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train · 85.1k rows

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ambig_qa	null	2022-11-03T16:31:34Z	810	2	ambigqa	[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended\|natural_questions", "source_datasets:original", "language:en", "license:cc-by-sa-3.0", "arxiv:2004.10645", "region:us" ]	2022-11-03T16:31:34Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|natural_questions - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: ambigqa pretty_name: 'AmbigQA: Answering Ambiguous Open-domain Questions' dataset_info: - config_name: light features: - name: id dtype: string - name: question dtype: string - name: annotations sequence: - name: type dtype: string - name: answer sequence: string - name: qaPairs sequence: - name: question dtype: string - name: answer sequence: string splits: - name: train num_bytes: 2739732 num_examples: 10036 - name: validation num_bytes: 805808 num_examples: 2002 download_size: 19700900 dataset_size: 3545540 - config_name: full features: - name: id dtype: string - name: question dtype: string - name: annotations sequence: - name: type dtype: string - name: answer sequence: string - name: qaPairs sequence: - name: question dtype: string - name: answer sequence: string - name: viewed_doc_titles sequence: string - name: used_queries sequence: - name: query dtype: string - name: results sequence: - name: title dtype: string - name: snippet dtype: string - name: nq_answer sequence: string - name: nq_doc_title dtype: string splits: - name: train num_bytes: 43538733 num_examples: 10036 - name: validation num_bytes: 15383368 num_examples: 2002 download_size: 19700900 dataset_size: 58922101 --- # Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [Homepage:](https://nlp.cs.washington.edu/ambigqa/) - [Repository:](https://github.com/shmsw25/AmbigQA) - [Paper:](https://arxiv.org/pdf/2004.10645.pdf) ### Dataset Summary AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with 14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity. We provide two distributions of our new dataset AmbigNQ: a `full` version with all annotation metadata and a `light` version with only inputs and outputs. ### Supported Tasks and Leaderboards `question-answering` ### Languages English ## Dataset Structure ### Data Instances An example from the data set looks as follows: ``` {'annotations': {'answer': [[]], 'qaPairs': [{'answer': [['April 19, 1987'], ['December 17, 1989']], 'question': ['When did the Simpsons first air on television as an animated short on the Tracey Ullman Show?', 'When did the Simpsons first air as a half-hour prime time show?']}], 'type': ['multipleQAs']}, 'id': '-4469503464110108318', 'nq_answer': ['December 17 , 1989'], 'nq_doc_title': 'The Simpsons', 'question': 'When did the simpsons first air on television?', 'used_queries': {'query': ['When did the simpsons first air on television?'], 'results': [{'snippet': ['The <b>Simpsons</b> is an American animated <b>television</b> sitcom starring the animated \nSimpson family, ... Since its <b>debut</b> on December 17, 1989, the show <b>has</b> \nbroadcast 673 episodes and its 30th season started ... The <b>Simpsons first</b> season \n<b>was</b> the Fox network's <b>first TV</b> series to rank among a season's top 30 highest-\nrated shows.', 'The <b>Simpsons</b> is an American animated sitcom created by Matt Groening for the \nFox ... Since its <b>debut</b> on December 17, 1989, 674 episodes of The <b>Simpsons</b> \nhave been broadcast. ... When producer James L. Brooks <b>was</b> working on the \n<b>television</b> variety show The Tracey Ullman Show, he decided to include small \nanimated ...', '... in shorts from The Tracey Ullman Show as their <b>television debut</b> in 1987. The \n<b>Simpsons</b> shorts are a series of animated shorts that <b>aired</b> as a recurring \nsegment on Fox variety <b>television</b> series The Tracey ... The final short to <b>air was</b> "\n<b>TV Simpsons</b>", originally airing on May 14, 1989. The <b>Simpsons</b> later debuted on\n ...', 'The <b>first</b> season of the American animated <b>television</b> series The <b>Simpsons</b> \noriginally <b>aired</b> on the Fox network between December 17, 1989, and May 13, \n1990, beginning with the Christmas special "<b>Simpsons</b> Roasting on an Open Fire\n". The executive producers for the <b>first</b> production season <b>were</b> Matt Groening, ...', 'The <b>Simpsons</b> is an American animated <b>television</b> sitcom created by Matt \nGroening for the Fox ... Since its <b>debut</b> on December 17, 1989, The <b>Simpsons</b> \n<b>has</b> broadcast 674 episodes. The show holds several American <b>television</b> \nlongevity ...', 'The opening sequence of the American animated <b>television</b> series The <b>Simpsons</b> \nis among the most popular opening sequences in <b>television</b> and is accompanied \nby one of <b>television's</b> most recognizable theme songs. The <b>first</b> episode to use \nthis intro <b>was</b> the series' second episode "Bart the ... <b>was</b> the <b>first</b> episode of The \n<b>Simpsons</b> to <b>air</b> in 720p high-definition <b>television</b>, ...', '"<b>Simpsons</b> Roasting on an Open Fire", titled onscreen as "The <b>Simpsons</b> \nChristmas Special", is the premiere episode of the American animated <b>TV</b> series \nThe <b>Simpsons</b>, ... The show <b>was</b> originally intended to <b>debut</b> earlier in 1989 with "\nSome Enchanted Evening", but due to animation problems with that episode, the \nshow ...', '"Stark Raving Dad" is the <b>first</b> episode of the third season of the American \nanimated <b>television</b> series The <b>Simpsons</b>. It <b>first aired</b> on the Fox network in the \nUnited States on September 19, 1991. ... The <b>Simpsons was</b> the second highest \nrated show on Fox the week it <b>aired</b>, behind Married... with Children. "Stark \nRaving Dad," ...', 'The <b>Simpsons</b>' twentieth season <b>aired</b> on Fox from September 28, 2008 to May \n17, 2009. With this season, the show tied Gunsmoke as the longest-running \nAmerican primetime <b>television</b> series in terms of total number ... It <b>was</b> the <b>first</b>-\never episode of the show to <b>air</b> in Europe before being seen in the United States.', 'The animated <b>TV</b> show The <b>Simpsons</b> is an American English language \nanimated sitcom which ... The <b>Simpsons was</b> dubbed for the <b>first</b> time in Punjabi \nand <b>aired</b> on Geo <b>TV</b> in Pakistan. The name of the localised Punjabi version is \nTedi Sim ...'], 'title': ['History of The Simpsons', 'The Simpsons', 'The Simpsons shorts', 'The Simpsons (season 1)', 'List of The Simpsons episodes', 'The Simpsons opening sequence', 'Simpsons Roasting on an Open Fire', 'Stark Raving Dad', 'The Simpsons (season 20)', 'Non-English versions of The Simpsons']}]}, 'viewed_doc_titles': ['The Simpsons']} ``` ### Data Fields Full ``` {'id': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'annotations': Sequence(feature={'type': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'qaPairs': Sequence(feature={'question': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, length=-1, id=None)}, length=-1, id=None), 'viewed_doc_titles': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'used_queries': Sequence(feature={'query': Value(dtype='string', id=None), 'results': Sequence(feature={'title': Value(dtype='string', id=None), 'snippet': Value(dtype='string', id=None)}, length=-1, id=None)}, length=-1, id=None), 'nq_answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'nq_doc_title': Value(dtype='string', id=None)} ``` In the original data format `annotations` have different keys depending on the `type` field = `singleAnswer` or `multipleQAs`. But this implementation uses an empty list `[]` for the unavailable keys please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details. ``` for example in train_light_dataset: for i,t in enumerate(example['annotations']['type']): if t =='singleAnswer': # use the example['annotations']['answer'][i] # example['annotations']['qaPairs'][i] - > is [] print(example['annotations']['answer'][i]) else: # use the example['annotations']['qaPairs'][i] # example['annotations']['answer'][i] - > is [] print(example['annotations']['qaPairs'][i]) ``` please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details. Light version only has `id`, `question`, `annotations` fields ### Data Splits - train: 10036 - validation: 2002 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data - Wikipedia - NQ-open: ``` @article{ kwiatkowski2019natural, title={ Natural questions: a benchmark for question answering research}, author={ Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and others }, journal={ Transactions of the Association for Computational Linguistics }, year={ 2019 } } ``` #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) ### Citation Information ``` @inproceedings{ min2020ambigqa, title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions }, author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke }, booktitle={ EMNLP }, year={2020} } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.	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TheBritishLibrary/blbooks	TheBritishLibrary	2022-11-03T16:31:29Z	803	6	null	[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:other", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:nl", "license:cc0-1.0", "digital-humanities-research", "region:us" ]	2022-11-03T16:31:29Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - no-annotation language_creators: - machine-generated language: - de - en - es - fr - it - nl license: - cc0-1.0 multilinguality: - multilingual pretty_name: British Library Books size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - fill-mask - other task_ids: - language-modeling - masked-language-modeling tags: - digital-humanities-research dataset_info: - config_name: all features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30394267732 num_examples: 14011953 download_size: 10486035662 dataset_size: 30394267732 - config_name: 1800s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30020434670 num_examples: 13781747 download_size: 10348577602 dataset_size: 30020434670 - config_name: 1700s features: - name: record_id dtype: string - name: date dtype: int32 - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 266382657 num_examples: 178224 download_size: 95137895 dataset_size: 266382657 - config_name: '1510_1699' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 107667469 num_examples: 51982 download_size: 42320165 dataset_size: 107667469 - config_name: '1500_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30452067039 num_examples: 14011953 download_size: 10486035662 dataset_size: 30452067039 - config_name: '1800_1899' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 30077284377 num_examples: 13781747 download_size: 10348577602 dataset_size: 30077284377 - config_name: '1700_1799' features: - name: record_id dtype: string - name: date dtype: timestamp[s] - name: raw_date dtype: string - name: title dtype: string - name: place dtype: string - name: empty_pg dtype: bool - name: text dtype: string - name: pg dtype: int32 - name: mean_wc_ocr dtype: float32 - name: std_wc_ocr dtype: float64 - name: name dtype: string - name: all_names dtype: string - name: Publisher dtype: string - name: Country of publication 1 dtype: string - name: all Countries of publication dtype: string - name: Physical description dtype: string - name: Language_1 dtype: string - name: Language_2 dtype: string - name: Language_3 dtype: string - name: Language_4 dtype: string - name: multi_language dtype: bool splits: - name: train num_bytes: 267117831 num_examples: 178224 download_size: 95137895 dataset_size: 267117831 --- # Dataset Card for British Library Books ## Table of Contents - [Dataset Card for British Library Books](#dataset-card-for-British-Library-Books) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Language model training](#language-model-training) - [Supervised tasks](#supervised-tasks) - [Languages](#languages) - [Language change](#language-change) - [Optical Character Recognition](#optical-character-recognition) - [OCR word confidence](#ocr-word-confidence) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Date normalization](#date-normalization) - [Metadata included](#metadata-included) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Colonialism](#colonialism) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.bl.uk/collection-guides/digitised-printed-books - Repository: https://doi.org/10.21250/db14 - Paper: - Leaderboard: - Point of Contact: labs@bl.uk ### Dataset Summary This dataset consists of books digitised by the British Library in partnership with Microsoft. The dataset includes ~25 million pages of out of copyright texts. The majority of the texts were published in the 18th and 19th Century, but the collection also consists of a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas, including geography, philosophy, history, poetry and literature and are published in various languages. While the books are predominately from the 18th and 19th Centuries, there are fewer books from earlier periods. The number of pages in the corpus by decade: \| \| page count \| \| ---- \| ---------- \| \| 1510 \| 94 \| \| 1520 \| 32 \| \| 1540 \| 184 \| \| 1550 \| 16 \| \| 1580 \| 276 \| \| 1590 \| 540 \| \| 1600 \| 1117 \| \| 1610 \| 1132 \| \| 1620 \| 1856 \| \| 1630 \| 9274 \| \| 1640 \| 4232 \| \| 1650 \| 2944 \| \| 1660 \| 5858 \| \| 1670 \| 11415 \| \| 1680 \| 8348 \| \| 1690 \| 13756 \| \| 1700 \| 10160 \| \| 1710 \| 9556 \| \| 1720 \| 10314 \| \| 1730 \| 13282 \| \| 1740 \| 10778 \| \| 1750 \| 12001 \| \| 1760 \| 21415 \| \| 1770 \| 28490 \| \| 1780 \| 32676 \| \| 1790 \| 50014 \| \| 1800 \| 307806 \| \| 1810 \| 478008 \| \| 1820 \| 589419 \| \| 1830 \| 681212 \| \| 1840 \| 1113473 \| \| 1850 \| 1726108 \| \| 1860 \| 1725407 \| \| 1870 \| 2069089 \| \| 1880 \| 2585159 \| \| 1890 \| 3365031 \| [More Information Needed] ### Supported Tasks and Leaderboards This collection has been previously used across various digital history and humanities projects since being published. The dataset consists of text and a range of metadata associated with this text. This metadata includes: - date of publication - place of publication - country of publication - language - OCR quality - physical description of the original physical item #### Language model training As a relatively large dataset, `blbooks` provides a source dataset for training language models. The presence of this metadata also offers interesting opportunities to use this dataset as a source for training language models based on: - specific time-periods - specific languages - certain OCR quality thresholds The above is not an exhaustive list but offer some suggestions of how the dataset can be used to explore topics such as the impact of OCR quality on language models, the ‘transferability’ of language models across time or the impact of training multilingual language models on historical languages. #### Supervised tasks Whilst this dataset does not have annotations for a specific NLP task, such as Named Entity Recognition, it does include a wide variety of metadata. This metadata has the potential to be used for training and/or evaluating a variety of supervised tasks predicting this metadata. ### Languages This dataset consists of books published in several languages. The breakdown of the languages included (at the page level) is: \| Language \| Pages \| \| --------------------- \| -------- \| \| English \| 10039463 \| \| French \| 1442929 \| \| German \| 1172793 \| \| Spanish \| 286778 \| \| Italian \| 214255 \| \| Dutch \| 204759 \| \| Russian \| 193347 \| \| Danish \| 93366 \| \| Hungarian \| 88094 \| \| Swedish \| 76225 \| \| Polish \| 58901 \| \| Greek, Modern (1453-) \| 26104 \| \| Latin \| 25611 \| \| Portuguese \| 25410 \| \| Czech \| 20160 \| \| Bulgarian \| 7891 \| \| Finnish \| 5677 \| \| Irish \| 2743 \| \| Serbian \| 1975 \| \| Romanian \| 1544 \| \| Norwegian Nynorsk \| 1398 \| \| Croatian \| 1306 \| \| Norwegian \| 1227 \| \| Icelandic \| 902 \| \| Slovak \| 840 \| \| Lithuanian \| 714 \| \| Welsh \| 580 \| \| Slovenian \| 545 \| \| Indonesian \| 418 \| \| Cornish \| 223 \| This breakdown was derived from the first language in the associated metadata field. Some books include multiple languages. Some of the languages codes for this data were also derived using computational methods. Therefore, the language fields in the dataset should be treated with some caution (discussed in more detail below). #### Language change The publication dates of books in the data cover a broad period of time (1500-1900). For languages in the dataset with broad temporal coverage, significant [language change](https://en.wikipedia.org/wiki/Language_change) might be found. The ability to study this change by taking reasonably large samples of languages covering different time periods is one of the opportunities offered by this dataset. The fact that the text in this dataset was produced via Optical Character Recognition (OCR) causes some challenges for this type of research (see below). #### Optical Character Recognition The digitised books in this collection were transformed into machine-readable text using Optical Character Recognition (OCR) software. The text produced via OCR software will usually include some errors. These errors include; mistakes at the character level; for example, an `i` is mistaken for an `l`, at the word level or across significant passages of text. The books in this dataset can pose some additional challenges for OCR software. OCR errors can stem from: - the quality of the original printing: printing technology was a developing technology during the time period covered by this corpus; some of the original book text will include misprints, blurred or faded ink that is hard to read - damage to the page: some of the books will have become damaged over time, this can obscure all or parts of the text on a page - poor quality scans: scanning books can be challenging; for example, if the book has tight bindings, it can be hard to capture text that has fallen into the [gutter](https://www.abaa.org/glossary/entry/gutter) of the book. - the language used in the books may differ from the languages OCR software is predominantly trained to recognise. ##### OCR word confidence Many OCR engines produce some form of confidence score alongside the predicted text. These confidence scores are usually at the character or word level. The word confidence score was given for each word in the original ALTO XML versions of the text in this dataset in this dataset. The OCR confidence scores should be treated with some scepticism. For historical text or in a lower resource language, for example, a low confidence score may be more likely for words not included in a modern dictionary but may be accurate transcriptions of the original text. With that said, the confidence scores do give some sense of the OCR quality. An example of text with a high (over 90% mean word confidence score): ``` 8 direction to the Conduit, round which is a wide open space, and a good broad pavement called the Parade. It commands a pleasant peep of the slopes and terrace throughout its entire length. The street continuing from the Conduit, in the same general direction, was known anciently as Lodborne Lane, and is now named South Street. From the Conduit two other streets, at right angles to these, are Long Street, leading Eastwards, and Half-Moon Street (formerly Lodborne), leading to Westbury, Trendle Street, and the Horsecastles Road. ``` An example of text with a score below 40%: ``` Hannover. Schrift und Druck von Fr. CultniTmn,', "LeMNs'utluirui.", 'ü 8u«llim» M^äalßwi 01de!lop 1<M.', 'p^dnalmw vom Xr^u/e, lpiti>»*Kmm lie« !»^2!M kleine lii!<! (,«> ttünee!<»e^ v»n tndzt Lievclum, 1872, ``` The quality of OCR - as measured by mean OCR confidence for a page - across the dataset correlates with other features. A groupby of publication decade and mean word confidence: \| decade \| mean_wc_ocr \| \| ------ \| ----------- \| \| 1510 \| 0.499151 \| \| 1520 \| 0.544818 \| \| 1540 \| 0.511589 \| \| 1550 \| 0.4505 \| \| 1580 \| 0.321858 \| \| 1590 \| 0.461282 \| \| 1600 \| 0.467318 \| \| 1610 \| 0.495895 \| \| 1620 \| 0.501257 \| \| 1630 \| 0.49766 \| \| 1640 \| 0.512095 \| \| 1650 \| 0.528534 \| \| 1660 \| 0.521014 \| \| 1670 \| 0.592575 \| \| 1680 \| 0.583901 \| \| 1690 \| 0.567202 \| \| 1700 \| 0.575175 \| \| 1710 \| 0.61436 \| \| 1720 \| 0.627725 \| \| 1730 \| 0.658534 \| \| 1740 \| 0.64214 \| \| 1750 \| 0.657357 \| \| 1760 \| 0.6389 \| \| 1770 \| 0.651883 \| \| 1780 \| 0.632326 \| \| 1790 \| 0.664279 \| \| 1800 \| 0.682338 \| \| 1810 \| 0.708915 \| \| 1820 \| 0.730015 \| \| 1830 \| 0.730973 \| \| 1840 \| 0.713886 \| \| 1850 \| 0.697106 \| \| 1860 \| 0.696701 \| \| 1870 \| 0.717233 \| \| 1880 \| 0.733331 \| \| 1890 \| 0.762364 \| As might be expected, the earlier periods have lower mean word confidence scores. Again, all of this should be treated with some scepticism, especially as the size of the data grows over time. As with time, the mean word confidence of the OCR software varies across languages: \| Language_1 \| mean_wc_ocr \| \| --------------------- \| ----------- \| \| Croatian \| 0.755565 \| \| Welsh \| 0.7528 \| \| Norwegian Nynorsk \| 0.751648 \| \| Slovenian \| 0.746007 \| \| French \| 0.740772 \| \| Finnish \| 0.738032 \| \| Czech \| 0.737849 \| \| Hungarian \| 0.736076 \| \| Dutch \| 0.734977 \| \| Cornish \| 0.733682 \| \| Danish \| 0.733106 \| \| English \| 0.733037 \| \| Irish \| 0.732658 \| \| Portuguese \| 0.727746 \| \| Spanish \| 0.725111 \| \| Icelandic \| 0.724427 \| \| Italian \| 0.715839 \| \| Swedish \| 0.715633 \| \| Polish \| 0.715133 \| \| Lithuanian \| 0.700003 \| \| Bulgarian \| 0.694657 \| \| Romanian \| 0.692957 \| \| Latin \| 0.689022 \| \| Russian \| 0.685847 \| \| Serbian \| 0.674329 \| \| Slovak \| 0.66739 \| \| Greek, Modern (1453-) \| 0.632195 \| \| German \| 0.631457 \| \| Indonesian \| 0.6155 \| \| Norwegian \| 0.597987 \| Again, these numbers should be treated sceptically since some languages appear very infrequently. For example, the above table suggests the mean word confidence for Welsh is relatively high. However, there isn’t much Welsh in the dataset. Therefore, it is unlikely that this data will be particularly useful for training (historic) Welsh language models. [More Information Needed] ## Dataset Structure The dataset has a number of configurations relating to the different dates of publication in the underlying data: - `1500_1899`: this configuration covers all years - `1800_1899`: this configuration covers the years between 1800 and 1899 - `1700_1799`: this configuration covers the years between 1700 and 1799 - `1510_1699`: this configuration covers the years between 1510 and 1699 ### Configuration option All of the configurations have an optional keyword argument `skip_empty_pages` which is set to `True` by default. The underlying dataset includes some pages where there is no text. This could either be because the underlying book page didn't have any text or the OCR software failed to detect this text. For many uses of this dataset it doesn't make sense to include empty pages so these are skipped by default. However, for some uses you may prefer to retain a representation of the data that includes these empty pages. Passing `skip_empty_pages=False` when loading the dataset will enable this option. ### Data Instances An example data instance: ```python {'Country of publication 1': 'England', 'Language_1': 'English', 'Language_2': None, 'Language_3': None, 'Language_4': None, 'Physical description': None, 'Publisher': None, 'all Countries of publication': 'England', 'all names': 'Settle, Elkanah [person]', 'date': 1689, 'empty_pg': True, 'mean_wc_ocr': 0.0, 'multi_language': False, 'name': 'Settle, Elkanah', 'pg': 1, 'place': 'London', 'raw_date': '1689', 'record_id': '001876770', 'std_wc_ocr': 0.0, 'text': None, ‘title’: ‘The Female Prelate: being the history and the life and death of Pope Joan. A tragedy [in five acts and in verse] . Written by a Person of Quality [i.e. Elkanah Settle]’} ``` Each instance in the dataset represents a single page from an original digitised book. ### Data Fields Included in this dataset are: \| Field \| Data Type \| Description \| \| ---------------------------- \| --------- \| ------------------------------------------------------------------------------------------------------------- \| \| record_id \| string \| British Library ID for the item \| \| date \| int \| parsed/normalised year for the item. i.e. 1850 \| \| raw_date \| string \| the original raw date for an item i.e. 1850- \| \| title \| string \| title of the book \| \| place \| string \| Place of publication, i.e. London \| \| empty_pg \| bool \| whether page contains text \| \| text \| string \| OCR generated text for a page \| \| pg \| int \| page in original book the instance refers to \| \| mean_wc_ocr \| float \| mean word confidence values for the page \| \| std_wc_ocr \| float \| standard deviation of the word confidence values for the page \| \| name \| string \| name associated with the item (usually author) \| \| all names \| string \| all names associated with a publication \| \| Publisher \| string \| publisher of the book \| \| Country of publication 1 \| string \| first country associated with publication \| \| all Countries of publication \| string \| all countries associated with a publication \| \| Physical description \| string \| physical description of the item (size). This requires some normalisation before use and isn’t always present \| \| Language_1 \| string \| first language associated with the book, this is usually present \| \| Language_2 \| string \| \| \| Language_3 \| string \| \| \| Language_4 \| string \| \| \| multi_language \| bool \| \| Some of these fields are not populated a large proportion of the time. You can get some sense of this from this [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) [report](https://davanstrien.github.io/BL-datasets-pandas-profile-reports/pandas_profile_report_MS_digitised_books_2021-01-09.html) The majority of these fields relate to metadata about the books. Most of these fields were created by staff working for the British Library. The notable exception is the “Languages” fields that have sometimes been determined using computational methods. This work is reported in more detail in [Automated Language Identification of Bibliographic Resources](https://doi.org/10.1080/01639374.2019.1700201). It is important to note that metadata is neither perfect nor static. The metadata associated with this book was generated based on export from the British Library catalogue in 2021. [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation Note this section is a work in progress. ### Curation Rationale The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization), i.e. projects intending to quickly digitise large volumes of materials shape the selection of materials to include in several ways. Some considerations which are often involved in the decision of whether to include items for digitisation include (but are not limited to): - copyright status - preservation needs - the size of an item, very large and very small items are often hard to digitise quickly These criteria can have knock-on effects on the makeup of a collection. For example, systematically excluding large books may result in some types of book content not being digitised. Large volumes are likely to be correlated to content to at least some extent, so excluding them from digitisation will mean that material is underrepresented. Similarly, copyright status is often (but not only) determined by publication date. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date. All of the above is largely to make clear that this collection was not curated to create a representative sample of the British Library’s holdings. Some material will be over-represented, and others under-represented. Similarly, the collection should not be considered a representative sample of what was published across the period covered by the dataset (nor that the relative proportions of the data for each time period represent a proportional sample of publications from that period). Finally, and this probably does not need stating, the language included in the text should not be considered representative of either written or spoken language(s) from that time period. [More Information Needed] ### Source Data The source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. “Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It’s existed in English law since 1662.” [source](https://www.bl.uk/legal-deposit/about-legal-deposit). The source data for this version of the data is derived from the original ALTO XML files and a recent metadata export #TODO add links [More Information Needed] #### Initial Data Collection and Normalization This version of the dataset was created using the original ALTO XML files and, where a match was found, updating the metadata associated with that item with more recent metadata using an export from the British Library catalogue. The process of creating this new dataset is documented here #TODO add link. There are a few decisions made in the above processing steps worth highlighting in particular: ##### Date normalization The metadata around date of publication for an item is not always exact. It often is represented as a date range e.g. `1850-1860`. The `date` field above takes steps to normalise this date to a single integer value. In most cases, this is taking the mean of the values associated with the item. The `raw_date` field includes the unprocessed date string. ##### Metadata included The metadata associated with each item includes most of the fields available via the ALTO XML. However, the data doesn’t include some metadata fields from the metadata export file. The reason fields were excluded because they are frequently not populated. A cut off of 50% was chosen, i.e. values from the metadata which are missing above 50% of the time were not included. This is slightly arbitrary, but since the aim of this version of the data was to support computational research using the collection it was felt that these fields with frequent missing values would be less valuable. #### Who are the source language producers? [More Information Needed] ### Annotations This dataset does not include annotations as usually understood in the context of NLP. The data does include metadata associated with the books. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data There a range of considerations around using the data. These include the representativeness of the dataset, the OCR quality and the language used. Depending on your use case, these may be more or less important. For example, the impact of OCR quality on downstream tasks will depend on the target task. It may also be possible to mitigate this negative impact from OCR through tokenizer choice, Language Model training objectives, oversampling high-quality OCR, etc. [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The text in this collection is derived from historical text. As a result, the text will reflect this time period's social beliefs and attitudes. The books include both fiction and non-fiction books. Examples of book titles that appear in the data (these are randomly sampled from all titles): - ‘Rhymes and Dreams, Legends of Pendle Forest, and other poems’, - “Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General’s Department, Horse Guards, War Office, etc”, - ‘The fan. A poem’, - ‘Grif; a story of Australian Life’, - ‘Calypso; a masque: in three acts, etc’, - ‘Tales Uncle told [With illustrative woodcuts.]’, - 'Questings', - 'Home Life on an Ostrich Farm. With ... illustrations’, - ‘Bulgarya i Bulgarowie’, - 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc', - ‘The Corsair, a tale’, ‘Poems ... With notes [With a portrait.]’, - ‘Report of the Librarian for the year 1898 (1899, 1901, 1909)’, - “The World of Thought. A novel. By the author of ‘Before I began to speak.’”, - 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc'] While using titles alone is insufficient to integrate bias in this collection, it gives some insight into the topics covered by books. Further, the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list. #### Colonialism Even in the above random sample of titles examples of colonial attitudes, we can see examples of titles. We can try and interrogate this further by searching for the name of places that were part of the British Empire when many of these books were published. Searching for the string `India` in the titles and randomly sampling 10 titles returns: - “Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the ‘Calcutta Weekly Englishman.’”, - ‘A Winter in India and Malaysia among the Methodist Missions’, - “The Tourist’s Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition”, - ‘Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson’, - "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]”, - ‘The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies’, - “From Tonkin to India : by the sources of the Irawadi, January’ 95-January ’96”, - ‘Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844’, - ‘The Andaman Islands; their colonisation, etc. A correspondence addressed to the India Office’, - ‘Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle’] Searching form the string `Africa` in the titles and randomly sampling 10 titles returns: - ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada', - ‘To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]’, - ‘Diamonds and Gold in South Africa ... With maps, etc’, - ‘Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition’, - ‘A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts’, - ‘Side Lights on South Africa ... With a map, etc’, - ‘My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc’, - ‘Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations’, - ‘[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder’, - ‘Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc’] [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license. ### Citation Information ```bibtext @misc{bBritishLibraryBooks2021, author = {British Library Labs}, title = {Digitised Books. c. 1510 - c. 1900. JSONL (OCR derived text + metadata)}, year = {2021}, publisher = {British Library}, howpublished={https://doi.org/10.23636/r7w6-zy15} ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.	