvqa_210_1", "docvqa_docvqa_210_2", "docvqa_docvqa_210_5", "docvqa_docvqa_216_3", "docvqa_docvqa_228_4", "docvqa_docvqa_234_1", "docvqa_docvqa_236_0", "docvqa_docvqa_241_0", "docvqa_docvqa_267_1", "docvqa_docvqa_270_2", "docvqa_docvqa_276_0", "docvqa_docvqa_301_1", "docvqa_docvqa_308_0", "docvqa_docvqa_314_0", "docvqa_docvqa_318_1", "docvqa_docvqa_319_2", "docvqa_docvqa_321_0", "docvqa_docvqa_324_0", "docvqa_docvqa_327_0", "docvqa_docvqa_333_1", "docvqa_docvqa_344_0", "docvqa_docvqa_360_0", "docvqa_docvqa_384_8", "docvqa_docvqa_397_0", "docvqa_docvqa_397_1", "docvqa_docvqa_401_1", "docvqa_docvqa_417_0", "docvqa_docvqa_420_0", "docvqa_docvqa_421_0", "docvqa_docvqa_426_0", "docvqa_docvqa_431_4", "docvqa_docvqa_437_3", "docvqa_docvqa_439_1", "docvqa_docvqa_439_4", "docvqa_docvqa_440_5", "docvqa_docvqa_446_1", "docvqa_docvqa_450_0", "docvqa_docvqa_456_5", "docvqa_docvqa_459_2", "docvqa_docvqa_465_2", "docvqa_docvqa_466_0", "docvqa_docvqa_476_0", "docvqa_docvqa_478_3", "docvqa_docvqa_487_1", "docvqa_docvqa_487_2", "docvqa_docvqa_491_0", "docvqa_docvqa_492_5", "docvqa_docvqa_499_1", "docvqa_docvqa_516_1", "docvqa_docvqa_517_3", "docvqa_docvqa_518_7", "docvqa_docvqa_519_1", "docvqa_docvqa_520_0", "docvqa_docvqa_524_0", "docvqa_docvqa_525_1", "docvqa_docvqa_526_4", "docvqa_docvqa_529_1", "docvqa_docvqa_538_1", "docvqa_docvqa_543_0", "docvqa_docvqa_543_1", "docvqa_docvqa_543_2", "docvqa_docvqa_562_2", "docvqa_docvqa_567_6", "docvqa_docvqa_569_0", "docvqa_docvqa_578_3", "docvqa_docvqa_599_0", "docvqa_docvqa_605_3", "docvqa_docvqa_609_0", "docvqa_docvqa_610_0", "docvqa_docvqa_626_7", "docvqa_docvqa_627_0", "docvqa_docvqa_652_7", "docvqa_docvqa_656_4", "docvqa_docvqa_656_5", "docvqa_docvqa_660_9", "docvqa_docvqa_678_5", "docvqa_docvqa_685_2", "docvqa_docvqa_691_2", "docvqa_docvqa_692_1", "docvqa_docvqa_694_3", "docvqa_docvqa_697_1", "docvqa_docvqa_707_1", "docvqa_docvqa_714_0", "docvqa_docvqa_714_4", "docvqa_docvqa_722_1", "docvqa_docvqa_724_1", "docvqa_docvqa_725_3", "docvqa_docvqa_725_4", "docvqa_docvqa_727_7", "docvqa_docvqa_732_1", "docvqa_docvqa_749_1", "docvqa_docvqa_758_0", "docvqa_docvqa_759_2", "docvqa_docvqa_763_0", "docvqa_docvqa_798_3", "docvqa_docvqa_802_1", "docvqa_docvqa_802_3", "docvqa_docvqa_803_3", "docvqa_docvqa_803_7", "docvqa_docvqa_811_2", "docvqa_docvqa_821_6", "docvqa_docvqa_830_1", "docvqa_docvqa_857_0", "docvqa_docvqa_861_1", "docvqa_docvqa_864_6", "docvqa_docvqa_865_1", "docvqa_docvqa_878_4", "docvqa_docvqa_880_0", "docvqa_docvqa_884_6", "docvqa_docvqa_898_7", "docvqa_docvqa_913_1", "docvqa_docvqa_932_0", "docvqa_docvqa_939_1", "docvqa_docvqa_952_0", "docvqa_docvqa_960_3", "docvqa_docvqa_963_2", "docvqa_docvqa_971_3", "docvqa_docvqa_974_1", "docvqa_docvqa_977_5", "docvqa_docvqa_1000_0", "docvqa_docvqa_1024_2", "docvqa_docvqa_1028_0", "docvqa_docvqa_1032_0", "docvqa_docvqa_1038_3", "docvqa_docvqa_1041_5", "docvqa_docvqa_1045_5", "docvqa_docvqa_1047_0", "docvqa_docvqa_1049_2", "docvqa_docvqa_1050_0", "docvqa_docvqa_1050_1", "docvqa_docvqa_1064_0", "docvqa_docvqa_1064_3", "docvqa_docvqa_1072_1", "docvqa_docvqa_1078_4", "docvqa_docvqa_1080_3", "docvqa_docvqa_1089_3", "docvqa_docvqa_1098_1", "docvqa_docvqa_1098_2", "docvqa_docvqa_1108_0", "docvqa_docvqa_1108_1", "docvqa_docvqa_1127_3", "docvqa_docvqa_1141_0", "docvqa_docvqa_1143_3", "docvqa_docvqa_1151_0", "docvqa_docvqa_1153_2", "docvqa_docvqa_1161_9", "docvqa_docvqa_1162_3", "docvqa_docvqa_1169_1", "docvqa_docvqa_1177_3", "docvqa_docvqa_1180_2", "docvqa_docvqa_1186_1", "docvqa_docvqa_1191_3", "docvqa_docvqa_1192_1", "docvqa_docvqa_1210_0", "docvqa_docvqa_1225_6", "docvqa_docvqa_1230_4", "docvqa_docvqa_1233_2", "docvqa_docvqa_1248_1", "docvqa_docvqa_1249_1", "docvqa_docvqa_1255_0", "docvqa_docvqa_1257_1", "docvqa_docvqa_1259_2", "docvqa_docvqa_1261_1", "docvqa_docvqa_1265_4", "docvqa_docvqa_1276_2", "docvqa_docvqa_1281_0"], "infographicvqa": ["infographicvqa_infographicvqa_1_0_2", "infographicvqa_infographicvqa_1_1_2", "infographicvqa_infographicvqa_1_3_0", "infographicvqa_infographicvqa_1_6_2", "infographicvqa_infographicvqa_1_12_1", "infographicvqa_infographicvqa_1_16_2", "infographicvqa_infographicvqa_1_17_0", "infographicvqa_infographicvqa_1_19_3", "infographicvqa_infographicvqa_1_20_5", "infographicvqa_infographicvqa_1_20_6", "infographicvqa_infographicvqa_1_25_5", "infographicvqa_infographicvqa_1_27_1", "infographicvqa_infographicvqa_1_28_2", "infographicvqa_infographicvqa_1_31_7", "infographicvqa_infographicvqa_1_32_6", "infographicvqa_infographicvqa_1_36_2", "infographicvqa_infographicvqa_1_37_10", "infographicvqa_infographicvqa_1_38_0", "infographicvqa_infographicvqa_1_38_1", "infographicvqa_infographicvqa_1_39_0", "infographicvqa_infographicvqa_1_41_12", "infographicvqa_infographicvqa_1_42_1", "infographicvqa_infographicvqa_1_45_3", "infographicvqa_infographicvqa_1_46_1", "infographicvqa_infographicvqa_1_49_2", "infographicvqa_infographicvqa_1_49_9", "infographicvqa_infographicvqa_1_49_13", "infographicvqa_infographicvqa_1_53_0", "infographicvqa_infographicvqa_1_56_1", "infographicvqa_infographicvqa_1_56_6", "infographicvqa_infographicvqa_1_56_7", "infographicvqa_infographicvqa_1_61_1", "infographicvqa_infographicvqa_1_66_4", "infographicvqa_infographicvqa_1_67_6", "infographicvqa_infographicvqa_1_68_1", "infographicvqa_infographicvqa_1_70_2", "infographicvqa_infographicvqa_1_75_2", "infographicvqa_infographicvqa_1_75_6", "infographicvqa_infographicvqa_1_76_9", "infographicvqa_infographicvqa_1_82_9", "infographicvqa_infographicvqa_1_83_4", "infographicvqa_infographicvqa_1_87_2", "infographicvqa_infographicvqa_1_95_4", "infographicvqa_infographicvqa_1_96_3", "infographicvqa_infographicvqa_1_99_1", "infographicvqa_infographicvqa_1_99_7", "infographicvqa_infographicvqa_1_99_9", "infographicvqa_infographicvqa_1_99_15", "infographicvqa_infographicvqa_1_100_2", "infographicvqa_infographicvqa_1_100_5", "infographicvqa_infographicvqa_1_101_3", "infographicvqa_infographicvqa_1_104_0", "infographicvqa_infographicvqa_1_107_7", "infographicvqa_infographicvqa_1_119_0", "infographicvqa_infographicvqa_1_123_4", "infographicvqa_infographicvqa_1_125_1", "infographicvqa_infographicvqa_1_133_2", "infographicvqa_infographicvqa_1_134_5", "infographicvqa_infographicvqa_1_134_8", "infographicvqa_infographicvqa_1_134_11", "infographicvqa_infographicvqa_1_135_10", "infographicvqa_infographicvqa_1_135_24", "infographicvqa_infographicvqa_1_135_27", "infographicvqa_infographicvqa_1_136_0", "infographicvqa_infographicvqa_1_137_0", "infographicvqa_infographicvqa_1_140_0", "infographicvqa_infographicvqa_1_143_6", "infographicvqa_infographicvqa_1_145_1", "infographicvqa_infographicvqa_1_149_4", "infographicvqa_infographicvqa_1_150_0", "infographicvqa_infographicvqa_1_157_1", "infographicvqa_infographicvqa_1_160_3", "infographicvqa_infographicvqa_1_165_0", "infographicvqa_infographicvqa_1_165_1", "infographicvqa_infographicvqa_1_167_4", "infographicvqa_infographicvqa_1_169_7", "infographicvqa_infographicvqa_1_180_1", "infographicvqa_infographicvqa_1_180_3", "infographicvqa_infographicvqa_1_180_8", "infographicvqa_infographicvqa_1_180_9", "infographicvqa_infographicvqa_1_181_0", "infographicvqa_infographicvqa_1_184_2", "infographicvqa_infographicvqa_1_184_3", "infographicvqa_infographicvqa_1_185_1", "infographicvqa_infographicvqa_1_186_1", "infographicvqa_infographicvqa_1_192_2", "infographicvqa_infographicvqa_1_192_14", "infographicvqa_infographicvqa_1_192_15", "infographicvqa_infographicvqa_1_198_6", "infographicvqa_infographicvqa_1_201_6", "infographicvqa_infographicvqa_1_201_8", "infographicvqa_infographicvqa_1_208_0", "infographicvqa_infographicvqa_1_219_0", "infographicvqa_infographicvqa_1_222_4", "infographicvqa_infographicvqa_1_225_0", "infographicvqa_infographicvqa_1_225_4", "infographicvqa_infographicvqa_1_232_1", "infographicvqa_infographicvqa_1_233_0", "infographicvqa_infographicvqa_1_243_2", "infographicvqa_infographicvqa_1_247_1", "infographicvqa_infographicvqa_1_248_9", "infographicvqa_infographicvqa_1_256_9", "infographicvqa_infographicvqa_1_260_3", "infographicvqa_infographicvqa_1_261_6", "infographicvqa_infographicvqa_1_262_0", "infographicvqa_infographicvqa_1_264_0", "infographicvqa_infographicvqa_1_265_3", "infographicvqa_infographicvqa_1_267_8", "infographicvqa_infographicvqa_1_271_1", "infographicvqa_infographicvqa_1_271_3", "infographicvqa_infographicvqa_1_275_0", "infographicvqa_infographicvqa_1_275_4", "infographicvqa_infographicvqa_1_276_0", "infographicvqa_infographicvqa_1_277_2", "infographicvqa_infographicvqa_1_279_1", "infographicvqa_infographicvqa_1_290_8", "infographicvqa_infographicvqa_1_292_6", "infographicvqa_infographicvqa_1_292_7", "infographicvqa_infographicvqa_1_293_1", "infographicvqa_infographicvqa_1_302_4", "infographicvqa_infographicvqa_1_303_4", "infographicvqa_infographicvqa_1_304_0", "infographicvqa_infographicvqa_1_304_2", "infographicvqa_infographicvqa_1_304_4", "infographicvqa_infographicvqa_1_307_4", "infographicvqa_infographicvqa_1_309_10", "infographicvqa_infographicvqa_1_312_6", "infographicvqa_infographicvqa_1_318_18", "infographicvqa_infographicvqa_1_321_1", "infographicvqa_infographicvqa_1_323_3", "infographicvqa_infographicvqa_1_324_0", "infographicvqa_infographicvqa_1_327_4", "infographicvqa_infographicvqa_1_327_6", "infographicvqa_infographicvqa_1_331_0", "infographicvqa_infographicvqa_1_334_1", "infographicvqa_infographicvqa_1_340_0", "infographicvqa_infographicvqa_1_347_1", "infographicvqa_infographicvqa_1_349_4", "infographicvqa_infographicvqa_1_353_1", "infographicvqa_infographicvqa_1_357_1", "infographicvqa_infographicvqa_1_359_4", "infographicvqa_infographicvqa_1_363_3", "infographicvqa_infographicvqa_1_365_0", "infographicvqa_infographicvqa_1_367_2", "infographicvqa_infographicvqa_1_375_0", "infographicvqa_infographicvqa_1_381_3", "infographicvqa_infographicvqa_1_382_0", "infographicvqa_infographicvqa_1_385_0", "infographicvqa_infographicvqa_1_389_0", "infographicvqa_infographicvqa_1_392_0", "infographicvqa_infographicvqa_1_393_8", 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"textbookqa_textbookqa_1_82_193_3_14", "textbookqa_textbookqa_1_82_228_3_14", "textbookqa_textbookqa_1_82_229_3_14", "textbookqa_textbookqa_1_82_279_3_16", "textbookqa_textbookqa_1_82_294_3_16", "textbookqa_textbookqa_1_82_309_3_14", "textbookqa_textbookqa_1_82_356_3_15", "textbookqa_textbookqa_1_82_371_3_16", "textbookqa_textbookqa_1_82_382_3_6", "textbookqa_textbookqa_1_82_383_3_6", "textbookqa_textbookqa_1_82_387_3_12", "textbookqa_textbookqa_1_82_395_3_16", "textbookqa_textbookqa_1_82_417_3_15", "textbookqa_textbookqa_1_82_430_3_10", "textbookqa_textbookqa_1_82_432_3_10", "textbookqa_textbookqa_1_82_451_3_21", "textbookqa_textbookqa_1_82_455_3_16", "textbookqa_textbookqa_1_82_457_3_16", "textbookqa_textbookqa_1_82_464_3_14", "textbookqa_textbookqa_1_82_477_3_18", "textbookqa_textbookqa_1_82_521_3_8", "textbookqa_textbookqa_1_82_535_3_13", "textbookqa_textbookqa_1_82_536_3_13", "textbookqa_textbookqa_1_82_544_3_13", "textbookqa_textbookqa_1_82_619_3_13", "textbookqa_textbookqa_1_82_629_3_8", "textbookqa_textbookqa_1_82_630_3_14", "textbookqa_textbookqa_1_82_632_3_14", "textbookqa_textbookqa_1_82_634_3_14", "textbookqa_textbookqa_1_82_655_3_14", "textbookqa_textbookqa_1_82_681_3_17", "textbookqa_textbookqa_1_82_695_3_15", "textbookqa_textbookqa_1_82_740_3_6", "textbookqa_textbookqa_1_82_750_3_14", "textbookqa_textbookqa_1_82_758_3_13", "textbookqa_textbookqa_1_82_759_3_13", "textbookqa_textbookqa_1_82_780_3_12", "textbookqa_textbookqa_1_82_782_3_12", "textbookqa_textbookqa_1_82_794_3_12", "textbookqa_textbookqa_1_82_818_3_7", "textbookqa_textbookqa_1_82_847_3_7", "textbookqa_textbookqa_1_82_891_3_21", "textbookqa_textbookqa_1_82_902_3_6", "textbookqa_textbookqa_1_84_6_3_14", "textbookqa_textbookqa_1_84_29_3_12", "textbookqa_textbookqa_1_84_35_3_2", "textbookqa_textbookqa_1_84_54_3_10", "textbookqa_textbookqa_1_84_64_3_7", "textbookqa_textbookqa_1_84_72_3_17", "textbookqa_textbookqa_1_84_104_3_7", "textbookqa_textbookqa_1_87_29_3_11", "textbookqa_textbookqa_1_87_35_3_6", "textbookqa_textbookqa_1_87_40_3_6", "textbookqa_textbookqa_1_87_49_3_11", "textbookqa_textbookqa_1_87_55_3_8", "textbookqa_textbookqa_1_87_64_3_10", "textbookqa_textbookqa_1_87_67_3_10", "textbookqa_textbookqa_1_87_75_3_11", "textbookqa_textbookqa_1_87_76_3_11", "textbookqa_textbookqa_1_87_77_3_11", "textbookqa_textbookqa_1_87_100_3_12", "textbookqa_textbookqa_1_87_101_3_12", "textbookqa_textbookqa_1_87_119_3_10", "textbookqa_textbookqa_1_87_129_3_6", "textbookqa_textbookqa_1_87_134_3_8", "textbookqa_textbookqa_1_88_4_3_3", "textbookqa_textbookqa_1_88_21_3_3", "textbookqa_textbookqa_1_88_22_3_3", "textbookqa_textbookqa_1_96_9_3_16", "textbookqa_textbookqa_1_134_1_3_3", "textbookqa_textbookqa_1_134_27_3_4", "textbookqa_textbookqa_1_134_31_3_1", "textbookqa_textbookqa_1_134_50_3_4", "textbookqa_textbookqa_1_134_55_3_0", "textbookqa_textbookqa_1_134_77_3_4", "textbookqa_textbookqa_1_134_79_3_0", "textbookqa_textbookqa_1_134_90_3_6", "textbookqa_textbookqa_1_134_102_3_1", "textbookqa_textbookqa_1_134_109_3_5", "textbookqa_textbookqa_1_134_121_3_3", "textbookqa_textbookqa_1_134_131_3_1", "textbookqa_textbookqa_1_134_132_3_1", "textbookqa_textbookqa_1_137_7_3_3", "textbookqa_textbookqa_1_141_1_3_4", "textbookqa_textbookqa_1_141_10_3_4", "textbookqa_textbookqa_1_141_14_3_4", "textbookqa_textbookqa_1_141_40_3_3", "textbookqa_textbookqa_1_141_43_3_3", "textbookqa_textbookqa_1_141_57_3_2", "textbookqa_textbookqa_1_141_59_3_2", "textbookqa_textbookqa_1_141_61_3_17", "textbookqa_textbookqa_1_141_65_3_17", "textbookqa_textbookqa_1_141_73_3_8", "textbookqa_textbookqa_1_142_20_3_11", "textbookqa_textbookqa_1_142_27_3_12", "textbookqa_textbookqa_1_142_50_3_9", "textbookqa_textbookqa_1_142_68_3_8", "textbookqa_textbookqa_1_142_74_3_11", "textbookqa_textbookqa_1_142_111_3_13", "textbookqa_textbookqa_1_142_117_3_13", "textbookqa_textbookqa_1_142_137_3_15"], "tabfact": ["tabfact_tabfact_1_0_9", "tabfact_tabfact_1_5_3", "tabfact_tabfact_1_10_6", "tabfact_tabfact_1_18_8", "tabfact_tabfact_1_41_0", "tabfact_tabfact_1_43_5", "tabfact_tabfact_1_48_9", "tabfact_tabfact_1_52_2", "tabfact_tabfact_1_52_7", "tabfact_tabfact_1_71_5", "tabfact_tabfact_1_75_4", "tabfact_tabfact_1_77_5", "tabfact_tabfact_1_92_0", "tabfact_tabfact_1_104_4", "tabfact_tabfact_1_108_9", "tabfact_tabfact_1_114_4", "tabfact_tabfact_1_117_4", "tabfact_tabfact_1_129_6", "tabfact_tabfact_1_131_2", "tabfact_tabfact_1_142_9", "tabfact_tabfact_1_149_5", "tabfact_tabfact_1_152_5", "tabfact_tabfact_1_154_1", "tabfact_tabfact_1_159_0", "tabfact_tabfact_1_166_0", "tabfact_tabfact_1_168_2", "tabfact_tabfact_1_168_7", "tabfact_tabfact_1_175_1", "tabfact_tabfact_1_180_4", "tabfact_tabfact_1_183_2", "tabfact_tabfact_1_183_5", "tabfact_tabfact_1_188_4", "tabfact_tabfact_1_205_4", "tabfact_tabfact_1_227_7", "tabfact_tabfact_1_229_7", "tabfact_tabfact_1_229_8", "tabfact_tabfact_1_235_2", "tabfact_tabfact_1_251_7", "tabfact_tabfact_1_256_0", "tabfact_tabfact_1_260_4", "tabfact_tabfact_1_262_9", "tabfact_tabfact_1_263_5", "tabfact_tabfact_1_266_0", "tabfact_tabfact_1_267_8", "tabfact_tabfact_1_269_1", "tabfact_tabfact_1_281_7", "tabfact_tabfact_1_295_3", "tabfact_tabfact_1_317_7", "tabfact_tabfact_1_328_7", "tabfact_tabfact_1_328_8", "tabfact_tabfact_1_346_2", "tabfact_tabfact_1_348_6", "tabfact_tabfact_1_351_4", "tabfact_tabfact_1_355_8", "tabfact_tabfact_1_361_4", "tabfact_tabfact_1_362_5", "tabfact_tabfact_1_363_0", "tabfact_tabfact_1_364_3", "tabfact_tabfact_1_368_9", "tabfact_tabfact_1_376_5", "tabfact_tabfact_1_378_4", "tabfact_tabfact_1_383_14", "tabfact_tabfact_1_383_17", "tabfact_tabfact_1_403_3", "tabfact_tabfact_1_403_5", "tabfact_tabfact_1_406_3", "tabfact_tabfact_1_414_2", "tabfact_tabfact_1_433_0", "tabfact_tabfact_1_434_1", "tabfact_tabfact_1_435_9", "tabfact_tabfact_1_436_5", "tabfact_tabfact_1_439_7", "tabfact_tabfact_1_440_0", "tabfact_tabfact_1_441_2", "tabfact_tabfact_1_444_8", "tabfact_tabfact_1_453_4", "tabfact_tabfact_1_458_0", "tabfact_tabfact_1_458_2", "tabfact_tabfact_1_458_5", "tabfact_tabfact_1_477_4", "tabfact_tabfact_1_483_9", "tabfact_tabfact_1_484_4", "tabfact_tabfact_1_492_5", "tabfact_tabfact_1_519_2", "tabfact_tabfact_1_520_5", "tabfact_tabfact_1_535_1", "tabfact_tabfact_1_555_5", "tabfact_tabfact_1_561_4", "tabfact_tabfact_1_561_5", "tabfact_tabfact_1_567_6", "tabfact_tabfact_1_586_9", "tabfact_tabfact_1_593_1", "tabfact_tabfact_1_597_7", "tabfact_tabfact_1_598_2", "tabfact_tabfact_1_602_9", "tabfact_tabfact_1_611_7", "tabfact_tabfact_1_618_0", "tabfact_tabfact_1_624_15", "tabfact_tabfact_1_626_0", "tabfact_tabfact_1_626_8", "tabfact_tabfact_1_627_1", "tabfact_tabfact_1_629_6", "tabfact_tabfact_1_634_8", "tabfact_tabfact_1_653_1", "tabfact_tabfact_1_659_3", "tabfact_tabfact_1_662_2", "tabfact_tabfact_1_692_6", "tabfact_tabfact_1_696_5", "tabfact_tabfact_1_696_9", "tabfact_tabfact_1_697_7", "tabfact_tabfact_1_707_2", "tabfact_tabfact_1_717_6", "tabfact_tabfact_1_741_4", "tabfact_tabfact_1_748_3", "tabfact_tabfact_1_748_8", "tabfact_tabfact_1_757_2", "tabfact_tabfact_1_758_2", "tabfact_tabfact_1_762_8", "tabfact_tabfact_1_771_3", "tabfact_tabfact_1_813_4", "tabfact_tabfact_1_822_9", "tabfact_tabfact_1_825_1", "tabfact_tabfact_1_833_1", "tabfact_tabfact_1_836_9", "tabfact_tabfact_1_840_0", "tabfact_tabfact_1_892_4", "tabfact_tabfact_1_895_2", "tabfact_tabfact_1_912_0", "tabfact_tabfact_1_921_2", "tabfact_tabfact_1_923_5", "tabfact_tabfact_1_930_0", "tabfact_tabfact_1_932_3", "tabfact_tabfact_1_952_3", "tabfact_tabfact_1_953_2", "tabfact_tabfact_1_969_5", "tabfact_tabfact_1_972_6", "tabfact_tabfact_1_984_1", "tabfact_tabfact_1_999_11", "tabfact_tabfact_1_999_13", "tabfact_tabfact_1_1017_10", "tabfact_tabfact_1_1045_0", "tabfact_tabfact_1_1058_1", "tabfact_tabfact_1_1062_4", "tabfact_tabfact_1_1081_1", "tabfact_tabfact_1_1084_1", "tabfact_tabfact_1_1098_7", "tabfact_tabfact_1_1100_0", "tabfact_tabfact_1_1124_0", "tabfact_tabfact_1_1130_4", "tabfact_tabfact_1_1149_0", "tabfact_tabfact_1_1150_1", "tabfact_tabfact_1_1181_3", "tabfact_tabfact_1_1203_4", "tabfact_tabfact_1_1211_0", "tabfact_tabfact_1_1213_3", "tabfact_tabfact_1_1231_2", "tabfact_tabfact_1_1233_7", "tabfact_tabfact_1_1243_4", "tabfact_tabfact_1_1244_2", "tabfact_tabfact_1_1245_2", "tabfact_tabfact_1_1248_0", "tabfact_tabfact_1_1256_4", "tabfact_tabfact_1_1270_2", "tabfact_tabfact_1_1274_1", "tabfact_tabfact_1_1288_1", "tabfact_tabfact_1_1307_0", "tabfact_tabfact_1_1340_8", "tabfact_tabfact_1_1366_6", "tabfact_tabfact_1_1381_3", "tabfact_tabfact_1_1382_0", "tabfact_tabfact_1_1385_4", "tabfact_tabfact_1_1390_1", "tabfact_tabfact_1_1391_5", "tabfact_tabfact_1_1401_2", "tabfact_tabfact_1_1432_0", "tabfact_tabfact_1_1443_2", "tabfact_tabfact_1_1445_4", "tabfact_tabfact_1_1455_3", "tabfact_tabfact_1_1490_7", "tabfact_tabfact_1_1497_2", "tabfact_tabfact_1_1504_3", "tabfact_tabfact_1_1507_8", "tabfact_tabfact_1_1512_1", "tabfact_tabfact_1_1537_2", "tabfact_tabfact_1_1538_3", "tabfact_tabfact_1_1563_1", "tabfact_tabfact_1_1580_6", "tabfact_tabfact_1_1582_2", "tabfact_tabfact_1_1591_1", "tabfact_tabfact_1_1597_1", "tabfact_tabfact_1_1608_3", "tabfact_tabfact_1_1624_2", "tabfact_tabfact_1_1634_2", "tabfact_tabfact_1_1647_1", "tabfact_tabfact_1_1649_3", "tabfact_tabfact_1_1653_1", "tabfact_tabfact_1_1671_3", "tabfact_tabfact_1_1671_7", "tabfact_tabfact_1_1686_0", "tabfact_tabfact_1_1695_0"]}

conf.json

@@ -1,15 +1,19 @@
 {
     "COMPETITION_TYPE": "generic",
-    "SUBMISSION_LIMIT": 30,
+    "SUBMISSION_LIMIT": 50,
     "TIME_LIMIT": 3600,
-    "SELECTION_LIMIT": 2,
+    "SELECTION_LIMIT": 1,
     "HARDWARE": "cpu-basic",
     "END_DATE": "2024-12-31",
-    "EVAL_HIGHER_IS_BETTER": 1,
+    "EVAL_HIGHER_IS_BETTER": true,
     "SUBMISSION_ID_COLUMN": "id",
     "SUBMISSION_COLUMNS": "id,pred",
-    "SUBMISSION_ROWS": 10,
+    "SUBMISSION_ROWS": 10000,
     "EVAL_METRIC": "custom",
-    "LOGO": "https://mysite.com/mylogo.png",
-    "SCORING_METRIC" : "metric1"
+    "LOGO": "https://i.ibb.co/TPYSkhS/mini-logo.png",
+    "DATASET": "",
+    "SUBMISSION_FILENAMES": [
+        "submission.csv"
+    ],
+    "SCORING_METRIC": "public_overall"
 }

metric.py

@@ -1,27 +1,437 @@
 import os
-from openai import OpenAI
-if "OPENAI" in os.environ:
-    print('Loaded OPENAI Key.')
-else:
-    print('Can not load the OPENAI key.')
-client = OpenAI(api_key = os.environ['OPENAI'])
+# from openai import OpenAI
+# if "OPENAI" in os.environ:
+#     print('Loaded OPENAI Key.')
+# else:
+#     print('Can not load the OPENAI key.')
+# client = OpenAI(api_key = os.environ['OPENAI'])
 
 import pandas as pd
 from huggingface_hub import hf_hub_download
 
+# from lib.MMMU.eval.utils.data_utils import save_json, CAT_SHORT2LONG
+print('current dir:', os.getcwd())
+# list all files and folders in the current directory
+print('list all files and folders in the current directory:', os.listdir())
+
+# from lib.MMMU.eval.utils.eval_utils import evaluate, parse_multi_choice_response, parse_open_response, calculate_ins_level_acc
+
+import numpy as np
+import tqdm
+import torch
+import re
+from torch import bfloat16
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import json
+from tqdm import tqdm
+# from eval_mixtral import LLM_eval, creat_prompt
+# from eval_mixtral import creat_prompt
+# from llm_evaluator import chat_llm
+
+from dotenv import load_dotenv
+
+from genai.client import Client
+from genai.credentials import Credentials
+from genai.schema import (
+    DecodingMethod,
+    HumanMessage,
+    ModerationHAP,
+    ModerationHAPInput,
+    ModerationHAPOutput,
+    ModerationParameters,
+    SystemMessage,
+    TextGenerationParameters,
+)
+from genai.text.generation import CreateExecutionOptions
+
+load_dotenv()
+
+
+#######################################################################################
+#######################################################################################
+############################  Mixtral eval ############################################
+#######################################################################################
+#######################################################################################
+def heading(text: str) -> str:
+    """Helper function for centering text."""
+    return "\n" + f" {text} ".center(80, "=") + "\n"
+
+
+def chat_llm_batch(model_id, prompts, limit= 20):
+    parameters = TextGenerationParameters(
+        decoding_method=DecodingMethod.SAMPLE, max_new_tokens=128, min_new_tokens=30, temperature=0.7, top_k=50, top_p=1
+    )
+    client = Client(credentials=Credentials.from_env())
+    response_list = []
+    for response in client.text.generation.create(
+        model_id = model_id,
+        inputs = prompts, #  here each prompt is the concatenation of system prompt and user prompt
+        execution_options=CreateExecutionOptions(concurrency_limit=limit, ordered=False),
+        parameters=parameters,
+    ):
+        response_list.append(response.results[0].generated_text)
+    return response_list
+
+def creat_prompt(model_id, tokenizer=None, question=None, answer=None, pred=None,):
+    messages = [
+        {
+            "role": "system",
+            "content":
+                "You are an intelligent chatbot designed for evaluating the correctness of generative outputs for question-answer pairs. "
+                "Your task is to compare the predicted answer with the correct answer and determine if they match meaningfully. Here's how you can accomplish the task:\n"
+                "------\n"
+                "##INSTRUCTIONS:\n"
+                "- Focus on the meaningful match between the predicted answer and the correct answer.\n"
+                "- Consider synonyms or paraphrases as valid matches.\n"
+                "- Evaluate the correctness of the prediction compared to the answer."
+        },
+        {
+            "role": "user",
+            "content":
+                "Please evaluate the following question-answer pair:\n\n"
+                f"Question: {question.capitalize()}\n"
+                f"Correct Answer: {answer.lower()}\n"
+                f"Predicted Answer: {pred.lower()}\n\n"
+                "Evaluate if the answer is correct with yes/no and assign a correctness score between 0 and 5, where 0 indicates incorrect answer, and 5 signifies the highest meaningful match. "
+                "Please generate the response in the form of a Python dictionary string with keys 'pred' and 'score', where value of 'pred' is  a string of 'yes' or 'no' and value of 'score' is in INTEGER, not STRING. "
+                "DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
+                "For example, your response should look like this: {'pred': 'no', 'score': 0}."
+        }
+    ]
+
+    if 'mistralai' in model_id:
+        prompt = f'<s>[INST] {messages[0]["content"].strip()}\n\n{messages[1]["content"].strip()} [/INST]'
+    elif 'NousResearch' in model_id:
+        prompt = tokenizer.apply_chat_template(messages, tokenize=False)
+        prompt = prompt + '<|im_start|>assistant'
+    elif 'api' in model_id:
+        prompt = messages
+    else:
+        raise NotImplementedError
+    return prompt
+
+## Mixtral Evaluation
+def mixtral_eval_api(submission_dict_of_dict, solution_dict_of_dict, category=None,
+                     model_id = "mistralai/mixtral-8x7b-instruct-v01", batch_size = 16,
+                     category_to_sample_ids_dict = None, answer_dict_for_mixtral_eval = None, limit = None):
+    id_list = category_to_sample_ids_dict[category]
+    examples_to_eval = []
+    evals = []
+
+    sample_id_list = []
+    # for row, output in submission_dict_of_dict.iterrows():
+    for output_id, output in submission_dict_of_dict.items():
+        if output_id in id_list:
+            sample_id_list.append(output_id)
+    steps = int(np.ceil(len(sample_id_list) / batch_size))
+    for step in tqdm(range(steps)):
+        prompts = []
+
+        # put prompts of a batch of samples into a list
+        for item in sample_id_list[step * batch_size: (step + 1) * batch_size]:
+            # sample_key = submission_dict_of_dict.iloc[item]['id']
+            # answer = str(submission_dict_of_dict.iloc[item]['pred'])
+            # label = str(solution_dict_of_dict.iloc[item]['pred'])
+            output_id = item
+            sample_key = output_id
+            answer = str(submission_dict_of_dict[output_id]['pred'])
+            label = str(solution_dict_of_dict[output_id]['pred'])
+            print('answer:', answer)
+            print('label:', label)
+            gt_answer = label
+            pred_answer = answer
+            question = answer_dict_for_mixtral_eval[sample_key]['question']
+            examples_to_eval.append({
+                'id': sample_key,
+                "question_type": answer_dict_for_mixtral_eval[sample_key]['question_type'],
+                'answer': gt_answer,
+                'question': question,
+                'parsed_pred': pred_answer,
+            })
+            # the system prompt and user prompt are concatenated
+            prompt = creat_prompt(model_id='mistralai', question=question, answer=gt_answer, pred=pred_answer)
+            prompts.append(prompt)
+        response_list = chat_llm_batch(model_id, prompts, limit=limit)
+        evals.extend(response_list)
+
+    judge_dict = {}
+    pred_correct = 0
+    score_sum = 0
+    for sample_data, sample_eval in zip(examples_to_eval, evals):
+        try:
+            sample_eval = re.match(r".*(\{.*?\}).*", sample_eval, re.S).group(1)
+            sample_eval = sample_eval.replace("'", '"')
+            sample_eval = json.loads(sample_eval)
+            pred = sample_eval['pred']
+            sample_score = sample_eval['score']
+            if pred == 'yes':
+                judge_dict[sample_data['id']] = {'pred': 'Correct', 'score': sample_score}
+                pred_correct += 1
+            else:
+                judge_dict[sample_data['id']] = {'pred': 'Wrong', 'score': sample_score}
+            score_sum += sample_score
+        except:
+            judge_dict[sample_data['id']] = {'pred': 'Wrong', 'score': 0}
+    if len(examples_to_eval) == 0:
+        return {'acc': 0, 'avg_score': 0}
+    # return {'acc': pred_correct / len(examples_to_eval),
+    #                     'avg_score': score_sum / len(examples_to_eval)}
+    return pred_correct / len(examples_to_eval)
+
+
+#######################################################################################
+#######################################################################################
+############################  MMMU eval ###############################################
+#######################################################################################
+#######################################################################################
+def extract_numbers(string):
+    """
+    Exact all forms of numbers from a string with regex.
+    """
+    # Pattern for numbers with commas
+    pattern_commas = r'-?\b\d{1,3}(?:,\d{3})+\b'
+    # Pattern for scientific notation
+    pattern_scientific = r'-?\d+(?:\.\d+)?[eE][+-]?\d+'
+    # Pattern for simple numbers without commas
+    pattern_simple = r'-?(?:\d+\.\d+|\.\d+|\d+\b)(?![eE][+-]?\d+)(?![,\d])'
+
+    # Extract numbers with commas
+    numbers_with_commas = re.findall(pattern_commas, string)
+    # Extract numbers in scientific notation
+    numbers_scientific = re.findall(pattern_scientific, string)
+    # Extract simple numbers without commas
+    numbers_simple = re.findall(pattern_simple, string)
+
+    # Combine all extracted numbers
+    all_numbers = numbers_with_commas + numbers_scientific + numbers_simple
+    return all_numbers
+
+def parse_open_response(response):
+    """
+    Parse the prediction from the generated response.
+    Return a list of predicted strings or numbers.
+    """
+
+    # content = content.strip("\n").strip(".").strip(" ")
+    def get_key_subresponses(response):
+        key_responses = []
+        response = response.strip().strip(".").lower()
+        sub_responses = re.split(r'\.\s(?=[A-Z])|\n', response)
+        indicators_of_keys = ['could be ', 'so ', 'is ',
+                              'thus ', 'therefore ', 'final ', 'answer ', 'result ']
+        key_responses = []
+        for index, resp in enumerate(sub_responses):
+            # if last one, accept it's an equation (the entire response can be just one sentence with equation)
+            if index == len(sub_responses) - 1:
+                indicators_of_keys.extend(['='])
+            shortest_key_response = None  # the shortest response that may contain the answer (tail part of the response)
+            for indicator in indicators_of_keys:
+                if indicator in resp:
+                    if not shortest_key_response:
+                        shortest_key_response = resp.split(indicator)[-1].strip()
+                    else:
+                        if len(resp.split(indicator)[-1].strip()) < len(shortest_key_response):
+                            shortest_key_response = resp.split(indicator)[-1].strip()
+                    # key_responses.append(resp.split(indicator)[1].strip())
+
+            if shortest_key_response:
+                # and it's not trivial
+                if shortest_key_response.strip() not in [":", ",", ".", "!", "?", ";", ":", "'"]:
+                    key_responses.append(shortest_key_response)
+        if len(key_responses) == 0:  # did not found any
+            return [response]
+        return key_responses
+
+    # pdb.set_trace()
+    key_responses = get_key_subresponses(response)
+
+    pred_list = key_responses.copy()  # keep the original string response
+    for resp in key_responses:
+        pred_list.extend(extract_numbers(resp))
+
+    tmp_pred_list = []
+    for i in range(len(pred_list)):
+        tmp_pred_list.extend(normalize_str(pred_list[i]))
+    pred_list = tmp_pred_list
+
+    # remove duplicates
+    pred_list = list(set(pred_list))
+
+    return pred_list
+def check_is_number(string):
+    """
+    Check if the given string a number.
+    """
+    try:
+        float(string.replace(',', ''))
+        return True
+    except ValueError:
+        # check if there's comma inside
+        return False
+def normalize_str(string):
+    """
+    Normalize the str to lower case and make them float numbers if possible.
+    """
+    # check if characters in the string
+
+    # if number, numerize it.
+    string = string.strip()
+
+    is_number = check_is_number(string)
+
+    if is_number:
+        string = string.replace(',', '')
+        string = float(string)
+        # leave 2 decimal
+        string = round(string, 2)
+        return [string]
+    else: # it's likely to be a string
+        # lower it
+        string = string.lower()
+        if len(string) == 1:
+            return [" " + string, string + " "] # avoid trivial matches
+        return [string]
+def eval_open(gold_i, pred_i):
+    """
+    Evaluate an open question instance
+    """
+    correct = False
+    if isinstance(gold_i, list):
+        # use float to avoid trivial matches
+        norm_answers = []
+        for answer in gold_i:
+            norm_answers.extend(normalize_str(answer))
+    else:
+        norm_answers = normalize_str(gold_i)
+    for pred in pred_i: # pred is already normalized in parse response phase
+        if isinstance(pred, str): # if it's a string, then find if ans in the pred_i
+            for norm_ans in norm_answers:
+                # only see if the string answer in the string pred
+                if isinstance(norm_ans, str) and norm_ans in pred:
+                    if not correct:
+                        correct = True
+                    break
+        else: # it's a float number
+            if pred in norm_answers:
+                if not correct:
+                    correct = True
+                break
+    return correct
+def eval_multi_choice(gold_i, pred_i):
+    """
+    Evaluate a multiple choice instance.
+    """
+    correct = False
+    # only they are exactly the same, we consider it as correct
+    if isinstance(gold_i, list):
+        for answer in gold_i:
+            if answer == pred_i:
+                correct = True
+                break
+    else: # gold_i is a string
+        if gold_i == pred_i:
+            correct = True
+    return correct
+
+def evaluate(samples):
+    """
+    Batch evaluation for multiple choice and open questions.
+    """
+    pred_correct = 0
+    judge_dict = dict()
+    for sample in samples:
+        gold_i = sample['answer']
+        pred_i = sample['parsed_pred']
+        if sample['question_type'] == 'multiple-choice':
+            correct = eval_multi_choice(gold_i, pred_i)
+        else: # open question
+            correct = eval_open(gold_i, pred_i)
+
+        if correct:
+            judge_dict[sample['id']] = 'Correct'
+            pred_correct += 1
+        else:
+            judge_dict[sample['id']] = 'Wrong'
+
+    if len(samples) == 0:
+        return None, {'acc': 0}
+    return judge_dict, {'acc': pred_correct / len(samples)}
+
+
+def mmmu_eval(submission_dict_of_dict, solution_dict_of_dict, category=None, category_to_sample_ids_dict = None):
+    """
+    MMMU evaluation on 6 datasets in the 1st phase: iconqa_fill_in_blank,funsd,iconqa_choose_txt,wildreceipt,textbookqa,tabfact
+    only checking whether gt answer is contained in the prediction
+
+    :param submission_dict_of_dict: outputs are predictions of all samples of all datasets
+    :param solution_dict_of_dict:  targets are gt of all samples of all datasets
+    :return:
+    """
+    id_list = category_to_sample_ids_dict[category]
+    examples_to_eval = []
+
+    # for row, output in submission_dict_of_dict.iterrows():  # row is the sample id, output is the prediction of one sample
+    for output_id, output in submission_dict_of_dict.items():  # row is the sample id, output is the prediction of one sample
+        if output_id in id_list:
+            # print('outputs type', type(submission_dict_of_dict), 'targets type', type(submission_dict_of_dict))
+            answer = str(output['pred'])
+            # label = str(solution_dict_of_dict.iloc[row]['pred'])
+            label = str(solution_dict_of_dict[output_id]['pred'])
+            print('answer:', answer)
+            print('label:', label)
+
+            # this is applied on samples with question type of "short-answer"
+            # currently all the samples have the question type of "short-answer"
+            parse_pred = parse_open_response(answer) # mainly for type consistency , e.g. for answer of number, convert str to float
+            examples_to_eval.append({
+                'id': output['id'],
+                "question_type": "short-answer",
+                'answer': label,
+                'parsed_pred': parse_pred,
+            })
+    # judge_dict: dict of sample_id: 'Correct'/'Wrong'
+    # metric_dict: dict of metric_name: metric_value, e.g.  'acc': accuracy
+    judge_dict, metric_dict = evaluate(examples_to_eval)
+    metric_dict.update({'num_example': len(examples_to_eval)})
+    return metric_dict['acc']
+
+
+
 def compute(params):
-    public_score = 0
-    private_score = 0
+    # Download the two json file
+    # category_
+    category_to_sample_ids_dict_file = hf_hub_download(
+        repo_id=params.competition_id,
+        filename="category_to_sample_ids_dict.json",
+        token=params.token,
+        repo_type="dataset",
+    )
+    # answer dictionary for mixtral evaluation
+    answer_dict_for_mixtral_eval_file = hf_hub_download(
+        repo_id=params.competition_id,
+        filename="answer_dict_for_mixtral_eval.json",
+        token=params.token,
+        repo_type="dataset",
+    )
+
+    category_to_sample_ids_dict = json.load(open(category_to_sample_ids_dict_file))
+    answer_dict_for_mixtral_eval = json.load(open(answer_dict_for_mixtral_eval_file))
 