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newsqa	null	2023-06-01T14:59:49Z	803	10	newsqa	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]	2023-06-01T14:59:49Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: newsqa pretty_name: NewsQA dataset_info: - config_name: combined-csv features: - name: story_id dtype: string - name: story_text dtype: string - name: question dtype: string - name: answer_char_ranges dtype: string splits: - name: train num_bytes: 465942194 num_examples: 119633 download_size: 0 dataset_size: 465942194 - config_name: combined-json features: - name: storyId dtype: string - name: text dtype: string - name: type dtype: string - name: questions sequence: - name: q dtype: string - name: isAnswerAbsent dtype: int32 - name: isQuestionBad dtype: int32 - name: consensus struct: - name: s dtype: int32 - name: e dtype: int32 - name: badQuestion dtype: bool - name: noAnswer dtype: bool - name: answers sequence: - name: sourcerAnswers sequence: - name: s dtype: int32 - name: e dtype: int32 - name: badQuestion dtype: bool - name: noAnswer dtype: bool - name: validated_answers sequence: - name: s dtype: int32 - name: e dtype: int32 - name: badQuestion dtype: bool - name: noAnswer dtype: bool - name: count dtype: int32 splits: - name: train num_bytes: 68667276 num_examples: 12744 download_size: 0 dataset_size: 68667276 - config_name: split features: - name: story_id dtype: string - name: story_text dtype: string - name: question dtype: string - name: answer_token_ranges dtype: string splits: - name: train num_bytes: 362031288 num_examples: 92549 - name: test num_bytes: 19763673 num_examples: 5126 - name: validation num_bytes: 19862778 num_examples: 5166 download_size: 0 dataset_size: 401657739 config_names: - combined-csv - combined-json - split --- # Dataset Card for NewsQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.microsoft.com/en-us/research/project/newsqa-dataset/ - Repository: https://github.com/Maluuba/newsqa - Paper: https://www.aclweb.org/anthology/W17-2623/ - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English ## Dataset Structure ### Data Instances ``` {'storyId': './cnn/stories/42d01e187213e86f5fe617fe32e716ff7fa3afc4.story', 'text': 'NEW DELHI, India (CNN) -- A high court in northern India on Friday acquitted a wealthy businessman facing the death sentence for the killing of a teen in a case dubbed "the house of horrors."\n\n\n\nMoninder Singh Pandher was sentenced to death by a lower court in February.\n\n\n\nThe teen was one of 19 victims -- children and young women -- in one of the most gruesome serial killings in India in recent years.\n\n\n\nThe Allahabad high court has acquitted Moninder Singh Pandher, his lawyer Sikandar B. Kochar told CNN.\n\n\n\nPandher and his domestic employee Surinder Koli were sentenced to death in February by a lower court for the rape and murder of the 14-year-old.\n\n\n\nThe high court upheld Koli\'s death sentence, Kochar said.\n\n\n\nThe two were arrested two years ago after body parts packed in plastic bags were found near their home in Noida, a New Delhi suburb. Their home was later dubbed a "house of horrors" by the Indian media.\n\n\n\nPandher was not named a main suspect by investigators initially, but was summoned as co-accused during the trial, Kochar said.\n\n\n\nKochar said his client was in Australia when the teen was raped and killed.\n\n\n\nPandher faces trial in the remaining 18 killings and could remain in custody, the attorney said.', 'type': 'train', 'questions': {'q': ['What was the amount of children murdered?', 'When was Pandher sentenced to death?', 'The court aquitted Moninder Singh Pandher of what crime?', 'who was acquitted', 'who was sentenced', 'What was Moninder Singh Pandher acquitted for?', 'Who was sentenced to death in February?', 'how many people died', 'How many children and young women were murdered?'], 'isAnswerAbsent': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'isQuestionBad': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'consensus': [{'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False}, {'s': 261, 'e': 271, 'badQuestion': False, 'noAnswer': False}, {'s': 624, 'e': 640, 'badQuestion': False, 'noAnswer': False}, {'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False}, {'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False}, {'s': 129, 'e': 151, 'badQuestion': False, 'noAnswer': False}, {'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False}, {'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False}, {'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False}], 'answers': [{'sourcerAnswers': [{'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}, {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}]}, {'sourcerAnswers': [{'s': [261], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [258], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [261], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [26], 'e': [33], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}, {'s': [624], 'e': [640], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}, {'s': [195, 232], 'e': [218, 271], 'badQuestion': [False, False], 'noAnswer': [False, False]}, {'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}]}, {'sourcerAnswers': [{'s': [129], 'e': [192], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [129], 'e': [151], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [133], 'e': [151], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}]}, {'sourcerAnswers': [{'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}, {'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}]}], 'validated_answers': [{'s': [0, 294], 'e': [0, 297], 'badQuestion': [False, False], 'noAnswer': [True, False], 'count': [1, 2]}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}, {'s': [624], 'e': [640], 'badQuestion': [False], 'noAnswer': [False], 'count': [2]}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}, {'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False], 'count': [2]}, {'s': [129], 'e': [151], 'badQuestion': [False], 'noAnswer': [False], 'count': [2]}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}, {'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}]}} ``` ### Data Fields Configuration: combined-csv - 'story_id': An identifier of the story. - 'story_text': Text of the story. - 'question': A question about the story. - 'answer_char_ranges': The raw data collected for character based indices to answers in story_text. E.g. 196:228\|196:202,217:228\|None. Answers from different crowdsourcers are separated by `\|`; within those, multiple selections from the same crowdsourcer are separated by `,`. `None` means the crowdsourcer thought there was no answer to the question in the story. The start is inclusive and the end is exclusive. The end may point to whitespace after a token. Configuration: combined-json - 'storyId': An identifier of the story. - 'text': Text of the story. - 'type': Split type. Will be "train", "validation" or "test". - 'questions': A list containing the following: - 'q': A question about the story. - 'isAnswerAbsent': Proportion of crowdsourcers that said there was no answer to the question in the story. - 'isQuestionBad': Proportion of crowdsourcers that said the question does not make sense. - 'consensus': The consensus answer. Use this field to pick the best continuous answer span from the text. If you want to know about a question having multiple answers in the text then you can use the more detailed "answers" and "validated_answers". The object can have start and end positions like in the example above or can be {"badQuestion": true} or {"noAnswer": true}. Note that there is only one consensus answer since it's based on the majority agreement of the crowdsourcers. - 's': Start of the answer. The first character of the answer in "text" (inclusive). - 'e': End of the answer. The last character of the answer in "text" (exclusive). - 'badQuestion': The validator said that the question did not make sense. - 'noAnswer': The crowdsourcer said that there was no answer to the question in the text. - 'answers': The answers from various crowdsourcers. - 'sourcerAnswers': The answer provided from one crowdsourcer. - 's': Start of the answer. The first character of the answer in "text" (inclusive). - 'e': End of the answer. The last character of the answer in "text" (exclusive). - 'badQuestion': The crowdsourcer said that the question did not make sense. - 'noAnswer': The crowdsourcer said that there was no answer to the question in the text. - 'validated_answers': The answers from the validators. - 's': Start of the answer. The first character of the answer in "text" (inclusive). - 'e': End of the answer. The last character of the answer in "text" (exclusive). - 'badQuestion': The validator said that the question did not make sense. - 'noAnswer': The validator said that there was no answer to the question in the text. - 'count': The number of validators that agreed with this answer. Configuration: split - 'story_id': An identifier of the story - 'story_text': text of the story - 'question': A question about the story. - 'answer_token_ranges': Word based indices to answers in story_text. E.g. 196:202,217:228. Multiple selections from the same answer are separated by `,`. The start is inclusive and the end is exclusive. The end may point to whitespace after a token. ### Data Splits \| name \| train \| validation \| test \| \|---------------\|-----------:\|-----------:\|--------:\| \| combined-csv \| 119633 \| \| \| \| combined-json \| 12744 \| \| \| \| split \| 92549 \| 5166 \| 5126 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information NewsQA Code Copyright (c) Microsoft Corporation All rights reserved. MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED AS IS, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. © 2020 GitHub, Inc. ### Citation Information @inproceedings{trischler2017newsqa, title={NewsQA: A Machine Comprehension Dataset}, author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer}, booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP}, pages={191--200}, year={2017} ### Contributions Thanks to [@rsanjaykamath](https://github.com/rsanjaykamath) for adding this dataset.	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tab_fact	null	2023-01-25T14:45:28Z	802	7	tabfact	[ "task_categories:text-classification", "task_ids:fact-checking", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:1909.02164", "region:us" ]	2023-01-25T14:45:28Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - fact-checking paperswithcode_id: tabfact pretty_name: TabFact dataset_info: - config_name: tab_fact features: - name: id dtype: int32 - name: table_id dtype: string - name: table_text dtype: string - name: table_caption dtype: string - name: statement dtype: string - name: label dtype: class_label: names: '0': refuted '1': entailed splits: - name: train num_bytes: 99852664 num_examples: 92283 - name: validation num_bytes: 13846872 num_examples: 12792 - name: test num_bytes: 13493391 num_examples: 12779 download_size: 196508436 dataset_size: 127192927 - config_name: blind_test features: - name: id dtype: int32 - name: table_id dtype: string - name: table_text dtype: string - name: table_caption dtype: string - name: statement dtype: string - name: test_id dtype: string splits: - name: test num_bytes: 10954442 num_examples: 9750 download_size: 196508436 dataset_size: 10954442 --- # Dataset Card for TabFact ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [TabFact](https://tabfact.github.io/index.html) - Repository: [GitHub](https://github.com/wenhuchen/Table-Fact-Checking) - Paper: [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://arxiv.org/abs/1909.02164) - Leaderboard: [Leaderboard](https://competitions.codalab.org/competitions/21611) - Point of Contact: [Wenhu Chen](wenhuchen@cs.ucsb.edu) ### Dataset Summary The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{2019TabFactA, title={TabFact : A Large-scale Dataset for Table-based Fact Verification}, author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang}, booktitle = {International Conference on Learning Representations (ICLR)}, address = {Addis Ababa, Ethiopia}, month = {April}, year = {2020} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.	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chr_en	null	2023-06-01T14:59:50Z	801	3	chren	[ "task_categories:fill-mask", "task_categories:text-generation", "task_categories:translation", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "annotations_creators:found", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "multilinguality:multilingual", "multilinguality:translation", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "source_datasets:original", "language:chr", "language:en", "license:other", "arxiv:2010.04791", "region:us" ]	2023-06-01T14:59:50Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - expert-generated - found - no-annotation language_creators: - found language: - chr - en license: - other multilinguality: - monolingual - multilingual - translation size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - fill-mask - text-generation - translation task_ids: - language-modeling - masked-language-modeling paperswithcode_id: chren dataset_info: - config_name: monolingual_raw features: - name: text_sentence dtype: string - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 1210828 num_examples: 5210 download_size: 28899321 dataset_size: 1210828 - config_name: parallel_raw features: - name: line_number dtype: string - name: sentence_pair dtype: translation: languages: - en - chr - name: text_title dtype: string - name: speaker dtype: string - name: date dtype: int32 - name: type dtype: string - name: dialect dtype: string splits: - name: full num_bytes: 5012923 num_examples: 14151 download_size: 28899321 dataset_size: 5012923 - config_name: monolingual features: - name: sentence dtype: string splits: - name: chr num_bytes: 882848 num_examples: 5210 - name: en5000 num_bytes: 615295 num_examples: 5000 - name: en10000 num_bytes: 1211645 num_examples: 10000 - name: en20000 num_bytes: 2432378 num_examples: 20000 - name: en50000 num_bytes: 6065780 num_examples: 49999 - name: en100000 num_bytes: 12130564 num_examples: 100000 download_size: 28899321 dataset_size: 23338510 - config_name: parallel features: - name: sentence_pair dtype: translation: languages: - en - chr splits: - name: train num_bytes: 3089658 num_examples: 11639 - name: dev num_bytes: 260409 num_examples: 1000 - name: out_dev num_bytes: 78134 num_examples: 256 - name: test num_bytes: 264603 num_examples: 1000 - name: out_test num_bytes: 80967 num_examples: 256 download_size: 28899321 dataset_size: 3773771 config_names: - monolingual - monolingual_raw - parallel - parallel_raw --- # Dataset Card for ChrEn ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [Github repository for ChrEn](https://github.com/ZhangShiyue/ChrEn) - Paper: [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://arxiv.org/abs/2010.04791) - Point of Contact: [benfrey@email.unc.edu](benfrey@email.unc.edu) ### Dataset Summary ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning. ### Supported Tasks and Leaderboards The dataset is intended to use for `machine-translation` between Enlish (`en`) and Cherokee (`chr`). ### Languages The dataset contains Enlish (`en`) and Cherokee (`chr`) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC). ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill. ### Licensing Information The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions). ### Citation Information ``` @inproceedings{zhang2020chren, title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization}, author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit}, booktitle={EMNLP2020}, year={2020} } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite), [@lhoestq](https://github.com/lhoestq) for adding this dataset.	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OpenLLM-France/Tutoriel	OpenLLM-France	2023-06-21T03:33:13Z	801	0	null	[ "region:us" ]	2023-06-21T03:33:13Z	2023-06-21T03:32:48.000Z	2023-06-21T03:32:48	Entry not found	[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -0.3992327153682709, 0.5791246294975281, -0.14494499564170837, -0.10751135647296906, 0.28233814239501953, -0.2768957316875458, -0.5409227013587952, -0.3685525059700012, -1.1902779340744019, 0.061491698026657104, 0.5316582918167114, 0.743514358997345, 0.7584061622619629, 0.3652168810367584, 0.643257737159729, 0.39322906732559204, -0.231389120221138, 0.48270541429519653, -0.04171793907880783, 0.0026038705836981535, -0.3524438440799713, -0.5516899824142456, -0.28596577048301697, 0.07584768533706665, 1.096130132675171, 0.9666873812675476, 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qangaroo	null	2023-04-05T13:37:06Z	798	0	null	[ "language:en", "region:us" ]	2023-04-05T13:37:06Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- language: - en paperswithcode_id: null pretty_name: qangaroo dataset_info: - config_name: medhop features: - name: query dtype: string - name: supports sequence: string - name: candidates sequence: string - name: answer dtype: string - name: id dtype: string splits: - name: train num_bytes: 93947725 num_examples: 1620 - name: validation num_bytes: 16463555 num_examples: 342 download_size: 339843061 dataset_size: 110411280 - config_name: masked_medhop features: - name: query dtype: string - name: supports sequence: string - name: candidates sequence: string - name: answer dtype: string - name: id dtype: string splits: - name: train num_bytes: 95823986 num_examples: 1620 - name: validation num_bytes: 16802484 num_examples: 342 download_size: 339843061 dataset_size: 112626470 - config_name: wikihop features: - name: query dtype: string - name: supports sequence: string - name: candidates sequence: string - name: answer dtype: string - name: id dtype: string splits: - name: train num_bytes: 325994029 num_examples: 43738 - name: validation num_bytes: 40869634 num_examples: 5129 download_size: 339843061 dataset_size: 366863663 - config_name: masked_wikihop features: - name: query dtype: string - name: supports sequence: string - name: candidates sequence: string - name: answer dtype: string - name: id dtype: string splits: - name: train num_bytes: 348290479 num_examples: 43738 - name: validation num_bytes: 43689810 num_examples: 5129 download_size: 339843061 dataset_size: 391980289 --- # Dataset Card for "qangaroo" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://qangaroo.cs.ucl.ac.uk/index.html](http://qangaroo.cs.ucl.ac.uk/index.html) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.36 GB - Size of the generated dataset: 981.89 MB - Total amount of disk used: 2.34 GB ### Dataset Summary We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference. Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps. Our aim is to build Reading Comprehension methods that perform multi-hop inference on text, where individual facts are spread out across different documents. The two QAngaroo datasets provide a training and evaluation resource for such methods. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### masked_medhop - Size of downloaded dataset files: 339.84 MB - Size of the generated dataset: 112.63 MB - Total amount of disk used: 452.47 MB An example of 'validation' looks as follows. ``` ``` #### masked_wikihop - Size of downloaded dataset files: 339.84 MB - Size of the generated dataset: 391.98 MB - Total amount of disk used: 731.82 MB An example of 'validation' looks as follows. ``` ``` #### medhop - Size of downloaded dataset files: 339.84 MB - Size of the generated dataset: 110.42 MB - Total amount of disk used: 450.26 MB An example of 'validation' looks as follows. ``` ``` #### wikihop - Size of downloaded dataset files: 339.84 MB - Size of the generated dataset: 366.87 MB - Total amount of disk used: 706.71 MB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### masked_medhop - `query`: a `string` feature. - `supports`: a `list` of `string` features. - `candidates`: a `list` of `string` features. - `answer`: a `string` feature. - `id`: a `string` feature. #### masked_wikihop - `query`: a `string` feature. - `supports`: a `list` of `string` features. - `candidates`: a `list` of `string` features. - `answer`: a `string` feature. - `id`: a `string` feature. #### medhop - `query`: a `string` feature. - `supports`: a `list` of `string` features. - `candidates`: a `list` of `string` features. - `answer`: a `string` feature. - `id`: a `string` feature. #### wikihop - `query`: a `string` feature. - `supports`: a `list` of `string` features. - `candidates`: a `list` of `string` features. - `answer`: a `string` feature. - `id`: a `string` feature. ### Data Splits \| name \|train\|validation\| \|--------------\|----:\|---------:\| \|masked_medhop \| 1620\| 342\| \|masked_wikihop\|43738\| 5129\| \|medhop \| 1620\| 342\| \|wikihop \|43738\| 5129\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.	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qanastek/EMEA-V3	qanastek	2022-10-22T15:18:02Z	794	6	null	[ "task_categories:translation", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:bg", "multilinguality:cs", "multilinguality:da", "multilinguality:de", "multilinguality:el", "multilinguality:en", "multilinguality:es", "multilinguality:et", "multilinguality:fi", "multilinguality:fr", "multilinguality:hu", "multilinguality:it", "multilinguality:lt", "multilinguality:lv", "multilinguality:mt", "multilinguality:nl", "multilinguality:pl", "multilinguality:pt", "multilinguality:ro", "multilinguality:sk", "multilinguality:sl", "multilinguality:sv", "size_categories:100K<n<1M", "source_datasets:extended", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "region:us" ]	2022-10-22T15:18:02Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - machine-generated - expert-generated language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv multilinguality: - bg - cs - da - de - el - en - es - et - fi - fr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv pretty_name: EMEA-V3 size_categories: - 100K<n<1M source_datasets: - extended task_categories: - translation - machine-translation task_ids: - translation - machine-translation --- # EMEA-V3 : European parallel translation corpus from the European Medicines Agency ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: https://opus.nlpl.eu/EMEA.php - Repository: https://github.com/qanastek/EMEA-V3/ - Paper: https://aclanthology.org/L12-1246/ - Leaderboard: [Needs More Information] - Point of Contact: [Yanis Labrak](mailto:yanis.labrak@univ-avignon.fr) ### Dataset Summary `EMEA-V3` is a parallel corpus for neural machine translation collected and aligned by [Tiedemann, Jorg](mailto:jorg.tiedemann@lingfil.uu.se) during the [OPUS project](https://opus.nlpl.eu/). ### Supported Tasks and Leaderboards `translation`: The dataset can be used to train a model for translation. ### Languages In our case, the corpora consists of a pair of source and target sentences for all 22 different languages from the European Union (EU). List of languages : `Bulgarian (bg)`,`Czech (cs)`,`Danish (da)`,`German (de)`,`Greek (el)`,`English (en)`,`Spanish (es)`,`Estonian (et)`,`Finnish (fi)`,`French (fr)`,`Hungarian (hu)`,`Italian (it)`,`Lithuanian (lt)`,`Latvian (lv)`,`Maltese (mt)`,`Dutch (nl)`,`Polish (pl)`,`Portuguese (pt)`,`Romanian (ro)`,`Slovak (sk)`,`Slovenian (sl)`,`Swedish (sv)`. ## Load the dataset with HuggingFace ```python from datasets import load_dataset dataset = load_dataset("qanastek/EMEA-V3", split='train', download_mode='force_redownload') print(dataset) print(dataset[0]) ``` ## Dataset Structure ### Data Instances ```plain lang,source_text,target_text bg-cs,EMEA/ H/ C/ 471,EMEA/ H/ C/ 471 bg-cs,ABILIFY,ABILIFY bg-cs,Какво представлява Abilify?,Co je Abilify? bg-cs,"Abilify е лекарство, съдържащо активното вещество арипипразол.","Abilify je léčivý přípravek, který obsahuje účinnou látku aripiprazol." bg-cs,"Предлага се под формата на таблетки от 5 mg, 10 mg, 15 mg и 30 mg, като диспергиращи се таблетки (таблетки, които се разтварят в устата) от 10 mg, 15 mg и 30 mg, като перорален разтвор (1 mg/ ml) и като инжекционен разтвор (7, 5 mg/ ml).","Je dostupný ve formě tablet s obsahem 5 mg, 10 mg, 15 mg a 30 mg, ve formě tablet dispergovatelných v ústech (tablet, které se rozpustí v ústech) s obsahem 10 mg, 15 mg a 30 mg, jako perorální roztok (1 mg/ ml) nebo jako injekční roztok (7, 5 mg/ ml)." bg-cs,За какво се използва Abilify?,Na co se přípravek Abilify používá? ``` ### Data Fields lang : The pair of source and target language of type `String`. source_text : The source text of type `String`. target_text : The target text of type `String`. ### Data Splits \| \| bg \| cs \| da \| de \| el \| en \| es \| et \| fi \| fr \| hu \| it \| lt \| lv \| mt \| nl \| pl \| pt \| ro \| sk \| sl \| sv \| \|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\|--------\| \| bg \| 0 \| 342378 \| 349675 \| 348061 \| 355696 \| 333066 \| 349936 \| 336142 \| 341732 \| 358045 \| 352763 \| 351669 \| 348679 \| 342721 \| 351097 \| 353942 \| 355005 \| 347925 \| 351099 \| 345572 \| 346954 \| 342927 \| \| cs \| 342378 \| 0 \| 354824 \| 353397 \| 364609 \| 335716 \| 356506 \| 340309 \| 349040 \| 363614 \| 358353 \| 357578 \| 353232 \| 347807 \| 334353 \| 355192 \| 358357 \| 351244 \| 330447 \| 346835 \| 348411 \| 346894 \| \| da \| 349675 \| 354824 \| 0 \| 387202 \| 397654 \| 360186 \| 387329 \| 347391 \| 379830 \| 396294 \| 367091 \| 388495 \| 360572 \| 353801 \| 342263 \| 388250 \| 368779 \| 382576 \| 340508 \| 356890 \| 357694 \| 373510 \| \| de \| 348061 \| 353397 \| 387202 \| 0 \| 390281 \| 364005 \| 386335 \| 346166 \| 378626 \| 393468 \| 366828 \| 381396 \| 360907 \| 353151 \| 340294 \| 377770 \| 367080 \| 381365 \| 337562 \| 355805 \| 358700 \| 376925 \| \| el \| 355696 \| 364609 \| 397654 \| 390281 \| 0 \| 372824 \| 393051 \| 354874 \| 384889 \| 403248 \| 373706 \| 391389 \| 368576 \| 360047 \| 348221 \| 396284 \| 372486 \| 387170 \| 342655 \| 364959 \| 363778 \| 384569 \| \| en \| 333066 \| 335716 \| 360186 \| 364005 \| 372824 \| 0 \| 366769 \| 333667 \| 357177 \| 373152 \| 349176 \| 361089 \| 339899 \| 336306 \| 324695 \| 360418 \| 348450 \| 361393 \| 321233 \| 338649 \| 338195 \| 352587 \| \| es \| 349936 \| 356506 \| 387329 \| 386335 \| 393051 \| 366769 \| 0 \| 348454 \| 378158 \| 394253 \| 368203 \| 378076 \| 360645 \| 354126 \| 340297 \| 381188 \| 367091 \| 376443 \| 337302 \| 358745 \| 357961 \| 379462 \| \| et \| 336142 \| 340309 \| 347391 \| 346166 \| 354874 \| 333667 \| 348454 \| 0 \| 341694 \| 358012 \| 352099 \| 351747 \| 345417 \| 339042 \| 337302 \| 350911 \| 354329 \| 345856 \| 325992 \| 343950 \| 342787 \| 340761 \| \| fi \| 341732 \| 349040 \| 379830 \| 378626 \| 384889 \| 357177 \| 378158 \| 341694 \| 0 \| 387478 \| 358869 \| 379862 \| 352968 \| 346820 \| 334275 \| 379729 \| 358760 \| 374737 \| 331135 \| 348559 \| 348680 \| 368528 \| \| fr \| 358045 \| 363614 \| 396294 \| 393468 \| 403248 \| 373152 \| 394253 \| 358012 \| 387478 \| 0 \| 373625 \| 385869 \| 368817 \| 361137 \| 347699 \| 388607 \| 372387 \| 388658 \| 344139 \| 363249 \| 366474 \| 383274 \| \| hu \| 352763 \| 358353 \| 367091 \| 366828 \| 373706 \| 349176 \| 368203 \| 352099 \| 358869 \| 373625 \| 0 \| 367937 \| 361015 \| 354872 \| 343831 \| 368387 \| 369040 \| 361652 \| 340410 \| 357466 \| 361157 \| 356426 \| \| it \| 351669 \| 357578 \| 388495 \| 381396 \| 391389 \| 361089 \| 378076 \| 351747 \| 379862 \| 385869 \| 367937 \| 0 \| 360783 \| 356001 \| 341552 \| 384018 \| 365159 \| 378841 \| 337354 \| 357562 \| 358969 \| 377635 \| \| lt \| 348679 \| 353232 \| 360572 \| 360907 \| 368576 \| 339899 \| 360645 \| 345417 \| 352968 \| 368817 \| 361015 \| 360783 \| 0 \| 350576 \| 337339 \| 362096 \| 361497 \| 357070 \| 335581 \| 351639 \| 350916 \| 349636 \| \| lv \| 342721 \| 347807 \| 353801 \| 353151 \| 360047 \| 336306 \| 354126 \| 339042 \| 346820 \| 361137 \| 354872 \| 356001 \| 350576 \| 0 \| 336157 \| 355791 \| 358607 \| 349590 \| 329581 \| 348689 \| 346862 \| 345016 \| \| mt \| 351097 \| 334353 \| 342263 \| 340294 \| 348221 \| 324695 \| 340297 \| 337302 \| 334275 \| 347699 \| 343831 \| 341552 \| 337339 \| 336157 \| 0 \| 341111 \| 344764 \| 335553 \| 338137 \| 335930 \| 334491 \| 335353 \| \| nl \| 353942 \| 355192 \| 388250 \| 377770 \| 396284 \| 360418 \| 381188 \| 350911 \| 379729 \| 388607 \| 368387 \| 384018 \| 362096 \| 355791 \| 341111 \| 0 \| 369694 \| 383913 \| 339047 \| 359126 \| 360054 \| 379771 \| \| pl \| 355005 \| 358357 \| 368779 \| 367080 \| 372486 \| 348450 \| 367091 \| 354329 \| 358760 \| 372387 \| 369040 \| 365159 \| 361497 \| 358607 \| 344764 \| 369694 \| 0 \| 357426 \| 335243 \| 352527 \| 355534 \| 353214 \| \| pt \| 347925 \| 351244 \| 382576 \| 381365 \| 387170 \| 361393 \| 376443 \| 345856 \| 374737 \| 388658 \| 361652 \| 378841 \| 357070 \| 349590 \| 335553 \| 383913 \| 357426 \| 0 \| 333365 \| 354784 \| 352673 \| 373392 \| \| ro \| 351099 \| 330447 \| 340508 \| 337562 \| 342655 \| 321233 \| 337302 \| 325992 \| 331135 \| 344139 \| 340410 \| 337354 \| 335581 \| 329581 \| 338137 \| 339047 \| 335243 \| 333365 \| 0 \| 332373 \| 330329 \| 331268 \| \| sk \| 345572 \| 346835 \| 356890 \| 355805 \| 364959 \| 338649 \| 358745 \| 343950 \| 348559 \| 363249 \| 357466 \| 357562 \| 351639 \| 348689 \| 335930 \| 359126 \| 352527 \| 354784 \| 332373 \| 0 \| 348396 \| 346855 \| \| sl \| 346954 \| 348411 \| 357694 \| 358700 \| 363778 \| 338195 \| 357961 \| 342787 \| 348680 \| 366474 \| 361157 \| 358969 \| 350916 \| 346862 \| 334491 \| 360054 \| 355534 \| 352673 \| 330329 \| 348396 \| 0 \| 347727 \| \| sv \| 342927 \| 346894 \| 373510 \| 376925 \| 384569 \| 352587 \| 379462 \| 340761 \| 368528 \| 383274 \| 356426 \| 377635 \| 349636 \| 345016 \| 335353 \| 379771 \| 353214 \| 373392 \| 331268 \| 346855 \| 347727 \| 0 \| ## Dataset Creation ### Curation Rationale For details, check the corresponding [pages](https://opus.nlpl.eu/EMEA.php). ### Source Data <!-- #### Initial Data Collection and Normalization ddd --> #### Who are the source language producers? Every data of this corpora as been uploaded by [Tiedemann, Jorg](mailto:jorg.tiedemann@lingfil.uu.se) on [Opus](https://opus.nlpl.eu/EMEA.php). ### Personal and Sensitive Information The corpora is free of personal or sensitive information. ## Considerations for Using the Data ### Other Known Limitations The nature of the task introduce a variability in the quality of the target translations. ## Additional Information ### Dataset Curators __Hugging Face EMEA-V3__: Labrak Yanis, Dufour Richard (Not affiliated with the original corpus) __OPUS : Parallel Data, Tools and Interfaces in OPUS__: [Tiedemann, Jorg](mailto:jorg.tiedemann@lingfil.uu.se). <!-- ### Licensing Information ddd --> ### Citation Information Please cite the following paper when using this dataset. ```latex @inproceedings{tiedemann-2012-parallel, title = Parallel Data, Tools and Interfaces in OPUS, author = { Tiedemann, Jorg }, booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)", month = may, year = 2012, address = Istanbul, Turkey, publisher = European Language Resources Association (ELRA), url = http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf, pages = 2214--2218, abstract = This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project., } ```	[ -0.8196666240692139, -0.508622407913208, 0.16061457991600037, 0.3144359588623047, -0.17121270298957825, 0.09365755319595337, -0.03200807422399521, -0.1331845074892044, 0.7783786654472351, 0.41239625215530396, -0.6202534437179565, -0.5326645970344543, -0.6838599443435669, 0.06186196580529213, 0.01156540960073471, 0.519660472869873, -0.19833388924598694, 0.16230545938014984, 0.1334056556224823, -0.07052057981491089, -0.26294055581092834, 0.009424077346920967, -0.46702125668525696, -0.14262999594211578, 0.1887245625257492, 0.42281973361968994, 0.711357831954956, 0.7515573501586914, 0.4500191807746887, 0.4613138437271118, -0.17776378989219666, 0.18447256088256836, -0.29056301712989807, 0.0304876696318388, -0.002060249447822571, -0.21921636164188385, -0.24653640389442444, 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dennlinger/eur-lex-sum	dennlinger	2022-11-11T14:25:06Z	794	23	null	[ "task_categories:translation", "task_categories:summarization", "annotations_creators:found", "annotations_creators:expert-generated", "language_creators:found", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:bg", "language:hr", "language:cs", "language:da", "language:nl", "language:en", "language:et", "language:fi", "language:fr", "language:de", "language:el", "language:hu", "language:ga", "language:it", "language:lv", "language:lt", "language:mt", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:es", "language:sv", "license:cc-by-4.0", "legal", "eur-lex", "expert summary", "parallel corpus", "multilingual", "arxiv:2210.13448", "region:us" ]	2022-11-11T14:25:06Z	2022-10-10T08:07:37.000Z	2022-10-10T08:07:37	--- annotations_creators: - found - expert-generated language: - bg - hr - cs - da - nl - en - et - fi - fr - de - el - hu - ga - it - lv - lt - mt - pl - pt - ro - sk - sl - es - sv language_creators: - found - expert-generated license: - cc-by-4.0 multilinguality: - multilingual pretty_name: eur-lex-sum size_categories: - 10K<n<100K source_datasets: - original tags: - legal - eur-lex - expert summary - parallel corpus - multilingual task_categories: - translation - summarization --- # Dataset Card for the EUR-Lex-Sum Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [Needs More Information] - Repository: https://github.com/achouhan93/eur-lex-sum - Paper: [EUR-Lex-Sum: A Multi-and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://arxiv.org/abs/2210.13448) - Leaderboard: [Needs More Information] - Point of Contact: [Dennis Aumiller](mailto:aumiller@informatik.uni-heidelberg.de) ### Dataset Summary The EUR-Lex-Sum dataset is a multilingual resource intended for text summarization in the legal domain. It is based on human-written summaries of legal acts issued by the European Union. It distinguishes itself by introducing a smaller set of high-quality human-written samples, each of which have much longer references (and summaries!) than comparable datasets. Additionally, the underlying legal acts provide a challenging domain-specific application to legal texts, which are so far underrepresented in non-English languages. For each legal act, the sample can be available in up to 24 languages (the officially recognized languages in the European Union); the validation and test samples consist entirely of samples available in all languages, and are aligned across all languages at the paragraph level. ### Supported Tasks and Leaderboards - `summarization`: The dataset is primarily suitable for summarization tasks, where it can be used as a small-scale training resource. The primary evaluation metric used in the underlying experiments is [ROUGE](https://huggingface.co/metrics/rouge). The EUR-Lex-Sum data is particularly interesting, because traditional lead-based baselines (such as lead-3) do not work well, given the extremely long reference summaries. However, we can provide reasonably good summaries by applying a modified LexRank approach on the paragraph level. - `cross-lingual-summarization`: Given that samples of the dataset exist across multiple languages, and both the validation and test set are fully aligned across languages, this dataset can further be used as a cross-lingual benchmark. In these scenarios, language pairs (e.g., EN to ES) can be compared against monolingual systems. Suitable baselines include automatic translations of gold summaries, or translations of simple LexRank-generated monolingual summaries. - `long-form-summarization`: We further note the particular case for long-form summarization. In comparison to news-based summarization datasets, this resource provides around 10x longer summary texts. This is particularly challenging for transformer-based models, which struggle with limited context lengths. ### Languages The dataset supports all [official languages of the European Union](https://european-union.europa.eu/principles-countries-history/languages_en). At the time of collection, those were 24 languages: Bulgarian, Croationa, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, and Swedish. Both the reference texts, as well as the summaries, are translated from an English original text (this was confirmed by private correspondence with the Publications Office of the European Union). Translations and summaries are written by external (professional) parties, contracted by the EU. Depending on availability of document summaries in particular languages, we have between 391 (Irish) and 1505 (French) samples available. Over 80% of samples are available in at least 20 languages. ## Dataset Structure ### Data Instances Data instances contain fairly minimal information. Aside from a unique identifier, corresponding to the Celex ID generated by the EU, two further fields specify the original long-form legal act and its associated summary. ``` { "celex_id": "3A32021R0847", "reference": "REGULATION (EU) 2021/847 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL\n [...]" "summary": "Supporting EU cooperation in the field of taxation: Fiscalis (2021-2027)\n\n [...]" } ``` ### Data Fields - `celex_id`: The [Celex ID](https://eur-lex.europa.eu/content/tools/eur-lex-celex-infographic-A3.pdf) is a naming convention used for identifying EU-related documents. Among other things, the year of publication and sector codes are embedded in the Celex ID. - `reference`: This is the full text of a Legal Act published by the EU. - `summary`: This field contains the summary associated with the respective Legal Act. ### Data Splits We provide pre-split training, validation and test splits. To obtain the validation and test splits, we randomly assigned all samples that are available across all 24 languages into two equally large portions. In total, 375 instances are available in 24 languages, which means we obtain a validation split of 187 samples and 188 test instances. All remaining instances are assigned to the language-specific training portions, which differ in their exact size. We particularly ensured that no duplicates exist across the three splits. For this purpose, we ensured that no exactly matching reference or summary exists for any sample. Further information on the length distributions (for the English subset) can be found in the paper. ## Dataset Creation ### Curation Rationale The dataset was curated to provide a resource for under-explored aspects of automatic text summarization research. In particular, we want to encourage the exploration of abstractive summarization systems that are not limited by the usual 512 token context window, which usually works well for (short) news articles, but fails to generate long-form summaries, or does not even work with longer source texts in the first place. Also, existing resources primarily focus on a single (and very specialized) domain, namely news article summarization. We wanted to provide a further resource for legal summarization, for which many languages do not even have any existing datasets. We further noticed that no previous system had utilized the human-written samples from the [EUR-Lex platform](https://eur-lex.europa.eu/homepage.html), which provide an excellent source for training instances suitable for summarization research. We later found out about a resource created in parallel based on EUR-Lex documents, which provides a [monolingual (English) corpus](https://github.com/svea-klaus/Legal-Document-Summarization) constructed in similar fashion. However, we provide a more thorough filtering, and extend the process to the remaining 23 EU languages. ### Source Data #### Initial Data Collection and Normalization The data was crawled from the aforementioned EUR-Lex platform. In particular, we only use samples which have HTML versions of the texts available, which ensure the alignment across languages, given that translations have to retain the original paragraph structure, which is encoded in HTML elements. We further filter out samples that do not have associated document summaries available. One particular design choice has to be expanded upon: For some summaries, several source documents are considered as an input by the EU. However, since we construct a single-document summarization corpus, we decided to use the longest reference document only. This means we explicitly drop the other reference texts from the corpus. One alternative would have been to concatenated all relevant source texts; however, this generally leads to degradation of positional biases in the text, which can be an important learned feature for summarization systems. Our paper details the effect of this decision in terms of n-gram novelty, which we find is affected by the processing choice. #### Who are the source language producers? The language producers are external professionals contracted by the European Union offices. As previously noted, all non-English texts are generated from the respective English document (all summaries are direct translations the English summary, all reference texts are translated from the English reference text). No further information on the demographic of annotators is provided. ### Annotations #### Annotation process The European Union publishes their [annotation guidelines](https://etendering.ted.europa.eu/cft/cft-documents.html?cftId=6490) for summaries, which targets a length between 600-800 words. No information on the guidelines for translations is known. #### Who are the annotators? The language producers are external professionals contracted by the European Union offices. No further information on the annotators is available. ### Personal and Sensitive Information The original text was not modified in any way by the authors of this dataset. Explicit mentions of personal names can occur in the dataset, however, we rely on the European Union that no further sensitive information is provided in these documents. ## Considerations for Using the Data ### Social Impact of Dataset The dataset can be used to provide summarization systems in languages that are previously under-represented. For example, language samples in Irish and Maltese (among others) enable the development and evaluation for these languages. A successful cross-lingual system would further enable the creation of automated legal summaries for legal acts, possibly enabling foreigners in European countries to automatically translate similar country-specific legal acts. Given the limited amount of training data, this dataset is also suitable as a test bed for low-resource approaches, especially in comparsion to strong unsupervised (extractive) summarization systems. We also note that the summaries are explicitly provided as "not legally binding" by the EU. The implication of left-out details (a necessary evil of summaries) implies the existence of differences between the (legally binding) original legal act. Risks associated with this dataset also largely stem from the potential application of systems trained on it. Decisions in the legal domain require careful analysis of the full context, and should not be made based on system-generated summaries at this point in time. Known biases of summarization, specifically factual hallucinations, should act as further deterrents. ### Discussion of Biases Given the availability bias, some of the languages in the dataset are more represented than others. We attempt to mitigate influence on the evaluation by providing validation and test sets of the same size across all languages. Given that we require the availability of HTML documents, we see a particular temporal bias in our dataset, which features more documents from the years of 1990 onwards, simply due to the increase in EU-related activities, but also the native use of the internet as a data storage. This could imply a particular focus on more recent topics (e.g., Brexit, renewable eneriges, etc. come to mind). Finally, due to the source of these documents being the EU, we expect a natural bias towards EU-centric (and therefore Western-centric) content; other nations and continents will be under-represented in the data. ### Other Known Limitations As previously outlined, we are aware of some summaries relating to multiple (different) legal acts. For these samples, only one (the longest) text will be available in our dataset. ## Additional Information ### Dataset Curators The web crawler was originally implemented by Ashish Chouhan. Post-filtering and sample correction was later performed by Dennis Aumiller. Both were PhD students employed at the Database Systems Research group of Heidelberg University, under the guidance of Prof. Dr. Michael Gertz. ### Licensing Information Data from the EUR-Lex platform is available under the CC-BY SA 4.0 license. We redistribute the dataset under the same license. ### Citation Information For the pre-print version, please cite: ``` @article{aumiller-etal-2022-eur, author = {Aumiller, Dennis and Chouhan, Ashish and Gertz, Michael}, title = {{EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain}}, journal = {CoRR}, volume = {abs/2210.13448}, eprinttype = {arXiv}, eprint = {2210.13448}, url = {https://arxiv.org/abs/2210.13448} } ```	[ -0.4071638882160187, -0.39235350489616394, 0.15381577610969543, 0.16455596685409546, -0.40557804703712463, 0.08897431939840317, -0.44838786125183105, -0.3660835921764374, 0.47650042176246643, 0.38328471779823303, -0.3675614595413208, -0.8136327266693115, -0.469609797000885, 0.5263323783874512, -0.3126947581768036, 1.2134075164794922, -0.04013807326555252, 0.12790359556674957, -0.07883957028388977, -0.5313242673873901, -0.16967113316059113, -0.6713084578514099, -0.345561146736145, -0.1320706158876419, 0.6030920743942261, 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atasoglu/flickr8k-dataset	atasoglu	2023-05-28T12:12:43Z	793	0	null	[ "task_categories:image-to-text", "task_categories:text-to-image", "size_categories:1K<n<10K", "language:en", "region:us" ]	2023-05-28T12:12:43Z	2023-05-28T11:52:48.000Z	2023-05-28T11:52:48	--- task_categories: - image-to-text - text-to-image language: - en pretty_name: flickr8k size_categories: - 1K<n<10K --- You must download the dataset files manually. You can visit [this](https://github.com/jbrownlee/Datasets/releases/tag/Flickr8k) page or run `download.sh` to get files. After, you can load dataset by referencing the directory: ```py import datasets ds = datasets.load_dataset("atasoglu/flickr8k-dataset", data_dir="data") print(ds) ``` ``` DatasetDict({ train: Dataset({ features: ['image_id', 'image_path', 'captions'], num_rows: 6000 }) test: Dataset({ features: ['image_id', 'image_path', 'captions'], num_rows: 1000 }) validation: Dataset({ features: ['image_id', 'image_path', 'captions'], num_rows: 1000 }) }) ``` I don't own the copyright of the images. Please [visit](https://forms.illinois.edu/sec/1713398) for more.	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LeoLM/MMLU_de	LeoLM	2023-06-15T01:41:53Z	793	0	null	[ "license:mit", "region:us" ]	2023-06-15T01:41:53Z	2023-06-03T22:07:16.000Z	2023-06-03T22:07:16	--- license: mit --- # Massive Multitask Language Understanding (MMLU) in German This dataset is to be used for the evaluation of LLM German language understanding. It is based on the hendrycksTest dataset ([here](https://huggingface.co/datasets/cais/mmlu) and [here](https://huggingface.co/datasets/tasksource/mmlu)) and was created by using the GPT-3.5 API to translate the entire test set and a few examples of the validation set. To make sure the answer options follow the intended sentence structure and are always of the correct format, GPT was prompted to output in a JSON format. This came with some complications that were later manually fixed. The prompt used to translate a single example was the following: ``` insert prompt here @TODO ``` This translation cost a total of ~13€ including iterating on the prompt and fixing broken examples.	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CodedotAI/code_clippy_github	CodedotAI	2022-08-05T02:57:36Z	790	10	null	[ "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "language:code", "license:mit", "arxiv:2107.03374", "region:us" ]	2022-08-05T02:57:36Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: ["code"] license: - mit multilinguality: - multilingual pretty_name: code-clippy-github-code size_categories: - unknown source_datasets: [] task_categories: - sequence-modeling task_ids: - language-modeling --- # Code Clippy Github Dataset ## Dataset Description The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totaling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BigQuery. ### How to use it This dataset is pretty large please use the streaming parameter from the `datasets` library as seen below: ```python from datasets import load_dataset ds = load_dataset("CodedotAI/code_clippy_github", streaming=True) ``` ## Data Structure ### Data Instances ```python { 'code_text': " a = mc^2", 'repo_name': 'NotEinstein', 'file_path': 'root/users/einstein.py', 'language': 'Python', 'license': 'isc', 'size': 2 } ``` ### Data Fields \|Field\|Type\|Description\| \|---\|---\|---\| \|code_text\|string\|string of the source code contained in the code file\| \|repo_name\|string\|name of the GitHub repository\| \|file_path\|string\|path of the code file within the repository \| \|language\|string\|programming language used in the file inferred by the file extension\| \|license\|string\|license of GitHub repository\| \|size\|int\|size of source file in bytes\| ### Data Splits Only a train split is provided in this dataset. ## Languages The dataset contains 22 programming languages with over 23 extensions: ```python { "C": [".c"], "C#": [".cs"], "C++": [".cpp"], "CSS": [".css"], "Dart" : [".dart"], "GO": [".go"], "HTML":[".html"], "Java": [".java"], "JavaScript": [".js"], "Jupyter Notebooks (Python)": [".ipynb"], "Kotlin" : [".kt"], "Lisp" : [".lisp"], "Matlab" : [".m"], "PHP": [".php"], "Perl": [".pl"], "Python": [".py"], "R" : [".r"], "Ruby": [".rb"], "Rust": [".rs"], "SQL": [".sql"], "Shell": [".sh"], "Swift" : [".swift"], "TypeScript": [".ts"], } ``` ## Licenses Each example is also annotated with the license of the associated repository. There are in total 15 licenses: ```python [ 'mit', 'apache-2.0', 'gpl-2.0', 'gpl-3.0', 'bsd-3-clause', 'bsd-2-clause', 'unlicense', 'apacheagpl-3.0', 'lgpl-3.0', 'cc0-1.0', 'epl-1.0', 'lgpl-2.1', 'mpl-2.0', 'isc', 'artistic-2.0' ] ``` ## Dataset Statistics The dataset is about ~ 18 TB uncompressed. We are currently working on processing it and applying further filtering. ## Dataset Creation The dataset was created in two steps: 1. Files with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery using the following query: ```sql SELECT f.id, f.repo_name, f.path, content.copies, content.size, content.content, lic.license FROM `bigquery-public-data.github_repos.files` AS f JOIN `bigquery-public-data.github_repos.contents` as content ON f.id = content.id JOIN `bigquery-public-data.github_repos.licenses` AS lic ON f.repo_name = lic.repo_name WHERE NOT content.binary AND ( (f.path LIKE '%.py') OR (f.path LIKE '%.java') OR (f.path LIKE '%.js') OR (f.path LIKE '%.html') OR (f.path LIKE '%.lisp') OR (f.path LIKE '%.sh') OR (f.path LIKE '%.r') OR (f.path LIKE '%.pl') OR (f.path LIKE '%.css') OR (f.path LIKE '%.sql') OR (f.path LIKE '%.c') OR (f.path LIKE '%.cpp') OR (f.path LIKE '%.ts') OR (f.path LIKE '%.cs') OR (f.path LIKE '%.go') OR (f.path LIKE '%.rs') OR (f.path LIKE '%.swift') OR (f.path LIKE '%.php') OR (f.path LIKE '%.dart') OR (f.path LIKE '%.kt') OR (f.path LIKE '%.m') OR (f.path LIKE '%.rb') OR (f.path LIKE '%.ipynb') ) -- make sure we dont go above 1 megabyte AND (content.size BETWEEN 1024 AND 1000000) ``` 2. Currently, our CodedotAI team is working on adding additional filters and cleaning this dataset. ### Personal and Sensitive Information Since this data was collected from public repositories, there exists potential for personal and sensitive information to be included in the data through developers accidentally or on purpose uploading their secret keys, passwords, API keys, emails, etc. ## Considerations for Using the Data ### Social Impact of Dataset The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discussion are highlighted here as it pertains to this dataset and models that may be trained from it. As well as some differences in views from the paper, particularly around legal implications. 1. Over-reliance: A language model trained on large datasets such as this one for the task of autogenerating code may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using a language model trained on this dataset. 2. Economic and labor market impacts: Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [ONET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software. 3. Security implications:* No filtering or checking of vulnerabilities or buggy code was performed. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, any model trained on this dataset may generate vulnerable, buggy, or malicious code. In safety-critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, a model trained on this dataset may be used to generate malicious code on purpose in order to perform ransomware or other such attacks. 4. Legal implications: No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there have been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset. ### v1.0 - The query was executed on _February 1, 2022, 12:15:59 AM EST_ ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/about/). We would also like to thank [Dr. Razvan Bunescu](https://webpages.charlotte.edu/rbunescu/) and [The College of Computing and Informatics at UNC Charlotte](https://cci.charlotte.edu/) for their generous contributions to this project, specifically in funding the BigQuery and Google Cloud Storage costs. We would also like to thank the [codeparrot team at Hugging face](https://huggingface.co/codeparrot) for open sourcing their documentation on [github-code](https://huggingface.co/datasets/codeparrot/github-code) which we used for the readme in this dataset. For another similar dataset to this please check github-code!	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kaist-ai/CoT-Collection	kaist-ai	2023-10-14T12:10:16Z	790	41	null	[ "task_categories:text-generation", "task_categories:text-classification", "size_categories:1M<n<10M", "language:en", "license:cc-by-4.0", "arxiv:2305.14045", "region:us" ]	2023-10-14T12:10:16Z	2023-06-05T07:11:17.000Z	2023-06-05T07:11:17	--- license: cc-by-4.0 task_categories: - text-generation - text-classification language: - en size_categories: - 1M<n<10M --- # Dataset Card for Dataset Name ## Dataset Description - Homepage:https://github.com/kaistAI/CoT-Collection - Repository:https://github.com/kaistAI/CoT-Collection - Paper:https://arxiv.org/abs/2305.14045 - Point of Contact:seungone@kaist.ac.kr ### Dataset Summary ![plot](./cot_collection.JPG) The CoT Collection is a dataset designed to induce Chain-of-Thought (CoT) capabilities into language models. While proprietary LLMs excel at generating Chain-of-Thoughts based on prompting, smaller LMs do not have this capability. Thus, by fine-tuning to generate Chain-of-Thoughts, it could acquire such abilities. The CoT Collection provides 1.84 million Chain-of-Thoughts augmented across 1060 tasks from the Flan Collection.\\ Experimental results show that fine-tuning on the CoT Collection results in (1) better zero-shot performance and (2) a better base model for few-shot learning. We also provide a multilingual version of CoT Collection at this [link](https://huggingface.co/datasets/kaist-ai/Multilingual-CoT-Collection). ### Supported Tasks and Leaderboards 1060 tasks chosen from the Flan Collection. The list of categories within the CoT Collection are: * Natural Language Inference * Extractive Question Answering * Closed Book Question Answering * Science * Toxic Classification * Arithmetic * Program Execution * Dialogue * Ethics * Commonsense Reasoning * Multiple Choice Question Answering ### Languages English ## Dataset Structure * source: The input that is given to the language model (LM). * target: The ground truth answer to the source. * rationale: The Chain of Thought (CoT) that explains how the target could be derived from the source. * task: A category that shows which dataset the source and target was extracted from. In our paper, we trained the underlying language model to generate in the following format: ``` \{rationale\} [RESULT] \{target\} ``` Then during evaluation, we parsed the prediction after the phrase ```[RESULT]```. ### Data Splits \| name \| train \| \|-------------------\|------:\| \|CoT-Collection\|1837928\| ### Citation Information If you find the following model helpful, please considering citing our paper! ``` @article{kim2023cot, title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning}, author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon}, journal={arXiv preprint arXiv:2305.14045}, year={2023} } ```	[ -0.49070024490356445, -0.9289440512657166, 0.3890596926212311, -0.241209015250206, -0.37096458673477173, 0.12214291095733643, -0.5527057647705078, -0.6144881248474121, 0.07821726053953171, 0.6284116506576538, -0.5334031581878662, 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dynabench/dynasent	dynabench	2021-04-29T11:30:24Z	788	3	null	[ "arxiv:2012.15349", "arxiv:1803.09010", "arxiv:1810.03993", "region:us" ]	2021-04-29T11:30:24Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	# DynaSent: Dynamic Sentiment Analysis Dataset DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. This dataset card is forked from the original [DynaSent Repository](https://github.com/cgpotts/dynasent). ## Contents * [Citation](#Citation) * [Dataset files](#dataset-files) * [Quick start](#quick-start) * [Data format](#data-format) * [Models](#models) * [Other files](#other-files) * [License](#license) ## Citation [Christopher Potts](http://web.stanford.edu/~cgpotts/), [Zhengxuan Wu](http://zen-wu.social), Atticus Geiger, and [Douwe Kiela](https://douwekiela.github.io). 2020. [DynaSent: A dynamic benchmark for sentiment analysis](https://arxiv.org/abs/2012.15349). Ms., Stanford University and Facebook AI Research. ```stex @article{potts-etal-2020-dynasent, title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis}, author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus and Kiela, Douwe}, journal={arXiv preprint arXiv:2012.15349}, url={https://arxiv.org/abs/2012.15349}, year={2020}} ``` ## Dataset files The dataset is [dynasent-v1.1.zip](dynasent-v1.1.zip), which is included in this repository. `v1.1` differs from `v1` only in that `v1.1` has proper unique ids for Round 1 and corrects a bug that led to some non-unique ids in Round 2. There are no changes to the examples or other metadata. The dataset consists of two rounds, each with a train/dev/test split: ### Round 1: Naturally occurring sentences * `dynasent-v1.1-round01-yelp-train.jsonl` * `dynasent-v1.1-round01-yelp-dev.jsonl` * `dynasent-v1.1-round01-yelp-test.jsonl` ### Round 1: Sentences crowdsourced using Dynabench * `dynasent-v1.1-round02-dynabench-train.jsonl` * `dynasent-v1.1-round02-dynabench-dev.jsonl` * `dynasent-v1.1-round02-dynabench-test.jsonl` ### SST-dev revalidation The dataset also contains a version of the [Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/) dev set in our format with labels from our validation task: * `sst-dev-validated.jsonl` ## Quick start This function can be used to load any subset of the files: ```python import json def load_dataset(src_filenames, labels=None): data = [] for filename in src_filenames: with open(filename) as f: for line in f: d = json.loads(line) if labels is None or d['gold_label'] in labels: data.append(d) return data ``` For example, to create a Round 1 train set restricting to examples with ternary gold labels: ```python import os r1_train_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round01-yelp-train.jsonl') ternary_labels = ('positive', 'negative', 'neutral') r1_train = load_dataset(r1_train_filename, labels=ternary_labels) X_train, y_train = zip([(d['sentence'], d['gold_label']) for d in r1_train]) ``` ## Data format ### Round 1 format ```python {'hit_ids': ['y5238'], 'sentence': 'Roto-Rooter is always good when you need someone right away.', 'indices_into_review_text': [0, 60], 'model_0_label': 'positive', 'model_0_probs': {'negative': 0.01173639390617609, 'positive': 0.7473671436309814, 'neutral': 0.24089649319648743}, 'text_id': 'r1-0000001', 'review_id': 'IDHkeGo-nxhqX4Exkdr08A', 'review_rating': 1, 'label_distribution': {'positive': ['w130', 'w186', 'w207', 'w264', 'w54'], 'negative': [], 'neutral': [], 'mixed': []}, 'gold_label': 'positive'} ``` Details: * `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset. * `'sentence'`: The example text. * `'indices_into_review_text':` indices of `'sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset). * `'model_0_label'`: prediction of Model 0 as described in the paper. The possible values are `'positive'`, `'negative'`, and `'neutral'`. * `'model_0_probs'`: probability distribution predicted by Model 0. The keys are `('positive', 'negative', 'neutral')` and the values are floats. * `'text_id'`: unique identifier for this entry. * `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'sentence'`. * `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset). The possible values are `1`, `2`, `3`, `4`, and `5`. * `'label_distribution':` response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset. * `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`. Here is some code one could use to augment a dataset, as loaded by `load_dataset`, with a field giving the full review text from the [Yelp Academic Dataset](https://www.yelp.com/dataset): ```python import json def index_yelp_reviews(yelp_src_filename='yelp_academic_dataset_review.json'): index = {} with open(yelp_src_filename) as f: for line in f: d = json.loads(line) index[d['review_id']] = d['text'] return index yelp_index = index_yelp_reviews() def add_review_text_round1(dataset, yelp_index): for d in dataset: review_text = yelp_index[d['text_id']] # Check that we can find the sentence as expected: start, end = d['indices_into_review_text'] assert review_text[start: end] == d['sentence'] d['review_text'] = review_text return dataset ``` ### Round 2 format ```python {'hit_ids': ['y22661'], 'sentence': "We enjoyed our first and last meal in Toronto at Bombay Palace, and I can't think of a better way to book our journey.", 'sentence_author': 'w250', 'has_prompt': True, 'prompt_data': {'indices_into_review_text': [2093, 2213], 'review_rating': 5, 'prompt_sentence': "Our first and last meals in Toronto were enjoyed at Bombay Palace and I can't think of a better way to bookend our trip.", 'review_id': 'Krm4kSIb06BDHternF4_pA'}, 'model_1_label': 'positive', 'model_1_probs': {'negative': 0.29140257835388184, 'positive': 0.6788994669914246, 'neutral': 0.029697999358177185}, 'text_id': 'r2-0000001', 'label_distribution': {'positive': ['w43', 'w26', 'w155', 'w23'], 'negative': [], 'neutral': [], 'mixed': ['w174']}, 'gold_label': 'positive'} ``` Details: * `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset. * `'sentence'`: The example text. * `'sentence_author'`: Anonymized MTurk id of the worker who wrote `'sentence'`. These are from the same family of ids as used in `'label_distribution'`, but this id is never one of the ids in `'label_distribution'` for this example. * `'has_prompt'`: `True` if the `'sentence'` was written with a Prompt else `False`. * `'prompt_data'`: None if `'has_prompt'` is False, else: * `'indices_into_review_text'`: indices of `'prompt_sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset). * `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset). * `'prompt_sentence'`: The prompt text. * `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'prompt_sentence'`. * `'model_1_label'`: prediction of Model 1 as described in the paper. The possible values are `'positive'`, `'negative'`, and '`neutral'`. * `'model_1_probs'`: probability distribution predicted by Model 1. The keys are `('positive', 'negative', 'neutral')` and the values are floats. * `'text_id'`: unique identifier for this entry. * `'label_distribution'`: response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset. * `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`. To add the review texts to the `'prompt_data'` field, one can extend the code above for Round 1 with the following function: ```python def add_review_text_round2(dataset, yelp_index): for d in dataset: if d['has_prompt']: prompt_data = d['prompt_data'] review_text = yelp_index[prompt_data['review_id']] # Check that we can find the sentence as expected: start, end = prompt_data['indices_into_review_text'] assert review_text[start: end] == prompt_data['prompt_sentence'] prompt_data['review_text'] = review_text return dataset ``` ### SST-dev format ```python {'hit_ids': ['s20533'], 'sentence': '-LRB- A -RRB- n utterly charming and hilarious film that reminded me of the best of the Disney comedies from the 60s.', 'tree': '(4 (2 (1 -LRB-) (2 (2 A) (3 -RRB-))) (4 (4 (2 n) (4 (3 (2 utterly) (4 (3 (4 charming) (2 and)) (4 hilarious))) (3 (2 film) (3 (2 that) (4 (4 (2 (2 reminded) (3 me)) (4 (2 of) (4 (4 (2 the) (4 best)) (2 (2 of) (3 (2 the) (3 (3 Disney) (2 comedies))))))) (2 (2 from) (2 (2 the) (2 60s)))))))) (2 .)))', 'text_id': 'sst-dev-validate-0000437', 'sst_label': '4', 'label_distribution': {'positive': ['w207', 'w3', 'w840', 'w135', 'w26'], 'negative': [], 'neutral': [], 'mixed': []}, 'gold_label': 'positive'} ``` Details: * `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset. * `'sentence'`: The example text. * `'tree'`: The parsetree for the example as given in the SST distribution. * `'text_id'`: A new identifier for this example. * `'sst_label'`: The root-node label from the SST. Possible values `'0'`, `'1'` `'2'`, `'3'`, and `'4'`. * `'label_distribution':` response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset. * `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`. ## Models Model 0 and Model 1 from the paper are available here: https://drive.google.com/drive/folders/1dpKrjNJfAILUQcJPAFc5YOXUT51VEjKQ?usp=sharing This repository includes a Python module `dynasent_models.py` that provides a [Hugging Face](https://huggingface.co)-based wrapper around these ([PyTorch](https://pytorch.org)) models. Simple examples: ```python import os from dynasent_models import DynaSentModel # `dynasent_model0` should be downloaded from the above Google Drive link and # placed in the `models` directory. `dynasent_model1` works the same way. model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin')) examples = [ "superb", "They said the experience would be amazing, and they were right!", "They said the experience would be amazing, and they were wrong!"] model.predict(examples) ``` This should return the list `['positive', 'positive', 'negative']`. The `predict_proba` method provides access to the predicted distribution over the class labels; see the demo at the bottom of `dynasent_models.py` for details. The following code uses `load_dataset` from above to reproduce the Round 2 dev-set report on Model 0 from the paper: ```python import os from sklearn.metrics import classification_report from dynasent_models import DynaSentModel dev_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round02-dynabench-dev.jsonl') dev = load_dataset(dev_filename) X_dev, y_dev = zip([(d['sentence'], d['gold_label']) for d in dev]) model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin')) preds = model.predict(X_dev) print(classification_report(y_dev, preds, digits=3)) ``` For a fuller report on these models, see our paper and [our model card](dynasent_modelcard.md). ## Other files ### Analysis notebooks The following notebooks reproduce the dataset statistics, figures, and random example selections from the paper: `analyses_comparative.ipynb` * `analysis_round1.ipynb` * `analysis_round2.ipynb` * `analysis_sst_dev_revalidate.ipynb` The Python module `dynasent_utils.py` contains functions that support those notebooks, and `dynasent.mplstyle` helps with styling the plots. ### Datasheet The [Datasheet](https://arxiv.org/abs/1803.09010) for our dataset: * [dynasent_datasheet.md](dynasent_datasheet.md) ### Model Card The [Model Card](https://arxiv.org/pdf/1810.03993.pdf) for our models: * [dynasent_modelcard.md](dynasent_modelcard.md) ### Tests The module `test_dataset.py` contains PyTest tests for the dataset. To use it, run ``` py.test -vv test_dataset.py ``` in the root directory of this repository. ### Validation HIT code The file `validation-hit-contents.html` contains the HTML/Javascript used in the validation task. It could be used directly on Amazon Mechanical Turk, by simply pasting its contents into the usual HIT creation window. ## License DynaSent has a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).	