+    print(f'params  {params}')
+    print(f'params.submission_id_col  {params.submission_id_col}')
+
+    print('Downloading solution files ...')
     solution_file = hf_hub_download(
         repo_id=params.competition_id,
         filename="solution.csv",
         token=params.token,
         repo_type="dataset",
     )
+    # Download the solution file
+    solution_df = pd.read_csv(solution_file)  # 3 columns: submission_id, pred, split
 
-    solution_df = pd.read_csv(solution_file)
-
+    print('Downloading submission files ...')
     submission_filename = f"submissions/{params.team_id}-{params.submission_id}.csv"
     submission_file = hf_hub_download(
         repo_id=params.competition_id,
@@ -29,7 +439,20 @@ def compute(params):
         token=params.token,
         repo_type="dataset",
     )
-    submission_df = pd.read_csv(submission_file)
+    # Download the submission file
+    submission_df = pd.read_csv(submission_file) # submission data of a team
+
+    # Download the logo
+    print('Downloading logo ...')
+    logo_filepath = 'img/tensorleap_logo.png'
+    logo_file = hf_hub_download(
+        repo_id=params.competition_id,
+        filename=logo_filepath,
+        token=params.token,
+        repo_type="dataset",
+    )
+
+
 
     public_ids = solution_df[solution_df.split == "public"][params.submission_id_col].values
     private_ids = solution_df[solution_df.split == "private"][params.submission_id_col].values
@@ -40,67 +463,161 @@ def compute(params):
     private_solution_df = solution_df[solution_df[params.submission_id_col].isin(private_ids)]
     private_submission_df = submission_df[submission_df[params.submission_id_col].isin(private_ids)]
 
-    public_solution_df = public_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
+    public_solution_df = public_solution_df.sort_values(params.submission_id_col).reset_index(drop=True) # sort by submission_id
     public_submission_df = public_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)
 
     private_solution_df = private_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
     private_submission_df = private_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)
 
+    print('public_solution_df', public_solution_df)
+    print('private_solution_df', private_solution_df)
 
+    # target_cols = [col for col in solution_df.columns if col not in [params.submission_id_col, "split"]]
 
-    # # METRICS Calculation Evaluation
-    # # _metric = SOME METRIC FUNCTION
-    # def _metric(outputs, targets):
-    #     # input example: public_solution_df[target_cols], public_submission_df[target_cols]
-        
-    #     score = 0.5
-    #     return score
+    # public_score = _metric(public_solution_df[target_cols], public_submission_df[target_cols])
+    # private_score = _metric(private_solution_df[target_cols], private_submission_df[target_cols])
+    #
+    # metric_name = "metric1"
+    #
+    # metric_dict = {"public_score": {metric_name: public_score},
+    #                "private_score": {metric_name: private_score}
+    #                }
 
+
+
+    ### turn csv data into dict of dict
+    def csv_to_dict_of_dict(df):
+        dict_of_dict = {}
+        for row, output in df.iterrows():
+            dict_of_dict[output['id']] = output
+        return dict_of_dict
+    public_solution_dict_of_dict = csv_to_dict_of_dict(public_solution_df)
+    public_submission_dict_of_dict = csv_to_dict_of_dict(public_submission_df)
+    private_solution_dict_of_dict = csv_to_dict_of_dict(private_solution_df)
+    private_submission_dict_of_dict = csv_to_dict_of_dict(private_submission_df)
+
+    # print(public_submission_dict_of_dict)
+
+    ####################################################### Wei's part ###############################################
+    all_keys = ['iconqa_fill','funsd','iconqa_choose','wildreceipt','textbookqa','tabfact',
+                'docvqa','infographicvqa','websrc','wtq',
+                'public_overall',
+                'private_overall']
+    mixtral_eval_batch_size = 16
+    limit = 20 # concurrency limit
+    public_category_score_dict = {}
+    private_category_score_dict = {}
+    for key_ in all_keys:
+        public_category_score_dict.update({key_: 0})
+        private_category_score_dict.update({key_: 0})
+
+    public_datasets_for_mmmu_eval = ['iconqa_fill','funsd','iconqa_choose','wildreceipt','textbookqa','tabfact']
+    public_datasets_for_mixtral_eval = ['docvqa','infographicvqa','websrc','wtq']
+    private_datasets_for_mmmu_eval = []
+    # private_datasets_for_mixtral_eval = ['private_dataset1','private_dataset2','private_dataset3']
+    private_datasets_for_mixtral_eval = []
+    ###################################################################################################################################################
+    ############################################# public eval  ########################################################################################
+    ###################################################################################################################################################
+
+    ## MMMU evaluation
+    for category in public_datasets_for_mmmu_eval:
+        print(f'#################################################### Public Eval: MMMU evaluation for {category} ###########################################')
+        public_category_score_dict[category] = mmmu_eval(public_submission_dict_of_dict, public_solution_dict_of_dict, category,  category_to_sample_ids_dict=  category_to_sample_ids_dict)
+    ## Mixtral evaluation
+    for category in public_datasets_for_mixtral_eval:
+        print(f'#################################################### Public Eval: Mixtral evaluation for {category} ###########################################')
+        # here the returned result is a dict {'acc': accuracy, 'avg_score': average score}
+        public_category_score_dict[category] = mixtral_eval_api(public_submission_dict_of_dict, public_solution_dict_of_dict, category,
+                                                  category_to_sample_ids_dict =category_to_sample_ids_dict, answer_dict_for_mixtral_eval = answer_dict_for_mixtral_eval,
+                                                                batch_size=mixtral_eval_batch_size, limit=limit)
+
+    n_total = 0
+    n_correct = 0
+    print('################# Public Evaluation #################')
+    # print the heading of category n_samples acc
+    # print('category\tn_samples\tacc')
+    # print category, n_samples, acc with left alignment: 30 for key, 10 for n_samples, 10 for acc
+    print('category'.ljust(29, ' '), 'n_samples'.ljust(10, ' '), 'acc'.ljust(10, ' '))
+    for category, acc in public_category_score_dict.items():
+        if category in ['public_overall', 'private_overall']:
+            continue
+        dataset_total = len(category_to_sample_ids_dict[category])
+        dataset_correct = acc * dataset_total
+        # print(f'{category}\t{dataset_total}\t{acc}')
+        print(f'{category:<30}{dataset_total:<10}{acc:<10}')
+        n_correct += dataset_correct
+        n_total += dataset_total
+    public_overall_acc = 0 if n_total == 0 else n_correct / float(n_total)
+    # print(f'overall\t{n_total}\t{public_overall_acc}')
+    print(f'overall'.ljust(30, ' '), f'{n_total}'.ljust(10, ' '), f'{public_overall_acc}'.ljust(10, ' '))
+    public_category_score_dict['public_overall'] = public_overall_acc
+
+    ###################################################################################################################################################
+    ############################################# private eval  #######################################################################################
+    ###################################################################################################################################################
+    ## Mixtral evaluation
+    for category in private_datasets_for_mixtral_eval:
+        # here the returned result is a dict {'acc': accuracy, 'avg_score': average score}
+        # private_category_score_dict.update({category: mixtral_eval_api(private_submission_df, private_solution_df, category, category_to_sample_ids_dict, answer_dict_for_mixtral_eval)})
+        print(
+            f'#################################################### Private Eval: MMMU evaluation for {category} ###########################################')
+        private_category_score_dict[category] = mmmu_eval(private_submission_dict_of_dict, private_solution_dict_of_dict, category, category_to_sample_ids_dict)
+    n_total = 0
+    n_correct = 0
+    print('################# Private Evaluation #################')
+    print('category'.ljust(29, ' '), 'n_samples'.ljust(10, ' '), 'acc'.ljust(10, ' '))
+    for category, acc in private_category_score_dict.items():
+        if category in ['public_overall', 'private_overall']:
+            continue
+        dataset_total = len(category_to_sample_ids_dict[category])
+        dataset_correct = acc * dataset_total
+        # print(f'{category}\t{dataset_total}\t{acc}')
+        print(f'{category:<30}{dataset_total:<10}{acc:<10}')
+        n_correct += dataset_correct
+        n_total += dataset_total
+
+    private_overall_acc = 0 if n_total == 0 else n_correct / float(n_total)
+    # print(f'overall\t{n_total}\t{private_overall_acc}')
+    print(f'overall'.ljust(30, ' '), f'{n_total}'.ljust(10, ' '), f'{private_overall_acc}'.ljust(10, ' '))
+    private_category_score_dict['private_overall'] = private_overall_acc
+
+    """
     
-    print('public_solution_df', public_solution_df)
-    print('private_solution_df', private_solution_df)
-    
-    ## LLM Scoring Evaluation
-    def _metric(outputs, targets):
-        # inputs: public_solution_df[target_cols], public_submission_df[target_cols]
-        # output: score
-        for row, output in outputs.iterrows():
-            print('outputs type', type(outputs), 'targets type', type(outputs))
-            
-            answer = str(output['pred'])
-            label = str(targets.iloc[row]['pred'])
-            
-            print('answer:', answer)
-            print('label:', label)
-            
-            prompt=f"Give me a score from 1 to 10 (higher is better) judging how similar these two captions are. Caption one: {answer}. Caption two: {label}\nScore: "
-
-            response = client.completions.create(
-                    model="gpt-3.5-turbo-instruct",
-                    prompt=prompt,
-                    temperature=0,
-                    max_tokens=1,
-                )
-            eval_result = response.choices[0].text.strip()
-            print('eval_result', eval_result)
-            score = int(eval_result)
-            # try:
+        metric_dict = {
+            "public_score": 
+                {
+                public_dataset1: acc, 
+                public_dataset2: acc, 
+                ...	
+                public_overall: acc
                 
+                private_dataset1: 0,   # dummyscore for private part
+                private_dataset2: 0,   # dummyscore for private part
+                ...	
+                private_overall: 0     # dummyscore for private part
+                },
+                           
+            "private_score": 
+                {
+                public_dataset1: 0,  # dummyscore for public part
+                public_dataset2: 0,  # dummyscore for public part
+                ...	
+                public_overall:  0   # dummyscore for public part
                 
-            # except:
-            #     print("Error: API Calling")
-            #     return
+                private_dataset1: acc,   
+                private_dataset2: acc,
+                ...
+                private_overall:  acc
+                }
+        }  
+    """
 
-        return score
+    # metric_dict = {"public_score": {'overall': public_overall_acc},
+    #                "private_score": {'overall': private_overall_acc} }
 