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fedyanin/feud	fedyanin	2023-10-23T10:55:56Z	788	0	null	[ "license:cc", "region:us" ]	2023-10-23T10:55:56Z	2023-07-25T11:59:02.000Z	2023-07-25T11:59:02	--- license: cc --- # Feud dataset Dataset of question and answers that resemble family feud tv show style. There multiple possible answers for each question. 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visheratin/laion-coco-nllb	visheratin	2023-11-22T13:41:00Z	786	14	null	[ "task_categories:image-to-text", "task_categories:translation", "size_categories:100K<n<1M", "language:ace", "language:acm", "language:acq", "language:aeb", "language:af", "language:ajp", "language:ak", "language:als", "language:am", "language:apc", "language:ar", "language:ars", "language:ary", "language:arz", "language:as", "language:ast", "language:awa", "language:ayr", "language:azb", "language:azj", "language:ba", "language:bm", "language:ban", "language:be", "language:bem", "language:bn", "language:bho", "language:bjn", "language:bo", "language:bs", "language:bug", "language:bg", "language:ca", "language:ceb", "language:cs", "language:cjk", "language:ckb", "language:crh", "language:cy", "language:da", "language:de", "language:dik", "language:dyu", "language:dz", "language:el", "language:en", "language:eo", "language:et", "language:eu", "language:ee", "language:fo", "language:fj", "language:fi", "language:fon", "language:fr", "language:fur", "language:fuv", "language:gaz", "language:gd", "language:ga", "language:gl", "language:gn", "language:gu", "language:ht", "language:ha", "language:he", "language:hi", "language:hne", "language:hr", "language:hu", "language:hy", "language:ig", "language:ilo", "language:id", "language:is", "language:it", "language:jv", "language:ja", "language:kab", "language:kac", "language:kam", "language:kn", "language:ks", "language:ka", "language:kk", "language:kbp", "language:kea", "language:khk", "language:km", "language:ki", "language:rw", "language:ky", "language:kmb", "language:kmr", "language:knc", "language:kg", "language:ko", "language:lo", "language:lij", "language:li", "language:ln", "language:lt", "language:lmo", "language:ltg", "language:lb", "language:lua", "language:lg", "language:luo", "language:lus", "language:lvs", "language:mag", "language:mai", "language:ml", "language:mar", "language:min", "language:mk", "language:mt", "language:mni", "language:mos", "language:mi", "language:my", "language:nl", "language:nn", "language:nb", "language:npi", "language:nso", "language:nus", "language:ny", "language:oc", "language:ory", "language:pag", "language:pa", "language:pap", "language:pbt", "language:pes", "language:plt", "language:pl", "language:pt", "language:prs", "language:quy", "language:ro", "language:rn", "language:ru", "language:sg", "language:sa", "language:sat", "language:scn", "language:shn", "language:si", "language:sk", "language:sl", "language:sm", "language:sn", "language:sd", "language:so", "language:st", "language:es", "language:sc", "language:sr", "language:ss", "language:su", "language:sv", "language:swh", "language:szl", "language:ta", "language:taq", "language:tt", "language:te", "language:tg", "language:tl", "language:th", "language:ti", "language:tpi", "language:tn", "language:ts", "language:tk", "language:tum", "language:tr", "language:tw", "language:tzm", "language:ug", "language:uk", "language:umb", "language:ur", "language:uzn", "language:vec", "language:vi", "language:war", "language:wo", "language:xh", "language:ydd", "language:yo", "language:yue", "language:zh", "language:zsm", "language:zu", "license:cc-by-nc-4.0", "arxiv:2309.01859", "doi:10.57967/hf/1006", "region:us" ]	2023-11-22T13:41:00Z	2023-06-18T06:58:28.000Z	2023-06-18T06:58:28	--- language: - ace - acm - acq - aeb - af - ajp - ak - als - am - apc - ar - ars - ary - arz - as - ast - awa - ayr - azb - azj - ba - bm - ban - be - bem - bn - bho - bjn - bo - bs - bug - bg - ca - ceb - cs - cjk - ckb - crh - cy - da - de - dik - dyu - dz - el - en - eo - et - eu - ee - fo - fj - fi - fon - fr - fur - fuv - gaz - gd - ga - gl - gn - gu - ht - ha - he - hi - hne - hr - hu - hy - ig - ilo - id - is - it - jv - ja - kab - kac - kam - kn - ks - ka - kk - kbp - kea - khk - km - ki - rw - ky - kmb - kmr - knc - kg - ko - lo - lij - li - ln - lt - lmo - ltg - lb - lua - lg - luo - lus - lvs - mag - mai - ml - mar - min - mk - mt - mni - mos - mi - my - nl - nn - nb - npi - nso - nus - ny - oc - ory - pag - pa - pap - pbt - pes - plt - pl - pt - prs - quy - ro - rn - ru - sg - sa - sat - scn - shn - si - sk - sl - sm - sn - sd - so - st - es - sc - sr - ss - su - sv - swh - szl - ta - taq - tt - te - tg - tl - th - ti - tpi - tn - ts - tk - tum - tr - tw - tzm - ug - uk - umb - ur - uzn - vec - vi - war - wo - xh - ydd - yo - yue - zh - zsm - zu license: cc-by-nc-4.0 size_categories: - 100K<n<1M task_categories: - image-to-text - translation pretty_name: LAION-COCO translated to 200 languages dataset_info: features: - name: id dtype: string - name: url dtype: string - name: eng_caption dtype: string - name: captions sequence: sequence: string - name: score dtype: float64 splits: - name: test num_bytes: 289987047 num_examples: 15937 - name: train num_bytes: 11720986151 num_examples: 644707 download_size: 7642919742 dataset_size: 12010973198 language_details: ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* --- # LAION COCO translated into 200 languages This dataset contains the samples of the [LAION-COCO](https://huggingface.co/datasets/laion/laion-coco) dataset translated to 200 languages using the largest [NLLB-200 model](https://huggingface.co/facebook/nllb-200-3.3B) (3.3B parameters). ## Fields description 1. `id` - unique ID of the image. 2. `url` - original URL of the image from the LAION-COCO dataset. 3. `eng_caption` - original English caption from the LAION-COCO dataset. 4. `captions` - a list of captions translated to the languages from the Flores 200 dataset. Every item in the list is a list where the first element is a BCP-47 language code, and the second one is a caption in this language. The list of all language codes for the Flores 200 dataset can be found [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200). 5. `score` - aesthetic score generated using [LAION aesthetic predictor](https://github.com/christophschuhmann/improved-aesthetic-predictor/). The images in the dataset have the score of 4.5+. ## Images The dataset was filtered to contain only working image URLs. However, the availability may change in the future. Because of that, all images from this dataset are available at [https://nllb-data.com/](https://nllb-data.com/). To get the image, use the following format: ``` https://nllb-data.com/{id}.jpg ``` ## Paper The dataset was used to train the models in the paper: "[NLLB-CLIP - train performant multilingual image retrieval model on a budget](https://arxiv.org/abs/2309.01859)".	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masakhane/afriqa	masakhane	2023-07-07T16:57:28Z	785	5	null	[ "task_categories:question-answering", "multilinguality:multilingual", "size_categories:10K<n<100K", "language:bem", "language:fon", "language:ha", "language:ig", "language:kin", "language:sw", "language:wo", "language:yo", "language:zu", "language:tw", "license:cc-by-sa-4.0", "cross-lingual", "question-answering", "qa", "arxiv:2305.06897", "region:us" ]	2023-07-07T16:57:28Z	2023-04-23T20:05:43.000Z	2023-04-23T20:05:43	--- license: cc-by-sa-4.0 task_categories: - question-answering language: - bem - fon - ha - ig - kin - sw - wo - yo - zu - tw pretty_name: AfriQA size_categories: - 10K<n<100K multilinguality: - multilingual tags: - cross-lingual - question-answering - qa --- # Dataset Card for AfriQA ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [homepage](https://github.com/masakhane-io/afriqa) - Repository: [github](https://github.com/masakhane-io/afriqa) - Paper: [paper]() - Point of Contact: [Masakhane](https://www.masakhane.io/) or oogundep@uwaterloo.ca ### Dataset Summary AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages. The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitable QA technology. The train/validation/test sets are available for all the 10 languages. ### Supported Tasks and Leaderboards - `question-answering`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better) and [Exact Match Accuracy](https://huggingface.co/spaces/evaluate-metric/exact_match). ### Languages There are 20 languages available : - Bemba (bem) - Fon (fon) - Hausa (hau) - Igbo (ibo) - Kinyarwanda (kin) - Swahili (swą) - Twi (twi) - Wolof (wol) - Yorùbá (yor) - Zulu (zul) ## Dataset Structure ### Data Instances - Data Format: - id : Question ID - question : Question in African Language - translated_question : Question translated into a pivot language (English/French) - answers : Answer in African Language - lang : Datapoint Language (African Language) e.g `bem` - split : Dataset Split - translated_answer : Answer in Pivot Language - translation_type : Translation type of question and answers ```bash { "id": 0, "question": "Bushe icaalo ca Egypt caali tekwapo ne caalo cimbi?", "translated_question": "Has the country of Egypt been colonized before?", "answers": "['Emukwai']", "lang": "bem", "split": "dev", "translated_answer": "['yes']", "translation_type": "human_translation" } ``` ### Data Splits For all languages, there are three splits. The original splits were named `train`, `dev` and `test` and they correspond to the `train`, `validation` and `test` splits. The splits have the following sizes : \| Language \| train \| dev \| test \| \|-----------------\|------:\|-----------:\|-----:\| \| Bemba \| 502 \| 503 \| 314 \| \| Fon \| 427 \| 428 \| 386 \| \| Hausa \| 435 \| 436 \| 300 \| \| Igbo \| 417 \| 418 \| 409 \| \| Kinyarwanda \| 407 \| 409 \| 347 \| \| Swahili \| 415 \| 417 \| 302 \| \| Twi \| 451 \| 452 \| 490 \| \| Wolof \| 503 \| 504 \| 334 \| \| Yoruba \| 360 \| 361 \| 332 \| \| Zulu \| 387 \| 388 \| 325 \| \| <b>Total</b> \| <b>4333</b> \| <b>4346</b> \|<b>3560</b> \| ## Dataset Creation ### Curation Rationale The dataset was introduced to introduce question-answering resources to 10 languages that were under-served for natural language processing. [More Information Needed] ### Source Data ... #### Initial Data Collection and Normalization ... #### Who are the source language producers? ... ### Annotations #### Annotation process Details can be found here ... #### Who are the annotators? Annotators were recruited from [Masakhane](https://www.masakhane.io/) ### Personal and Sensitive Information ... ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains. ## Additional Information ### Dataset Curators ### Licensing Information The licensing status of the data is CC 4.0 Non-Commercial ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @misc{ogundepo2023afriqa, title={AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages}, author={Odunayo Ogundepo and Tajuddeen R. Gwadabe and Clara E. Rivera and Jonathan H. Clark and Sebastian Ruder and David Ifeoluwa Adelani and Bonaventure F. P. Dossou and Abdou Aziz DIOP and Claytone Sikasote and Gilles Hacheme and Happy Buzaaba and Ignatius Ezeani and Rooweither Mabuya and Salomey Osei and Chris Emezue and Albert Njoroge Kahira and Shamsuddeen H. Muhammad and Akintunde Oladipo and Abraham Toluwase Owodunni and Atnafu Lambebo Tonja and Iyanuoluwa Shode and Akari Asai and Tunde Oluwaseyi Ajayi and Clemencia Siro and Steven Arthur and Mofetoluwa Adeyemi and Orevaoghene Ahia and Aremu Anuoluwapo and Oyinkansola Awosan and Chiamaka Chukwuneke and Bernard Opoku and Awokoya Ayodele and Verrah Otiende and Christine Mwase and Boyd Sinkala and Andre Niyongabo Rubungo and Daniel A. Ajisafe and Emeka Felix Onwuegbuzia and Habib Mbow and Emile Niyomutabazi and Eunice Mukonde and Falalu Ibrahim Lawan and Ibrahim Said Ahmad and Jesujoba O. Alabi and Martin Namukombo and Mbonu Chinedu and Mofya Phiri and Neo Putini and Ndumiso Mngoma and Priscilla A. Amuok and Ruqayya Nasir Iro and Sonia Adhiambo}, year={2023}, eprint={2305.06897}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@ToluClassics](https://github.com/ToluClassics) for adding this dataset.	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IlyaGusev/headline_cause	IlyaGusev	2023-02-12T00:02:58Z	777	3	null	[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ru", "language:en", "license:cc0-1.0", "causal-reasoning", "arxiv:2108.12626", "region:us" ]	2023-02-12T00:02:58Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - found language: - ru - en license: - cc0-1.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification pretty_name: HeadlineCause tags: - causal-reasoning --- # Dataset Card for HeadlineCause ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/IlyaGusev/HeadlineCause - Paper: [HeadlineCause: A Dataset of News Headlines for Detecting Causalities](https://arxiv.org/abs/2108.12626) - Point of Contact: [Ilya Gusev](ilya.gusev@phystech.edu) ### Dataset Summary A dataset for detecting implicit causal relations between pairs of news headlines. The dataset includes over 5000 headline pairs from English news and over 9000 headline pairs from Russian news labeled through crowdsourcing. The pairs vary from totally unrelated or belonging to the same general topic to the ones including causation and refutation relations. ### Usage Loading Russian Simple task: ```python from datasets import load_dataset dataset = load_dataset("IlyaGusev/headline_cause", "ru_simple") ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages This dataset consists of two parts, Russian and English. ## Dataset Structure ### Data Instances There is an URL, a title, and a timestamp for each of the two headlines in every data instance. A label is presented in three fields. 'Result' field is a textual label, 'label' field is a numeric label, and the 'agreement' field shows the majority vote agreement between annotators. Additional information includes instance ID and the presence of the link between two articles. ``` { 'left_url': 'https://www.kommersant.ru/doc/4347456', 'right_url': 'https://tass.ru/kosmos/8488527', 'left_title': 'NASA: информация об отказе сотрудничать с Россией по освоению Луны некорректна', 'right_title': 'NASA назвало некорректными сообщения о нежелании США включать РФ в соглашение по Луне', 'left_timestamp': datetime.datetime(2020, 5, 15, 19, 46, 20), 'right_timestamp': datetime.datetime(2020, 5, 15, 19, 21, 36), 'label': 0, 'result': 'not_cause', 'agreement': 1.0, 'id': 'ru_tg_101', 'has_link': True } ``` ### Data Splits \| Dataset \| Split \| Number of Instances \| \|:---------\|:---------\|:---------\| \| ru_simple \| train \| 7,641 \| \| \| validation \| 955 \| \| \| test \| 957 \| \| en_simple \| train \| 4,332 \| \| \| validation \| 542 \| \| \| test \| 542 \| \| ru_full \| train \| 5,713 \| \| \| validation \| 715 \| \| \| test \| 715 \| \| en_full \| train \| 2,009 \| \| \| validation \| 251 \| \| \| test \| 252 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process Every candidate pair was annotated with [Yandex Toloka](https://toloka.ai/), a crowdsourcing platform. The task was to determine a relationship between two headlines, A and B. There were seven possible options: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is not linked to B. An annotation guideline was in Russian for Russian news and in English for English news. Guidelines: * Russian: [link](https://ilyagusev.github.io/HeadlineCause/toloka/ru/instruction.html) * English: [link](https://ilyagusev.github.io/HeadlineCause/toloka/en/instruction.html) Ten workers annotated every pair. The total annotation budget was 870$, with the estimated hourly wage paid to participants of 45 cents. Annotation management was semi-automatic. Scripts are available in the [Github repository](https://github.com/IlyaGusev/HeadlineCause). #### Who are the annotators? Yandex Toloka workers were the annotators, 457 workers for the Russian part, 180 workers for the English part. ### Personal and Sensitive Information The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset. No information about annotators is included except a platform worker ID. ## Considerations for Using the Data ### Social Impact of Dataset We do not see any direct malicious applications of our work. The data probably do not contain offensive content, as news agencies usually do not produce it, and a keyword search returned nothing. However, there are news documents in the dataset on several sensitive topics. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The data was collected by Ilya Gusev. ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @misc{gusev2021headlinecause, title={HeadlineCause: A Dataset of News Headlines for Detecting Causalities}, author={Ilya Gusev and Alexey Tikhonov}, year={2021}, eprint={2108.12626}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions [N/A]	[ -0.21737875044345856, -0.6235857009887695, 0.49415120482444763, 0.45513463020324707, -0.30843672156333923, -0.15789128839969635, -0.25233542919158936, -0.4510038197040558, 0.4770621657371521, 0.3450299799442291, -0.6381241083145142, -1.0063058137893677, -0.6875836253166199, 0.24233195185661316, -0.27929022908210754, 1.2967664003372192, 0.04963954538106918, -0.10699906945228577, 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cassandra-themis/QR-AN	cassandra-themis	2022-10-24T20:31:22Z	776	2	null	[ "task_categories:summarization", "task_categories:text-classification", "task_categories:text-generation", "task_ids:multi-class-classification", "task_ids:topic-classification", "size_categories:10K<n<100K", "language:fr", "conditional-text-generation", "region:us" ]	2022-10-24T20:31:22Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- language: - fr size_categories: 10K<n<100K task_categories: - summarization - text-classification - text-generation task_ids: - multi-class-classification - topic-classification tags: - conditional-text-generation --- QR-AN Dataset: a classification and generation dataset of french Parliament questions-answers. This is a dataset for theme/topic classification, made of questions and answers from https://www2.assemblee-nationale.fr/recherche/resultats_questions . \ It contains 188 unbalanced classes, 80k questions-answers divided into 3 splits: train (60k), val (10k) and test (10k). \ Can be used for generation with 'qran_generation' This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable: ```python "ccdv/cass-summarization": ("question", "answer") ``` Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script: ``` export MODEL_NAME=camembert-base export MAX_SEQ_LENGTH=512 python run_glue.py \ --model_name_or_path $MODEL_NAME \ --dataset_name cassandra-themis/QR-AN \ --do_train \ --do_eval \ --max_seq_length $MAX_SEQ_LENGTH \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 4 \ --learning_rate 2e-5 \ --num_train_epochs 1 \ --max_eval_samples 500 \ --output_dir tmp/QR-AN ```	[ -0.5777299404144287, -0.2031145989894867, 0.16159819066524506, 0.17405149340629578, -0.2738822400569916, 0.018022075295448303, -0.028775930404663086, 0.3636779487133026, 0.14899572730064392, 0.6266669034957886, -0.4965599775314331, -0.4507380723953247, -0.43791571259498596, 0.1509123146533966, -0.5213558673858643, 1.1783543825149536, 0.05966239795088768, 0.028477992862462997, -0.2617771625518799, -0.13871197402477264, -0.2821265161037445, -0.6379629969596863, -0.491705983877182, -0.2608136832714081, 0.3548782169818878, 0.6702089905738831, 0.6032325029373169, 0.46165043115615845, 0.419495552778244, 0.3796294033527374, -0.08785589784383774, 0.04020117223262787, -0.3713330328464508, -0.018376264721155167, -0.11359523236751556, -0.45443612337112427, -0.2825506925582886, -0.20920003950595856, 0.9687241315841675, 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SkelterLabsInc/JaQuAD	SkelterLabsInc	2022-10-25T09:06:40Z	775	6	null	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ja", "license:cc-by-sa-3.0", "arxiv:2202.01764", "region:us" ]	2022-10-25T09:06:40Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - ja license: - cc-by-sa-3.0 multilinguality: - monolingual paperswithcode_id: null pretty_name: "JaQuAD: Japanese Question Answering Dataset" size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa --- # Dataset Card for JaQuAD ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splitting](#data-splitting) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Acknowledgements](#acknowledgements) ## Dataset Description - Repository: https://github.com/SkelterLabsInc/JaQuAD - Paper: [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension]() - Point of Contact: [jaquad@skelterlabs.com](jaquad@skelterlabs.com) - Size of dataset files: 24.6 MB - Size of the generated dataset: 48.6 MB - Total amount of disk used: 73.2 MB ### Dataset Summary Japanese Question Answering Dataset (JaQuAD), released in 2022, is a human-annotated dataset created for Japanese Machine Reading Comprehension. JaQuAD is developed to provide a SQuAD-like QA dataset in Japanese. JaQuAD contains 39,696 question-answer pairs. Questions and answers are manually curated by human annotators. Contexts are collected from Japanese Wikipedia articles. Fine-tuning [BERT-Japanese](https://huggingface.co/cl-tohoku/bert-base-japanese) on JaQuAD achieves 78.92% for an F1 score and 63.38% for an exact match. ### Supported Tasks - `extractive-qa`: This dataset is intended to be used for `extractive-qa`. ### Languages Japanese (`ja`) ## Dataset Structure ### Data Instances - Size of dataset files: 24.6 MB - Size of the generated dataset: 48.6 MB - Total amount of disk used: 73.2 MB An example of 'validation': ```python { "id": "de-001-00-000", "title": "イタセンパラ", "context": "イタセンパラ(板鮮腹、Acheilognathuslongipinnis)は、コイ科のタナゴ亜科タナゴ属に分類される淡水>魚の一種。\n別名はビワタナゴ(琵琶鱮、琵琶鰱)。", "question": "ビワタナゴの正式名称は何?", "question_type": "Multiple sentence reasoning", "answers": { "text": "イタセンパラ", "answer_start": 0, "answer_type": "Object", }, }, ``` ### Data Fields - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `question_type`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. - `answer_type`: a `string` feature. ### Data Splitting JaQuAD consists of three sets, `train`, `validation`, and `test`. They were created from disjoint sets of Wikipedia articles. The `test` set is not publicly released yet. The following table shows statistics for each set. Set \| Number of Articles \| Number of Contexts \| Number of Questions --------------\|--------------------\|--------------------\|-------------------- Train \| 691 \| 9713 \| 31748 Validation \| 101 \| 1431 \| 3939 Test \| 109 \| 1479 \| 4009 ## Dataset Creation ### Curation Rationale The JaQuAD dataset was created by [Skelter Labs](https://skelterlabs.com/) to provide a SQuAD-like QA dataset in Japanese. Questions are original and based on Japanese Wikipedia articles. ### Source Data The articles used for the contexts are from [Japanese Wikipedia](https://ja.wikipedia.org/). 88.7% of articles are from the curated list of Japanese high-quality Wikipedia articles, e.g., [featured articles](https://ja.wikipedia.org/wiki/Wikipedia:%E8%89%AF%E8%B3%AA%E3%81%AA%E8%A8%98%E4%BA%8B) and [good articles](https://ja.wikipedia.org/wiki/Wikipedia:%E7%A7%80%E9%80%B8%E3%81%AA%E8%A8%98%E4%BA%8B). ### Annotations Wikipedia articles were scrapped and divided into one more multiple paragraphs as contexts. Annotations (questions and answer spans) are written by fluent Japanese speakers, including natives and non-natives. Annotators were given a context and asked to generate non-trivial questions about information in the context. ### Personal and Sensitive Information No personal or sensitive information is included in this dataset. Dataset annotators has been manually verified it. ## Considerations for Using the Data Users should consider that the articles are sampled from Wikipedia articles but not representative of all Wikipedia articles. ### Social Impact of Dataset The social biases of this dataset have not yet been investigated. ### Discussion of Biases The social biases of this dataset have not yet been investigated. Articles and questions have been selected for quality and diversity. ### Other Known Limitations The JaQuAD dataset has limitations as follows: - Most of them are short answers. - Assume that a question is answerable using the corresponding context. This dataset is incomplete yet. If you find any errors in JaQuAD, please contact us. ## Additional Information ### Dataset Curators Skelter Labs: [https://skelterlabs.com/](https://skelterlabs.com/) ### Licensing Information The JaQuAD dataset is licensed under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. ### Citation Information ```bibtex @misc{so2022jaquad, title={{JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension}}, author={ByungHoon So and Kyuhong Byun and Kyungwon Kang and Seongjin Cho}, year={2022}, eprint={2202.01764}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Acknowledgements This work was supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/). For training models, we used cloud TPUs provided by TRC. We also thank annotators who generated JaQuAD.	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kaitchup/ultrachat-100k-flattened	kaitchup	2023-10-19T15:13:49Z	769	2	null	[ "region:us" ]	2023-10-19T15:13:49Z	2023-10-19T15:07:12.000Z	2023-10-19T15:07:12	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 632072903 num_examples: 100000 - name: test num_bytes: 32563073 num_examples: 5140 download_size: 330831956 dataset_size: 664635976 --- # Dataset Card for "ultrachat-100k-flattened" A random sample of 100k dialogues from [stingning/ultrachat](https://huggingface.co/datasets/stingning/ultrachat). The dialogues are flattened into one single sequence of dialogue turns where each turn is introduced by one of the following roles: * Assistant * User This conversion and subsampling of ultrachat was made to facilitate and speed up training with HuggingFace's TRL.	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allenai/lila	allenai	2023-03-15T18:36:28Z	768	15	null	[ "license:cc-by-4.0", "region:us" ]	2023-03-15T18:36:28Z	2023-02-08T21:39:35.000Z	2023-02-08T21:39:35	--- license: cc-by-4.0 --- ## Dataset Description - Repository: [allenai/lila](https://github.com/allenai/lila) - Paper: [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392.pdf) - Point of Contact: [Matthew Finlayson](https://mattf1n.github.io/), [Sean Welleck](https://wellecks.com/) # Lila: A Unified Benchmark for Mathematical Reasoning - Homepage: https://lila.apps.allenai.org/ - Repository: https://github.com/allenai/lila - Paper: https://aclanthology.org/2022.emnlp-main.392.pdf ### Licensing Information Creative Commons Attribution 4.0 International ### Citation Information Cite this dataset and the source datasets (see [sources.bib](https://github.com/allenai/Lila/blob/main/sources.bib)). ```bib @INPROCEEDINGS{Mishra2022Lila, author = { Swaroop Mishra and Matthew Finlayson and Pan Lu and Leonard Tang and Sean Welleck and Chitta Baral and Tanmay Rajpurohit and Oyvind Tafjord and Ashish Sabharwal and Peter Clark and Ashwin Kalyan}, title = {Lila: A Unified Benchmark for Mathematical Reasoning}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022} } ```	[ -0.2784588932991028, -0.5200451612472534, 0.34462082386016846, 0.5351587533950806, -0.1004553958773613, -0.06256426870822906, -0.05477076396346092, -0.3891855776309967, -0.02794710174202919, 0.44506722688674927, -0.6893370747566223, -0.689995527267456, -0.31817370653152466, 0.317951500415802, -0.21030522882938385, 0.6808629035949707, -0.2292250394821167, 0.3023873269557953, -0.2493181973695755, -0.7145610451698303, -0.21136894822120667, -0.358428418636322, -0.4786028265953064, -0.21083968877792358, 0.05669758468866348, 0.14245189726352692, 0.4421611726284027, 0.5730530619621277, 0.6446186900138855, 0.2918684780597687, 0.044287096709012985, 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ai4bharat/IN22-Conv	ai4bharat	2023-11-13T06:36:05Z	767	2	null	[ "task_categories:translation", "language_creators:expert-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:1K<n<10K", "language:as", "language:bn", "language:brx", "language:doi", "language:en", "language:gom", "language:gu", "language:hi", "language:kn", "language:ks", "language:mai", "language:ml", "language:mr", "language:mni", "language:ne", "language:or", "language:pa", "language:sa", "language:sat", "language:sd", "language:ta", "language:te", "language:ur", "license:cc-by-4.0", "arxiv:2305.16307", "region:us" ]	2023-11-13T06:36:05Z	2023-09-09T17:35:58.000Z	2023-09-09T17:35:58	--- language: - as - bn - brx - doi - en - gom - gu - hi - kn - ks - mai - ml - mr - mni - ne - or - pa - sa - sat - sd - ta - te - ur language_details: >- asm_Beng, ben_Beng, brx_Deva, doi_Deva, eng_Latn, gom_Deva, guj_Gujr, hin_Deva, kan_Knda, kas_Arab, mai_Deva, mal_Mlym, mar_Deva, mni_Mtei, npi_Deva, ory_Orya, pan_Guru, san_Deva, sat_Olck, snd_Deva, tam_Taml, tel_Telu, urd_Arab license: cc-by-4.0 language_creators: - expert-generated multilinguality: - multilingual - translation pretty_name: in22-conv size_categories: - 1K<n<10K task_categories: - translation --- # IN22-Conv IN-22 is a newly created comprehensive benchmark for evaluating machine translation performance in multi-domain, n-way parallel contexts across 22 Indic languages. IN22-Conv is the conversation domain subset of IN22. It is designed to assess translation quality in typical day-to-day conversational-style applications. The evaluation subset consists of 1503 sentences translated across 22 Indic languages enabling evaluation of MT systems across 506 directions. Currently, we use it for sentence-level evaluation of MT systems but it can be repurposed for document translation evaluation as well. Here is the domain distribution of our IN22-Conv evaluation subset. <table style="width:25%"> <tr> <td>domain</td> <td>count</td> </tr> <tr> <td>hobbies</td> <td>120</td> </tr> <tr> <td>daily_dialogue</td> <td>117</td> </tr> <tr> <td>government</td> <td>116</td> </tr> <tr> <td>geography</td> <td>114</td> </tr> <tr> <td>sports</td> <td>100</td> </tr> <tr> <td>entertainment</td> <td>97</td> </tr> <tr> <td>history</td> <td>97</td> </tr> <tr> <td>legal</td> <td>96</td> </tr> <tr> <td>arts</td> <td>95</td> </tr> <tr> <td>college_life</td> <td>94</td> </tr> <tr> <td>tourism</td> <td>91</td> </tr> <tr> <td>school_life</td> <td>87</td> </tr> <tr> <td>insurance</td> <td>82</td> </tr> <tr> <td>culture</td> <td>73</td> </tr> <tr> <td>healthcare</td> <td>67</td> </tr> <tr> <td>banking</td> <td>57</td> </tr> <tr> <td>total</td> <td>1503</td> </tr> </table> Please refer to the `Appendix E: Dataset Card` of the [preprint](https://arxiv.org/abs/2305.16307) on detailed description of dataset curation, annotation and quality control process. ### Dataset Structure #### Dataset Fields - `id`: Row number for the data entry, starting at 1. - `doc_id`: Unique identifier of the conversation. - `sent_id`: Unique identifier of the sentence order in each conversation. - `topic`: The specific topic of the conversation within the domain. - `domain`: The domain of the conversation. - `prompt`: The prompt provided to annotators to simulate the conversation. - `scenario`: The scenario or context in which the conversation takes place. - `speaker`: The speaker identifier in the conversation. - `turn`: The turn within the conversation. #### Data Instances A sample from the `gen` split for the English language (`eng_Latn` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits. ```python { "id": 1, "doc_id": 0, "sent_id": 1, "topic": "Festivities", "domain": "culture", "prompt": "14th April a holiday", "scenario": "Historical importance", "speaker": 1, "turn": 1, "sentence": "Mom, let's go for a movie tomorrow." } ``` When using a hyphenated pairing or using the `all` function, data will be presented as follows: ```python { "id": 1, "doc_id": 0, "sent_id": 1, "topic": "Festivities", "domain": "culture", "prompt": "14th April a holiday", "scenario": "Historical importance", "speaker": 1, "turn": 1, "sentence_eng_Latn": "Mom, let's go for a movie tomorrow.", "sentence_hin_Deva": "माँ, चलो कल एक फिल्म देखने चलते हैं।" } ``` #### Sample Conversation <table> <tr> <td>Speaker</td> <td>Turn</td> </tr> <tr> <td>Speaker 1</td> <td>Mom, let's go for a movie tomorrow. I don't have to go to school. It is a holiday.</td> </tr> <tr> <td>Speaker 2</td> <td>Oh, tomorrow is the 14th of April right? Your dad will also have the day off from work. We can make a movie plan!</td> </tr> <tr> <td>Speaker 1</td> <td>That's a good news! Why is it a holiday though? Are all schools, colleges and offices closed tomorrow?</td> </tr> <tr> <td>Speaker 2</td> <td>It is Ambedkar Jayanti tomorrow! This day is celebrated annually to mark the birth of Dr. B. R Ambedkar. Have you heard of him?</td> </tr> <tr> <td>Speaker 1</td> <td>I think I have seen him in my History and Civics book. Is he related to our Constitution?</td> </tr> <tr> <td>Speaker 2</td> <td>Absolutely! He is known as the father of the Indian Constitution. He was a civil rights activist who played a major role in formulating the Constitution. He played a crucial part in shaping the vibrant democratic structure that India prides itself upon.</td> </tr> <tr> <td></td> <td>...</td> </tr> </table> ### Usage Instructions ```python from datasets import load_dataset # download and load all the pairs dataset = load_dataset("ai4bharat/IN22-Conv", "all") # download and load specific pairs dataset = load_dataset("ai4bharat/IN22-Conv", "eng_Latn-hin_Deva") ``` ### Languages Covered <table style="width: 40%"> <tr> <td>Assamese (asm_Beng)</td> <td>Kashmiri (Arabic) (kas_Arab)</td> <td>Punjabi (pan_Guru)</td> </tr> <tr> <td>Bengali (ben_Beng)</td> <td>Kashmiri (Devanagari) (kas_Deva)</td> <td>Sanskrit (san_Deva)</td> </tr> <tr> <td>Bodo (brx_Deva)</td> <td>Maithili (mai_Deva)</td> <td>Santali (sat_Olck)</td> </tr> <tr> <td>Dogri (doi_Deva)</td> <td>Malayalam (mal_Mlym)</td> <td>Sindhi (Arabic) (snd_Arab)</td> </tr> <tr> <td>English (eng_Latn)</td> <td>Marathi (mar_Deva)</td> <td>Sindhi (Devanagari) (snd_Deva)</td> </tr> <tr> <td>Konkani (gom_Deva)</td> <td>Manipuri (Bengali) (mni_Beng)</td> <td>Tamil (tam_Taml)</td> </tr> <tr> <td>Gujarati (guj_Gujr)</td> <td>Manipuri (Meitei) (mni_Mtei)</td> <td>Telugu (tel_Telu)</td> </tr> <tr> <td>Hindi (hin_Deva)</td> <td>Nepali (npi_Deva)</td> <td>Urdu (urd_Arab)</td> </tr> <tr> <td>Kannada (kan_Knda)</td> <td>Odia (ory_Orya)</td> </tr> </table> ### Citation If you consider using our work then please cite using: ``` @article{ai4bharat2023indictrans2, title = {IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages}, author = {AI4Bharat and Jay Gala and Pranjal A. Chitale and Raghavan AK and Sumanth Doddapaneni and Varun Gumma and Aswanth Kumar and Janki Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M. Khapra and Raj Dabre and Anoop Kunchukuttan}, year = {2023}, journal = {arXiv preprint arXiv: 2305.16307} } ```	[ -0.4368833601474762, -0.5792968273162842, 0.07238788902759552, 0.29809629917144775, -0.4459080696105957, 0.1690196692943573, -0.2960478961467743, -0.14370962977409363, 0.2753959894180298, 0.18696731328964233, -0.5911213755607605, -0.6019898653030396, -0.5541195869445801, 0.2670816481113434, -0.24103578925132751, 0.706619143486023, -0.09891096502542496, 0.17904981970787048, -0.25686079263687134, -0.2822343707084656, -0.3660886883735657, -0.5017406940460205, -0.5615698099136353, -0.19608227908611298, -0.17738573253154755, 0.5871055126190186, 0.5206794738769531, 0.6965528726577759, 0.6905114054679871, 0.3283357322216034, -0.016072077676653862, 0.32453325390815735, -0.04089878872036934, 0.0850389152765274, -0.1517036408185959, -0.46133679151535034, -0.42380669713020325, -0.025647751986980438, 0.7409297227859497, 0.5577582716941833, -0.01508921105414629, 0.17444971203804016, 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hyperpartisan_news_detection	null	2023-06-13T07:46:19Z	766	8	null	[ "task_categories:text-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-4.0", "bias-classification", "region:us" ]	2023-06-13T07:46:19Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: HyperpartisanNewsDetection tags: - bias-classification dataset_info: - config_name: byarticle features: - name: text dtype: string - name: title dtype: string - name: hyperpartisan dtype: bool - name: url dtype: string - name: published_at dtype: string splits: - name: train num_bytes: 2803943 num_examples: 645 download_size: 1000352 dataset_size: 2803943 - config_name: bypublisher features: - name: text dtype: string - name: title dtype: string - name: hyperpartisan dtype: bool - name: url dtype: string - name: published_at dtype: string - name: bias dtype: class_label: names: '0': right '1': right-center '2': least '3': left-center '4': left splits: - name: train num_bytes: 2805711609 num_examples: 600000 - name: validation num_bytes: 960356598 num_examples: 150000 download_size: 1003195420 dataset_size: 5611423218 --- # Dataset Card for "hyperpartisan_news_detection" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://pan.webis.de/semeval19/semeval19-web/](https://pan.webis.de/semeval19/semeval19-web/) - Repository: https://github.com/pan-webis-de/pan-code/tree/master/semeval19 - Paper: https://aclanthology.org/S19-2145 - Data: https://doi.org/10.5281/zenodo.1489920 - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.00 GB - Size of the generated dataset: 5.61 GB - Total amount of disk used: 6.62 GB ### Dataset Summary Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4. Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person. There are 2 parts: - byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed. - bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### byarticle - Size of downloaded dataset files: 1.00 MB - Size of the generated dataset: 2.80 MB - Total amount of disk used: 3.81 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "hyperpartisan": true, "published_at": "2020-01-01", "text": "\"<p>This is a sample article which will contain lots of text</p>\\n \\n<p>Lorem ipsum dolor sit amet, consectetur adipiscing el...", "title": "Example article 1", "url": "http://www.example.com/example1" } ``` #### bypublisher - Size of downloaded dataset files: 1.00 GB - Size of the generated dataset: 5.61 GB - Total amount of disk used: 6.61 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "bias": 3, "hyperpartisan": false, "published_at": "2020-01-01", "text": "\"<p>This is a sample article which will contain lots of text</p>\\n \\n<p>Phasellus bibendum porta nunc, id venenatis tortor fi...", "title": "Example article 4", "url": "https://example.com/example4" } ``` ### Data Fields The data fields are the same among all splits. #### byarticle - `text`: a `string` feature. - `title`: a `string` feature. - `hyperpartisan`: a `bool` feature. - `url`: a `string` feature. - `published_at`: a `string` feature. #### bypublisher - `text`: a `string` feature. - `title`: a `string` feature. - `hyperpartisan`: a `bool` feature. - `url`: a `string` feature. - `published_at`: a `string` feature. - `bias`: a classification label, with possible values including `right` (0), `right-center` (1), `least` (2), `left-center` (3), `left` (4). ### Data Splits #### byarticle \| \|train\| \|---------\|----:\| \|byarticle\| 645\| #### bypublisher \| \|train \|validation\| \|-----------\|-----:\|---------:\| \|bypublisher\|600000\| 150000\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The collection (including labels) are licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @inproceedings{kiesel-etal-2019-semeval, title = "{S}em{E}val-2019 Task 4: Hyperpartisan News Detection", author = "Kiesel, Johannes and Mestre, Maria and Shukla, Rishabh and Vincent, Emmanuel and Adineh, Payam and Corney, David and Stein, Benno and Potthast, Martin", booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation", month = jun, year = "2019", address = "Minneapolis, Minnesota, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S19-2145", doi = "10.18653/v1/S19-2145", pages = "829--839", abstract = "Hyperpartisan news is news that takes an extreme left-wing or right-wing standpoint. If one is able to reliably compute this meta information, news articles may be automatically tagged, this way encouraging or discouraging readers to consume the text. It is an open question how successfully hyperpartisan news detection can be automated, and the goal of this SemEval task was to shed light on the state of the art. We developed new resources for this purpose, including a manually labeled dataset with 1,273 articles, and a second dataset with 754,000 articles, labeled via distant supervision. The interest of the research community in our task exceeded all our expectations: The datasets were downloaded about 1,000 times, 322 teams registered, of which 184 configured a virtual machine on our shared task cloud service TIRA, of which in turn 42 teams submitted a valid run. The best team achieved an accuracy of 0.822 on a balanced sample (yes : no hyperpartisan) drawn from the manually tagged corpus; an ensemble of the submitted systems increased the accuracy by 0.048.", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.	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matinf	null	2023-04-05T10:09:38Z	764	3	matinf	[ "region:us" ]	2023-04-05T10:09:38Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- paperswithcode_id: matinf pretty_name: Maternal and Infant Dataset dataset_info: - config_name: age_classification features: - name: question dtype: string - name: description dtype: string - name: label dtype: class_label: names: '0': 0-1岁 '1': 1-2岁 '2': 2-3岁 - name: id dtype: int32 splits: - name: train num_bytes: 33901977 num_examples: 134852 - name: test num_bytes: 9616194 num_examples: 38318 - name: validation num_bytes: 4869685 num_examples: 19323 download_size: 0 dataset_size: 48387856 - config_name: topic_classification features: - name: question dtype: string - name: description dtype: string - name: label dtype: class_label: names: '0': 产褥期保健 '1': 儿童过敏 '2': 动作发育 '3': 婴幼保健 '4': 婴幼心理 '5': 婴幼早教 '6': 婴幼期喂养 '7': 婴幼营养 '8': 孕期保健 '9': 家庭教育 '10': 幼儿园 '11': 未准父母 '12': 流产和不孕 '13': 疫苗接种 '14': 皮肤护理 '15': 宝宝上火 '16': 腹泻 '17': 婴幼常见病 - name: id dtype: int32 splits: - name: train num_bytes: 153326538 num_examples: 613036 - name: test num_bytes: 43877443 num_examples: 175363 - name: validation num_bytes: 21834951 num_examples: 87519 download_size: 0 dataset_size: 219038932 - config_name: summarization features: - name: description dtype: string - name: question dtype: string - name: id dtype: int32 splits: - name: train num_bytes: 181245403 num_examples: 747888 - name: test num_bytes: 51784189 num_examples: 213681 - name: validation num_bytes: 25849900 num_examples: 106842 download_size: 0 dataset_size: 258879492 - config_name: qa features: - name: question dtype: string - name: answer dtype: string - name: id dtype: int32 splits: - name: train num_bytes: 188047511 num_examples: 747888 - name: test num_bytes: 53708532 num_examples: 213681 - name: validation num_bytes: 26931809 num_examples: 106842 download_size: 0 dataset_size: 268687852 --- # Dataset Card for "matinf" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/WHUIR/MATINF](https://github.com/WHUIR/MATINF) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 795.00 MB - Total amount of disk used: 795.00 MB ### Dataset Summary MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization. MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MATINF. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### age_classification - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 48.39 MB - Total amount of disk used: 48.39 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "description": "\"6个月的时候去儿宝检查，医生说宝宝的分胯动作做的不好，说最好去儿童医院看看，但我家宝宝很好，感觉没有什么不正常啊，请教一下，分胯做的不好，有什么不好吗？\"...", "id": 88016, "label": 0, "question": "医生说宝宝的分胯动作不好" } ``` #### qa - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 268.69 MB - Total amount of disk used: 268.69 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "\"我一个同学的孩子就是发现了肾积水，治疗了一段时间，结果还是越来越多，没办法就打掉了。虽然舍不得，但是还是要忍痛割爱，不然以后孩子真的有问题，大人和孩子都受罪。不过，这个最后的决定还要你自己做，毕竟是你的宝宝。，、、、、\"...", "id": 536714, "question": "孕5个月检查右侧肾积水孩子能要吗？" } ``` #### summarization - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 258.88 MB - Total amount of disk used: 258.88 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "description": "\"宝宝有中度HIE，但原因未查明，这是他出生后脸上红的几道，嘴唇深红近紫，请问这是像缺氧的表现吗？\"...", "id": 173649, "question": "宝宝脸上红的几道嘴唇深红近紫是像缺氧的表现吗？" } ``` #### topic_classification - Size of downloaded dataset files: 0.00 MB - Size of the generated dataset: 219.04 MB - Total amount of disk used: 219.04 MB An example of 'train' looks as follows. ``` { "description": "媳妇怀孕五个月了经检查右侧肾积水、过了半月左侧也出现肾积水、她要拿掉孩子、怎么办？", "id": 536714, "label": 8, "question": "孕5个月检查右侧肾积水孩子能要吗？" } ``` ### Data Fields The data fields are the same among all splits. #### age_classification - `question`: a `string` feature. - `description`: a `string` feature. - `label`: a classification label, with possible values including `0-1岁` (0), `1-2岁` (1), `2-3岁` (2). - `id`: a `int32` feature. #### qa - `question`: a `string` feature. - `answer`: a `string` feature. - `id`: a `int32` feature. #### summarization - `description`: a `string` feature. - `question`: a `string` feature. - `id`: a `int32` feature. #### topic_classification - `question`: a `string` feature. - `description`: a `string` feature. - `label`: a classification label, with possible values including `产褥期保健` (0), `儿童过敏` (1), `动作发育` (2), `婴幼保健` (3), `婴幼心理` (4). - `id`: a `int32` feature. ### Data Splits \| name \|train \|validation\| test \| \|--------------------\|-----:\|---------:\|-----:\| \|age_classification \|134852\| 19323\| 38318\| \|qa \|747888\| 106842\|213681\| \|summarization \|747888\| 106842\|213681\| \|topic_classification\|613036\| 87519\|175363\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{xu-etal-2020-matinf, title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization", author = "Xu, Canwen and Pei, Jiaxin and Wu, Hongtao and Liu, Yiyu and Li, Chenliang", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.330", pages = "3586--3596", } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.	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DFKI-SLT/scidtb_argmin	DFKI-SLT	2023-11-17T20:51:31Z	762	0	null	[ "region:us" ]	2023-11-17T20:51:31Z	2023-06-26T10:14:08.000Z	2023-06-26T10:14:08	# Dataset Card for "SciDTB Argmin" ### Dataset Summary Built from 60 English scientific abstracts in the larger annotated dataset, Discourse Dependency TreeBank for Scientific Abstracts ([Yang & Li, 2018](https://aclanthology.org/P18-2071)), [Accuosto and Saggion (2020)](https://aclanthology.org/W19-4505.pdf) offered a fine-grained annotated dataset for the argumentative component classification and relation classification tasks. The dataset contains 327 sentences, 8012 tokens, 862 discourse units and 352 argumentative units (of 6 labels) linked by 292 argumentative relations (of 5 labels). ### Supported Tasks and Leaderboards - Tasks: Argument Mining, Component Classification, Relation Classification - Leaderboards: \[More Information Needed\] ### Languages The language in the dataset is English (academic). ## Dataset Structure ### Data Instances - Size of downloaded dataset files: 32.4 KB ``` { 'id': 'D14-1002-fexp-corpus', 'data': { 'token': [ "This", "paper", "presents", "a", "deep", "semantic",...] 'unit-bio': [ 0, 1, 1, 1, 1,...] 'unit-label': [ 0, 0, 0, 0, 0,...] "role": [ 4, 4, 4, 4, 4,...] 'parent-offset': [ 0, 0, 0, 0, 0,...] } } ``` ### Data Fields - `id`: the instance `id` of the document, a `string` feature - `data`: a `dictionary` feature, contains: - `token`: word tokens of the whole document, a `list` of `string` feature - `unit-bio`: the BIO label indicating whether the token at a particular index is the beginning of a unit (labeled as 0) or not (labeled as 1), a `list` of `int` feature - `unit-label`: the span label (which the token belongs to) indicating the argumentation type, a `list` of `int` feature (see [label list](https://huggingface.co/datasets/DFKI-SLT/scidtb_argmin/blob/main/scidtb_argmin.py#L42-50)) - `role`: the relation label (of the span which the token belongs to) indicating the argumentation relation to another span, a `list` of `int` feature (see [label list](https://huggingface.co/datasets/DFKI-SLT/scidtb_argmin/blob/main/scidtb_argmin.py#L51)) - `parent-offset`: the distance from the current span to the span it has a relation with (as indicated in `role`), a `list` of `int` feature ### Data Splits \| \| train \| \| ------------------------------------------------------------------------------------------------------------------- \| -----------------------------------------: \| \| Size \| 60 \| \| Span Labels<br/>- `Proposal`<br/>- `Mean`<br/>- `Result`<br/>- `Observation`<br/> - `Assertion`<br/>- `Description` \| <br/>110<br/>63<br/>74<br/>11<br/>88<br/>7 \| \| Relation Labels<br/>- `Support`<br/>- `Attack`<br/>- `Detail`<br/>- `Sequence`<br/>- `Additional` \| <br/>126<br/>0<br/>129<br/>11<br/>27 \| ## Dataset Creation ### Curation Rationale "We propose to tackle the limitations posed by the lack of annotated data for argument mining in the scientific domain by leveraging existing Rhetorical Structure Theory (RST) (Mann et al., 1992) annotations in a corpus of computational linguistics abstracts (SciDTB) (Yang and Li, 2018)." (p. 42) "We introduce a fine-grained annotation scheme aimed at capturing information that accounts for the specificities of the scientific discourse, including the type of evidence that is offered to support a statement (e.g., background information, experimental data or interpretation of results). This can provide relevant information, for instance, to assess the argumentative strength of a text." (p. 44) ### Source Data The source data is available online at https://emnlp2014.org/. #### Initial Data Collection and Normalization "This work is informed by previous research in the areas of argument mining, argumentation quality assessment and the relationship between discourse and argumentative structures and, from the methodological perspective, to transfer learning approaches." Previously, Yang and Li (2018) divided a passage into non-overlapping text spans, which are named elementary discourse units (EDUs). They followed the criterion of Polanyi (1988) and Irmer (2011) and the guidelines defined by (Carlson and Marcu, 2001). For more information about the initial data collection and annotation, please see the SciDTB's [dataset card](https://huggingface.co/datasets/DFKI-SLT/scidtb). The current authors added a new annotation layer for to the elementary discourse units (EDUs) annotated by Yang & Li (2018), namely, fine-grained argumentative labels and relation labels from the RST Framework. (p. 43) #### Who are the source language producers? No demography or identity information of the source language producer is reported by the authors, but we infer that the dataset was human-generated, specifically the academics in the field of computational linguistics/NLP, and possibly edited by human reviewers. ### Annotations #### Annotation process "We consider a subset of the SciDTB corpus consisting of 60 abstracts from the Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) and transformed them into a format suitable for the GraPAT graph annotation tool (Sonntag and Stede, 2014). "...The corpus enriched with the argumentation level contains a total of 327 sentences, 8012 tokens, 862 discourse units and 352 argumentative units linked by 292 argumentative relations." (p. 43) #### Who are the annotators? \[More Information Needed\] ### Personal and Sensitive Information \[More Information Needed\] ## Considerations for Using the Data ### Social Impact of Dataset "The development of automatic systems to support the quality assessment of scientific texts can facilitate the work of editors and referees of scientific publications and, at the same time, be of value for researchers to obtain feedback that can lead to improve the communication of their results...Aspects such as the argumentative structure of the text are key when analyzing its effectiveness with respect to its communication objectives (Walton and Walton, 1989)." (p. 41) "Being able to extract not only what is being stated by the authors of a text but also the reasons they provide to support it can be useful in multiple applications, ranging from a finegrained analysis of opinions to the generation of abstractive summaries of texts." (p. 41) ### Discussion of Biases "The types of argumentative units are distributed as follows: 31% of the units are of type proposal, 25% assertion, 21% result, 18% means, 3% observation, and 2% description. In turn, the relations are distributed: 45% of type detail, 42% support, 9% additional, and 4% sequence. No attack relations were identified in the set of currently annotated texts." "When considering the distance of the units to their parent unit in the argumentation tree, we observe that the majority (57%) are linked to a unit that occurs right before or after it in the text, while 19% are linked to a unit with a distance of 1 unit in-between, 12% to a unit with a distance of 2 units, 6% to a unit with a distance of 3, and 6% to a unit with a distance of 4 or more." (p. 44) ### Other Known Limitations \[More Information Needed\] ## Additional Information ### Dataset Curators This work is (partly) supported by the Spanish Government under the Marı´a de Maeztu Units of Excellence Programme (MDM-2015-0502). (p. 49) ### Licensing Information \[More Information Needed\] ### Citation Information The current dataset: ``` @inproceedings{accuosto-saggion-2019-transferring, title = "Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts", author = "Accuosto, Pablo and Saggion, Horacio", editor = "Stein, Benno and Wachsmuth, Henning", booktitle = "Proceedings of the 6th Workshop on Argument Mining", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W19-4505", doi = "10.18653/v1/W19-4505", pages = "41--51", } ``` The original SciDTB dataset: ``` @inproceedings{yang-li-2018-scidtb, title = "{S}ci{DTB}: Discourse Dependency {T}ree{B}ank for Scientific Abstracts", author = "Yang, An and Li, Sujian", editor = "Gurevych, Iryna and Miyao, Yusuke", booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2018", address = "Melbourne, Australia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P18-2071", doi = "10.18653/v1/P18-2071", pages = "444--449", } ``` ### Contributions Thanks to [@idalr](https://github.com/idalr) for adding this dataset.	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yuvalkirstain/pickapic_v1_no_images	yuvalkirstain	2023-04-16T14:53:35Z	761	0	null	[ "region:us" ]	2023-04-16T14:53:35Z	2023-04-16T14:52:20.000Z	2023-04-16T14:52:20	--- dataset_info: features: - name: are_different dtype: bool - name: best_image_uid dtype: string - name: caption dtype: string - name: created_at dtype: timestamp[ns] - name: has_label dtype: bool - name: image_0_uid dtype: string - name: image_0_url dtype: string - name: image_1_uid dtype: string - name: image_1_url dtype: string - name: label_0 dtype: float64 - name: label_1 dtype: float64 - name: model_0 dtype: string - name: model_1 dtype: string - name: ranking_id dtype: int64 - name: user_id dtype: int64 - name: num_example_per_prompt dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 308923563 num_examples: 583747 - name: validation num_bytes: 8759568 num_examples: 17439 - name: test num_bytes: 7194410 num_examples: 14073 - name: validation_unique num_bytes: 248229 num_examples: 500 - name: test_unique num_bytes: 256313 num_examples: 500 download_size: 175013617 dataset_size: 325382083 --- # Dataset Card for "pick_a_pic_v1_no_images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.7070796489715576, -0.3171277940273285, 0.173021599650383, 0.3361775279045105, -0.6463616490364075, -0.2905769646167755, 0.6360108852386475, -0.2713265120983124, 1.0680371522903442, 0.5355578064918518, -0.9265021681785583, -0.7465808987617493, -0.7100085020065308, -0.263421893119812, -0.321388304233551, 1.0889441967010498, 0.15764158964157104, 0.028950337320566177, -0.59279865026474, -0.10526886582374573, -0.23863759636878967, -0.19907331466674805, -0.9173093438148499, -0.20361509919166565, 0.8174306750297546, 0.7809275984764099, 0.5536245107650757, 0.3811415135860443, 0.9534416794776917, 0.11656509339809418, 0.037072598934173584, -0.35338714718818665, -0.45464465022087097, 0.027369806542992592, 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NeelNanda/c4-tokenized-2b	NeelNanda	2022-11-14T00:26:59Z	759	0	null	[ "region:us" ]	2022-11-14T00:26:59Z	2022-11-14T00:15:38.000Z	2022-11-14T00:15:38	--- dataset_info: features: - name: tokens sequence: int64 splits: - name: train num_bytes: 11145289620 num_examples: 1359845 download_size: 2530851147 dataset_size: 11145289620 --- # Dataset Card for "c4-tokenized-2b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.3734208941459656, -0.26926782727241516, 0.11349277198314667, 0.42653128504753113, -0.35576149821281433, 0.2610257863998413, 0.2737118899822235, -0.4609658718109131, 0.8254308104515076, 0.4324452877044678, -0.6883285641670227, -0.7655689120292664, -0.6773478984832764, -0.15042391419410706, -0.22774241864681244, 1.2743730545043945, 0.15640147030353546, 0.38615649938583374, -0.344222754240036, -0.07415715605020523, -0.6190133690834045, -0.8225517868995667, -0.7212029099464417, 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dynabench/qa	dynabench	2022-07-02T20:17:58Z	755	0	null	[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "arxiv:2002.00293", "arxiv:1606.05250", "region:us" ]	2022-07-02T20:17:58Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa --- # Dataset Card for Dynabench.QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Dynabench.QA](https://dynabench.org/tasks/2#overall) - Paper: [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://arxiv.org/abs/2002.00293) - Leaderboard: [Dynabench QA Round 1 Leaderboard](https://dynabench.org/tasks/2#overall) - Point of Contact: [Max Bartolo](max.bartolo@ucl.ac.uk) ### Dataset Summary Dynabench.QA is an adversarially collected Reading Comprehension dataset spanning over multiple rounds of data collect. For round 1, it is identical to the [adversarialQA dataset](https://adversarialqa.github.io/), where we have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples. The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods. ### Supported Tasks and Leaderboards `extractive-qa`: The dataset can be used to train a model for Extractive Question Answering, which consists in selecting the answer to a question from a passage. Success on this task is typically measured by achieving a high word-overlap [F1 score](https://huggingface.co/metrics/f1). The [RoBERTa-Large](https://huggingface.co/roberta-large) model trained on all the data combined with [SQuAD](https://arxiv.org/abs/1606.05250) currently achieves 64.35% F1. This task has an active leaderboard and is available as round 1 of the QA task on [Dynabench](https://dynabench.org/tasks/2#overall) and ranks models based on F1 score. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Data is provided in the same format as SQuAD 1.1. An example is shown below: ``` { "data": [ { "title": "Oxygen", "paragraphs": [ { "context": "Among the most important classes of organic compounds that contain oxygen are (where \"R\" is an organic group): alcohols (R-OH); ethers (R-O-R); ketones (R-CO-R); aldehydes (R-CO-H); carboxylic acids (R-COOH); esters (R-COO-R); acid anhydrides (R-CO-O-CO-R); and amides (R-C(O)-NR2). There are many important organic solvents that contain oxygen, including: acetone, methanol, ethanol, isopropanol, furan, THF, diethyl ether, dioxane, ethyl acetate, DMF, DMSO, acetic acid, and formic acid. Acetone ((CH3)2CO) and phenol (C6H5OH) are used as feeder materials in the synthesis of many different substances. Other important organic compounds that contain oxygen are: glycerol, formaldehyde, glutaraldehyde, citric acid, acetic anhydride, and acetamide. Epoxides are ethers in which the oxygen atom is part of a ring of three atoms.", "qas": [ { "id": "22bbe104aa72aa9b511dd53237deb11afa14d6e3", "question": "In addition to having oxygen, what do alcohols, ethers and esters have in common, according to the article?", "answers": [ { "answer_start": 36, "text": "organic compounds" } ] }, { "id": "4240a8e708c703796347a3702cf1463eed05584a", "question": "What letter does the abbreviation for acid anhydrides both begin and end in?", "answers": [ { "answer_start": 244, "text": "R" } ] }, { "id": "0681a0a5ec852ec6920d6a30f7ef65dced493366", "question": "Which of the organic compounds, in the article, contains nitrogen?", "answers": [ { "answer_start": 262, "text": "amides" } ] }, { "id": "2990efe1a56ccf81938fa5e18104f7d3803069fb", "question": "Which of the important classes of organic compounds, in the article, has a number in its abbreviation?", "answers": [ { "answer_start": 262, "text": "amides" } ] } ] } ] } ] } ``` ### Data Fields - title: the title of the Wikipedia page from which the context is sourced - context: the context/passage - id: a string identifier for each question - answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text ### Data Splits For round 1, the dataset is composed of three different datasets constructed using different models in the loop: BiDAF, BERT-Large, and RoBERTa-Large. Each of these has 10,000 training examples, 1,000 validation examples, and 1,000 test examples for a total of 30,000/3,000/3,000 train/validation/test examples. ## Dataset Creation ### Curation Rationale This dataset was collected to provide a more challenging and diverse Reading Comprehension dataset to state-of-the-art models. ### Source Data #### Initial Data Collection and Normalization The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250). #### Who are the source language producers? The source language produces are Wikipedia editors for the passages, and human annotators on Mechanical Turk for the questions. ### Annotations #### Annotation process The dataset is collected through an adversarial human annotation process which pairs a human annotator and a reading comprehension model in an interactive setting. The human is presented with a passage for which they write a question and highlight the correct answer. The model then tries to answer the question, and, if it fails to answer correctly, the human wins. Otherwise, the human modifies or re-writes their question until the successfully fool the model. #### Who are the annotators? The annotators are from Amazon Mechanical Turk, geographically restricted the the USA, UK and Canada, having previously successfully completed at least 1,000 HITs, and having a HIT approval rate greater than 98%. Crowdworkers undergo intensive training and qualification prior to annotation. ### Personal and Sensitive Information No annotator identifying details are provided. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a test bed for questions which contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question. It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application. ### Discussion of Biases The dataset may exhibit various biases in terms of the source passage selection, annotated questions and answers, as well as algorithmic biases resulting from the adversarial annotation protocol. ### Other Known Limitations N/a ## Additional Information ### Dataset Curators This dataset was initially created by Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp, during work carried out at University College London (UCL). ### Licensing Information This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{bartolo2020beat, author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus}, title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension}, journal = {Transactions of the Association for Computational Linguistics}, volume = {8}, number = {}, pages = {662-678}, year = {2020}, doi = {10.1162/tacl\_a\_00338}, URL = { https://doi.org/10.1162/tacl_a_00338 }, eprint = { https://doi.org/10.1162/tacl_a_00338 }, abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). } } ``` ### Contributions Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset.	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BeIR/trec-covid-qrels	BeIR	2022-10-23T06:01:04Z	755	0	beir	[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]	2022-10-23T06:01:04Z	2022-06-05T15:38:00.000Z	2022-06-05T15:38:00	--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/UKPLab/beir - Repository: https://github.com/UKPLab/beir - Paper: https://openreview.net/forum?id=wCu6T5xFjeJ - Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - Point of Contact: nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits \| Dataset \| Website\| BEIR-Name \| Type \| Queries \| Corpus \| Rel D/Q \| Down-load \| md5 \| \| -------- \| -----\| ---------\| --------- \| ----------- \| ---------\| ---------\| :----------: \| :------:\| \| MSMARCO \| [Homepage](https://microsoft.github.io/msmarco/)\| ``msmarco`` \| ``train``<br>``dev``<br>``test``\| 6,980 \| 8.84M \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) \| ``444067daf65d982533ea17ebd59501e4`` \| \| TREC-COVID \| [Homepage](https://ir.nist.gov/covidSubmit/index.html)\| ``trec-covid``\| ``test``\| 50\| 171K\| 493.5 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) \| ``ce62140cb23feb9becf6270d0d1fe6d1`` \| \| NFCorpus \| [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) \| ``nfcorpus`` \| ``train``<br>``dev``<br>``test``\| 323 \| 3.6K \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) \| ``a89dba18a62ef92f7d323ec890a0d38d`` \| \| BioASQ \| [Homepage](http://bioasq.org) \| ``bioasq``\| ``train``<br>``test`` \| 500 \| 14.91M \| 8.05 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) \| \| NQ \| [Homepage](https://ai.google.com/research/NaturalQuestions) \| ``nq``\| ``train``<br>``test``\| 3,452 \| 2.68M \| 1.