-    target_cols = [col for col in solution_df.columns if col not in [params.submission_id_col, "split"]]
-    public_score = _metric(public_solution_df[target_cols], public_submission_df[target_cols])
-    private_score = _metric(private_solution_df[target_cols], private_submission_df[target_cols])
-
-    metric_name = "metric1"
-    
-    metric_dict = {"public_score": {metric_name: public_score},
-                   "private_score": {metric_name: private_score}
-                   }
+    metric_dict = {"public_score": public_category_score_dict,
+                   "private_score": private_category_score_dict }
+    # return metric_dict, public_category_score_dict, private_category_score_dict
 
     return metric_dict

requirements.txt

@@ -1 +1,5 @@
-openai
+python-dotenv
+ibm-generative-ai
+tqdm
+#openai==1.26.0
+#genai

solution.csv

@@ -1,4 +1,2001 @@
-id,pred,split
-0,"The picture depicts a boy getting ready to swing at a baseball",public
-1,"The picture depicts a boy holding a cup",private
-
+id,pred,split
+docvqa_docvqa_0_4,0.24,public
+docvqa_docvqa_5_0,11:14 to 11:39 a.m.,public
+docvqa_docvqa_12_0,70,public
+docvqa_docvqa_21_2,Report and Accounts,public
+docvqa_docvqa_35_1,1969,public
+docvqa_docvqa_46_1,brown & williamson tobacco corporation,public
+docvqa_docvqa_51_1,78.2,public
+docvqa_docvqa_58_6,RESPONSE CODE REQUEST,public
+docvqa_docvqa_62_0,14-3006-14,public
+docvqa_docvqa_63_0,Sidestream Smoke Chamber,public
+docvqa_docvqa_83_2,209.54,public
+docvqa_docvqa_88_1,Olive Investment & Finance Limited,public
+docvqa_docvqa_90_1,1994,public
+docvqa_docvqa_107_3,Cott Corp.,public
+docvqa_docvqa_111_4,P3(7) FAST TRACK,public
+docvqa_docvqa_123_3,$0.00,public
+docvqa_docvqa_123_7,"$41,141,706.19",public
+docvqa_docvqa_127_3,70,public
+docvqa_docvqa_131_3,AMSTAR,public
+docvqa_docvqa_132_1,G. L. K. Hunter,public
+docvqa_docvqa_136_0,five,public
+docvqa_docvqa_151_2,11,public
+docvqa_docvqa_152_1,Megestrol acetate,public
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+infographicvqa_infographicvqa_1_474_6,46,public
+infographicvqa_infographicvqa_1_474_8,"direct sourcing, internal transfers",public
+infographicvqa_infographicvqa_1_476_0,27%,public
+infographicvqa_infographicvqa_1_477_0,3%,public
+infographicvqa_infographicvqa_1_479_6,easy access to learning and professional development,public
+infographicvqa_infographicvqa_1_480_1,youtube,public
+infographicvqa_infographicvqa_1_484_3,42%,public
+infographicvqa_infographicvqa_1_487_4,20,public
+infographicvqa_infographicvqa_1_488_3,"romania, vietnam, china",public
+infographicvqa_infographicvqa_1_491_5,1.51%,public
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+infographicvqa_infographicvqa_1_495_11,"50,000",public
+infographicvqa_infographicvqa_1_497_2,76,public
+infographicvqa_infographicvqa_1_497_3,94,public
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+websrc_websrc_1_0_213,"3.0 L Intercooled Turbo Hybrid V-6, 335 HP",public
+websrc_websrc_1_0_426,"2.0 L Intercooled Turbo Premium Unleaded I-4, 255 HP",public
+websrc_websrc_1_0_753,"6.2L Gas V8, 490 HP",public
+websrc_websrc_1_0_1640,5,public
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+websrc_websrc_1_0_4338,Front Wheel Drive,public
+websrc_websrc_1_0_4559,Front Wheel Drive,public
+websrc_websrc_1_0_4680,"4 Years / 50,000 Miles",public
+websrc_websrc_1_0_5165,"4 Years / 50,000 Miles",public
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+websrc_websrc_1_0_5698,"22 mpg City, 29 mpg Hwy",public
+websrc_websrc_1_0_5958,"23 mpg City, 34 mpg Hwy",public
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+websrc_websrc_1_1_732,AC Electric Motor,public
+websrc_websrc_1_1_738,Dual AC Electric Motors,public
+websrc_websrc_1_1_951,228 @ 5000 RPM,public
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+websrc_websrc_1_2_1188,Yotam Ottolenghi,public
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+websrc_websrc_1_2_1744,$22.99,public
+websrc_websrc_1_2_2237,$14.99,public
+websrc_websrc_1_2_3130,$14.99,public
+websrc_websrc_1_3_351,Borderlands 2 (Xbox 360),public
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+websrc_websrc_1_3_1050,06 November 2010,public
+websrc_websrc_1_3_1146,84,public
+websrc_websrc_1_3_1254,Jason Gress,public
+websrc_websrc_1_3_1430,74,public
+websrc_websrc_1_3_1648,84,public
+websrc_websrc_1_3_1693,96,public
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+websrc_websrc_1_3_3369,100,public
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+websrc_websrc_1_3_4581,Rock Band 3 (Xbox 360),public
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+websrc_websrc_1_3_4866,"Warhammer 40,000: Space Marine (Xbox 360)",public
+websrc_websrc_1_3_5011,86,public
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+websrc_websrc_1_3_6533,93,public
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+websrc_websrc_1_5_174,1 - 4 yrs.,public
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+websrc_websrc_1_5_383,few days ago,public
+websrc_websrc_1_5_734,Krishna Consultants,public
+websrc_websrc_1_5_978,Rs 4.00 - 9.00 Lacs p.a.,public
+websrc_websrc_1_5_1281,Rs 40.00 - 70.00 Lacs p.a.,public
+websrc_websrc_1_5_1341,5 - 10 yrs.,public
+websrc_websrc_1_5_1372,a month ago,public
+websrc_websrc_1_5_1385,Rs 2.15 - 5.20 Lacs p.a.,public
+websrc_websrc_1_5_1483,4 - 9 yrs.,public
+websrc_websrc_1_5_1690,5 - 10 yrs.,public
+websrc_websrc_1_5_2402,Rs 4.00 - 8.90 Lacs p.a.,public
+websrc_websrc_1_5_2649,2 - 7 yrs.,public
+websrc_websrc_1_5_2764,Rs 3.00 - 6.50 Lacs p.a.,public
+websrc_websrc_1_5_3967,"Ahmedabad, Bengaluru / Bangal...",public
+websrc_websrc_1_5_4125,1 - 6 yrs.,public
+websrc_websrc_1_5_4140,12 - 17 yrs.,public
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+websrc_websrc_1_6_156,Captain America: Civil War (En Español),public
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+websrc_websrc_1_9_2315,St. Louis Rams,public
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+websrc_websrc_1_9_5155,Philadelphia Eagles,public
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+websrc_websrc_1_9_7251,99,public
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+wtq_wtq_1_1_0,Lake Palas Tuzla,public
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+wtq_wtq_1_9_9,2007,public
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+wtq_wtq_1_31_3,5:20,public
+wtq_wtq_1_32_6,53,public
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+wtq_wtq_1_96_1,90,public
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+wtq_wtq_1_100_0,5th,public
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+wtq_wtq_1_135_9,Felipe Massa,public
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+wtq_wtq_1_150_6,U.S. Route 630,public
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+wtq_wtq_1_162_8,Yes,public
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+wtq_wtq_1_180_0,EW58,public
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+wtq_wtq_1_195_7,Yarum Ariya,public
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+wtq_wtq_1_201_1,will.i.am,public
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+wtq_wtq_1_214_1,8,public
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+wtq_wtq_1_218_3,4 years,public
+wtq_wtq_1_218_5,Royal Daisy Airlines,public
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+wtq_wtq_1_223_4,Savannah College of Art and Design at Atlanta,public
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+wtq_wtq_1_250_9,24.73,public
+wtq_wtq_1_250_10,"Guadalajara, Mexico",public
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+wtq_wtq_1_252_4,Arequipa,public
+wtq_wtq_1_253_12,12,public
+wtq_wtq_1_257_11,Dawid Daniuszewski,public
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+funsd_funsd_1_37_102_102_55,value,public
+funsd_funsd_1_37_102_102_70,value,public
+funsd_funsd_1_38_26_23_19,key,public
+funsd_funsd_1_39_37_35_0,title,public
+funsd_funsd_1_39_37_35_15,value,public
+funsd_funsd_1_39_37_35_26,value,public
+funsd_funsd_1_39_37_35_27,key,public
+funsd_funsd_1_39_37_35_31,key,public
+funsd_funsd_1_40_42_41_4,key,public
+funsd_funsd_1_40_42_41_26,key,public
+funsd_funsd_1_40_42_41_39,value,public
+funsd_funsd_1_40_42_41_41,other,public
+funsd_funsd_1_41_47_47_18,key,public
+funsd_funsd_1_41_47_47_22,other,public
+funsd_funsd_1_41_47_47_24,key,public
+funsd_funsd_1_41_47_47_31,key,public
+funsd_funsd_1_41_47_47_44,value,public
+funsd_funsd_1_43_89_83_8,value,public
+funsd_funsd_1_43_89_83_22,value,public
+funsd_funsd_1_43_89_83_23,value,public
+funsd_funsd_1_43_89_83_31,key,public
+funsd_funsd_1_43_89_83_37,key,public
+funsd_funsd_1_43_89_83_52,key,public
+funsd_funsd_1_43_89_83_55,other,public
+funsd_funsd_1_43_89_83_71,key,public
+funsd_funsd_1_44_107_103_32,key,public
+funsd_funsd_1_44_107_103_42,value,public
+funsd_funsd_1_44_107_103_43,key,public
+funsd_funsd_1_44_107_103_45,key,public
+funsd_funsd_1_44_107_103_47,key,public
+funsd_funsd_1_44_107_103_48,key,public
+funsd_funsd_1_44_107_103_68,value,public
+funsd_funsd_1_44_107_103_79,title,public
+funsd_funsd_1_44_107_103_94,other,public
+funsd_funsd_1_45_70_70_11,key,public
+funsd_funsd_1_45_70_70_32,key,public
+funsd_funsd_1_45_70_70_54,key,public
+funsd_funsd_1_45_70_70_56,value,public
+funsd_funsd_1_46_53_50_0,value,public
+funsd_funsd_1_46_53_50_8,key,public
+funsd_funsd_1_46_53_50_9,value,public
+funsd_funsd_1_46_53_50_30,key,public
+funsd_funsd_1_46_53_50_32,key,public
+funsd_funsd_1_46_53_50_44,key,public
+funsd_funsd_1_46_53_50_45,key,public
+funsd_funsd_1_47_74_70_14,key,public
+funsd_funsd_1_47_74_70_19,value,public
+funsd_funsd_1_47_74_70_38,key,public
+funsd_funsd_1_47_74_70_39,key,public
+funsd_funsd_1_47_74_70_48,key,public
+funsd_funsd_1_48_82_82_3,other,public
+funsd_funsd_1_48_82_82_20,other,public
+funsd_funsd_1_48_82_82_21,title,public
+funsd_funsd_1_48_82_82_22,key,public
+funsd_funsd_1_48_82_82_33,key,public
+funsd_funsd_1_48_82_82_53,key,public
+funsd_funsd_1_48_82_82_76,other,public
+funsd_funsd_1_49_46_46_5,key,public
+funsd_funsd_1_49_46_46_24,other,public
+funsd_funsd_1_49_46_46_34,key,public
+funsd_funsd_1_49_46_46_42,other,public
+iconqa_choose_txt_iconqa_1_4,8,public
+iconqa_choose_txt_iconqa_1_26,no,public
+iconqa_choose_txt_iconqa_1_53,5,public
+iconqa_choose_txt_iconqa_1_94,sun,public
+iconqa_choose_txt_iconqa_1_204,8,public
+iconqa_choose_txt_iconqa_1_217,black,public
+iconqa_choose_txt_iconqa_1_244,green,public
+iconqa_choose_txt_iconqa_1_260,10,public
+iconqa_choose_txt_iconqa_1_262,black,public
+iconqa_choose_txt_iconqa_1_355,yellow,public
+iconqa_choose_txt_iconqa_1_375,yellow,public
+iconqa_choose_txt_iconqa_1_385,turn,public
+iconqa_choose_txt_iconqa_1_458,9,public
+iconqa_choose_txt_iconqa_1_520,Wendy,public
+iconqa_choose_txt_iconqa_1_542,6/9,public
+iconqa_choose_txt_iconqa_1_569,9,public
+iconqa_choose_txt_iconqa_1_584,fed the fish,public
+iconqa_choose_txt_iconqa_1_645,6:00 A.M.,public
+iconqa_choose_txt_iconqa_1_653,tractor,public
+iconqa_choose_txt_iconqa_1_712,7/8,public
+iconqa_choose_txt_iconqa_1_744,about 50,public
+iconqa_choose_txt_iconqa_1_759,7,public
+iconqa_choose_txt_iconqa_1_767,Ben,public
+iconqa_choose_txt_iconqa_1_792,odd,public
+iconqa_choose_txt_iconqa_1_827,54,public
+iconqa_choose_txt_iconqa_1_837,no,public
+iconqa_choose_txt_iconqa_1_839,1/3,public
+iconqa_choose_txt_iconqa_1_871,71,public
+iconqa_choose_txt_iconqa_1_898,drew a picture,public
+iconqa_choose_txt_iconqa_1_912,3:00 P.M.,public
+iconqa_choose_txt_iconqa_1_913,flat,public
+iconqa_choose_txt_iconqa_1_938,crab,public
+iconqa_choose_txt_iconqa_1_1022,7,public
+iconqa_choose_txt_iconqa_1_1133,barn,public
+iconqa_choose_txt_iconqa_1_1143,bear,public
+iconqa_choose_txt_iconqa_1_1170,impossible,public
+iconqa_choose_txt_iconqa_1_1252,5:00 P.M.,public
+iconqa_choose_txt_iconqa_1_1273,4,public
+iconqa_choose_txt_iconqa_1_1295,8/12,public
+iconqa_choose_txt_iconqa_1_1307,10,public
+iconqa_choose_txt_iconqa_1_1310,no,public
+iconqa_choose_txt_iconqa_1_1323,1:00 P.M.,public
+iconqa_choose_txt_iconqa_1_1332,no,public
+iconqa_choose_txt_iconqa_1_1338,turn,public
+iconqa_choose_txt_iconqa_1_1401,Wendy,public
+iconqa_choose_txt_iconqa_1_1463,no,public
+iconqa_choose_txt_iconqa_1_1575,9,public
+iconqa_choose_txt_iconqa_1_1628,paw,public
+iconqa_choose_txt_iconqa_1_1629,even,public
+iconqa_choose_txt_iconqa_1_1716,5,public
+iconqa_choose_txt_iconqa_1_1728,8,public
+iconqa_choose_txt_iconqa_1_1741,no,public
+iconqa_choose_txt_iconqa_1_1763,sub,public
+iconqa_choose_txt_iconqa_1_1791,sub,public
+iconqa_choose_txt_iconqa_1_1796,certain,public
+iconqa_choose_txt_iconqa_1_1799,9/12,public
+iconqa_choose_txt_iconqa_1_1805,Myra,public
+iconqa_choose_txt_iconqa_1_1828,about 60,public
+iconqa_choose_txt_iconqa_1_1866,51,public
+iconqa_choose_txt_iconqa_1_1876,2:00 P.M.,public
+iconqa_choose_txt_iconqa_1_1905,no,public
+iconqa_choose_txt_iconqa_1_1907,white,public
+iconqa_choose_txt_iconqa_1_2005,6,public
+iconqa_choose_txt_iconqa_1_2006,turn,public
+iconqa_choose_txt_iconqa_1_2020,probable,public
+iconqa_choose_txt_iconqa_1_2059,6,public
+iconqa_choose_txt_iconqa_1_2152,yes,public
+iconqa_choose_txt_iconqa_1_2157,4,public
+iconqa_choose_txt_iconqa_1_2166,2/7,public
+iconqa_choose_txt_iconqa_1_2169,slide,public
+iconqa_choose_txt_iconqa_1_2185,7:00 A.M.,public
+iconqa_choose_txt_iconqa_1_2187,green,public
+iconqa_choose_txt_iconqa_1_2193,2:30 P.M.,public
+iconqa_choose_txt_iconqa_1_2211,turn,public
+iconqa_choose_txt_iconqa_1_2253,7:00 P.M.,public
+iconqa_choose_txt_iconqa_1_2276,unlikely,public
+iconqa_choose_txt_iconqa_1_2277,8,public
+iconqa_choose_txt_iconqa_1_2278,yes,public
+iconqa_choose_txt_iconqa_1_2370,77,public
+iconqa_choose_txt_iconqa_1_2401,about 50,public
+iconqa_choose_txt_iconqa_1_2404,6,public
+iconqa_choose_txt_iconqa_1_2444,91,public
+iconqa_choose_txt_iconqa_1_2577,train,public
+iconqa_choose_txt_iconqa_1_2584,about 50,public
+iconqa_choose_txt_iconqa_1_2655,wrote a poem,public
+iconqa_choose_txt_iconqa_1_2656,even,public
+iconqa_choose_txt_iconqa_1_2757,2,public
+iconqa_choose_txt_iconqa_1_2786,about 20,public
+iconqa_choose_txt_iconqa_1_2787,97,public
+iconqa_choose_txt_iconqa_1_2817,3:00 P.M.,public
+iconqa_choose_txt_iconqa_1_2910,even,public
+iconqa_choose_txt_iconqa_1_2940,black,public
+iconqa_choose_txt_iconqa_1_2962,3:00 P.M.,public
+iconqa_choose_txt_iconqa_1_2965,yes,public
+iconqa_choose_txt_iconqa_1_2988,55,public
+iconqa_choose_txt_iconqa_1_3032,8,public
+iconqa_choose_txt_iconqa_1_3063,3/5,public
+iconqa_choose_txt_iconqa_1_3100,plane,public
+iconqa_choose_txt_iconqa_1_3108,9,public
+iconqa_choose_txt_iconqa_1_3112,walked the dog,public
+iconqa_choose_txt_iconqa_1_3113,77,public
+iconqa_choose_txt_iconqa_1_3126,flip,public
+iconqa_choose_txt_iconqa_1_3152,6,public
+iconqa_choose_txt_iconqa_1_3241,blue,public
+iconqa_choose_txt_iconqa_1_3271,black,public
+iconqa_choose_txt_iconqa_1_3286,6,public
+iconqa_choose_txt_iconqa_1_3436,barn,public
+iconqa_choose_txt_iconqa_1_3456,yes,public
+iconqa_choose_txt_iconqa_1_3458,3,public
+iconqa_choose_txt_iconqa_1_3462,2,public
+iconqa_choose_txt_iconqa_1_3509,no,public
+iconqa_choose_txt_iconqa_1_3561,flip,public
+iconqa_choose_txt_iconqa_1_3679,about 20,public
+iconqa_choose_txt_iconqa_1_3714,87,public
+iconqa_choose_txt_iconqa_1_3716,no,public
+iconqa_choose_txt_iconqa_1_3758,1/2,public
+iconqa_choose_txt_iconqa_1_3763,flip,public
+iconqa_choose_txt_iconqa_1_3786,8,public
+iconqa_choose_txt_iconqa_1_3834,3,public
+iconqa_choose_txt_iconqa_1_4002,no,public
+iconqa_choose_txt_iconqa_1_4042,5,public
+iconqa_choose_txt_iconqa_1_4089,10,public
+iconqa_choose_txt_iconqa_1_4100,4,public
+iconqa_choose_txt_iconqa_1_4139,7:00 P.M.,public
+iconqa_choose_txt_iconqa_1_4158,8,public
+iconqa_choose_txt_iconqa_1_4174,3,public
+iconqa_choose_txt_iconqa_1_4334,paw,public
+iconqa_choose_txt_iconqa_1_4344,8,public
+iconqa_choose_txt_iconqa_1_4375,8/9,public
+iconqa_choose_txt_iconqa_1_4392,51,public
+iconqa_choose_txt_iconqa_1_4398,flip,public
+iconqa_choose_txt_iconqa_1_4415,Owen,public
+iconqa_choose_txt_iconqa_1_4418,blue,public
+iconqa_choose_txt_iconqa_1_4464,cylinder,public
+iconqa_choose_txt_iconqa_1_4467,moon,public
+iconqa_choose_txt_iconqa_1_4522,38,public
+iconqa_choose_txt_iconqa_1_4532,7,public
+iconqa_choose_txt_iconqa_1_4562,7:30 P.M.,public
+iconqa_choose_txt_iconqa_1_4597,slide,public
+iconqa_choose_txt_iconqa_1_4598,white,public
+iconqa_choose_txt_iconqa_1_4646,4,public
+iconqa_choose_txt_iconqa_1_4729,even,public
+iconqa_choose_txt_iconqa_1_4780,7,public
+iconqa_choose_txt_iconqa_1_4788,2:30 P.M.,public
+iconqa_choose_txt_iconqa_1_4827,sub,public
+iconqa_choose_txt_iconqa_1_4837,flip,public
+iconqa_choose_txt_iconqa_1_4881,71,public
+iconqa_choose_txt_iconqa_1_4885,8:00 P.M.,public
+iconqa_choose_txt_iconqa_1_4906,no,public
+iconqa_choose_txt_iconqa_1_4931,even,public
+iconqa_choose_txt_iconqa_1_4945,10,public
+iconqa_choose_txt_iconqa_1_4988,5,public
+iconqa_choose_txt_iconqa_1_4990,about 30,public
+iconqa_choose_txt_iconqa_1_5066,bucket,public
+iconqa_choose_txt_iconqa_1_5122,plane,public
+iconqa_choose_txt_iconqa_1_5140,Frank,public
+iconqa_choose_txt_iconqa_1_5149,no,public
+iconqa_choose_txt_iconqa_1_5201,unlikely,public
+iconqa_choose_txt_iconqa_1_5207,listened to music,public
+iconqa_choose_txt_iconqa_1_5234,94,public
+iconqa_choose_txt_iconqa_1_5238,4,public
+iconqa_choose_txt_iconqa_1_5242,2,public
+iconqa_choose_txt_iconqa_1_5250,rocket,public
+iconqa_choose_txt_iconqa_1_5266,flip,public
+iconqa_choose_txt_iconqa_1_5308,no,public
+iconqa_choose_txt_iconqa_1_5323,even,public
+iconqa_choose_txt_iconqa_1_5369,2:00 P.M.,public
+iconqa_choose_txt_iconqa_1_5417,slide,public
+iconqa_choose_txt_iconqa_1_5490,no,public
+iconqa_choose_txt_iconqa_1_5543,Linda,public
+iconqa_choose_txt_iconqa_1_5572,2,public
+iconqa_choose_txt_iconqa_1_5574,5:00 A.M.,public
+iconqa_choose_txt_iconqa_1_5584,sun,public
+iconqa_choose_txt_iconqa_1_5599,8,public
+iconqa_choose_txt_iconqa_1_5608,about 20,public
+iconqa_choose_txt_iconqa_1_5637,7:00 A.M.,public
+iconqa_choose_txt_iconqa_1_5719,bucket,public
+iconqa_choose_txt_iconqa_1_5745,white,public
+iconqa_choose_txt_iconqa_1_5749,2,public
+iconqa_choose_txt_iconqa_1_5771,9:00 A.M.,public
+iconqa_choose_txt_iconqa_1_5865,no,public
+iconqa_choose_txt_iconqa_1_5881,5,public
+iconqa_choose_txt_iconqa_1_5895,square,public
+iconqa_choose_txt_iconqa_1_5904,about 30,public
+iconqa_choose_txt_iconqa_1_5915,6,public
+iconqa_choose_txt_iconqa_1_5973,about 20,public
+iconqa_choose_txt_iconqa_1_5977,white,public
+iconqa_choose_txt_iconqa_1_6033,fish,public
+iconqa_choose_txt_iconqa_1_6067,barn,public
+iconqa_choose_txt_iconqa_1_6074,Owen,public
+iconqa_choose_txt_iconqa_1_6102,black,public
+iconqa_choose_txt_iconqa_1_6119,slide,public
+iconqa_choose_txt_iconqa_1_6151,8,public
+iconqa_choose_txt_iconqa_1_6191,turn,public
+iconqa_choose_txt_iconqa_1_6216,no,public
+iconqa_choose_txt_iconqa_1_6241,2,public
+iconqa_choose_txt_iconqa_1_6246,odd,public
+iconqa_choose_txt_iconqa_1_6254,8,public
+iconqa_choose_txt_iconqa_1_6294,impossible,public
+iconqa_choose_txt_iconqa_1_6296,2:00 P.M.,public
+wildreceipt_wildreceipt_1_3_73_73_7,Others,public
+wildreceipt_wildreceipt_1_4_41_40_26,Prod_price_value,public
+wildreceipt_wildreceipt_1_11_35_34_6,Others,public
+wildreceipt_wildreceipt_1_12_36_36_4,Others,public
+wildreceipt_wildreceipt_1_14_52_35_6,Prod_price_value,public
+wildreceipt_wildreceipt_1_15_74_72_11,Prod_item_value,public
+wildreceipt_wildreceipt_1_15_74_72_16,Prod_price_value,public
+wildreceipt_wildreceipt_1_20_27_26_15,Others,public
+wildreceipt_wildreceipt_1_21_34_34_28,Others,public
+wildreceipt_wildreceipt_1_22_42_41_15,Tax_value,public
+wildreceipt_wildreceipt_1_26_39_29_4,Tel_value,public
+wildreceipt_wildreceipt_1_29_44_44_33,Others,public
+wildreceipt_wildreceipt_1_31_40_40_3,Tel_value,public
+wildreceipt_wildreceipt_1_32_55_55_23,Prod_price_value,public
+wildreceipt_wildreceipt_1_33_24_19_6,Date_value,public
+wildreceipt_wildreceipt_1_37_35_35_2,Store_addr_value,public
+wildreceipt_wildreceipt_1_37_35_35_18,Others,public
+wildreceipt_wildreceipt_1_41_29_29_17,Others,public
+wildreceipt_wildreceipt_1_43_34_33_9,Others,public
+wildreceipt_wildreceipt_1_44_35_34_12,Prod_item_value,public
+wildreceipt_wildreceipt_1_44_35_34_28,Others,public
+wildreceipt_wildreceipt_1_45_65_63_47,Prod_item_value,public
+wildreceipt_wildreceipt_1_48_59_59_1,Others,public
+wildreceipt_wildreceipt_1_48_59_59_21,Prod_price_value,public
+wildreceipt_wildreceipt_1_48_59_59_26,Prod_quantity_value,public
+wildreceipt_wildreceipt_1_51_47_46_29,Subtotal_key,public
+wildreceipt_wildreceipt_1_52_30_30_17,Total_value,public
+wildreceipt_wildreceipt_1_54_50_50_19,Prod_price_value,public
+wildreceipt_wildreceipt_1_59_46_45_12,Prod_item_value,public
+wildreceipt_wildreceipt_1_66_31_31_5,Store_addr_value,public
+wildreceipt_wildreceipt_1_66_31_31_25,Tax_key,public
+wildreceipt_wildreceipt_1_66_31_31_26,Total_value,public
+wildreceipt_wildreceipt_1_68_37_36_17,Prod_quantity_value,public
+wildreceipt_wildreceipt_1_73_73_72_14,Others,public
+wildreceipt_wildreceipt_1_73_73_72_57,Others,public
+wildreceipt_wildreceipt_1_76_28_26_20,Others,public
+wildreceipt_wildreceipt_1_77_45_45_1,Store_addr_value,public
+wildreceipt_wildreceipt_1_78_25_25_6,Others,public
+wildreceipt_wildreceipt_1_78_25_25_19,Others,public
+wildreceipt_wildreceipt_1_83_31_31_10,Others,public
+wildreceipt_wildreceipt_1_95_41_31_4,Store_name_value,public
+wildreceipt_wildreceipt_1_98_46_45_17,Tax_key,public
+wildreceipt_wildreceipt_1_103_45_44_21,Prod_item_value,public
+wildreceipt_wildreceipt_1_104_31_31_5,Others,public
+wildreceipt_wildreceipt_1_105_31_30_2,Store_addr_value,public
+wildreceipt_wildreceipt_1_106_29_23_21,Time_value,public
+wildreceipt_wildreceipt_1_108_31_31_14,Others,public
+wildreceipt_wildreceipt_1_108_31_31_25,Others,public
+wildreceipt_wildreceipt_1_109_45_45_0,Store_name_value,public
+wildreceipt_wildreceipt_1_109_45_45_13,Others,public
+wildreceipt_wildreceipt_1_110_31_29_14,Prod_price_value,public
+wildreceipt_wildreceipt_1_112_87_87_34,Prod_item_value,public
+wildreceipt_wildreceipt_1_112_87_87_53,Others,public
+wildreceipt_wildreceipt_1_113_88_88_48,Others,public
+wildreceipt_wildreceipt_1_113_88_88_51,Others,public
+wildreceipt_wildreceipt_1_119_41_41_26,Total_value,public
+wildreceipt_wildreceipt_1_119_41_41_28,Tips_key,public
+wildreceipt_wildreceipt_1_120_36_35_19,Prod_price_value,public
+wildreceipt_wildreceipt_1_122_53_50_33,Others,public
+wildreceipt_wildreceipt_1_128_42_41_28,Tax_value,public
+wildreceipt_wildreceipt_1_130_20_19_0,Store_name_value,public
+wildreceipt_wildreceipt_1_130_20_19_6,Others,public
+wildreceipt_wildreceipt_1_131_28_28_22,Tax_value,public
+wildreceipt_wildreceipt_1_132_26_25_6,Others,public
+wildreceipt_wildreceipt_1_133_32_31_18,Prod_item_value,public
+wildreceipt_wildreceipt_1_136_54_52_14,Prod_price_value,public
+wildreceipt_wildreceipt_1_137_54_54_22,Prod_item_value,public
+wildreceipt_wildreceipt_1_137_54_54_24,Prod_price_value,public
+wildreceipt_wildreceipt_1_137_54_54_27,Prod_item_value,public
+wildreceipt_wildreceipt_1_140_74_72_55,Time_value,public
+wildreceipt_wildreceipt_1_142_41_36_22,Subtotal_key,public
+wildreceipt_wildreceipt_1_149_26_23_20,Others,public
+wildreceipt_wildreceipt_1_150_40_39_10,Prod_price_value,public
+wildreceipt_wildreceipt_1_154_33_33_24,Others,public
+wildreceipt_wildreceipt_1_159_38_38_35,Others,public
+wildreceipt_wildreceipt_1_162_62_61_3,Store_addr_value,public
+wildreceipt_wildreceipt_1_163_21_21_18,Others,public
+wildreceipt_wildreceipt_1_170_25_22_13,Others,public
+wildreceipt_wildreceipt_1_172_39_39_8,Time_key,public
+wildreceipt_wildreceipt_1_173_60_59_18,Others,public
+wildreceipt_wildreceipt_1_173_60_59_23,Tax_key,public
+wildreceipt_wildreceipt_1_174_32_31_15,Prod_item_value,public
+wildreceipt_wildreceipt_1_178_35_30_3,Tel_key,public
+wildreceipt_wildreceipt_1_182_31_31_1,Store_addr_value,public
+wildreceipt_wildreceipt_1_182_31_31_16,Prod_quantity_value,public
+wildreceipt_wildreceipt_1_182_31_31_24,Total_key,public
+wildreceipt_wildreceipt_1_182_31_31_27,Date_value,public
+wildreceipt_wildreceipt_1_184_31_29_5,Others,public
+wildreceipt_wildreceipt_1_186_22_20_14,Tax_value,public
+wildreceipt_wildreceipt_1_191_48_47_36,Others,public
+wildreceipt_wildreceipt_1_194_28_28_18,Others,public
+wildreceipt_wildreceipt_1_195_62_61_20,Prod_item_value,public
+wildreceipt_wildreceipt_1_204_43_41_4,Others,public
+wildreceipt_wildreceipt_1_205_43_39_6,Others,public
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+textbookqa_textbookqa_1_10_2_3_5,Solid iron inner core,public
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+textbookqa_textbookqa_1_10_130_3_9,movement in the liquid iron core creates the earth's magnetic field,public
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+textbookqa_textbookqa_1_12_29_3_7,No,public
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+textbookqa_textbookqa_1_80_45_3_7,It filters pathogens out of the blood,public
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+textbookqa_textbookqa_1_82_629_3_8,Its function is to form ribosomes,public
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+textbookqa_textbookqa_1_134_102_3_1,The other bulb would continue to work normally,public
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+textbookqa_textbookqa_1_134_121_3_3,the bulbs will not light,public
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+textbookqa_textbookqa_1_137_7_3_3,Nail,public
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+textbookqa_textbookqa_1_141_61_3_17,solid,public
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+textbookqa_textbookqa_1_142_111_3_13,Turn a liquid into a solid.,public
+textbookqa_textbookqa_1_142_117_3_13,It becomes solid,public
+textbookqa_textbookqa_1_142_137_3_15,sublimation,public
+tabfact_tabfact_1_0_9,no,public
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+tabfact_tabfact_1_10_6,no,public
+tabfact_tabfact_1_18_8,no,public
+tabfact_tabfact_1_41_0,yes,public
+tabfact_tabfact_1_43_5,no,public
+tabfact_tabfact_1_48_9,no,public
+tabfact_tabfact_1_52_2,yes,public
+tabfact_tabfact_1_52_7,no,public
+tabfact_tabfact_1_71_5,no,public
+tabfact_tabfact_1_75_4,yes,public
+tabfact_tabfact_1_77_5,no,public
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+infographicvqa_infographicvqa_1_56_7,2,public
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+infographicvqa_infographicvqa_1_66_4,2,public
+infographicvqa_infographicvqa_1_67_6,105,public
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+infographicvqa_infographicvqa_1_70_2,2,public
+infographicvqa_infographicvqa_1_75_2,18-24,public
+infographicvqa_infographicvqa_1_75_6,18-24,public
+infographicvqa_infographicvqa_1_76_9,16x19,public
+infographicvqa_infographicvqa_1_82_9,Cornucopia,public
+infographicvqa_infographicvqa_1_83_4,Facebook,public
+infographicvqa_infographicvqa_1_87_2,42,public
+infographicvqa_infographicvqa_1_95_4,Fish,public
+infographicvqa_infographicvqa_1_96_3,Waxing,public
+infographicvqa_infographicvqa_1_99_1,1,public
+infographicvqa_infographicvqa_1_99_7,Nba,public
+infographicvqa_infographicvqa_1_99_9,Nfl,public
+infographicvqa_infographicvqa_1_99_15,New york yankees,public
+infographicvqa_infographicvqa_1_100_2,Snapchat is used for sharing photos and videos.,public
+infographicvqa_infographicvqa_1_100_5,Twitter,public
+infographicvqa_infographicvqa_1_101_3,Difficult,public
+infographicvqa_infographicvqa_1_104_0,Heart,public
+infographicvqa_infographicvqa_1_107_7,4,public
+infographicvqa_infographicvqa_1_119_0,"Solid, Liquid, Gas",public
+infographicvqa_infographicvqa_1_123_4,0,public
+infographicvqa_infographicvqa_1_125_1,Biology,public
+infographicvqa_infographicvqa_1_133_2,0,public
+infographicvqa_infographicvqa_1_134_5,1 in 6,public
+infographicvqa_infographicvqa_1_134_8,90,public
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+infographicvqa_infographicvqa_1_135_10,2008,public
+infographicvqa_infographicvqa_1_135_24,Facebook,public
+infographicvqa_infographicvqa_1_135_27,2010,public
+infographicvqa_infographicvqa_1_136_0,42,public
+infographicvqa_infographicvqa_1_137_0,35,public
+infographicvqa_infographicvqa_1_140_0,85000,public
+infographicvqa_infographicvqa_1_143_6,0,public
+infographicvqa_infographicvqa_1_145_1,43,public
+infographicvqa_infographicvqa_1_149_4,Battle of el alamein,public
+infographicvqa_infographicvqa_1_150_0,10 hours,public
+infographicvqa_infographicvqa_1_157_1,Calm,public
+infographicvqa_infographicvqa_1_160_3,2001,public
+infographicvqa_infographicvqa_1_165_0,15%,public
+infographicvqa_infographicvqa_1_165_1,30%,public
+infographicvqa_infographicvqa_1_167_4,12,public
+infographicvqa_infographicvqa_1_169_7,Not exist explicitly as a span of document,public
+infographicvqa_infographicvqa_1_180_1,29,public
+infographicvqa_infographicvqa_1_180_3,Great britain,public
+infographicvqa_infographicvqa_1_180_8,China,public
+infographicvqa_infographicvqa_1_180_9,Mexico,public
+infographicvqa_infographicvqa_1_181_0,3 million,public
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+infographicvqa_infographicvqa_1_184_3,200,public
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+infographicvqa_infographicvqa_1_186_1,Union,public
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+infographicvqa_infographicvqa_1_192_14,2008,public
+infographicvqa_infographicvqa_1_192_15,1,public
+infographicvqa_infographicvqa_1_198_6,Facebook,public
+infographicvqa_infographicvqa_1_201_6,"1,000,000",public
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+infographicvqa_infographicvqa_1_208_0,1,public
+infographicvqa_infographicvqa_1_219_0,17.4,public
+infographicvqa_infographicvqa_1_222_4,14 days,public
+infographicvqa_infographicvqa_1_225_0,$10 million,public
+infographicvqa_infographicvqa_1_225_4,Cyber security,public
+infographicvqa_infographicvqa_1_232_1,3,public
+infographicvqa_infographicvqa_1_233_0,14,public
+infographicvqa_infographicvqa_1_243_2,85,public
+infographicvqa_infographicvqa_1_247_1,Accommodation & food services,public
+infographicvqa_infographicvqa_1_248_9,Law,public
+infographicvqa_infographicvqa_1_256_9,2,public
+infographicvqa_infographicvqa_1_260_3,0,public
+infographicvqa_infographicvqa_1_261_6,68,public
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+infographicvqa_infographicvqa_1_264_0,2.5,public
+infographicvqa_infographicvqa_1_265_3,42,public
+infographicvqa_infographicvqa_1_267_8,Yes,public
+infographicvqa_infographicvqa_1_271_1,72,public