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) \| ``d4d3d2e48787a744b6f6e691ff534307`` \| \| HotpotQA \| [Homepage](https://hotpotqa.github.io) \| ``hotpotqa``\| ``train``<br>``dev``<br>``test``\| 7,405 \| 5.23M \| 2.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) \| ``f412724f78b0d91183a0e86805e16114`` \| \| FiQA-2018 \| [Homepage](https://sites.google.com/view/fiqa/) \| ``fiqa`` \| ``train``<br>``dev``<br>``test``\| 648 \| 57K \| 2.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) \| ``17918ed23cd04fb15047f73e6c3bd9d9`` \| \| Signal-1M(RT) \| [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)\| ``signal1m`` \| ``test``\| 97 \| 2.86M \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) \| \| TREC-NEWS \| [Homepage](https://trec.nist.gov/data/news2019.html) \| ``trec-news`` \| ``test``\| 57 \| 595K \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) \| \| ArguAna \| [Homepage](http://argumentation.bplaced.net/arguana/data) \| ``arguana``\| ``test`` \| 1,406 \| 8.67K \| 1.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) \| ``8ad3e3c2a5867cdced806d6503f29b99`` \| \| Touche-2020\| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) \| ``webis-touche2020``\| ``test``\| 49 \| 382K \| 19.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) \| ``46f650ba5a527fc69e0a6521c5a23563`` \| \| CQADupstack\| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) \| ``cqadupstack``\| ``test``\| 13,145 \| 457K \| 1.4 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) \| ``4e41456d7df8ee7760a7f866133bda78`` \| \| Quora\| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) \| ``quora``\| ``dev``<br>``test``\| 10,000 \| 523K \| 1.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) \| ``18fb154900ba42a600f84b839c173167`` \| \| DBPedia \| [Homepage](https://github.com/iai-group/DBpedia-Entity/) \| ``dbpedia-entity``\| ``dev``<br>``test``\| 400 \| 4.63M \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) \| ``c2a39eb420a3164af735795df012ac2c`` \| \| SCIDOCS\| [Homepage](https://allenai.org/data/scidocs) \| ``scidocs``\| ``test``\| 1,000 \| 25K \| 4.9 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) \| ``38121350fc3a4d2f48850f6aff52e4a9`` \| \| FEVER \| [Homepage](http://fever.ai) \| ``fever``\| ``train``<br>``dev``<br>``test``\| 6,666 \| 5.42M \| 1.2\| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) \| ``5a818580227bfb4b35bb6fa46d9b6c03`` \| \| Climate-FEVER\| [Homepage](http://climatefever.ai) \| ``climate-fever``\|``test``\| 1,535 \| 5.42M \| 3.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) \| ``8b66f0a9126c521bae2bde127b4dc99d`` \| \| SciFact\| [Homepage](https://github.com/allenai/scifact) \| ``scifact``\| ``train``<br>``test``\| 300 \| 5K \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) \| ``5f7d1de60b170fc8027bb7898e2efca1`` \| \| Robust04 \| [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) \| ``robust04``\| ``test``\| 249 \| 528K \| 69.9 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.	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gbharti/finance-alpaca	gbharti	2023-09-26T04:13:35Z	755	48	null	[ "language:en", "region:us" ]	2023-09-26T04:13:35Z	2023-03-29T03:37:58.000Z	2023-03-29T03:37:58	--- language: - en --- This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5 Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora GitHub repo with performance analyses, training and data generation scripts, and inference notebooks: https://github.com/gaurangbharti1/wealth-alpaca Cleaner dataset: https://huggingface.co/datasets/gbharti/wealth-alpaca_lora (no major changes, just cleaned up) CSV format: https://huggingface.co/datasets/gbharti/finance-alpaca-csv	[ -0.3914183974266052, -0.7195168733596802, 0.045999590307474136, 0.11762411892414093, -0.3523216247558594, -0.12452883273363113, 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HuggingFaceH4/CodeAlpaca_20K	HuggingFaceH4	2023-03-28T17:26:28Z	753	40	null	[ "task_categories:text-generation", "license:cc", "region:us" ]	2023-03-28T17:26:28Z	2023-03-28T17:18:25.000Z	2023-03-28T17:18:25	--- license: cc task_categories: - text-generation --- This dataset splits the original [CodeAlpaca dataset](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) into train and test splits.	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-0.1213812306523323, -0.01086068619042635, 0.36838826537132263, 0.4369833171367645, 0.07710233330726624, -0.3342304527759552, -0.40624183416366577, 0.3314063847064972, -0.5256334543228149, -1.2436655759811401 ]	null	null	null	null	null	null	null	null	null	null	null	null	null
Matthijs/snacks	Matthijs	2022-04-12T14:26:59Z	750	6	null	[ "task_categories:image-classification", "license:cc-by-4.0", "region:us" ]	2022-04-12T14:26:59Z	2022-04-12T08:30:24.000Z	2022-04-12T08:30:24	--- pretty_name: Snacks task_categories: - image-classification - computer-vision license: cc-by-4.0 --- # Dataset Card for Snacks ## Dataset Summary This is a dataset of 20 different types of snack foods that accompanies the book [Machine Learning by Tutorials](https://www.raywenderlich.com/books/machine-learning-by-tutorials/v2.0). The images were taken from the [Google Open Images dataset](https://storage.googleapis.com/openimages/web/index.html), release 2017_11. ## Dataset Structure Number of images in the train/validation/test splits: ```nohighlight train 4838 val 955 test 952 total 6745 ``` Total images in each category: ```nohighlight apple 350 banana 350 cake 349 candy 349 carrot 349 cookie 349 doughnut 350 grape 350 hot dog 350 ice cream 350 juice 350 muffin 348 orange 349 pineapple 340 popcorn 260 pretzel 204 salad 350 strawberry 348 waffle 350 watermelon 350 ``` To save space in the download, the images were resized so that their smallest side is 256 pixels. All EXIF information was removed. ### Data Splits Train, Test, Validation ## Licensing Information Just like the images from Google Open Images, the snacks dataset is licensed under the terms of the Creative Commons license. The images are listed as having a [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/) license. The annotations are licensed by Google Inc. under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. The credits.csv file contains the original URL, author information and license for each image.	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BeIR/fiqa-qrels	BeIR	2022-10-23T06:06:29Z	749	0	beir	[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]	2022-10-23T06:06:29Z	2022-06-05T17:26:38.000Z	2022-06-05T17:26:38	--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/UKPLab/beir - Repository: https://github.com/UKPLab/beir - Paper: https://openreview.net/forum?id=wCu6T5xFjeJ - Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - Point of Contact: nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits \| Dataset \| Website\| BEIR-Name \| Type \| Queries \| Corpus \| Rel D/Q \| Down-load \| md5 \| \| -------- \| -----\| ---------\| --------- \| ----------- \| ---------\| ---------\| :----------: \| :------:\| \| MSMARCO \| [Homepage](https://microsoft.github.io/msmarco/)\| ``msmarco`` \| ``train``<br>``dev``<br>``test``\| 6,980 \| 8.84M \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) \| ``444067daf65d982533ea17ebd59501e4`` \| \| TREC-COVID \| [Homepage](https://ir.nist.gov/covidSubmit/index.html)\| ``trec-covid``\| ``test``\| 50\| 171K\| 493.5 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) \| ``ce62140cb23feb9becf6270d0d1fe6d1`` \| \| NFCorpus \| [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) \| ``nfcorpus`` \| ``train``<br>``dev``<br>``test``\| 323 \| 3.6K \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) \| ``a89dba18a62ef92f7d323ec890a0d38d`` \| \| BioASQ \| [Homepage](http://bioasq.org) \| ``bioasq``\| ``train``<br>``test`` \| 500 \| 14.91M \| 8.05 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) \| \| NQ \| [Homepage](https://ai.google.com/research/NaturalQuestions) \| ``nq``\| ``train``<br>``test``\| 3,452 \| 2.68M \| 1.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) \| ``d4d3d2e48787a744b6f6e691ff534307`` \| \| HotpotQA \| [Homepage](https://hotpotqa.github.io) \| ``hotpotqa``\| ``train``<br>``dev``<br>``test``\| 7,405 \| 5.23M \| 2.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) \| ``f412724f78b0d91183a0e86805e16114`` \| \| FiQA-2018 \| [Homepage](https://sites.google.com/view/fiqa/) \| ``fiqa`` \| ``train``<br>``dev``<br>``test``\| 648 \| 57K \| 2.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) \| ``17918ed23cd04fb15047f73e6c3bd9d9`` \| \| Signal-1M(RT) \| [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)\| ``signal1m`` \| ``test``\| 97 \| 2.86M \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) \| \| TREC-NEWS \| [Homepage](https://trec.nist.gov/data/news2019.html) \| ``trec-news`` \| ``test``\| 57 \| 595K \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) \| \| ArguAna \| [Homepage](http://argumentation.bplaced.net/arguana/data) \| ``arguana``\| ``test`` \| 1,406 \| 8.67K \| 1.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) \| ``8ad3e3c2a5867cdced806d6503f29b99`` \| \| Touche-2020\| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) \| ``webis-touche2020``\| ``test``\| 49 \| 382K \| 19.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) \| ``46f650ba5a527fc69e0a6521c5a23563`` \| \| CQADupstack\| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) \| ``cqadupstack``\| ``test``\| 13,145 \| 457K \| 1.4 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) \| ``4e41456d7df8ee7760a7f866133bda78`` \| \| Quora\| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) \| ``quora``\| ``dev``<br>``test``\| 10,000 \| 523K \| 1.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) \| ``18fb154900ba42a600f84b839c173167`` \| \| DBPedia \| [Homepage](https://github.com/iai-group/DBpedia-Entity/) \| ``dbpedia-entity``\| ``dev``<br>``test``\| 400 \| 4.63M \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) \| ``c2a39eb420a3164af735795df012ac2c`` \| \| SCIDOCS\| [Homepage](https://allenai.org/data/scidocs) \| ``scidocs``\| ``test``\| 1,000 \| 25K \| 4.9 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) \| ``38121350fc3a4d2f48850f6aff52e4a9`` \| \| FEVER \| [Homepage](http://fever.ai) \| ``fever``\| ``train``<br>``dev``<br>``test``\| 6,666 \| 5.42M \| 1.2\| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) \| ``5a818580227bfb4b35bb6fa46d9b6c03`` \| \| Climate-FEVER\| [Homepage](http://climatefever.ai) \| ``climate-fever``\|``test``\| 1,535 \| 5.42M \| 3.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) \| ``8b66f0a9126c521bae2bde127b4dc99d`` \| \| SciFact\| [Homepage](https://github.com/allenai/scifact) \| ``scifact``\| ``train``<br>``test``\| 300 \| 5K \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) \| ``5f7d1de60b170fc8027bb7898e2efca1`` \| \| Robust04 \| [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) \| ``robust04``\| ``test``\| 249 \| 528K \| 69.9 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.	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tomekkorbak/detoxify-pile-chunk3-0-50000	tomekkorbak	2022-10-06T02:57:39Z	749	0	null	[ "region:us" ]	2022-10-06T02:57:39Z	2022-10-06T02:57:31.000Z	2022-10-06T02:57:31	Entry not found	[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -0.3992327153682709, 0.5791246294975281, -0.14494499564170837, -0.10751135647296906, 0.28233814239501953, -0.2768957316875458, -0.5409227013587952, -0.3685525059700012, -1.1902779340744019, 0.061491698026657104, 0.5316582918167114, 0.743514358997345, 0.7584061622619629, 0.3652168810367584, 0.643257737159729, 0.39322906732559204, -0.231389120221138, 0.48270541429519653, -0.04171793907880783, 0.0026038705836981535, -0.3524438440799713, -0.5516899824142456, -0.28596577048301697, 0.07584768533706665, 1.096130132675171, 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ContextualAI/hellaswag	ContextualAI	2023-10-06T23:57:13Z	749	1	null	[ "region:us" ]	2023-10-06T23:57:13Z	2023-10-06T23:56:54.000Z	2023-10-06T23:56:54	--- dataset_info: features: - name: query dtype: string - name: choices sequence: string - name: gold_generation dtype: string splits: - name: dev num_bytes: 9610103 num_examples: 10042 - name: test num_bytes: 7885767 num_examples: 10003 download_size: 10451785 dataset_size: 17495870 --- # Dataset Card for "hellaswag" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6128870248794556, -0.29142898321151733, 0.20823931694030762, 0.20621995627880096, -0.43477490544319153, -0.12314988672733307, 0.28245270252227783, -0.11252515763044357, 0.8611869812011719, 0.5932539701461792, -1.050764560699463, -0.9999287128448486, -0.7216940522193909, -0.288851261138916, -0.26935553550720215, 1.496099591255188, 0.2125922292470932, -0.20594629645347595, 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cs_restaurants	null	2022-11-18T19:49:56Z	742	1	czech-restaurant-information	[ "task_categories:text2text-generation", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:found", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended\|other-san-francisco-restaurants", "language:cs", "license:cc-by-4.0", "intent-to-text", "arxiv:1910.05298", "region:us" ]	2022-11-18T19:49:56Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - found language_creators: - expert-generated - machine-generated language: - cs license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended\|other-san-francisco-restaurants task_categories: - text2text-generation - text-generation - fill-mask task_ids: - dialogue-modeling - language-modeling - masked-language-modeling paperswithcode_id: czech-restaurant-information pretty_name: Czech Restaurant tags: - intent-to-text dataset_info: features: - name: dialogue_act dtype: string - name: delexicalized_dialogue_act dtype: string - name: text dtype: string - name: delexicalized_text dtype: string config_name: CSRestaurants splits: - name: train num_bytes: 654071 num_examples: 3569 - name: validation num_bytes: 181528 num_examples: 781 - name: test num_bytes: 191334 num_examples: 842 download_size: 1463019 dataset_size: 1026933 --- # Dataset Card for Czech Restaurant ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [Czech restaurants homepage](https://github.com/UFAL-DSG/cs_restaurant_dataset) - Paper: [Czech restaurants on Arxiv](https://arxiv.org/abs/1910.05298) ### Dataset Summary This is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as a translation of the [English San Francisco Restaurants dataset](https://www.repository.cam.ac.uk/handle/1810/251304) by Wen et al. (2015). The domain is restaurant information in Prague, with random/fictional values. It includes input dialogue acts and the corresponding outputs in Czech. ### Supported Tasks and Leaderboards - `other-intent-to-text`: The dataset can be used to train a model for data-to-text generation: from a desired dialogue act, the model must produce textual output that conveys this intention. ### Languages The entire dataset is in Czech, translated from the English San Francisco dataset by professional translators. ## Dataset Structure ### Data Instances Example of a data instance: ``` { "da": "?request(area)", "delex_da": "?request(area)", "text": "Jakou lokalitu hledáte ?", "delex_text": "Jakou lokalitu hledáte ?" } ``` ### Data Fields - `da`: input dialogue act - `delex_da`: input dialogue act, delexicalized - `text`: output text - `delex_text`: output text, delexicalized ### Data Splits The order of the instances is random; the split is roughly 3:1:1 between train, development, and test, ensuring that the different sections don't share the same DAs (so the generators need to generalize to unseen DAs), but they share as many generic different DA types as possible (e.g., confirm, inform_only_match etc.). DA types that only have a single corresponding DA (e.g., bye()) are included in the training set. The training, development, and test set contain 3569, 781, and 842 instances, respectively. ## Dataset Creation ### Curation Rationale While most current neural NLG systems do not explicitly contain language-specific components and are thus capable of multilingual generation in principle, there has been little work to test these capabilities experimentally. This goes hand in hand with the scarcity of non-English training datasets for NLG – the only data-to-text NLG set known to us is a small sportscasting Korean dataset (Chenet al., 2010), which only contains a limited number of named entities, reducing the need for their inflection. Since most generators are only tested on English, they do not need to handle grammar complexities not present in English. A prime example is the delexicalization technique used by most current generators. We create a novel dataset for Czech delexicalized generation; this extends the typical task of data-to-text NLG by requiring attribute value inflection. We choose Czech as an example of a morphologically complex language with a large set of NLP tools readily available. ### Source Data #### Initial Data Collection and Normalization The original data was collected from the [English San Francisco Restaurants dataset](https://www.repository.cam.ac.uk/handle/1810/251304) by Wen et al. (2015). #### Who are the source language producers? The original data was produced in interactions between Amazon Mechanical Turk workers and themed around San Francisco restaurants. This data was then translated into Czech and localized to Prague restaurants by professional translators. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information This data does not contain personal information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Ondřej Dušek, Filip Jurčíček, Josef Dvořák, Petra Grycová, Matěj Hejda, Jana Olivová, Michal Starý, Eva Štichová, Charles University. This work was funded by the Ministry of Education, Youth and Sports of the Czech Republic under the grant agreement LK11221 and core research funding, SVV project 260 333, and GAUK grant 2058214 of Charles University in Prague. It used language resources stored and distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (project LM2015071). ### Licensing Information [Creative Commons 4.0 BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @article{DBLP:journals/corr/abs-1910-05298, author = {Ondrej Dusek and Filip Jurcicek}, title = {Neural Generation for Czech: Data and Baselines}, journal = {CoRR}, volume = {abs/1910.05298}, year = {2019}, url = {http://arxiv.org/abs/1910.05298}, archivePrefix = {arXiv}, eprint = {1910.05298}, timestamp = {Wed, 16 Oct 2019 16:25:53 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-05298.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset.	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fhamborg/news_sentiment_newsmtsc	fhamborg	2022-10-25T09:20:03Z	742	9	null	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]	2022-10-25T09:20:03Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual pretty_name: 'NewsMTSC' size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification language_bcp47: - en-US --- # NewsMTSC dataset NewsMTSC is a high-quality dataset consisting of more than 11k manually labeled sentences sampled from English news articles. Each sentence was labeled by five human coders (the dataset contains only examples where the five coders assessed same or similar sentiment). The dataset is published as a [full paper at EACL 2021: NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles](https://aclanthology.org/2021.eacl-main.142.pdf). ## Subsets and splits The dataset consists of two subsets (`rw` and `mt`), each consisting of three splits (train, validation, and test). We recommend to use the `rw` subset, which is also the default subset. Both subsets share the same train set, in which the three sentiment classes have similar frequency since we applied class boosting. The two subsets differ in their validation and test sets: `rw` contains validation and test sets that resemble real-world distribution of sentiment in news articles. In contrast, `mt`'s validation and test sets contain only sentences that each have two or more (different) targets, where each target's sentiment was labeled individually. More information on the subsets can be found in our [paper](https://aclanthology.org/2021.eacl-main.142.pdf). ## Format Each split is stored in a JSONL file. In JSONL, each line represents one JSON object. In our dataset, each JSON object consists of the following attributes. When using the dataset, you most likely will need (only) the attributes highlighted in bold. 1. `mention`: text of the mention within `sentence` 2. `polarity`: sentiment of the sentence concerning the target's mention (-1 = negative, 0 = neutral, 1 = positive) 3. `from`: character-based, 0-indexed position of the first character of the target's mention within `sentence` 4. `to`: last character of the target's mention 5. `sentence`: sentence 6. `id`: identifier that is unique within NewsMTSC ## Contact If you find an issue with the dataset or model or have a question concerning either, please open an issue in the repository. * Repository: [https://github.com/fhamborg/NewsMTSC](https://github.com/fhamborg/NewsMTSC) * Web: [https://felix.hamborg.eu/](https://felix.hamborg.eu/) ## How to cite If you use the dataset or parts of it, please cite our paper: ``` @InProceedings{Hamborg2021b, author = {Hamborg, Felix and Donnay, Karsten}, title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles}, booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)}, year = {2021}, month = {Apr.}, location = {Virtual Event}, } ```	[ -0.5319080948829651, -0.5897260904312134, 0.28729328513145447, 0.28211674094200134, -0.43317022919654846, 0.23047871887683868, -0.32055386900901794, -0.21326199173927307, 0.3481038808822632, 0.33231207728385925, -0.5626454949378967, -0.811676561832428, -0.526943027973175, 0.2752832770347595, -0.34267932176589966, 1.3884339332580566, -0.0131803834810853, 0.08259601145982742, -0.1591201275587082, -0.4617435038089752, -0.10359708219766617, -0.8001183867454529, -0.3686141073703766, 0.021843109279870987, 0.6920166015625, 0.309103399515152, 0.5700552463531494, 0.7178257703781128, 0.5028066635131836, 0.18109293282032013, -0.2186986804008484, 0.1335335522890091, -0.19585365056991577, -0.08639304339885712, 0.06626392155885696, -0.42514657974243164, 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HuggingFaceM4/vqav2_task_finetuning	HuggingFaceM4	2023-11-23T16:31:14Z	741	0	null	[ "region:us" ]	2023-11-23T16:31:14Z	2023-11-23T16:11:33.000Z	2023-11-23T16:11:33	Invalid username or password.	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-0.3269882798194885, -0.8494763970375061, -0.5256158113479614, -0.28295063972473145, 0.622323751449585, 0.1574394404888153, 0.1579095870256424 ]	null	null	null	null	null	null	null	null	null	null	null	null	null
xiyuez/red-dot-design-award-product-description	xiyuez	2023-07-07T18:32:48Z	740	6	null	[ "task_categories:text-generation", "size_categories:10k<n<100K", "language:en", "license:odc-by", "region:us" ]	2023-07-07T18:32:48Z	2023-07-05T17:26:58.000Z	2023-07-05T17:26:58	--- license: odc-by task_categories: - text-generation language: - en pretty_name: Red Dot Design Award Dataset size_categories: - 10k<n<100K --- # Red Dot Design Award Dataset This dataset contains information about the products that have won the Red Dot Design Award, a prestigious international design competition. The data was extracted from the official website of the award: <https://www.red-dot.org/>. ## Task The task for this dataset is text generation, specifically product description generation. Given a product name and category, the goal is to generate a concise and informative description that highlights the features and benefits of the product. ## Limitations The dataset may have some limitations, such as: - The data may contain false or outdated information, as it reflects the information available on the website at the time of extraction. - The data only covers the products that have won the award, which may introduce some selection bias or limit the diversity of the data. - The data is only in English, although the website also has a German version that could be crawled in the future. - The data does not include any images of the products, which could be useful for multimodal language models. Images are planned to be scraped in the future. ## License This public extract is licensed under the Open Data Commons Attribution License: <http://opendatacommons.org/licenses/by/1.0/>. ## Data Format The dataset consists of 21183 unique rows, each containing the following columns: - `product`: The name of the product that won the award. - `category`: The category of the product, such as "Video Camera", "Bathroom Shelf", or "Mobile Home". - `description`: A short paragraph describing the product, its features, and its benefits. There is no predefined train/test split for this dataset. Near-duplicates have been removed. ## Data Quality The data quality may vary depending on the source and accuracy of the information on the website. We have not verified, filtered, or modified the data in any way. The data may contain content that is toxic, biased, copyrighted, or false. Use of this dataset is at your own risk. We do not provide any warranties or liability. ## Acknowledgements We would like to acknowledge the Red Dot Design Award for hosting and maintaining the website that provided the data for this dataset. We do not claim any ownership or affiliation with the award or the website.	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pharaouk/dharma-1	pharaouk	2023-09-14T23:50:58Z	740	20	null	[ "region:us" ]	2023-09-14T23:50:58Z	2023-08-18T19:34:12.000Z	2023-08-18T19:34:12	--- configs: - config_name: default data_files: - split: 'dharma_1_full' path: dharma_1_full* - split: 'dharma_1_mini' path: dharma_1_mini* - split: 'dharma_1_micro' path: dharma_1_micro* - split: 'dharma_1_unshuffled' path: dharma_eval_unshuffled* --- # "Dharma-1" A new carefully curated benchmark set, designed for a new era where the true end user uses LLM's for zero-shot and one-shot tasks, for a vast majority of the time. Stop training your models on mindless targets (eval_loss, train_loss), start training your LLM on lightweight Dharma as an eval target. A mix of all the top benchmarks. Formed to have an equal distribution of some of the most trusted benchmarks used by those developing SOTA LLMs, comprised of only 3,000 examples for the largest size, as well as 450 and 90 for Dharma-mini and Dharma-micro respectively. The current version of Dharma is comprised of a curated sampling of the following benchmarks: - AGIEval - Bigbench - MMLU - Winogrande - Arc-C - Arc- E - OBQA - TruthfulQA - Bool-q Each of these original benchmark datasets have their own subsections, careful work has gone into also choosing an equal distribution of the important subsections of each these, to have the best representation of the original benchmark creators goals. Dharma-1 is now integrated into Axolotl as well!, so you can focus on optimizing the other aspects of your training pipeline, model architecture and/or dataset, as opposed to worrying about what is the best evaluation measurement or optimization target that will best represent capabilities for the end user. Benchmarking for top base model will be listed here when completed and verified. Special thanks to @LDJnr for their contributions. Check out their Puffin dataset here: https://huggingface.co/LDJnr [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.2608586549758911, -0.49740222096443176, 0.30244842171669006, 0.09227284044027328, -0.619132399559021, -0.1984916776418686, 0.04819190129637718, -0.3075184226036072, 0.29093828797340393, 0.5620014667510986, -1.0055086612701416, -0.5254119038581848, -0.39619317650794983, -0.1587146520614624, -0.3348660469055176, 1.1514729261398315, 0.1063304916024208, 0.2159898281097412, -0.19787158071994781, -0.7136114835739136, -0.3490692377090454, -0.44228851795196533, -0.655640184879303, -0.21907244622707367, 0.8321682214736938, 0.12861163914203644, 0.36340513825416565, 0.7225781083106995, 0.5561084747314453, 0.12144169211387634, -0.007244758307933807, 0.09934167563915253, -0.7745264172554016, 0.031439535319805145, -0.02149355039000511, -0.4483802020549774, -0.7233376502990723, 0.2073654681444168, 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qanta	null	2023-04-05T13:37:09Z	738	3	quizbowl	[ "task_categories:question-answering", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:unknown", "quizbowl", "arxiv:1904.04792", "region:us" ]	2023-04-05T13:37:09Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - machine-generated language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: Quizbowl size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: quizbowl tags: - quizbowl dataset_info: features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 config_name: mode=first,char_skip=25 splits: - name: adversarial num_bytes: 1258844 num_examples: 1145 - name: buzzdev num_bytes: 1553636 num_examples: 1161 - name: buzztest num_bytes: 2653425 num_examples: 1953 - name: buzztrain num_bytes: 19699736 num_examples: 16706 - name: guessdev num_bytes: 1414882 num_examples: 1055 - name: guesstest num_bytes: 2997123 num_examples: 2151 - name: guesstrain num_bytes: 117599750 num_examples: 96221 download_size: 170754918 dataset_size: 147177396 --- # Dataset Card for "qanta" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://www.qanta.org/](http://www.qanta.org/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) - Point of Contact: [Jordan Boyd-Graber](mailto:jbg@umiacs.umd.edu) - Size of downloaded dataset files: 170.75 MB - Size of the generated dataset: 147.18 MB - Total amount of disk used: 317.93 MB ### Dataset Summary The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### mode=first,char_skip=25 - Size of downloaded dataset files: 170.75 MB - Size of the generated dataset: 147.18 MB - Total amount of disk used: 317.93 MB An example of 'guessdev' looks as follows. ``` This example was too long and was cropped: { "answer": "Apollo_program", "category": "History", "char_idx": -1, "dataset": "quizdb.org", "difficulty": "easy_college", "first_sentence": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "fold": "guessdev", "full_question": "\"As part of this program, William Anders took a photo that Galen Rowell called \\\"the most influential environmental photograph e...", "gameplay": false, "id": "127028-first", "page": "Apollo_program", "proto_id": "", "qanta_id": 127028, "qdb_id": 126689, "raw_answer": "Apollo program [or Project Apollo; accept Apollo 8; accept Apollo 1; accept Apollo 11; prompt on landing on the moon]", "sentence_idx": -1, "subcategory": "American", "text": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "tokenizations": [[0, 137], [138, 281], [282, 412], [413, 592], [593, 675]], "tournament": "ACF Fall", "year": 2016 } ``` ### Data Fields The data fields are the same among all splits. #### mode=first,char_skip=25 - `id`: a `string` feature. - `qanta_id`: a `int32` feature. - `proto_id`: a `string` feature. - `qdb_id`: a `int32` feature. - `dataset`: a `string` feature. - `text`: a `string` feature. - `full_question`: a `string` feature. - `first_sentence`: a `string` feature. - `char_idx`: a `int32` feature. - `sentence_idx`: a `int32` feature. - `tokenizations`: a dictionary feature containing: - `feature`: a `int32` feature. - `answer`: a `string` feature. - `page`: a `string` feature. - `raw_answer`: a `string` feature. - `fold`: a `string` feature. - `gameplay`: a `bool` feature. - `category`: a `string` feature. - `subcategory`: a `string` feature. - `tournament`: a `string` feature. - `difficulty`: a `string` feature. - `year`: a `int32` feature. ### Data Splits \| name \|adversarial\|buzzdev\|buzztrain\|guessdev\|guesstrain\|buzztest\|guesstest\| \|-----------------------\|----------:\|------:\|--------:\|-------:\|---------:\|-------:\|--------:\| \|mode=first,char_skip=25\| 1145\| 1161\| 16706\| 1055\| 96221\| 1953\| 2151\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Rodriguez2019QuizbowlTC, title={Quizbowl: The Case for Incremental Question Answering}, author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber}, journal={ArXiv}, year={2019}, volume={abs/1904.04792} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.	