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+infographicvqa_infographicvqa_1_275_0,5 years,public
+infographicvqa_infographicvqa_1_275_4,10,public
+infographicvqa_infographicvqa_1_276_0,5,public
+infographicvqa_infographicvqa_1_277_2,"1,000,000",public
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+infographicvqa_infographicvqa_1_290_8,-1.2,public
+infographicvqa_infographicvqa_1_292_6,Cd,public
+infographicvqa_infographicvqa_1_292_7,Vinyl,public
+infographicvqa_infographicvqa_1_293_1,1,public
+infographicvqa_infographicvqa_1_302_4,0,public
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+infographicvqa_infographicvqa_1_304_0,Gif,public
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+infographicvqa_infographicvqa_1_304_4,Jpg,public
+infographicvqa_infographicvqa_1_307_4,15,public
+infographicvqa_infographicvqa_1_309_10,50,public
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+infographicvqa_infographicvqa_1_318_18,1955,public
+infographicvqa_infographicvqa_1_321_1,16,public
+infographicvqa_infographicvqa_1_323_3,China,public
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+infographicvqa_infographicvqa_1_327_4,2,public
+infographicvqa_infographicvqa_1_327_6,2,public
+infographicvqa_infographicvqa_1_331_0,0,public
+infographicvqa_infographicvqa_1_334_1,1911,public
+infographicvqa_infographicvqa_1_340_0,12,public
+infographicvqa_infographicvqa_1_347_1,China,public
+infographicvqa_infographicvqa_1_349_4,2,public
+infographicvqa_infographicvqa_1_353_1,Cyprus,public
+infographicvqa_infographicvqa_1_357_1,3 out of 5 americans,public
+infographicvqa_infographicvqa_1_359_4,2,public
+infographicvqa_infographicvqa_1_363_3,Water,public
+infographicvqa_infographicvqa_1_365_0,37.5,public
+infographicvqa_infographicvqa_1_367_2,7,public
+infographicvqa_infographicvqa_1_375_0,1,public
+infographicvqa_infographicvqa_1_381_3,11,public
+infographicvqa_infographicvqa_1_382_0,South america,public
+infographicvqa_infographicvqa_1_385_0,Sites like Groupon,public
+infographicvqa_infographicvqa_1_389_0,18,public
+infographicvqa_infographicvqa_1_392_0,2,public
+infographicvqa_infographicvqa_1_393_8,20%,public
+infographicvqa_infographicvqa_1_395_0,100,public
+infographicvqa_infographicvqa_1_395_8,College,public
+infographicvqa_infographicvqa_1_395_9,12%,public
+infographicvqa_infographicvqa_1_396_0,0,public
+infographicvqa_infographicvqa_1_403_0,2.5,public
+infographicvqa_infographicvqa_1_403_1,63,public
+infographicvqa_infographicvqa_1_406_3,1.3 billion,public
+infographicvqa_infographicvqa_1_407_5,Not exist explicitly as a span of document,public
+infographicvqa_infographicvqa_1_407_10,2,public
+infographicvqa_infographicvqa_1_409_1,$15 billion,public
+infographicvqa_infographicvqa_1_413_3,30,public
+infographicvqa_infographicvqa_1_413_4,"$67,000",public
+infographicvqa_infographicvqa_1_422_0,10,public
+infographicvqa_infographicvqa_1_432_8,Coca cola,public
+infographicvqa_infographicvqa_1_436_6,30%,public
+infographicvqa_infographicvqa_1_436_10,2,public
+infographicvqa_infographicvqa_1_440_1,1,public
+infographicvqa_infographicvqa_1_441_4,2,public
+infographicvqa_infographicvqa_1_445_8,1,public
+infographicvqa_infographicvqa_1_446_1,0,public
+infographicvqa_infographicvqa_1_447_2,2,public
+infographicvqa_infographicvqa_1_452_1,2012,public
+infographicvqa_infographicvqa_1_453_3,Sir robert mcalpine,public
+infographicvqa_infographicvqa_1_454_6,Usain bolt,public
+infographicvqa_infographicvqa_1_455_6,Michael phelps,public
+infographicvqa_infographicvqa_1_455_7,Michael phelps,public
+infographicvqa_infographicvqa_1_461_2,2,public
+infographicvqa_infographicvqa_1_466_1,15%,public
+infographicvqa_infographicvqa_1_467_6,16,public
+infographicvqa_infographicvqa_1_468_3,1,public
+infographicvqa_infographicvqa_1_471_5,2,public
+infographicvqa_infographicvqa_1_472_2,2019,public
+infographicvqa_infographicvqa_1_474_4,Facebook,public
+infographicvqa_infographicvqa_1_474_6,0,public
+infographicvqa_infographicvqa_1_474_8,1. Direct sourcing,public
+infographicvqa_infographicvqa_1_476_0,84,public
+infographicvqa_infographicvqa_1_477_0,0,public
+infographicvqa_infographicvqa_1_479_6,Instant communication,public
+infographicvqa_infographicvqa_1_480_1,Facebook,public
+infographicvqa_infographicvqa_1_484_3,45%,public
+infographicvqa_infographicvqa_1_487_4,250,public
+infographicvqa_infographicvqa_1_488_3,None,public
+infographicvqa_infographicvqa_1_491_5,42,public
+infographicvqa_infographicvqa_1_493_2,53,public
+infographicvqa_infographicvqa_1_495_11,30,public
+infographicvqa_infographicvqa_1_497_2,42,public
+infographicvqa_infographicvqa_1_497_3,42,public
+infographicvqa_infographicvqa_1_497_8,25,public
+infographicvqa_infographicvqa_1_499_3,3,public
+websrc_websrc_1_0_213,3.0,public
+websrc_websrc_1_0_426,1-425 hp,public
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+websrc_websrc_1_0_1640,4,public
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+websrc_websrc_1_0_1953,Yes,public
+websrc_websrc_1_0_2083,Automatic,public
+websrc_websrc_1_0_2104,Automatic,public
+websrc_websrc_1_0_2848,Front wheel drive,public
+websrc_websrc_1_0_3004,Front wheel drive,public
+websrc_websrc_1_0_3088,All wheel drive,public
+websrc_websrc_1_0_3666,All wheel drive,public
+websrc_websrc_1_0_4164,Front wheel drive,public
+websrc_websrc_1_0_4338,Automatic,public
+websrc_websrc_1_0_4559,Front wheel drive,public
+websrc_websrc_1_0_4680,3 years,public
+websrc_websrc_1_0_5165,Unlimited miles,public
+websrc_websrc_1_0_5230,4 years,public
+websrc_websrc_1_0_5698,22 mpg,public
+websrc_websrc_1_0_5958,23 mpg,public
+websrc_websrc_1_0_6079,4,public
+websrc_websrc_1_0_6141,4,public
+websrc_websrc_1_0_6339,4,public
+websrc_websrc_1_0_6620,4,public
+websrc_websrc_1_0_6699,3,public
+websrc_websrc_1_0_6718,4,public
+websrc_websrc_1_0_6974,4,public
+websrc_websrc_1_0_7186,148 hp,public
+websrc_websrc_1_0_7297,1-428 hp,public
+websrc_websrc_1_0_7311,1-428,public
+websrc_websrc_1_0_7508,30,public
+websrc_websrc_1_0_8181,22,public
+websrc_websrc_1_1_653,19 gallons,public
+websrc_websrc_1_1_732,113 mpg,public
+websrc_websrc_1_1_738,Diesel,public
+websrc_websrc_1_1_951,228,public
+websrc_websrc_1_2_545,Liane moriarty,public
+websrc_websrc_1_2_718,George orwell,public
+websrc_websrc_1_2_894,Anthony Field,public
+websrc_websrc_1_2_992,Jamie oliver,public
+websrc_websrc_1_2_1013,Jamie Oliver,public
+websrc_websrc_1_2_1188,Jamie oliver,public
+websrc_websrc_1_2_1237,Jeff Kinney,public
+websrc_websrc_1_2_1744,Good,public
+websrc_websrc_1_2_2237,$14.99,public
+websrc_websrc_1_2_3130,$19.99,public
+websrc_websrc_1_3_351,Xbox 360,public
+websrc_websrc_1_3_355,None,public
+websrc_websrc_1_3_1050,15 November 2010,public
+websrc_websrc_1_3_1146,0,public
+websrc_websrc_1_3_1254,Jason gross,public
+websrc_websrc_1_3_1430,No,public
+websrc_websrc_1_3_1648,0,public
+websrc_websrc_1_3_1693,0,public
+websrc_websrc_1_3_1712,No,public
+websrc_websrc_1_3_1766,Cheryl gress,public
+websrc_websrc_1_3_1948,0,public
+websrc_websrc_1_3_2255,None,public
+websrc_websrc_1_3_2330,30 october,public
+websrc_websrc_1_3_2567,Jason greg,public
+websrc_websrc_1_3_2576,2019,public
+websrc_websrc_1_3_3363,2017,public
+websrc_websrc_1_3_3369,0,public
+websrc_websrc_1_3_3482,27 march 2019,public
+websrc_websrc_1_3_3796,J.r. sommerfeldt,public
+websrc_websrc_1_3_4539,0,public
+websrc_websrc_1_3_4581,0,public
+websrc_websrc_1_3_4655,Zombie,public
+websrc_websrc_1_3_4679,8,public
+websrc_websrc_1_3_4806,2008,public
+websrc_websrc_1_3_4816,J.r. sommerfeldt,public
+websrc_websrc_1_3_4866,Xbox 360,public
+websrc_websrc_1_3_5011,No,public
+websrc_websrc_1_3_5344,Nix,public
+websrc_websrc_1_3_5530,Law of new,public
+websrc_websrc_1_3_5537,85,public
+websrc_websrc_1_3_5551,09/13/2018,public
+websrc_websrc_1_3_6285,2016,public
+websrc_websrc_1_3_6533,No,public
+websrc_websrc_1_3_6554,Switch,public
+websrc_websrc_1_3_6878,Switch,public
+websrc_websrc_1_4_626,Capcom,public
+websrc_websrc_1_4_677,Ubisoft montreal studios,public
+websrc_websrc_1_4_1256,Yes,public
+websrc_websrc_1_4_2063,Hamster corporation,public
+websrc_websrc_1_4_2297,Darkened sky,public
+websrc_websrc_1_4_2302,"January 27, 2002",public
+websrc_websrc_1_4_2545,Yes,public
+websrc_websrc_1_4_3287,Yes,public
+websrc_websrc_1_4_3530,No,public
+websrc_websrc_1_5_174,4 years,public
+websrc_websrc_1_5_197,2,public
+websrc_websrc_1_5_383,0,public
+websrc_websrc_1_5_734,Ios developer,public
+websrc_websrc_1_5_978,30000,public
+websrc_websrc_1_5_1281,40000,public
+websrc_websrc_1_5_1341,Yes,public
+websrc_websrc_1_5_1372,2 days ago,public
+websrc_websrc_1_5_1385,15000,public
+websrc_websrc_1_5_1483,2 years,public
+websrc_websrc_1_5_1690,2 years,public
+websrc_websrc_1_5_2402,10000,public
+websrc_websrc_1_5_2649,2 years,public
+websrc_websrc_1_5_2764,3.5 lacs,public
+websrc_websrc_1_5_3967,Ahmedabad,public
+websrc_websrc_1_5_4125,1-6 years,public
+websrc_websrc_1_5_4140,12 years,public
+websrc_websrc_1_5_4170,1,public
+websrc_websrc_1_5_4551,1.5 years,public
+websrc_websrc_1_6_156,Captain america civil war,public
+websrc_websrc_1_6_1103,Teenage mutant ninja turtles: out of the shadows,public
+websrc_websrc_1_6_1378,Action,public
+websrc_websrc_1_6_1399,Yes,public
+websrc_websrc_1_6_1735,Peter,public
+websrc_websrc_1_6_1772,2020,public
+websrc_websrc_1_6_1958,15,public
+websrc_websrc_1_6_2338,No,public
+websrc_websrc_1_6_4007,Fantasy,public
+websrc_websrc_1_7_112,3 gb,public
+websrc_websrc_1_7_201,7900mah,public
+websrc_websrc_1_7_513,16mp,public
+websrc_websrc_1_7_665,Yes,public
+websrc_websrc_1_7_666,Samsung galaxy a20,public
+websrc_websrc_1_7_787,15.6,public
+websrc_websrc_1_7_2071,6,public
+websrc_websrc_1_7_2400,Yes,public
+websrc_websrc_1_7_2537,Android,public
+websrc_websrc_1_7_2585,16mp,public
+websrc_websrc_1_7_2717,Ufs,public
+websrc_websrc_1_7_3108,145,public
+websrc_websrc_1_7_3136,2 gb,public
+websrc_websrc_1_7_3573,Android,public
+websrc_websrc_1_7_3651,16mp,public
+websrc_websrc_1_7_3895,Android,public
+websrc_websrc_1_7_4176,16mp,public
+websrc_websrc_1_7_4184,Android,public
+websrc_websrc_1_7_4565,150 g,public
+websrc_websrc_1_7_5232,Vivo nex 3,public
+websrc_websrc_1_8_51,206,public
+websrc_websrc_1_8_120,22,public
+websrc_websrc_1_8_427,Player,public
+websrc_websrc_1_8_507,220,public
+websrc_websrc_1_8_861,None,public
+websrc_websrc_1_8_895,25,public
+websrc_websrc_1_8_1262,240,public
+websrc_websrc_1_8_1374,220,public
+websrc_websrc_1_8_1718,Player,public
+websrc_websrc_1_8_1729,1999,public
+websrc_websrc_1_8_2344,Vanderbilt,public
+websrc_websrc_1_8_2788,220,public
+websrc_websrc_1_8_2929,6-9,public
+websrc_websrc_1_8_3007,220,public
+websrc_websrc_1_8_3422,Kentucky,public
+websrc_websrc_1_8_3469,College,public
+websrc_websrc_1_8_3683,Point guard,public
+websrc_websrc_1_8_3714,R,public
+websrc_websrc_1_8_3752,29,public
+websrc_websrc_1_8_3815,No,public
+websrc_websrc_1_8_3938,Unknown,public
+websrc_websrc_1_8_4278,19,public
+websrc_websrc_1_8_4399,SG,public
+websrc_websrc_1_8_4763,26,public
+websrc_websrc_1_8_5145,No,public
+websrc_websrc_1_8_5729,3/26/95,public
+websrc_websrc_1_8_6157,250,public
+websrc_websrc_1_8_6392,214,public
+websrc_websrc_1_8_6405,218,public
+websrc_websrc_1_8_6485,6/19/1996,public
+websrc_websrc_1_8_6605,No one,public
+websrc_websrc_1_8_6675,Man,public
+websrc_websrc_1_8_6908,Wt,public
+websrc_websrc_1_9_245,1,public
+websrc_websrc_1_9_454,0,public
+websrc_websrc_1_9_486,0,public
+websrc_websrc_1_9_666,6.5,public
+websrc_websrc_1_9_1417,No,public
+websrc_websrc_1_9_1541,0,public
+websrc_websrc_1_9_1656,No,public
+websrc_websrc_1_9_1725,101,public
+websrc_websrc_1_9_1815,16,public
+websrc_websrc_1_9_2276,12,public
+websrc_websrc_1_9_2315,Packers,public
+websrc_websrc_1_9_2757,Yes,public
+websrc_websrc_1_9_3032,Ssk,public
+websrc_websrc_1_9_3090,Patriots,public
+websrc_websrc_1_9_3315,22,public
+websrc_websrc_1_9_3448,A,public
+websrc_websrc_1_9_3701,Total,public
+websrc_websrc_1_9_4022,20,public
+websrc_websrc_1_9_4221,13,public
+websrc_websrc_1_9_4427,4,public
+websrc_websrc_1_9_4463,29,public
+websrc_websrc_1_9_4528,6,public
+websrc_websrc_1_9_4844,6,public
+websrc_websrc_1_9_4862,No,public
+websrc_websrc_1_9_5155,Team,public
+websrc_websrc_1_9_5347,Sox,public
+websrc_websrc_1_9_6401,28,public
+websrc_websrc_1_9_7251,22,public
+websrc_websrc_1_9_7302,1.5,public
+wtq_wtq_1_1_0,Lake tuz,public
+wtq_wtq_1_2_0,Sydney,public
+wtq_wtq_1_4_3,M939,public
+wtq_wtq_1_9_3,34,public
+wtq_wtq_1_9_9,2012,public
+wtq_wtq_1_11_0,2,public
+wtq_wtq_1_11_8,0,public
+wtq_wtq_1_11_9,Yes,public
+wtq_wtq_1_16_5,Yes,public
+wtq_wtq_1_17_5,1,public
+wtq_wtq_1_17_10,2,public
+wtq_wtq_1_21_2,No,public
+wtq_wtq_1_23_9,2,public
+wtq_wtq_1_24_9,1,public
+wtq_wtq_1_25_10,2,public
+wtq_wtq_1_26_4,2,public
+wtq_wtq_1_28_9,1901,public
+wtq_wtq_1_29_1,None,public
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+wtq_wtq_1_34_3,Not exist explicitly as span of document,public
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+wtq_wtq_1_36_10,Yes,public
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+wtq_wtq_1_56_14,Power forward,public
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+wtq_wtq_1_59_2,"10,000",public
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+wtq_wtq_1_72_2,Not exist explicitly as span of document,public
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+wtq_wtq_1_121_6,2,public
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+wtq_wtq_1_126_8,No,public
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+wtq_wtq_1_135_9,Felipe Massa,public
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+wtq_wtq_1_192_4,No,public
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+wtq_wtq_1_199_0,No,public
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+wtq_wtq_1_277_5,No,public
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+wtq_wtq_1_284_5,Not exist explicitly as span of document,public
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+wtq_wtq_1_288_10,Not exist explicitly as span of document,public
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+wtq_wtq_1_308_9,David nedohin,public
+wtq_wtq_1_309_0,No,public
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+funsd_funsd_1_49_46_46_34,Key,public
+funsd_funsd_1_49_46_46_42,Other,public
+iconqa_choose_txt_iconqa_1_4,8,public
+iconqa_choose_txt_iconqa_1_26,No,public
+iconqa_choose_txt_iconqa_1_53,5,public
+iconqa_choose_txt_iconqa_1_94,Sun,public
+iconqa_choose_txt_iconqa_1_204,6,public
+iconqa_choose_txt_iconqa_1_217,Black,public
+iconqa_choose_txt_iconqa_1_244,Green,public
+iconqa_choose_txt_iconqa_1_260,8,public
+iconqa_choose_txt_iconqa_1_262,Black,public
+iconqa_choose_txt_iconqa_1_355,Yellow,public
+iconqa_choose_txt_iconqa_1_375,Yellow,public
+iconqa_choose_txt_iconqa_1_385,Flip,public
+iconqa_choose_txt_iconqa_1_458,8,public
+iconqa_choose_txt_iconqa_1_520,Wendy,public
+iconqa_choose_txt_iconqa_1_542,3/6,public
+iconqa_choose_txt_iconqa_1_569,5,public
+iconqa_choose_txt_iconqa_1_584,Got mail,public
+iconqa_choose_txt_iconqa_1_645,6:00 a.m.,public
+iconqa_choose_txt_iconqa_1_653,Tractor,public
+iconqa_choose_txt_iconqa_1_712,7/10,public
+iconqa_choose_txt_iconqa_1_744,About 50,public
+iconqa_choose_txt_iconqa_1_759,8,public
+iconqa_choose_txt_iconqa_1_767,Ben,public
+iconqa_choose_txt_iconqa_1_792,Even,public
+iconqa_choose_txt_iconqa_1_827,64,public
+iconqa_choose_txt_iconqa_1_837,No,public
+iconqa_choose_txt_iconqa_1_839,1/3,public
+iconqa_choose_txt_iconqa_1_871,69,public
+iconqa_choose_txt_iconqa_1_898,Watched a movie,public
+iconqa_choose_txt_iconqa_1_912,3:00 p.m.,public
+iconqa_choose_txt_iconqa_1_913,Solid,public
+iconqa_choose_txt_iconqa_1_938,Boat,public
+iconqa_choose_txt_iconqa_1_1022,3,public
+iconqa_choose_txt_iconqa_1_1133,Barn,public
+iconqa_choose_txt_iconqa_1_1143,Bear,public
+iconqa_choose_txt_iconqa_1_1170,Unlikely,public
+iconqa_choose_txt_iconqa_1_1252,5:00 p.m.,public
+iconqa_choose_txt_iconqa_1_1273,2,public
+iconqa_choose_txt_iconqa_1_1295,8/12,public
+iconqa_choose_txt_iconqa_1_1307,8,public
+iconqa_choose_txt_iconqa_1_1310,No,public
+iconqa_choose_txt_iconqa_1_1323,1:00 p.m.,public
+iconqa_choose_txt_iconqa_1_1332,No,public
+iconqa_choose_txt_iconqa_1_1338,Flip,public
+iconqa_choose_txt_iconqa_1_1401,Wendy,public
+iconqa_choose_txt_iconqa_1_1463,No,public
+iconqa_choose_txt_iconqa_1_1575,9,public
+iconqa_choose_txt_iconqa_1_1628,House,public
+iconqa_choose_txt_iconqa_1_1629,Even,public
+iconqa_choose_txt_iconqa_1_1716,1,public
+iconqa_choose_txt_iconqa_1_1728,8,public
+iconqa_choose_txt_iconqa_1_1741,No,public
+iconqa_choose_txt_iconqa_1_1763,Sub,public
+iconqa_choose_txt_iconqa_1_1791,Turtle,public
+iconqa_choose_txt_iconqa_1_1796,Probable,public
+iconqa_choose_txt_iconqa_1_1799,10/12,public
+iconqa_choose_txt_iconqa_1_1805,Nick,public
+iconqa_choose_txt_iconqa_1_1828,90,public
+iconqa_choose_txt_iconqa_1_1866,12,public
+iconqa_choose_txt_iconqa_1_1876,2:00 a.m.,public
+iconqa_choose_txt_iconqa_1_1905,No,public
+iconqa_choose_txt_iconqa_1_1907,Pink,public
+iconqa_choose_txt_iconqa_1_2005,6,public
+iconqa_choose_txt_iconqa_1_2006,Flip,public
+iconqa_choose_txt_iconqa_1_2020,Unlikely,public
+iconqa_choose_txt_iconqa_1_2059,5,public
+iconqa_choose_txt_iconqa_1_2152,No,public
+iconqa_choose_txt_iconqa_1_2157,5,public
+iconqa_choose_txt_iconqa_1_2166,1/5,public
+iconqa_choose_txt_iconqa_1_2169,Flip,public
+iconqa_choose_txt_iconqa_1_2185,7:00 a.m.,public
+iconqa_choose_txt_iconqa_1_2187,Green,public
+iconqa_choose_txt_iconqa_1_2193,2:30 p.m.,public
+iconqa_choose_txt_iconqa_1_2211,Flip,public
+iconqa_choose_txt_iconqa_1_2253,7:00 p.m.,public
+iconqa_choose_txt_iconqa_1_2276,Unlikely,public
+iconqa_choose_txt_iconqa_1_2277,4,public
+iconqa_choose_txt_iconqa_1_2278,No,public
+iconqa_choose_txt_iconqa_1_2370,77,public
+iconqa_choose_txt_iconqa_1_2401,About 80,public
+iconqa_choose_txt_iconqa_1_2404,5,public
+iconqa_choose_txt_iconqa_1_2444,100,public
+iconqa_choose_txt_iconqa_1_2577,Plane,public
+iconqa_choose_txt_iconqa_1_2584,About 50,public
+iconqa_choose_txt_iconqa_1_2655,Practiced piano,public
+iconqa_choose_txt_iconqa_1_2656,Even,public
+iconqa_choose_txt_iconqa_1_2757,1,public
+iconqa_choose_txt_iconqa_1_2786,About 90,public
+iconqa_choose_txt_iconqa_1_2787,85,public
+iconqa_choose_txt_iconqa_1_2817,3:00 p.m.,public
+iconqa_choose_txt_iconqa_1_2910,Even,public
+iconqa_choose_txt_iconqa_1_2940,Pink,public
+iconqa_choose_txt_iconqa_1_2962,3:00 p.m.,public
+iconqa_choose_txt_iconqa_1_2965,Yes,public
+iconqa_choose_txt_iconqa_1_2988,50,public
+iconqa_choose_txt_iconqa_1_3032,5,public
+iconqa_choose_txt_iconqa_1_3063,0,public
+iconqa_choose_txt_iconqa_1_3100,Bear,public
+iconqa_choose_txt_iconqa_1_3108,8,public
+iconqa_choose_txt_iconqa_1_3112,Walked the dog,public
+iconqa_choose_txt_iconqa_1_3113,65,public
+iconqa_choose_txt_iconqa_1_3126,Flip,public
+iconqa_choose_txt_iconqa_1_3152,4,public
+iconqa_choose_txt_iconqa_1_3241,White,public
+iconqa_choose_txt_iconqa_1_3271,Pink,public
+iconqa_choose_txt_iconqa_1_3286,2,public
+iconqa_choose_txt_iconqa_1_3436,Tractor,public
+iconqa_choose_txt_iconqa_1_3456,Yes,public
+iconqa_choose_txt_iconqa_1_3458,4,public
+iconqa_choose_txt_iconqa_1_3462,4,public
+iconqa_choose_txt_iconqa_1_3509,Yes,public
+iconqa_choose_txt_iconqa_1_3561,Flip,public
+iconqa_choose_txt_iconqa_1_3679,About 70,public
+iconqa_choose_txt_iconqa_1_3714,92,public
+iconqa_choose_txt_iconqa_1_3716,No,public
+iconqa_choose_txt_iconqa_1_3758,1/2,public
+iconqa_choose_txt_iconqa_1_3763,Flip,public
+iconqa_choose_txt_iconqa_1_3786,4,public
+iconqa_choose_txt_iconqa_1_3834,5,public
+iconqa_choose_txt_iconqa_1_4002,No,public
+iconqa_choose_txt_iconqa_1_4042,4,public
+iconqa_choose_txt_iconqa_1_4089,10,public
+iconqa_choose_txt_iconqa_1_4100,4,public
+iconqa_choose_txt_iconqa_1_4139,7:00 p.m.,public
+iconqa_choose_txt_iconqa_1_4158,6,public
+iconqa_choose_txt_iconqa_1_4174,5,public
+iconqa_choose_txt_iconqa_1_4334,House,public
+iconqa_choose_txt_iconqa_1_4344,8,public
+iconqa_choose_txt_iconqa_1_4375,0,public
+iconqa_choose_txt_iconqa_1_4392,51,public
+iconqa_choose_txt_iconqa_1_4398,Flip,public
+iconqa_choose_txt_iconqa_1_4415,Ruben,public
+iconqa_choose_txt_iconqa_1_4418,Blue,public
+iconqa_choose_txt_iconqa_1_4464,Cylinder,public
+iconqa_choose_txt_iconqa_1_4467,Third,public
+iconqa_choose_txt_iconqa_1_4522,12,public
+iconqa_choose_txt_iconqa_1_4532,10,public
+iconqa_choose_txt_iconqa_1_4562,7:30 p.m.,public
+iconqa_choose_txt_iconqa_1_4597,Flip,public
+iconqa_choose_txt_iconqa_1_4598,Blue,public
+iconqa_choose_txt_iconqa_1_4646,4,public
+iconqa_choose_txt_iconqa_1_4729,Even,public
+iconqa_choose_txt_iconqa_1_4780,5,public
+iconqa_choose_txt_iconqa_1_4788,2:30 p.m.,public
+iconqa_choose_txt_iconqa_1_4827,Turtle,public
+iconqa_choose_txt_iconqa_1_4837,Flip,public
+iconqa_choose_txt_iconqa_1_4881,24,public
+iconqa_choose_txt_iconqa_1_4885,8:00 p.m.,public
+iconqa_choose_txt_iconqa_1_4906,Yes,public
+iconqa_choose_txt_iconqa_1_4931,Even,public
+iconqa_choose_txt_iconqa_1_4945,10,public
+iconqa_choose_txt_iconqa_1_4988,5,public
+iconqa_choose_txt_iconqa_1_4990,30,public
+iconqa_choose_txt_iconqa_1_5066,Crab,public
+iconqa_choose_txt_iconqa_1_5122,No,public
+iconqa_choose_txt_iconqa_1_5140,Brad,public
+iconqa_choose_txt_iconqa_1_5149,No,public
+iconqa_choose_txt_iconqa_1_5201,Probable,public
+iconqa_choose_txt_iconqa_1_5207,Wrote a poem,public
+iconqa_choose_txt_iconqa_1_5234,100,public
+iconqa_choose_txt_iconqa_1_5238,4,public
+iconqa_choose_txt_iconqa_1_5242,2,public
+iconqa_choose_txt_iconqa_1_5250,Rocket,public
+iconqa_choose_txt_iconqa_1_5266,Flip,public
+iconqa_choose_txt_iconqa_1_5308,No,public
+iconqa_choose_txt_iconqa_1_5323,Even,public
+iconqa_choose_txt_iconqa_1_5369,2:00 p.m.,public
+iconqa_choose_txt_iconqa_1_5417,Flip,public
+iconqa_choose_txt_iconqa_1_5490,Yes,public
+iconqa_choose_txt_iconqa_1_5543,Linda,public
+iconqa_choose_txt_iconqa_1_5572,4,public
+iconqa_choose_txt_iconqa_1_5574,5:00 a.m.,public
+iconqa_choose_txt_iconqa_1_5584,Barn,public
+iconqa_choose_txt_iconqa_1_5599,6,public
+iconqa_choose_txt_iconqa_1_5608,50,public
+iconqa_choose_txt_iconqa_1_5637,7:00 a.m.,public
+iconqa_choose_txt_iconqa_1_5719,Boat,public
+iconqa_choose_txt_iconqa_1_5745,Blue,public
+iconqa_choose_txt_iconqa_1_5749,4,public
+iconqa_choose_txt_iconqa_1_5771,9:00 a.m.,public
+iconqa_choose_txt_iconqa_1_5865,No,public
+iconqa_choose_txt_iconqa_1_5881,5,public
+iconqa_choose_txt_iconqa_1_5895,Square,public
+iconqa_choose_txt_iconqa_1_5904,About 80,public
+iconqa_choose_txt_iconqa_1_5915,5,public
+iconqa_choose_txt_iconqa_1_5973,About 50,public
+iconqa_choose_txt_iconqa_1_5977,White,public
+iconqa_choose_txt_iconqa_1_6033,Turtle,public
+iconqa_choose_txt_iconqa_1_6067,Sun,public
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+iconqa_choose_txt_iconqa_1_6102,Orange,public
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+iconqa_choose_txt_iconqa_1_6151,8,public
+iconqa_choose_txt_iconqa_1_6191,Flip,public
+iconqa_choose_txt_iconqa_1_6216,No,public
+iconqa_choose_txt_iconqa_1_6241,3,public
+iconqa_choose_txt_iconqa_1_6246,Even,public
+iconqa_choose_txt_iconqa_1_6254,8,public
+iconqa_choose_txt_iconqa_1_6294,Probable,public
+iconqa_choose_txt_iconqa_1_6296,2:00 p.m.,public
+wildreceipt_wildreceipt_1_3_73_73_7,Prod_item_key,public
+wildreceipt_wildreceipt_1_4_41_40_26,Store_name_value,public
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+wildreceipt_wildreceipt_1_12_36_36_4,Store_name_value,public
+wildreceipt_wildreceipt_1_14_52_35_6,Tax,public
+wildreceipt_wildreceipt_1_15_74_72_11,Store_name_value,public
+wildreceipt_wildreceipt_1_15_74_72_16,Total,public
+wildreceipt_wildreceipt_1_20_27_26_15,Store_name_value,public
+wildreceipt_wildreceipt_1_21_34_34_28,Store_name_value,public
+wildreceipt_wildreceipt_1_22_42_41_15,Tax,public
+wildreceipt_wildreceipt_1_26_39_29_4,Store_name_key,public
+wildreceipt_wildreceipt_1_29_44_44_33,Store_name_value,public
+wildreceipt_wildreceipt_1_31_40_40_3,Tel,public
+wildreceipt_wildreceipt_1_32_55_55_23,Prod_price_key,public
+wildreceipt_wildreceipt_1_33_24_19_6,Date_key,public
+wildreceipt_wildreceipt_1_37_35_35_2,Store_name_value,public
+wildreceipt_wildreceipt_1_37_35_35_18,Prod_item_key,public
+wildreceipt_wildreceipt_1_41_29_29_17,Store_name_key,public
+wildreceipt_wildreceipt_1_43_34_33_9,Gst1,public
+wildreceipt_wildreceipt_1_44_35_34_12,Prod_item_key,public
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+wildreceipt_wildreceipt_1_45_65_63_47,Store_name_value,public
+wildreceipt_wildreceipt_1_48_59_59_1,Store_name_key,public
+wildreceipt_wildreceipt_1_48_59_59_21,Prod_item_key,public
+wildreceipt_wildreceipt_1_48_59_59_26,Prod_item_key,public
+wildreceipt_wildreceipt_1_51_47_46_29,Store_name_key,public
+wildreceipt_wildreceipt_1_52_30_30_17,Total_key,public
+wildreceipt_wildreceipt_1_54_50_50_19,Subtotal,public
+wildreceipt_wildreceipt_1_59_46_45_12,Prod_item_key,public
+wildreceipt_wildreceipt_1_66_31_31_5,Store_name_key,public
+wildreceipt_wildreceipt_1_66_31_31_25,Eat in tax,public
+wildreceipt_wildreceipt_1_66_31_31_26,Prod_price_key,public
+wildreceipt_wildreceipt_1_68_37_36_17,Prod_quantity_key,public
+wildreceipt_wildreceipt_1_73_73_72_14,Store_name_key,public
+wildreceipt_wildreceipt_1_73_73_72_57,Store_name_key,public
+wildreceipt_wildreceipt_1_76_28_26_20,Prod_price_key,public
+wildreceipt_wildreceipt_1_77_45_45_1,Store_name_key,public
+wildreceipt_wildreceipt_1_78_25_25_6,Store_name_key,public
+wildreceipt_wildreceipt_1_78_25_25_19,Others,public
+wildreceipt_wildreceipt_1_83_31_31_10,Store_name_key,public
+wildreceipt_wildreceipt_1_95_41_31_4,Store_name_key,public
+wildreceipt_wildreceipt_1_98_46_45_17,Tax,public
+wildreceipt_wildreceipt_1_103_45_44_21,Prod_item_key,public
+wildreceipt_wildreceipt_1_104_31_31_5,Store_name_key,public
+wildreceipt_wildreceipt_1_105_31_30_2,Store_addr_key,public
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+wildreceipt_wildreceipt_1_108_31_31_14,Subtotal,public
+wildreceipt_wildreceipt_1_108_31_31_25,Store_name_key,public
+wildreceipt_wildreceipt_1_109_45_45_0,Store_name_value,public
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+wildreceipt_wildreceipt_1_113_88_88_48,Sorry,public
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+wildreceipt_wildreceipt_1_119_41_41_28,Tips_key,public
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+wildreceipt_wildreceipt_1_122_53_50_33,Prod_item_key,public
+wildreceipt_wildreceipt_1_128_42_41_28,Store_name_value,public
+wildreceipt_wildreceipt_1_130_20_19_0,Store_name_key,public
+wildreceipt_wildreceipt_1_130_20_19_6,Store_name_key,public
+wildreceipt_wildreceipt_1_131_28_28_22,Subtotal,public
+wildreceipt_wildreceipt_1_132_26_25_6,Store_name_value,public
+wildreceipt_wildreceipt_1_133_32_31_18,Prod_item_key,public
+wildreceipt_wildreceipt_1_136_54_52_14,Total_key,public
+wildreceipt_wildreceipt_1_137_54_54_22,Store_name_value,public
+wildreceipt_wildreceipt_1_137_54_54_24,Subtotal,public
+wildreceipt_wildreceipt_1_137_54_54_27,Prod_item_key,public
+wildreceipt_wildreceipt_1_140_74_72_55,Store_name_value,public
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+wildreceipt_wildreceipt_1_149_26_23_20,Postbill,public
+wildreceipt_wildreceipt_1_150_40_39_10,Store_name_key,public
+wildreceipt_wildreceipt_1_154_33_33_24,Total,public
+wildreceipt_wildreceipt_1_159_38_38_35,Store_name_value,public
+wildreceipt_wildreceipt_1_162_62_61_3,Store_name_value,public
+wildreceipt_wildreceipt_1_163_21_21_18,Total,public
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+wildreceipt_wildreceipt_1_172_39_39_8,Prod_item_key,public
+wildreceipt_wildreceipt_1_173_60_59_18,Subtotal_key,public
+wildreceipt_wildreceipt_1_173_60_59_23,Tax,public
+wildreceipt_wildreceipt_1_174_32_31_15,Prod_item_key,public
+wildreceipt_wildreceipt_1_178_35_30_3,Tel_key,public
+wildreceipt_wildreceipt_1_182_31_31_1,Store_name_value,public
+wildreceipt_wildreceipt_1_182_31_31_16,Store_name_key,public
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+wildreceipt_wildreceipt_1_184_31_29_5,Store_name_key,public
+wildreceipt_wildreceipt_1_186_22_20_14,Total,public
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+wildreceipt_wildreceipt_1_228_82_77_46,Store_name_value,public
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+wildreceipt_wildreceipt_1_249_41_39_18,Store_name_value,public
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+wildreceipt_wildreceipt_1_272_195_195_95,Store_name_key,public
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+wildreceipt_wildreceipt_1_329_69_69_15,Prod_quantity_key,public
+wildreceipt_wildreceipt_1_329_69_69_19,Subtotal,public
+wildreceipt_wildreceipt_1_329_69_69_39,Total_key,public
+wildreceipt_wildreceipt_1_330_33_32_19,Subtotal_key,public
+wildreceipt_wildreceipt_1_331_51_45_4,Store_name_value,public
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+wildreceipt_wildreceipt_1_348_26_26_19,Time,public
+wildreceipt_wildreceipt_1_353_37_37_26,Store_name_key,public
+wildreceipt_wildreceipt_1_353_37_37_35,Store_name_value,public
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+wildreceipt_wildreceipt_1_359_34_32_24,Receipt,public
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+wildreceipt_wildreceipt_1_361_33_32_23,Price,public
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+wildreceipt_wildreceipt_1_365_38_38_3,Store_name_key,public
+wildreceipt_wildreceipt_1_367_39_38_16,Prod_item_key,public
+wildreceipt_wildreceipt_1_369_39_24_2,Store_name_key,public
+wildreceipt_wildreceipt_1_372_62_53_20,Total,public
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+wildreceipt_wildreceipt_1_377_38_38_32,Store_name_value,public
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+wildreceipt_wildreceipt_1_395_42_42_1,Store_name_key,public
+wildreceipt_wildreceipt_1_395_42_42_22,Others,public
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+wildreceipt_wildreceipt_1_422_42_37_23,Tax,public
+wildreceipt_wildreceipt_1_422_42_37_28,Store_name_value,public
+wildreceipt_wildreceipt_1_423_50_50_7,Tbl2/1,public
+wildreceipt_wildreceipt_1_425_30_28_6,Store_name_key,public
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+wildreceipt_wildreceipt_1_430_35_35_31,Store_name_value,public
+wildreceipt_wildreceipt_1_435_36_35_23,Prod_item_key,public
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+wildreceipt_wildreceipt_1_436_26_24_2,Store_name_key,public
+wildreceipt_wildreceipt_1_440_50_50_10,Auth#01336C,public
+wildreceipt_wildreceipt_1_442_30_29_0,Store_name_key,public
+wildreceipt_wildreceipt_1_450_31_29_14,Store_name_value,public
+wildreceipt_wildreceipt_1_451_35_32_12,Prod_item_key,public
+wildreceipt_wildreceipt_1_455_22_22_2,Store_name_key,public
+wildreceipt_wildreceipt_1_456_36_36_33,Store_name_value,public
+wildreceipt_wildreceipt_1_462_50_48_38,Store_name_value,public
+wildreceipt_wildreceipt_1_466_28_28_9,Store_name_key,public
+wildreceipt_wildreceipt_1_466_28_28_17,Prod_price_key,public
+textbookqa_textbookqa_1_10_2_3_5,Crust,public
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+textbookqa_textbookqa_1_10_47_3_5,'R',public
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+textbookqa_textbookqa_1_10_102_3_2,Crust,public
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+textbookqa_textbookqa_1_10_122_3_13,5,public
+textbookqa_textbookqa_1_10_130_3_9,To keep the core in place,public
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+textbookqa_textbookqa_1_10_187_3_3,Inner core,public
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+textbookqa_textbookqa_1_10_260_3_9,4,public
+textbookqa_textbookqa_1_10_262_3_3,Outer core,public
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+textbookqa_textbookqa_1_10_283_3_13,6378 km,public
+textbookqa_textbookqa_1_10_284_3_4,Inner core,public
+textbookqa_textbookqa_1_10_292_3_2,4,public
+textbookqa_textbookqa_1_10_322_3_4,Inner core,public
+textbookqa_textbookqa_1_10_326_3_7,Inner core,public
+textbookqa_textbookqa_1_10_350_3_3,3,public
+textbookqa_textbookqa_1_10_356_3_5,Fault plane,public
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+textbookqa_textbookqa_1_10_379_3_3,1,public
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+textbookqa_textbookqa_1_11_14_3_5,Magma,public
+textbookqa_textbookqa_1_11_15_3_5,1,public
+textbookqa_textbookqa_1_12_6_3_6,Converging plates,public
+textbookqa_textbookqa_1_12_20_3_15,Trench,public
+textbookqa_textbookqa_1_12_27_3_7,Asthenosphere,public
+textbookqa_textbookqa_1_12_29_3_7,Yes,public
+textbookqa_textbookqa_1_12_40_3_5,Converging plates,public
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+textbookqa_textbookqa_1_18_36_3_6,Yes,public
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+textbookqa_textbookqa_1_18_87_3_8,1,public
+textbookqa_textbookqa_1_18_98_3_4,Lava,public
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+textbookqa_textbookqa_1_18_112_3_4,Magma,public
+textbookqa_textbookqa_1_18_117_3_1,Intrusive,public
+textbookqa_textbookqa_1_18_120_3_1,Magma,public
+textbookqa_textbookqa_1_18_127_3_6,Somma volcano,public
+textbookqa_textbookqa_1_18_151_3_6,8,public
+textbookqa_textbookqa_1_18_182_3_15,Lava,public
+textbookqa_textbookqa_1_19_26_3_8,A,public
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+textbookqa_textbookqa_1_23_21_3_8,Yes,public
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+textbookqa_textbookqa_1_23_42_3_15,Cirrus,public
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+textbookqa_textbookqa_1_23_73_3_11,5,public
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+textbookqa_textbookqa_1_23_93_3_26,Cumulonimbus,public
+textbookqa_textbookqa_1_66_24_3_20,Gulf stream,public
+textbookqa_textbookqa_1_79_0_3_28,A phagosome is formed.,public
+textbookqa_textbookqa_1_79_16_3_5,Phagosome formation,public
+textbookqa_textbookqa_1_79_17_3_5,Lysosome,public
+textbookqa_textbookqa_1_79_24_3_9,Vacuole containing microbes forms.,public
+textbookqa_textbookqa_1_79_29_3_21,Bacterium is digested,public
+textbookqa_textbookqa_1_79_43_3_13,Degradation,public
+textbookqa_textbookqa_1_80_37_3_8,Lymph nodes,public
+textbookqa_textbookqa_1_80_39_3_8,Spleen,public
+textbookqa_textbookqa_1_80_44_3_5,1,public
+textbookqa_textbookqa_1_80_45_3_7,Filter,public
+textbookqa_textbookqa_1_80_46_3_7,Bone marrow,public
+textbookqa_textbookqa_1_80_53_3_3,Spleen,public
+textbookqa_textbookqa_1_80_54_3_3,Spleen,public
+textbookqa_textbookqa_1_80_57_3_3,Spleen,public
+textbookqa_textbookqa_1_82_6_3_16,M,public
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+textbookqa_textbookqa_1_82_40_3_16,"['C', 'D', 'W', 'M']",public
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+textbookqa_textbookqa_1_82_160_3_16,Mitochondria,public
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+textbookqa_textbookqa_1_82_229_3_14,Anatomy of an animal cell,public
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+textbookqa_textbookqa_1_82_371_3_16,Cytosol,public
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+textbookqa_textbookqa_1_82_395_3_16,Centrioles,public
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+textbookqa_textbookqa_1_82_455_3_16,Cytoplasm,public
+textbookqa_textbookqa_1_82_457_3_16,Chloroplast,public
+textbookqa_textbookqa_1_82_464_3_14,Cytoplasm,public
+textbookqa_textbookqa_1_82_477_3_18,Vesicle,public
+textbookqa_textbookqa_1_82_521_3_8,1,public
+textbookqa_textbookqa_1_82_535_3_13,1,public
+textbookqa_textbookqa_1_82_536_3_13,Lysosome,public
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+textbookqa_textbookqa_1_82_629_3_8,To guard the nucleus,public
+textbookqa_textbookqa_1_82_630_3_14,Chloroplast,public
+textbookqa_textbookqa_1_82_632_3_14,Cell membrane,public
+textbookqa_textbookqa_1_82_634_3_14,1,public
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+textbookqa_textbookqa_1_82_695_3_15,Chloroplast,public
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+textbookqa_textbookqa_1_82_758_3_13,Mitochondria,public
+textbookqa_textbookqa_1_82_759_3_13,Chloroplast,public
+textbookqa_textbookqa_1_82_780_3_12,Mitochondria,public
+textbookqa_textbookqa_1_82_782_3_12,1,public
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+textbookqa_textbookqa_1_82_818_3_7,Cell membrane,public
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+textbookqa_textbookqa_1_82_902_3_6,5,public
+textbookqa_textbookqa_1_84_6_3_14,Mitosis,public
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+textbookqa_textbookqa_1_84_35_3_2,Mitosis will not occur and the cell will not divide.,public
+textbookqa_textbookqa_1_84_54_3_10,Anaphase,public
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+textbookqa_textbookqa_1_84_104_3_7,Mitosis,public
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+textbookqa_textbookqa_1_87_40_3_6,Micronucleus,public
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+textbookqa_textbookqa_1_87_55_3_8,Cytoplasm,public
+textbookqa_textbookqa_1_87_64_3_10,1,public
+textbookqa_textbookqa_1_87_67_3_10,Contractile vacuole,public
+textbookqa_textbookqa_1_87_75_3_11,One,public
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+textbookqa_textbookqa_1_87_119_3_10,Cilia,public
+textbookqa_textbookqa_1_87_129_3_6,Oral groove,public
+textbookqa_textbookqa_1_87_134_3_8,Amoeba,public
+textbookqa_textbookqa_1_88_4_3_3,Spores grow hyphae,public
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+textbookqa_textbookqa_1_88_22_3_3,Spores grow hyphae,public
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+textbookqa_textbookqa_1_134_1_3_3,Flicker,public
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+textbookqa_textbookqa_1_134_55_3_0,Light bulb,public
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+textbookqa_textbookqa_1_134_90_3_6,1,public
+textbookqa_textbookqa_1_134_102_3_1,The other bulb would flicker,public
+textbookqa_textbookqa_1_134_109_3_5,2,public
+textbookqa_textbookqa_1_134_121_3_3,Only one bulb will light,public
+textbookqa_textbookqa_1_134_131_3_1,1,public
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+textbookqa_textbookqa_1_137_7_3_3,Nail,public
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+textbookqa_textbookqa_1_141_40_3_3,4,public
+textbookqa_textbookqa_1_141_43_3_3,Solid,public
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+textbookqa_textbookqa_1_141_61_3_17,Solid,public
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+textbookqa_textbookqa_1_142_50_3_9,Condensation,public
+textbookqa_textbookqa_1_142_68_3_8,Melting,public
+textbookqa_textbookqa_1_142_74_3_11,Melting,public
+textbookqa_textbookqa_1_142_111_3_13,Turn liquid into solid,public
+textbookqa_textbookqa_1_142_117_3_13,It becomes solid,public
+textbookqa_textbookqa_1_142_137_3_15,Vaporization,public
+tabfact_tabfact_1_0_9,No,public
+tabfact_tabfact_1_5_3,Yes,public
+tabfact_tabfact_1_10_6,Yes,public
+tabfact_tabfact_1_18_8,No,public
+tabfact_tabfact_1_41_0,Yes,public
+tabfact_tabfact_1_43_5,No,public
+tabfact_tabfact_1_48_9,No,public
+tabfact_tabfact_1_52_2,No,public
+tabfact_tabfact_1_52_7,No,public
+tabfact_tabfact_1_71_5,No,public
+tabfact_tabfact_1_75_4,Yes,public
+tabfact_tabfact_1_77_5,No,public
+tabfact_tabfact_1_92_0,Yes,public
+tabfact_tabfact_1_104_4,Yes,public
+tabfact_tabfact_1_108_9,No,public
+tabfact_tabfact_1_114_4,No,public
+tabfact_tabfact_1_117_4,Yes,public
+tabfact_tabfact_1_129_6,No,public
+tabfact_tabfact_1_131_2,No,public
+tabfact_tabfact_1_142_9,No,public
+tabfact_tabfact_1_149_5,Yes,public
+tabfact_tabfact_1_152_5,No,public
+tabfact_tabfact_1_154_1,No,public
+tabfact_tabfact_1_159_0,No,public
+tabfact_tabfact_1_166_0,No,public
+tabfact_tabfact_1_168_2,No,public
+tabfact_tabfact_1_168_7,No,public
+tabfact_tabfact_1_175_1,No,public
+tabfact_tabfact_1_180_4,No,public
+tabfact_tabfact_1_183_2,No,public
+tabfact_tabfact_1_183_5,No,public
+tabfact_tabfact_1_188_4,Yes,public
+tabfact_tabfact_1_205_4,No,public
+tabfact_tabfact_1_227_7,Yes,public
+tabfact_tabfact_1_229_7,No,public
+tabfact_tabfact_1_229_8,Yes,public
+tabfact_tabfact_1_235_2,No,public
+tabfact_tabfact_1_251_7,No,public
+tabfact_tabfact_1_256_0,Yes,public
+tabfact_tabfact_1_260_4,No,public
+tabfact_tabfact_1_262_9,Yes,public
+tabfact_tabfact_1_263_5,No,public
+tabfact_tabfact_1_266_0,Yes,public
+tabfact_tabfact_1_267_8,No,public
+tabfact_tabfact_1_269_1,Yes,public
+tabfact_tabfact_1_281_7,No,public
+tabfact_tabfact_1_295_3,Yes,public
+tabfact_tabfact_1_317_7,No,public
+tabfact_tabfact_1_328_7,No,public
+tabfact_tabfact_1_328_8,Yes,public
+tabfact_tabfact_1_346_2,Yes,public
+tabfact_tabfact_1_348_6,No,public
+tabfact_tabfact_1_351_4,No,public
+tabfact_tabfact_1_355_8,No,public
+tabfact_tabfact_1_361_4,No,public
+tabfact_tabfact_1_362_5,No,public
+tabfact_tabfact_1_363_0,Yes,public
+tabfact_tabfact_1_364_3,No,public
+tabfact_tabfact_1_368_9,Yes,public
+tabfact_tabfact_1_376_5,No,public
+tabfact_tabfact_1_378_4,Yes,public
+tabfact_tabfact_1_383_14,Yes,public
+tabfact_tabfact_1_383_17,No,public
+tabfact_tabfact_1_403_3,No,public
+tabfact_tabfact_1_403_5,No,public
+tabfact_tabfact_1_406_3,No,public
+tabfact_tabfact_1_414_2,No,public
+tabfact_tabfact_1_433_0,No,public
+tabfact_tabfact_1_434_1,No,public
+tabfact_tabfact_1_435_9,No,public
+tabfact_tabfact_1_436_5,No,public
+tabfact_tabfact_1_439_7,Yes,public
+tabfact_tabfact_1_440_0,No,public
+tabfact_tabfact_1_441_2,No,public
+tabfact_tabfact_1_444_8,No,public
+tabfact_tabfact_1_453_4,Yes,public
+tabfact_tabfact_1_458_0,No,public
+tabfact_tabfact_1_458_2,No,public
+tabfact_tabfact_1_458_5,Yes,public
+tabfact_tabfact_1_477_4,No,public
+tabfact_tabfact_1_483_9,Yes,public
+tabfact_tabfact_1_484_4,Yes,public
+tabfact_tabfact_1_492_5,No,public
+tabfact_tabfact_1_519_2,No,public
+tabfact_tabfact_1_520_5,No,public
+tabfact_tabfact_1_535_1,No,public
+tabfact_tabfact_1_555_5,No,public
+tabfact_tabfact_1_561_4,No,public
+tabfact_tabfact_1_561_5,Yes,public
+tabfact_tabfact_1_567_6,No,public
+tabfact_tabfact_1_586_9,No,public
+tabfact_tabfact_1_593_1,Yes,public
+tabfact_tabfact_1_597_7,Yes,public
+tabfact_tabfact_1_598_2,No,public
+tabfact_tabfact_1_602_9,Yes,public
+tabfact_tabfact_1_611_7,No,public
+tabfact_tabfact_1_618_0,No,public
+tabfact_tabfact_1_624_15,Yes,public
+tabfact_tabfact_1_626_0,No,public
+tabfact_tabfact_1_626_8,No,public
+tabfact_tabfact_1_627_1,Yes,public
+tabfact_tabfact_1_629_6,No,public
+tabfact_tabfact_1_634_8,Yes,public
+tabfact_tabfact_1_653_1,No,public
+tabfact_tabfact_1_659_3,No,public
+tabfact_tabfact_1_662_2,No,public
+tabfact_tabfact_1_692_6,No,public
+tabfact_tabfact_1_696_5,No,public
+tabfact_tabfact_1_696_9,No,public
+tabfact_tabfact_1_697_7,No,public
+tabfact_tabfact_1_707_2,Yes,public
+tabfact_tabfact_1_717_6,No,public
+tabfact_tabfact_1_741_4,Yes,public
+tabfact_tabfact_1_748_3,Yes,public
+tabfact_tabfact_1_748_8,No,public
+tabfact_tabfact_1_757_2,Yes,public
+tabfact_tabfact_1_758_2,No,public
+tabfact_tabfact_1_762_8,No,public
+tabfact_tabfact_1_771_3,No,public
+tabfact_tabfact_1_813_4,No,public
+tabfact_tabfact_1_822_9,No,public
+tabfact_tabfact_1_825_1,Yes,public
+tabfact_tabfact_1_833_1,Yes,public
+tabfact_tabfact_1_836_9,No,public
+tabfact_tabfact_1_840_0,No,public
+tabfact_tabfact_1_892_4,No,public
+tabfact_tabfact_1_895_2,No,public
+tabfact_tabfact_1_912_0,No,public
+tabfact_tabfact_1_921_2,No,public
+tabfact_tabfact_1_923_5,No,public
+tabfact_tabfact_1_930_0,No,public
+tabfact_tabfact_1_932_3,Yes,public
+tabfact_tabfact_1_952_3,No,public
+tabfact_tabfact_1_953_2,No,public
+tabfact_tabfact_1_969_5,No,public
+tabfact_tabfact_1_972_6,No,public
+tabfact_tabfact_1_984_1,No,public
+tabfact_tabfact_1_999_11,No,public
+tabfact_tabfact_1_999_13,No,public
+tabfact_tabfact_1_1017_10,No,public
+tabfact_tabfact_1_1045_0,No,public
+tabfact_tabfact_1_1058_1,No,public
+tabfact_tabfact_1_1062_4,Yes,public
+tabfact_tabfact_1_1081_1,No,public
+tabfact_tabfact_1_1084_1,No,public
+tabfact_tabfact_1_1098_7,No,public
+tabfact_tabfact_1_1100_0,Yes,public
+tabfact_tabfact_1_1124_0,Yes,public
+tabfact_tabfact_1_1130_4,No,public
+tabfact_tabfact_1_1149_0,No,public
+tabfact_tabfact_1_1150_1,No,public
+tabfact_tabfact_1_1181_3,No,public
+tabfact_tabfact_1_1203_4,No,public
+tabfact_tabfact_1_1211_0,No,public
+tabfact_tabfact_1_1213_3,No,public
+tabfact_tabfact_1_1231_2,Yes,public
+tabfact_tabfact_1_1233_7,No,public
+tabfact_tabfact_1_1243_4,Yes,public
+tabfact_tabfact_1_1244_2,Yes,public
+tabfact_tabfact_1_1245_2,No,public
+tabfact_tabfact_1_1248_0,No,public
+tabfact_tabfact_1_1256_4,Yes,public
+tabfact_tabfact_1_1270_2,No,public
+tabfact_tabfact_1_1274_1,No,public
+tabfact_tabfact_1_1288_1,Yes,public
+tabfact_tabfact_1_1307_0,No,public
+tabfact_tabfact_1_1340_8,No,public
+tabfact_tabfact_1_1366_6,No,public
+tabfact_tabfact_1_1381_3,No,public
+tabfact_tabfact_1_1382_0,Yes,public
+tabfact_tabfact_1_1385_4,No,public
+tabfact_tabfact_1_1390_1,No,public
+tabfact_tabfact_1_1391_5,Yes,public
+tabfact_tabfact_1_1401_2,No,public
+tabfact_tabfact_1_1432_0,No,public
+tabfact_tabfact_1_1443_2,No,public
+tabfact_tabfact_1_1445_4,No,public
+tabfact_tabfact_1_1455_3,Yes,public
+tabfact_tabfact_1_1490_7,No,public
+tabfact_tabfact_1_1497_2,Yes,public
+tabfact_tabfact_1_1504_3,No,public
+tabfact_tabfact_1_1507_8,No,public
+tabfact_tabfact_1_1512_1,No,public
+tabfact_tabfact_1_1537_2,Yes,public
+tabfact_tabfact_1_1538_3,No,public
+tabfact_tabfact_1_1563_1,No,public
+tabfact_tabfact_1_1580_6,No,public
+tabfact_tabfact_1_1582_2,Yes,public
+tabfact_tabfact_1_1591_1,No,public
+tabfact_tabfact_1_1597_1,No,public
+tabfact_tabfact_1_1608_3,No,public
+tabfact_tabfact_1_1624_2,No,public
+tabfact_tabfact_1_1634_2,Yes,public
+tabfact_tabfact_1_1647_1,No,public
+tabfact_tabfact_1_1649_3,Yes,public
+tabfact_tabfact_1_1653_1,No,public
+tabfact_tabfact_1_1671_3,No,public
+tabfact_tabfact_1_1671_7,No,public
+tabfact_tabfact_1_1686_0,Yes,public
+tabfact_tabfact_1_1695_0,Yes,public