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bavard/personachat_truecased	bavard	2021-04-23T13:28:30Z	738	26	null	[ "region:us" ]	2021-04-23T13:28:30Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	# A More Natural PersonaChat ## Dataset Summary This dataset is a true-cased version of the PersonaChat dataset by Zhang et al. (2018). The original PersonaChat dataset is all lower case, and has extra space around each clause/sentence separating punctuation mark. This version of the dataset has more of a natural language look, with sentence capitalization, proper noun capitalization, and normalized whitespace. Also, each dialogue turn includes a pool of distractor candidate responses, which can be used by a multiple choice regularization loss during training. As an example, here is an utterance from the original PersonaChat dataset: ``` "i really like celine dion . what about you ?" ``` In this dataset, that example is: ``` "I really like Celine Dion. What about you?" ``` ## Languages The text in the dataset is in English (en). ## Data Fields Each instance of the dataset represents a conversational utterance that a crowdworker made, while pretending to have a certain personality. Each instance has these fields: \| Field Name \| Datatype \| Description \| \|---------------\|----------------\|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| `conv_id` \| int \| A unique identifier for the instance's conversation. \| \| `utterance_idx` \| int \| The index of the instance in the conversation. \| \| `personality` \| list of string \| Sentences describing the personality of the current speaker. \| \| `history` \| list of string \| The conversation's utterances so far, alternating between speakers with one utterance per speaker. \| \| `candidates` \| list of string \| A list of utterances including distractor utterances as well as the true utterance the speaker gave, given their personality and the conversation history thus far. The true utterance is always the last utterance in this list. \| ## Dataset Curation The dataset was sourced from HuggingFace's version of the dataset used in the code for their ConvAI 2018 submission, which was described in their [blog article](https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313) on that submission. This version of the dataset has had extra white spaces removed, and a StanfordNLP [stanza](https://stanfordnlp.github.io/stanza/) NLP pipeline was used to conduct part-of-speech tagging to identify proper nouns, which were then capitalized. The pipeline was also used to conduct sentence segmentation, allowing the beginning of sentences to then be capitalized. Finally, all instances of the pronoun "I" were capitalized, along with its contractions. ## Citation Information For the PersonaChat dataset, please cite: ``` @article{zhang2018personalizing, title={Personalizing dialogue agents: I have a dog, do you have pets too?}, author={Zhang, Saizheng and Dinan, Emily and Urbanek, Jack and Szlam, Arthur and Kiela, Douwe and Weston, Jason}, journal={arXiv preprint arXiv:1801.07243}, year={2018} } ```	[ -0.5385639071464539, -0.5317596197128296, 0.3846190571784973, 0.3691977858543396, -0.11882920563220978, 0.15314121544361115, -0.34805741906166077, -0.38058215379714966, 0.4415634274482727, 0.6358447670936584, -0.802573025226593, -0.6733909845352173, -0.49315494298934937, 0.12445686757564545, 0.04658679664134979, 1.1780484914779663, 0.3541945517063141, -0.07444053888320923, -0.29685401916503906, -0.40339791774749756, -0.7006638646125793, -0.7628790736198425, -0.8496547937393188, -0.24151773750782013, 0.3498484790325165, 0.6822406053543091, 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koutch/staqc	koutch	2023-03-27T14:53:22Z	738	3	null	[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "code", "arxiv:1803.09371", "region:us" ]	2023-03-27T14:53:22Z	2023-03-27T11:43:26.000Z	2023-03-27T11:43:26	--- dataset_info: - config_name: mca_python features: - name: id dtype: int32 - name: question_id dtype: int32 - name: question dtype: string - name: snippet sequence: string splits: - name: train num_bytes: 23286786 num_examples: 40391 download_size: 72054260 dataset_size: 23286786 - config_name: mca_sql features: - name: id dtype: int32 - name: question_id dtype: int32 - name: question dtype: string - name: snippet sequence: string splits: - name: train num_bytes: 15164206 num_examples: 26052 download_size: 50304531 dataset_size: 15164206 - config_name: sca_python features: - name: id dtype: int32 - name: question_id dtype: int32 - name: question dtype: string - name: snippet dtype: string splits: - name: train num_bytes: 39678168 num_examples: 85294 download_size: 47378850 dataset_size: 39678168 - config_name: sca_sql features: - name: id dtype: int32 - name: question_id dtype: int32 - name: question dtype: string - name: snippet dtype: string splits: - name: train num_bytes: 28656467 num_examples: 75637 download_size: 34194025 dataset_size: 28656467 - config_name: man_python features: - name: id dtype: int32 - name: question_id dtype: int32 - name: question dtype: string - name: snippet sequence: - name: text dtype: string - name: is_sda dtype: bool splits: - name: train num_bytes: 1445103 num_examples: 2052 download_size: 71250225 dataset_size: 1445103 - config_name: man_sql features: - name: id dtype: int32 - name: question_id dtype: int32 - name: question dtype: string - name: snippet sequence: - name: text dtype: string - name: is_sda dtype: bool splits: - name: train num_bytes: 1123721 num_examples: 1587 download_size: 49745860 dataset_size: 1123721 license: cc-by-4.0 task_categories: - question-answering language: - en tags: - code pretty_name: staqc size_categories: - 10K<n<100K --- # Dataset Card for StaQC (A Systematically Mined Question-Code Dataset from Stack Overflow) ## Dataset Description - Homepage: [GitHub](https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset) - Paper: [StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow](https://arxiv.org/abs/1803.09371) ### Dataset Summary StaQC (Stack Overflow Question-Code pairs) is a large dataset of around 148K Python and 120K SQL domain question-code pairs, which are automatically mined from Stack Overflow using a Bi-View Hierarchical Neural Network. StaQC is collected from three sources: multi-code answer posts, single-code answer posts, and manual annotations on multi-code answer posts. The dataset was originally released by the main authors on [GitHub](https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset). This version is a non-modified redistributed copy (under the [license](#licensing-information) permission) made available on the hub for easier access. #### Standalone solutions As noted in the paper, the authors define a code snippet as a code solution when the questioner can solve the problem solely based on it (also named as “standalone” solution). #### Manual annotations The manual annotations are the collection of multi-code answer posts for which each code snippet was annotated with a boolean indicating whether or not the snippet is a standalone solution to the question. #### Multi-code answer posts A Multi-code answer post is an (accepted) answer post that contains multiple code snippets, some of which may not be a standalone code solution to the question (see Section 1 in [paper](http://web.cse.ohio-state.edu/~sun.397/docs/StaQC-www18.pdf)). For example, in [this multi-code answer post](https://stackoverflow.com/a/5996949), the third code snippet is not a code solution to the question "How to limit a number to be within a specified range? (Python)". Note: the multi-code answer posts contain also the manual annotations. #### Single-code answer posts A Single-code answer post is an (accepted) answer post that contains only one code snippet. We pair such code snippets with the question title as a question-code pair. ### Supported Tasks and Leaderboards This dataset can be used for Natural Language to Code Generation tasks. ### Languages Python, SQL, English ## Dataset Structure ### Data Instances Each configuration correspond to one of the three parts, in a given programming language. There are three parts for the dataset: - mca (Multi-code answer posts) - sca (Single-code answer posts) - man (Manual annotations) And two programming/query languages: - python - sql One can obtain obtain a configuration as a combination of a part in a programing language. For instance, one can obtain the automatically mined multi-code answers in python using: ```python dataset = load_dataset("koutch/staqc", 'mca_python') DatasetDict({ train: Dataset({ features: ['id', 'question_id', 'question', 'snippet'], num_rows: 40391 }) }) ``` or the manual annotations using: ```python dataset = load_dataset("koutch/staqc", 'man_sql') DatasetDict({ train: Dataset({ features: ['id', 'question_id', 'question', 'snippet'], num_rows: 1587 }) }) ``` #### Manual annotations The manual annotations contain, for a given stackoverflow questions, for each individual code block in the accepted answer of that post, information on whether or not the given code block is a standalone solution to the question asked (the question title). ``` { 'question_id': 5947137, 'question': 'How can I use a list comprehension to extend a list in python?', 'snippet': {'text': ['import itertools as it\n\nreturn sum(it.imap(doSomething, originalList), [])\n', 'return sum(map(doSomething, originalList), [])\n', 'return sum((doSomething(x) for x in originalList), [])\n', 'accumulationList = []\nfor x in originalList:\n accumulationList.extend(doSomething(x))\nreturn accumulationList\n'], 'is_sda': [True, True, True, True]} } ``` #### Multi-code answer posts ``` { 'question_id': 35349290, 'question': 'Python: Generating YYMM string between two dates', 'snippet': ['start_year = 2005\nend_year = 2007\nstart_month = 3\nend_month = 2\nyymm = [(yy, mm) for yy in range(start_year, end_year + 1) for mm in range(1, 13)\n if (start_year, start_month) <= (yy, mm) <= (end_year, end_month)]\n', "formatted_yymm = ['{:>02}{:>02}.mat'.format(yy % 100, mm) for yy, mm in yymm]\n"] } ``` #### Single-code answer posts ``` { 'question_id': 19387200, 'question': 'Python: get OS language', 'snippet': "import locale\nloc = locale.getlocale() # get current locale\nlocale.getdefaultlocale() # Tries to determine the default locale settings and returns them as a tuple of the form (language code, encoding); e.g, ('en_US', 'UTF-8').\n" } ``` ### Data Fields - `question_id`: id of the stackoverflow question - `question`: title of the stackoverflow question repurposed as the natural language intent - `snippet`: mined or annotated standalone solution(s) (potentially) answerring the question - `is_sda`: for the manual annotations, whether or not the given code snippet is a standalone solution to the question. ### Data Splits Each configuration of the dataset contains only a training split. ## Dataset Creation ### Source Data StackOverflow data dump. ### Annotations See section 2.3 "Annotating QC Pairs for Model Training" of the [paper](https://arxiv.org/abs/1803.09371) ## Additional Information ### Licensing Information <a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. ### Citation Information If you use the dataset or the code in your research, please cite the following paper: ``` @inproceedings{yao2018staqc, title={StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow}, author={Yao, Ziyu and Weld, Daniel S and Chen, Wei-Peng and Sun, Huan}, booktitle={Proceedings of the 2018 World Wide Web Conference on World Wide Web}, pages={1693--1703}, year={2018}, organization={International World Wide Web Conferences Steering Committee} } ``` ### Contributions information I did not contribute to the creation of this dataset, only to the redistribution. All credits should be attributed to the original authors.	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movie_rationales	null	2023-04-05T10:09:59Z	737	3	null	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]	2023-04-05T10:09:59Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: MovieRationales dataset_info: features: - name: review dtype: string - name: label dtype: class_label: names: '0': NEG '1': POS - name: evidences sequence: string splits: - name: test num_bytes: 1046377 num_examples: 199 - name: train num_bytes: 6853624 num_examples: 1600 - name: validation num_bytes: 830417 num_examples: 200 download_size: 3899487 dataset_size: 8730418 --- # Dataset Card for "movie_rationales" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: https://github.com/jayded/eraserbenchmark - Paper: [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://aclanthology.org/2020.acl-main.408/) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 3.90 MB - Size of the generated dataset: 8.73 MB - Total amount of disk used: 12.62 MB ### Dataset Summary The movie rationale dataset contains human annotated rationales for movie reviews. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 3.90 MB - Size of the generated dataset: 8.73 MB - Total amount of disk used: 12.62 MB An example of 'validation' looks as follows. ``` { "evidences": ["Fun movie"], "label": 1, "review": "Fun movie\n" } ``` ### Data Fields The data fields are the same among all splits. #### default - `review`: a `string` feature. - `label`: a classification label, with possible values including `NEG` (0), `POS` (1). - `evidences`: a `list` of `string` features. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\| 1600\| 200\| 199\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{deyoung-etal-2020-eraser, title = "{ERASER}: {A} Benchmark to Evaluate Rationalized {NLP} Models", author = "DeYoung, Jay and Jain, Sarthak and Rajani, Nazneen Fatema and Lehman, Eric and Xiong, Caiming and Socher, Richard and Wallace, Byron C.", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.acl-main.408", doi = "10.18653/v1/2020.acl-main.408", pages = "4443--4458", } @InProceedings{zaidan-eisner-piatko-2008:nips, author = {Omar F. Zaidan and Jason Eisner and Christine Piatko}, title = {Machine Learning with Annotator Rationales to Reduce Annotation Cost}, booktitle = {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning}, month = {December}, year = {2008} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.	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poleval2019_mt	null	2022-11-18T21:39:08Z	737	0	null	[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:expert-generated", "language_creators:found", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "language:pl", "language:ru", "license:unknown", "region:us" ]	2022-11-18T21:39:08Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - no-annotation language_creators: - expert-generated - found language: - en - pl - ru license: - unknown multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: Poleval2019Mt dataset_info: - config_name: ru-pl features: - name: translation dtype: translation: languages: - ru - pl splits: - name: train num_bytes: 2818015 num_examples: 20001 - name: validation num_bytes: 415735 num_examples: 3001 - name: test num_bytes: 266462 num_examples: 2969 download_size: 3355801 dataset_size: 3500212 - config_name: en-pl features: - name: translation dtype: translation: languages: - en - pl splits: - name: train num_bytes: 13217798 num_examples: 129255 - name: validation num_bytes: 1209168 num_examples: 10001 - name: test num_bytes: 562482 num_examples: 9845 download_size: 13851405 dataset_size: 14989448 - config_name: pl-ru features: - name: translation dtype: translation: languages: - pl - ru splits: - name: train num_bytes: 2818015 num_examples: 20001 - name: validation num_bytes: 415735 num_examples: 3001 - name: test num_bytes: 149423 num_examples: 2967 download_size: 3355801 dataset_size: 3383173 - config_name: pl-en features: - name: translation dtype: translation: languages: - pl - en splits: - name: train num_bytes: 13217798 num_examples: 129255 - name: validation num_bytes: 1209168 num_examples: 10001 - name: test num_bytes: 16 num_examples: 1 download_size: 13591306 dataset_size: 14426982 --- # Dataset Card for poleval2019_mt ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: PolEval-2019 competition. http://2019.poleval.pl/ - Repository: Links available [in this page](http://2019.poleval.pl/index.php/tasks/task4) - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures. One of the tasks in PolEval-2019 was Machine Translation (Task-4). The task is to train as good as possible machine translation system, using any technology,with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions). Here, Polish-English is also made available to allow for training in both directions. However, the test data is ONLY available for English-Polish ### Supported Tasks and Leaderboards Supports Machine Translation between Russian to Polish and English to Polish (and vice versa). ### Languages - Polish (pl) - Russian (ru) - English (en) ## Dataset Structure ### Data Instances As the training data set, a set of bi-lingual corpora aligned at the sentence level has been prepared. The corpora are saved in UTF-8 encoding as plain text, one language per file. ### Data Fields One example of the translation is as below: ``` { 'translation': {'ru': 'не содержала в себе моделей. Модели это сравнительно новое явление. ', 'pl': 'nie miała w sobie modeli. Modele to względnie nowa dziedzina. Tak więc, jeśli '} } ``` ### Data Splits The dataset is divided into two splits. All the headlines are scraped from news websites on the internet. \| \| train \| validation \| test \| \|-------\|-------:\|-----------:\|-----:\| \| ru-pl \| 20001 \| 3001 \| 2969 \| \| pl-ru \| 20001 \| 3001 \| 2969 \| \| en-pl \| 129255 \| 1000 \| 9845 \| ## Dataset Creation ### Curation Rationale This data was curated as a task for the PolEval-2019. The task is to train as good as possible machine translation system, using any technology, with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions). PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted tools compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures. PolEval 2019-related papers were presented at AI & NLP Workshop Day (Warsaw, May 31, 2019). The links for the top performing models on various tasks (including the Task-4: Machine Translation) is present in [this](http://2019.poleval.pl/index.php/publication) link ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The organization details of PolEval is present in this [link](http://2019.poleval.pl/index.php/organizers) ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @proceedings{ogr:kob:19:poleval, editor = {Maciej Ogrodniczuk and Łukasz Kobyliński}, title = {{Proceedings of the PolEval 2019 Workshop}}, year = {2019}, address = {Warsaw, Poland}, publisher = {Institute of Computer Science, Polish Academy of Sciences}, url = {http://2019.poleval.pl/files/poleval2019.pdf}, isbn = "978-83-63159-28-3"} } ``` ### Contributions Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset.	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arampacha/rsicd	arampacha	2022-04-11T15:34:07Z	737	4	null	[ "region:us" ]	2022-04-11T15:34:07Z	2022-04-11T15:31:49.000Z	2022-04-11T15:31:49	Entry not found	[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -0.3992327153682709, 0.5791246294975281, -0.14494499564170837, -0.10751135647296906, 0.28233814239501953, -0.2768957316875458, -0.5409227013587952, -0.3685525059700012, -1.1902779340744019, 0.061491698026657104, 0.5316582918167114, 0.743514358997345, 0.7584061622619629, 0.3652168810367584, 0.643257737159729, 0.39322906732559204, -0.231389120221138, 0.48270541429519653, -0.04171793907880783, 0.0026038705836981535, -0.3524438440799713, -0.5516899824142456, -0.28596577048301697, 0.07584768533706665, 1.096130132675171, 0.9666873812675476, 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laion/laion-coco	laion	2022-10-23T18:55:09Z	737	45	null	[ "license:cc-by-4.0", "region:us" ]	2022-10-23T18:55:09Z	2022-09-30T20:29:42.000Z	2022-09-30T20:29:42	--- license: cc-by-4.0 --- # LAION COCO: 600M SYNTHETIC CAPTIONS FROM LAION2B-EN by: Christoph Schuhmann, Andreas Köpf, Richard Vencu, Theo Coombes, Romain Beaumont, 10 Oct, 2022 Author: Christoph Schuhmann, Andreas Köpf , Theo Coombes, Richard Vencu, Benjamin Trom , Romain Beaumont We present LAION-COCO, the world’s largest dataset of 600M generated high-quality captions for publicly available web-images Laion5B has five billion natural captions. They provide a lot of information, but could synthetic captions complement them? To answer this question, we use a combination of existing, publicly available models to produce high quality captions for images in the style of MS COCO. We captioned 600M images from the english subset of Laion-5B with an ensemble of BLIP L/14 and 2 CLIP versions (L/14 and RN50x64). This will make it possible to investigate the value of generated captions to train models. We’re curious on how these synthetic captions could impact models trained on them! The 600M samples are provided in parquet files. Columns include the original caption, the url, the top caption and a list of alternative captions with lower CLIP-similarity scores. ## Method The method we used to generate these captions was to: - We use Blip L/14 to generate 40 captions - Rank them using openai Clip Open AI L/14 ; selected the best 5 captions - Rank using Open AI RN50x64 Clip model to select the best one - Use a small, fine-tuned T0 model to roughly repair grammar and punctuation of the texts The hyperparameters were chosen through a grid search (settings) by Andreas Köpf to best match the style ( ROUGE scores ) of MS COCO texts. ## Evaluation We evaluated these generated captions by asking human evaluators to guess whether a caption is coming from a human or an AI model. We also asked them to rate the quality on a scale from 0(bad) to 5 (good). In a first round we presented the evaluators each 200 samples, that contained 100 AI generated and 100 human written MS COCO captions. ## Observations Mean rating & standard deviation of samples, that were written by a human: Mean: 3.98 Stdev: 0.99 Mean rating & standard deviation of samples, that were written by an AI Mean: 3.89 Stdev: 1.12 Mean rating & standard deviation of samples, where the annotator believed they were written by a human: Mean: 4.44 Stdev: 0.61 Mean rating & standard deviation of samples, where the annotator believed they were generated by an AI Mean: 3.50 Stdev: 1.15 ## Interpretation It is very interesting that the mean scores of the samples generated by humans and generated by the model are very similar. We also notice that the standard deviation of the generated captions is a little bit higher. We hypothesize that most in most cases the quality of the generated captions is perceived as as good as the quality of the human written captions. But sometimes the captioning model obviously fails and the quality of the results is pretty low because the model doesn't relevant understand concepts about what is going on in the picture, because it's knowledge is not grounded in a sufficiently sophisticated world model. ## Failure cases “Two people posing for the camera in their wedding attire, one with an umbrella over his head and another with long red hair.” “An older man having a heart attack, with his hand on the chest.” When we remove all samples from the evaluations that have ratings of either 0 or 1, we Observe that the mean ratings and standard deviations move closer together. Scores without ratings of 0 and 1 Mean rating & standard deviation of samples, that were written by a human: Mean: 4.07 Stdev: 0.81 Mean rating & standard deviation of samples, that were written by an AI Mean: 4.02 Stdev: 0.94 The mean ratings of the generated captions are still a little bit lower and the standard deviation is still a little bit higher, but the trend is pretty clear. By removing samples with rating 2, the gap between the qualities would probably decrease even further. Presentation only generated captions: In a next step, we presented the human evaluators 400 captions that were only generated by the model (no human written captions in between): Mean rating of all samples 3.81 Standard deviation of all samples 0.94 % rated as human 47.5 % rated as AI 52.5 We observe that the human evaluators thought in 47.5% of all cases, that the captions were written by a human. This makes us confident that our captains are on average pretty good. When we told the evaluators later that all captions were generated by the model they told us that it was very hard for them to judge whether a caption was written by a model or a human, and that it only was easy for them in obvious failure cases. ## Conclusions We conclude that Our ensemble of BLIP and CLIP is already pretty good and capable of generating captions with a quality that is on average pretty close to the human written captions of MS Coco. It would be very interesting for future work to let people rate our generated captions at larger scale and then filter out the samples with low rating values. These results could be used to train models to rate the quality of captions and to predict whether a caption looks like a generated or a human written caption. And even without further automated filtering, an ensemble of our captions and human evaluators would be a pretty good workflow to curate high quality captions at much lower costs than if we would ask humans to write them from scratch. ## Credit assignments - Christoph Schuhmann lead the project, implemented a first version of the code, ran most of the generations & conducted the human evaluations - Andreas Köpf conducted the hyperparameter search & wrote the code to execute BLIP + CLIP filtering at scale - Theo Coombes managed the server that coordinated which GPU worker got which part of LAION to work on - Romain Beaumont packaged the .json into parquet files, sent to HF and wrote the first draft of this post - Richard Vencu provided the infra structure to use the idle compute for this project - Benjamin Trom wrote code that help us to convert the .json files to parquet We thank stability.ai for providing the compute used to generate the captions in the dataset.	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laion/wuerstchen-dataset	laion	2023-11-22T20:06:25Z	737	2	null	[ "license:cc0-1.0", "region:us" ]	2023-11-22T20:06:25Z	2023-10-12T19:26:49.000Z	2023-10-12T19:26:49	--- license: cc0-1.0 dataset_info: features: - name: caption dtype: string - name: image dtype: image - name: link dtype: string - name: message_id dtype: string - name: timestamp dtype: string configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.04782553389668465, -0.38947567343711853, 0.8470754623413086, -0.09598255157470703, 0.024005301296710968, 0.04711989313364029, -0.1431780606508255, -0.6121035814285278, -0.04771772027015686, -1.052453637123108, -0.06787454336881638, 0.30022722482681274, 0.5120980143547058, 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EMBO/biolang	EMBO	2023-01-11T15:31:53Z	733	0	null	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n>1M", "language:en", "license:cc-by-4.0", "region:us" ]	2023-01-11T15:31:53Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - machine-generated language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - n>1M source_datasets: [] task_categories: - text-generation task_ids: - language-modeling --- # Dataset Card for BioLang ## Table of Contents - [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://sourcedata.embo.org - Repository: https://github.com/source-data/soda-roberta - Paper: - Leaderboard: - Point of Contact: thomas.lemberger@embo.org - Download Size: 5_299_878_661 ### Dataset Summary BioLang is a dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology. The dataset can be used for random masked language modeling or for language modeling using only specific part-of-speech maksing. More details on generation and use of the dataset at https://github.com/source-data/soda-roberta . ### Supported Tasks and Leaderboards - `MLM`: masked language modeling - `DET`: part-of-speach masked language model, with determinants (`DET`) tagged - `SMALL`: part-of-speech masked language model, with "small" words (`DET`, `CCONJ`, `SCONJ`, `ADP`, `PRON`) tagged - `VERB`: part-of-speach masked language model, with verbs (`VERB`) tagged ### Languages English ## Dataset Structure ### Data Instances ```json { "input_ids":[ 0, 2444, 6997, 46162, 7744, 35, 20632, 20862, 3457, 36, 500, 23858, 29, 43, 32, 3919, 716, 15, 49, 4476, 4, 1398, 6, 52, 1118, 5, 20862, 819, 9, 430, 23305, 248, 23858, 29, 4, 256, 40086, 104, 35, 1927, 1069, 459, 1484, 58, 4776, 13, 23305, 634, 16706, 493, 2529, 8954, 14475, 73, 34263, 6, 4213, 718, 833, 12, 24291, 4473, 22500, 14475, 73, 510, 705, 73, 34263, 6, 5143, 4313, 2529, 8954, 14475, 73, 34263, 6, 8, 5143, 4313, 2529, 8954, 14475, 248, 23858, 29, 23, 4448, 225, 4722, 2392, 11, 9341, 261, 4, 49043, 35, 96, 746, 6, 5962, 9, 38415, 4776, 408, 36, 3897, 4, 398, 8871, 56, 23305, 4, 20, 15608, 21, 8061, 6164, 207, 13, 70, 248, 23858, 29, 6, 150, 5, 42561, 21, 8061, 5663, 207, 13, 80, 3457, 4, 509, 1296, 5129, 21567, 3457, 36, 398, 23528, 8748, 22065, 11654, 35, 7253, 15, 49, 4476, 6, 70, 3457, 4682, 65, 189, 28, 5131, 13, 23305, 9726, 4, 2 ], "label_ids": [ "X", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "ADJ", "ADJ", "NOUN", "PUNCT", "PROPN", "PROPN", "PROPN", "PUNCT", "AUX", "VERB", "VERB", "ADP", "DET", "NOUN", "PUNCT", "ADV", "PUNCT", "PRON", "VERB", "DET", "ADJ", "NOUN", "ADP", "ADJ", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "ADJ", "ADJ", "ADJ", "PUNCT", "NOUN", "NOUN", "NOUN", "NOUN", "AUX", "VERB", "ADP", "NOUN", "VERB", "PROPN", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "CCONJ", "ADJ", "PROPN", "PROPN", "PROPN", "PROPN", "NOUN", "NOUN", "NOUN", "ADP", "PROPN", "PROPN", "PROPN", "PROPN", "ADP", "PROPN", "PROPN", "PUNCT", "PROPN", "PUNCT", "ADP", "NOUN", "PUNCT", "NUM", "ADP", "NUM", "VERB", "NOUN", "PUNCT", "NUM", "NUM", "NUM", "NOUN", "AUX", "NOUN", "PUNCT", "DET", "NOUN", "AUX", "X", "NUM", "NOUN", "ADP", "DET", "NOUN", "NOUN", "NOUN", "PUNCT", "SCONJ", "DET", "NOUN", "AUX", "X", "NUM", "NOUN", "ADP", "NUM", "NOUN", "PUNCT", "NUM", "NOUN", "VERB", "ADJ", "NOUN", "PUNCT", "NUM", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "VERB", "ADP", "DET", "NOUN", "PUNCT", "DET", "NOUN", "SCONJ", "PRON", "VERB", "AUX", "VERB", "ADP", "NOUN", "NOUN", "PUNCT", "X" ], "special_tokens_mask": [ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 ] } ``` ### Data Fields `MLM`: - `input_ids`: a `list` of `int32` features. - `special_tokens_mask`: a `list` of `int8` features. `DET`, `VERB`, `SMALL`: - `input_ids`: a `list` of `int32` features. - `tag_mask`: a `list` of `int8` features. ### Data Splits - `train`: - features: ['input_ids', 'special_tokens_mask'], - num_rows: 12_005_390 - `test`: - features: ['input_ids', 'special_tokens_mask'], - num_rows: 37_112 - `validation`: - features: ['input_ids', 'special_tokens_mask'], - num_rows: 36_713 ## Dataset Creation ### Curation Rationale The dataset was assembled to train language models in the field of cell and molecular biology. To expand the size of the dataset and to include many examples with highly technical language, abstracts were complemented with figure legends (or figure 'captions'). ### Source Data #### Initial Data Collection and Normalization The xml content of papers were downloaded in January 2021 from the open access section of [EuropePMC]("https://europepmc.org/downloads/openaccess"). Figure legends and abstracts were extracted from the JATS XML, tokenized with the `roberta-base` tokenizer and part-of-speech tagged with Spacy's `en_core_web_sm` model (https://spacy.io). More details at https://github.com/source-data/soda-roberta #### Who are the source language producers? Experts scientists. ### Annotations #### Annotation process Part-of-speech was tagged automatically. #### Who are the annotators? Spacy's `en_core_web_sm` model (https://spacy.io) was used for part-of-speech tagging. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Thomas Lemberger ### Licensing Information CC-BY 4.0 ### Citation Information [More Information Needed] ### Contributions Thanks to [@tlemberger](https://github.com/tlemberger) for adding this dataset.	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sharc_modified	null	2022-11-03T16:31:23Z	730	0	null	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended\|sharc", "language:en", "license:unknown", "conversational-qa", "arxiv:1909.03759", "arxiv:2009.06354", "region:us" ]	2022-11-03T16:31:23Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - crowdsourced - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|sharc task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: null pretty_name: SharcModified tags: - conversational-qa dataset_info: - config_name: mod features: - name: id dtype: string - name: utterance_id dtype: string - name: source_url dtype: string - name: snippet dtype: string - name: question dtype: string - name: scenario dtype: string - name: history list: - name: follow_up_question dtype: string - name: follow_up_answer dtype: string - name: evidence list: - name: follow_up_question dtype: string - name: follow_up_answer dtype: string - name: answer dtype: string splits: - name: train num_bytes: 15138034 num_examples: 21890 - name: validation num_bytes: 1474239 num_examples: 2270 download_size: 21197271 dataset_size: 16612273 - config_name: mod_dev_multi features: - name: id dtype: string - name: utterance_id dtype: string - name: source_url dtype: string - name: snippet dtype: string - name: question dtype: string - name: scenario dtype: string - name: history list: - name: follow_up_question dtype: string - name: follow_up_answer dtype: string - name: evidence list: - name: follow_up_question dtype: string - name: follow_up_answer dtype: string - name: answer dtype: string - name: all_answers sequence: string splits: - name: validation num_bytes: 1553940 num_examples: 2270 download_size: 2006124 dataset_size: 1553940 - config_name: history features: - name: id dtype: string - name: utterance_id dtype: string - name: source_url dtype: string - name: snippet dtype: string - name: question dtype: string - name: scenario dtype: string - name: history list: - name: follow_up_question dtype: string - name: follow_up_answer dtype: string - name: evidence list: - name: follow_up_question dtype: string - name: follow_up_answer dtype: string - name: answer dtype: string splits: - name: train num_bytes: 15083103 num_examples: 21890 - name: validation num_bytes: 1468604 num_examples: 2270 download_size: 21136658 dataset_size: 16551707 - config_name: history_dev_multi features: - name: id dtype: string - name: utterance_id dtype: string - name: source_url dtype: string - name: snippet dtype: string - name: question dtype: string - name: scenario dtype: string - name: history list: - name: follow_up_question dtype: string - name: follow_up_answer dtype: string - name: evidence list: - name: follow_up_question dtype: string - name: follow_up_answer dtype: string - name: answer dtype: string - name: all_answers sequence: string splits: - name: validation num_bytes: 1548305 num_examples: 2270 download_size: 2000489 dataset_size: 1548305 --- # Dataset Card for SharcModified ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [More info needed] - Repository: [github](https://github.com/nikhilweee/neural-conv-qa) - Paper: [Neural Conversational QA: Learning to Reason v.s. Exploiting Patterns](https://arxiv.org/abs/1909.03759) - Leaderboard: [More info needed] - Point of Contact: [More info needed] ### Dataset Summary ShARC, a conversational QA task, requires a system to answer user questions based on rules expressed in natural language text. However, it is found that in the ShARC dataset there are multiple spurious patterns that could be exploited by neural models. SharcModified is a new dataset which reduces the patterns identified in the original dataset. To reduce the sensitivity of neural models, for each occurence of an instance conforming to any of the patterns, we automatically construct alternatives where we choose to either replace the current instance with an alternative instance which does not exhibit the pattern; or retain the original instance. The modified ShARC has two versions sharc-mod and history-shuffled. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is in english (en). ## Dataset Structure ### Data Instances Example of one instance: ``` { "annotation": { "answer": [ { "paragraph_reference": { "end": 64, "start": 35, "string": "syndactyly affecting the feet" }, "sentence_reference": { "bridge": false, "end": 64, "start": 35, "string": "syndactyly affecting the feet" } } ], "explanation_type": "single_sentence", "referential_equalities": [ { "question_reference": { "end": 40, "start": 29, "string": "webbed toes" }, "sentence_reference": { "bridge": false, "end": 11, "start": 0, "string": "Webbed toes" } } ], "selected_sentence": { "end": 67, "start": 0, "string": "Webbed toes is the common name for syndactyly affecting the feet . " } }, "example_id": 9174646170831578919, "original_nq_answers": [ { "end": 45, "start": 35, "string": "syndactyly" } ], "paragraph_text": "Webbed toes is the common name for syndactyly affecting the feet . It is characterised by the fusion of two or more digits of the feet . This is normal in many birds , such as ducks ; amphibians , such as frogs ; and mammals , such as kangaroos . In humans it is considered unusual , occurring in approximately one in 2,000 to 2,500 live births .", "question": "what is the medical term for webbed toes", "sentence_starts": [ 0, 67, 137, 247 ], "title_text": "Webbed toes", "url": "https: //en.wikipedia.org//w/index.php?title=Webbed_toes&oldid=801229780" } ``` ### Data Fields - `example_id`: a unique integer identifier that matches up with NQ - `title_text`: the title of the wikipedia page containing the paragraph - `url`: the url of the wikipedia page containing the paragraph - `question`: a natural language question string from NQ - `paragraph_text`: a paragraph string from a wikipedia page containing the answer to question - `sentence_starts`: a list of integer character offsets indicating the start of sentences in the paragraph - `original_nq_answers`: the original short answer spans from NQ - `annotation`: the QED annotation, a dictionary with the following items and further elaborated upon below: - `referential_equalities`: a list of dictionaries, one for each referential equality link annotated - `answer`: a list of dictionaries, one for each short answer span - `selected_sentence`: a dictionary representing the annotated sentence in the passage - `explanation_type`: one of "single_sentence", "multi_sentence", or "none" ### Data Splits The dataset is split into training and validation splits. \| \| train \| validation \| \|--------------\|------:\|-----------:\| \| N. Instances \| 7638 \| 1355 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Unknown. ### Citation Information ``` @misc{lamm2020qed, title={QED: A Framework and Dataset for Explanations in Question Answering}, author={Matthew Lamm and Jennimaria Palomaki and Chris Alberti and Daniel Andor and Eunsol Choi and Livio Baldini Soares and Michael Collins}, year={2020}, eprint={2009.06354}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.	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GEM/BiSECT	GEM	2022-09-02T21:58:17Z	730	2	null	[ "annotations_creators:none", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:de", "language:en", "language:fr", "language:es", "license:other", "region:us" ]	2022-09-02T21:58:17Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - none language_creators: - unknown language: - de - en - fr - es license: - other multilinguality: - unknown pretty_name: BiSECT size_categories: - unknown source_datasets: - original task_categories: - simplification task_ids: - unknown --- # Dataset Card for GEM/BiSECT ## Dataset Description - Homepage: https://github.com/mounicam/BiSECT - Repository: https://github.com/mounicam/BiSECT/tree/main/bisect - Paper: https://aclanthology.org/2021.emnlp-main.500/ - Leaderboard: N/A - Point of Contact: Joongwon Kim, Mounica Maddela, Reno Kriz ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/BiSECT). ### Dataset Summary This dataset is composed of 1 million complex sentences with the task to split and simplify them while retaining the full meaning. Compared to other simplification corpora, BiSECT requires more significant edits. BiSECT offers splits in English, German, French, and Spanish. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/BiSECT') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/BiSECT). #### website [Link](https://github.com/mounicam/BiSECT) #### paper [Link](https://aclanthology.org/2021.emnlp-main.500/) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Link](https://github.com/mounicam/BiSECT) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Link](https://github.com/mounicam/BiSECT/tree/main/bisect) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [Link](https://aclanthology.org/2021.emnlp-main.500/) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{kim-etal-2021-bisect, title = "{B}i{SECT}: Learning to Split and Rephrase Sentences with Bitexts", author = "Kim, Joongwon and Maddela, Mounica and Kriz, Reno and Xu, Wei and Callison-Burch, Chris", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.500", pages = "6193--6209" } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Joongwon Kim, Mounica Maddela, Reno Kriz #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> jkim0118@seas.upenn.edu, mmaddela3@gatech.edu, rkriz1@jh.edu #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> yes #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English`, `German`, `French`, `Spanish, Castilian` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> other: Other license #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> Split and Rephrase. #### Add. License Info <!-- info: What is the 'other' license of the dataset? --> <!-- scope: periscope --> The dataset is not licensed by itself, and the source OPUS data consists solely of publicly available parallel corpora. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Simplification #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> To rewrite a long, complex sentence into shorter, readable, meaning-equivalent sentences. ### Credit ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> - `gem_id` (string): a unique identifier for the instance - `source_sentence` (string): sentence to be simplified - `target_sentence` (string)" simplified text that was split and rephrased #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { "gem_id": "bisect-train-0", "source_sentence": "The report on the visit to Bhutan states that the small community has made the task of coordination less complex and success is manifested in the synchronized programming cycles which now apply to all but one of the agencies ( the World Health Organization ) .", "target_sentence": "The report on the visit to Bhutan says that the small community has made the coordination work less complex . Success manifests itself in synchronized programming cycles that now apply to all but one organism ( the World Health Organization ) ." } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> For the main English BiSECT dataset, the splits are as follows: 1. Train (n=928440) 2. Validation (n=9079) 3. Test (n=583) Additional challenge sets were derived from the data presented in the paper. Please refer to the challenge set sections. The train/validation/test splits for other languages are as follows: German (n=184638/n=864/n=735) Spanish (n=282944/n=3638/n=3081) French (n=491035/n=2400/n=1036) #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> While all training data were derived from subsets of the OPUS corpora, different source subsets were used for training v.s., validation and testing. The training set comprised more web crawl data, whereas development and test sets comprised EMEA and EU texts. Details can be found in the BiSECT paper. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> Understanding long and complex sentences is challenging for both humans and NLP models. The BiSECT dataset helps facilitate more research on Split and Rephrase as a task within itself, as well as how it can benefit downstream NLP applications. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> yes #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> BiSECT is the largest available corpora for the Split and Rephrase task. In addition, it has been shown that BiSECT is of higher quality than previous Split and Rephrase corpora and contains a wider variety of splitting operations. Most previous Split and Rephrase corpora (HSplit-Wiki, Cont-Benchmark, and Wiki-Benchmark) were manually written at a small scale and focused on evaluation, while the one corpus of comparable size, WikiSplit, contains around 25% of pairs contain significant errors. This is because Wikipedia editors are not only trying to split a sentence, but also often simultaneously modifying the sentence for other purposes, which results in changes of the initial meaning. ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `data points added` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> The original BiSECT training, validation, and test splits are maintained to ensure a fair comparison. Note that the original BiSECT test set was created by manually selecting 583 high-quality Split and Rephrase instances from 1000 random source-target pairs sampled from the EMEA and JRC-Acquis corpora from OPUS. As the first challenge set, we include the HSPLIT-Wiki test set, containing 359 pairs. For each complex sentence, there are four reference splits; To ensure replicability, as reference splits, we again follow the BiSECT paper and present only the references from HSplit2-full. In addition to the two evaluation sets used in the original BiSECT paper, we also introduce a second challenge set. For this, we initially consider all 7,293 pairs from the EMEA and JRC-Acquis corpora. From there, we classify each pair using the classification algorithm from Section 4.2 of the original BiSECT paper. The three classes are as follows: 1. Direct Insertion: when a long sentence l contains two independent clauses and requires only minor changes in order to make a fluent and meaning-preserving split s. 2. Changes near Split, when l contains one independent and one dependent clause, but modifications are restricted to the region where l is split. 3. Changes across Sentences, where major changes are required throughout l in order to create a fluent split s. We keep only pairs labeled as Type 3, and after filtering out pairs with significant length differences (signaling potential content addition/deletion), we present a second challenge set of 1,798 pairs. #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> The dataset can be downloaded from the original repository by the authors. The original BiSECT paper proposes several transformer-based models that can be used as baselines, which also compares against Copy512, an LSTM-based model and the previous state-of-the-art. The common metric used for automatic evaluation of Split and Rephrase, and sentence simplification more generally is SARI. The BiSECT paper also evaluates using BERTScore. Note that automatic evaluations tend to not correlate well with human judgments, so a human evaluation for quality is generally expected for publication. The original BiSECT paper provides templates for collecting quality annotations from Amazon Mechanical Turk. ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Text comprehension (needed to generate meaning-equivalent output) and notions of complexity (what is more 'readable' in terms of syntactic structure, lexical choice, punctuation). #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `Other: Other Metrics`, `BERT-Score` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> SARI is a metric used for evaluating automatic text simplification systems. The metric compares the predicted simplified sentences against the reference and the source sentences. It explicitly measures the goodness of words that are added, deleted and kept by the system. #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> Existing automatic metrics, such as BLEU (Papineni et al., 2002) and SAMSA (Sulem et al., 2018), are not optimal for the Split and Rephrase task as they rely on lexical overlap between the output and the target (or source) and underestimate the splitting capability of the models that rephrase often. As such, the dataset creators focused on BERTScore (Zhang et al., 2020) and SARI (Xu et al., 2016). BERTScore captures meaning preservation and fluency well (Scialom et al., 2021). SARI can provide three separate F1/precision scores that explicitly measure the correctness of inserted, kept and deleted n-grams when compared to both the source and the target. The authors used an extended version of SARI that considers lexical paraphrases of the reference. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> BiSECT was constructed to satisfy the need of a Split and Rephrase corpus that is both large-scale and high-quality. Most previous Split and Rephrase corpora (HSplit-Wiki, Cont-Benchmark, and Wiki-Benchmark) were manually written at a small scale and focused on evaluation, while the one corpus of comparable size, WikiSplit, contains around 25% of pairs contain significant errors. This is because Wikipedia editors are not only trying to split a sentence, but also often simultaneously modifying the sentence for other purposes, which results in changes of the initial meaning. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> The goal of Split and Rephrase is to break down longer sentences into multiple shorter sentences, which has downstream applications for many NLP tasks, including machine translation and dependency parsing. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> no ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Found` #### Where was it found? <!-- info: If found, where from? --> <!-- scope: telescope --> `Other` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> N/A. #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> There is a range of topics spanning domains such as web crawl and government documents (European Parliament, United Nations, EMEA). #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by data curator #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> The construction of the BiSECT corpus relies on leveraging the sentence-level alignments from OPUS), a collection of bilingual parallel corpora over many language pairs. Given a target language A, this work extracts all 1-2 and 2-1 sentence alignments from parallel corpora between A and a set of foreign languages B. Next, the foreign sentences are translated into English using Google Translate’s Web API service to obtain sentence alignments between a single long sentence and two corresponding split sentences, both in the desired language. The authors further filtered the data in a hybrid fashion. #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> hybrid #### Filter Criteria <!-- info: What were the selection criteria? --> <!-- scope: microscope --> To remove noise, the authors remove pairs where the single long sentence (l) contains a token with a punctuation after the first two and before the last two alphabetic characters. The authors also removed instances where l contains more than one unconnected component in its dependency tree, generated via SpaCy. ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> none #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> no #### Justification for Using the Data <!-- info: If not, what is the justification for reusing the data? --> <!-- scope: microscope --> Since this data is collected from OPUS, all instances are already in the public domain. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> unlikely #### Categories of PII <!-- info: What categories of PII are present or suspected in the data? --> <!-- scope: periscope --> `generic PII` #### Any PII Identification? <!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? --> <!-- scope: periscope --> no identification ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> yes #### Details on how Dataset Addresses the Needs <!-- info: Describe how this dataset addresses the needs of underserved communities. --> <!-- scope: microscope --> The data as provided in GEMv2 is in English, which is a language with abundant existing resources. However, the original paper also provides Split and Rephrase pairs for French, Spanish, and German, while providing a framework for leveraging bilingual corpora from any language pair found within OPUS. ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> The language produced in the dataset is limited to what is captured in the used subset of the OPUS corpora, which might not represent the full distribution of speakers from all locations. For example, the corpora used are from a limited set of relatively formal domains, so it is possible that high performance on the BiSECT test set may not transfer to more informal text. ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk <!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. --> <!-- scope: microscope --> Since this data is collected from OPUS, all pairs are already in the public domain. ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `public domain` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `public domain` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The creation of English BiSECT relies on translating non-English text back to English. While machine translation systems tend to perform well on high-resource languages, there is still a non-negligible chance that there these systems make errors; through a manual evaluation of a subset of BiSECT, it was found that 15% of pairs contained significant errors, while an additional 22% contained minor adequacy/fluency errors. This problem is exacerbated slightly when creating German BiSECT (22% significant errors, 24% minor errors), and these numbers would likely get larger if lower-resource languages were used.	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flax-community/german_common_crawl	flax-community	2023-10-02T16:46:37Z	730	0	null	[ "language:de", "region:us" ]	2023-10-02T16:46:37Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- language: - de --- The dataset script is more or less ready and one file has correctly been converted so far: `https://opendata.iisys.de/systemintegration/Datasets/CommonCrawl/head/de_head_0000_2015-48.tar.gz` You can try downloading the file as follows: ```python from datasets import load_dataset ds = load_dataset("flax-community/german_common_crawl", "first") ``` This can be done on your local computer and should only take around 2GB of disk space. This however only loads the first of >100 files. We now need to add all other files to this repo. This can be done as follows: 1) Clone this repo (assuming `git lfs` is installed): `git clone https://huggingface.co/datasets/flax-community/german_common_crawl` 2) For each file: `https://opendata.iisys.de/systemintegration/Datasets/CommonCrawl/head/de_head_0000_2016-18.tar.gz` - `https://opendata.iisys.de/systemintegration/Datasets/CommonCrawl/middle/de_middle_0009_2019-47.tar.gz` run the command `./convert_file.sh <file_name>` This command will download the file via `wget`, filter out all text that is below a threshold as explained here: https://opendata.iisys.de/systemintegration/Datasets/CommonCrawl/middle/de_middle_0009_2019-47.tar.gz and then converts the file into the correct format. 3) Upload the file to this repo: `git add . && git commit -m "add file x" && git push Ideally this can be done in a loop on a computer that has enough CPU memory (Note that if this is done on a TPU VM, make sure to disable the TPU via `export JAX_PLATFORM_NAME=cpu`. Also some description and file names have to be added correctly to the dataset.py script	[ -0.616102397441864, -0.33663952350616455, 0.18119148910045624, 0.17185437679290771, -0.28939682245254517, -0.11483222991228104, -0.47653529047966003, -0.2822420597076416, 0.4084365963935852, 0.630645751953125, -0.6435532569885254, -0.7281674146652222, -0.4990619122982025, 0.4520062506198883, -0.5117666721343994, 1.1783632040023804, -0.24957498908042908, -0.03926844894886017, 0.3126763105392456, -0.14515769481658936, -0.3144203722476959, -0.11425156891345978, -0.19559675455093384, -0.050944384187459946, 0.1782500445842743, 0.6795880794525146, 0.6366345286369324, 1.0075454711914062, 0.6670661568641663, 0.27286043763160706, 0.42292913794517517, -0.013423356227576733, -0.434800386428833, 0.21974724531173706, -0.43891453742980957, -0.38365137577056885, -0.13690562546253204, 0.10463463515043259, 0.39198037981987, 0.6068413853645325, -0.20017842948436737, 0.4205501973628998, 0.06680644303560257, 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bigbio/hallmarks_of_cancer	bigbio	2022-12-22T15:44:44Z	730	1	null	[ "multilinguality:monolingual", "language:en", "license:gpl-3.0", "region:us" ]	2022-12-22T15:44:44Z	2022-11-13T22:08:53.000Z	2022-11-13T22:08:53	--- language: - en bigbio_language: - English license: gpl-3.0 multilinguality: monolingual bigbio_license_shortname: GPL_3p0 pretty_name: Hallmarks of Cancer homepage: https://github.com/sb895/Hallmarks-of-Cancer bigbio_pubmed: True bigbio_public: True bigbio_tasks: - TEXT_CLASSIFICATION --- # Dataset Card for Hallmarks of Cancer ## Dataset Description - Homepage: https://github.com/sb895/Hallmarks-of-Cancer - Pubmed: True - Public: True - Tasks: TXTCLASS The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication abstracts manually annotated by experts according to a taxonomy. The taxonomy consists of 37 classes in a hierarchy. Zero or more class labels are assigned to each sentence in the corpus. The labels are found under the "labels" directory, while the tokenized text can be found under "text" directory. The filenames are the corresponding PubMed IDs (PMID). ## Citation Information ``` @article{DBLP:journals/bioinformatics/BakerSGAHSK16, author = {Simon Baker and Ilona Silins and Yufan Guo and Imran Ali and Johan H{"{o}}gberg and Ulla Stenius and Anna Korhonen}, title = {Automatic semantic classification of scientific literature according to the hallmarks of cancer}, journal = {Bioinform.}, volume = {32}, number = {3}, pages = {432--440}, year = {2016}, url = {https://doi.org/10.1093/bioinformatics/btv585}, doi = {10.1093/bioinformatics/btv585}, timestamp = {Thu, 14 Oct 2021 08:57:44 +0200}, biburl = {https://dblp.org/rec/journals/bioinformatics/BakerSGAHSK16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```	[ -0.21507947146892548, -0.32592374086380005, 0.3446207344532013, -0.10006620734930038, -0.4688519835472107, 0.14892975986003876, -0.07900287955999374, -0.3027849495410919, 0.24526676535606384, 0.5662534236907959, -0.3828754425048828, 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mozilla-foundation/common_voice_6_1	mozilla-foundation	2023-07-29T16:00:07Z	728	4	common-voice	[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended\|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]	2023-07-29T16:00:07Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - crowdsourced license: - cc0-1.0 multilinguality: - multilingual size_categories: ab: - n<1K ar: - 10K<n<100K as: - n<1K br: - 10K<n<100K ca: - 100K<n<1M cnh: - 1K<n<10K cs: - 10K<n<100K cv: - 10K<n<100K cy: - 10K<n<100K de: - 100K<n<1M dv: - 10K<n<100K el: - 10K<n<100K en: - 1M<n<10M eo: - 10K<n<100K es: - 100K<n<1M et: - 10K<n<100K eu: - 10K<n<100K fa: - 100K<n<1M fi: - 1K<n<10K fr: - 100K<n<1M fy-NL: - 10K<n<100K ga-IE: - 1K<n<10K hi: - n<1K hsb: - 1K<n<10K hu: - 1K<n<10K ia: - 1K<n<10K id: - 10K<n<100K it: - 100K<n<1M ja: - 1K<n<10K ka: - 1K<n<10K kab: - 100K<n<1M ky: - 10K<n<100K lg: - 1K<n<10K lt: - 1K<n<10K lv: - 1K<n<10K mn: - 10K<n<100K mt: - 10K<n<100K nl: - 10K<n<100K or: - 1K<n<10K pa-IN: - 1K<n<10K pl: - 100K<n<1M pt: - 10K<n<100K rm-sursilv: - 1K<n<10K rm-vallader: - 1K<n<10K ro: - 1K<n<10K ru: - 10K<n<100K rw: - 1M<n<10M sah: - 1K<n<10K sl: - 1K<n<10K sv-SE: - 10K<n<100K ta: - 10K<n<100K th: - 10K<n<100K tr: - 10K<n<100K tt: - 10K<n<100K uk: - 10K<n<100K vi: - 1K<n<10K vot: - n<1K zh-CN: - 10K<n<100K zh-HK: - 10K<n<100K zh-TW: - 10K<n<100K source_datasets: - extended\|common_voice paperswithcode_id: common-voice pretty_name: Common Voice Corpus 6.1 language_bcp47: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa-IN - pl - pt - rm-sursilv - rm-vallader - ro - ru - rw - sah - sl - sv-SE - ta - th - tr - tt - uk - vi - vot - zh-CN - zh-HK - zh-TW extra_gated_prompt: By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset. task_categories: - automatic-speech-recognition --- # Dataset Card for Common Voice Corpus 6.1 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://commonvoice.mozilla.org/en/datasets - Repository: https://github.com/common-voice/common-voice - Paper: https://arxiv.org/abs/1912.06670 - Leaderboard: https://paperswithcode.com/dataset/common-voice - Point of Contact: [Anton Lozhkov](mailto:anton@huggingface.co) ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 7335 validated hours in 60 languages, but more voices and languages are always added. Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing. ### Supported Tasks and Leaderboards The results for models trained on the Common Voice datasets are available via the [🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) ### Languages ``` Abkhaz, Arabic, Assamese, Basque, Breton, Catalan, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, Finnish, French, Frisian, Georgian, German, Greek, Hakha Chin, Hindi, Hungarian, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Lithuanian, Luganda, Maltese, Mongolian, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Slovenian, Sorbian, Upper, Spanish, Swedish, Tamil, Tatar, Thai, Turkish, Ukrainian, Vietnamese, Votic, Welsh ``` ## Dataset Structure ### Data Instances A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`. ```python { 'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 'path': 'et/clips/common_voice_et_18318995.mp3', 'audio': { 'path': 'et/clips/common_voice_et_18318995.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000 }, 'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 'up_votes': 2, 'down_votes': 0, 'age': 'twenties', 'gender': 'male', 'accent': '', 'locale': 'et', 'segment': '' } ``` ### Data Fields `client_id` (`string`): An id for which client (voice) made the recording `path` (`string`): The path to the audio file `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. `sentence` (`string`): The sentence the user was prompted to speak `up_votes` (`int64`): How many upvotes the audio file has received from reviewers `down_votes` (`int64`): How many downvotes the audio file has received from reviewers `age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`) `gender` (`string`): The gender of the speaker `accent` (`string`): Accent of the speaker `locale` (`string`): The locale of the speaker `segment` (`string`): Usually an empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Data Preprocessing Recommended by Hugging Face The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_. In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation. ```python from datasets import load_dataset ds = load_dataset("mozilla-foundation/common_voice_6_1", "en", use_auth_token=True) def prepare_dataset(batch): """Function to preprocess the dataset with the .map method""" transcription = batch["sentence"] if transcription.startswith('"') and transcription.endswith('"'): # we can remove trailing quotation marks as they do not affect the transcription transcription = transcription[1:-1] if transcription[-1] not in [".", "?", "!"]: # append a full-stop to sentences that do not end in punctuation transcription = transcription + "." batch["sentence"] = transcription return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ``` ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ```	[ -0.539664089679718, -0.7319751977920532, 0.1318860948085785, 0.45115622878074646, -0.2534431517124176, 0.03309357166290283, -0.5736495852470398, -0.22932881116867065, 0.4320584237575531, 0.5503373742103577, -0.7707356214523315, -0.957775354385376, -0.4422262907028198, 0.24771031737327576, -0.3119956851005554, 0.9572769403457642, 0.46783268451690674, 0.07838047295808792, -0.11705450713634491, -0.15286792814731598, -0.3534087538719177, -0.6628702878952026, -0.6481004357337952, -0.03285644203424454, 0.1845950335264206, 0.5895600318908691, 0.4502083361148834, 0.5309678912162781, 0.19139565527439117, 0.341501384973526, -0.3279404044151306, 0.057754192501306534, -0.34041786193847656, 0.03134985640645027, 0.11021455377340317, -0.3552730977535248, -0.44552698731422424, 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web_of_science	null	2023-04-05T13:42:58Z	727	3	web-of-science-dataset	[ "language:en", "region:us" ]	2023-04-05T13:42:58Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- language: - en paperswithcode_id: web-of-science-dataset pretty_name: Web of Science Dataset dataset_info: - config_name: WOS5736 features: - name: input_data dtype: string - name: label dtype: int32 - name: label_level_1 dtype: int32 - name: label_level_2 dtype: int32 splits: - name: train num_bytes: 8051533 num_examples: 5736 download_size: 60222421 dataset_size: 8051533 - config_name: WOS11967 features: - name: input_data dtype: string - name: label dtype: int32 - name: label_level_1 dtype: int32 - name: label_level_2 dtype: int32 splits: - name: train num_bytes: 16248391 num_examples: 11967 download_size: 60222421 dataset_size: 16248391 - config_name: WOS46985 features: - name: input_data dtype: string - name: label dtype: int32 - name: label_level_1 dtype: int32 - name: label_level_2 dtype: int32 splits: - name: train num_bytes: 65471726 num_examples: 46985 download_size: 60222421 dataset_size: 65471726 --- # Dataset Card for "web_of_science" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://data.mendeley.com/datasets/9rw3vkcfy4/6](https://data.mendeley.com/datasets/9rw3vkcfy4/6) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 180.67 MB - Size of the generated dataset: 89.81 MB - Total amount of disk used: 270.48 MB ### Dataset Summary Copyright (c) 2017 Kamran Kowsari Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the "Dataset"), to deal in the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset. If you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Description of Dataset: Here is three datasets which include WOS-11967 , WOS-46985, and WOS-5736 Each folder contains: -X.txt -Y.txt -YL1.txt -YL2.txt X is input data that include text sequences Y is target value YL1 is target value of level one (parent label) YL2 is target value of level one (child label) Web of Science Dataset WOS-5736 -This dataset contains 5,736 documents with 11 categories which include 3 parents categories. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### WOS11967 - Size of downloaded dataset files: 60.22 MB - Size of the generated dataset: 16.25 MB - Total amount of disk used: 76.48 MB An example of 'train' looks as follows. ``` ``` #### WOS46985 - Size of downloaded dataset files: 60.22 MB - Size of the generated dataset: 65.50 MB - Total amount of disk used: 125.72 MB An example of 'train' looks as follows. ``` ``` #### WOS5736 - Size of downloaded dataset files: 60.22 MB - Size of the generated dataset: 8.05 MB - Total amount of disk used: 68.27 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### WOS11967 - `input_data`: a `string` feature. - `label`: a `int32` feature. - `label_level_1`: a `int32` feature. - `label_level_2`: a `int32` feature. #### WOS46985 - `input_data`: a `string` feature. - `label`: a `int32` feature. - `label_level_1`: a `int32` feature. - `label_level_2`: a `int32` feature. #### WOS5736 - `input_data`: a `string` feature. - `label`: a `int32` feature. - `label_level_1`: a `int32` feature. - `label_level_2`: a `int32` feature. ### Data Splits \| name \|train\| \|--------\|----:\| \|WOS11967\|11967\| \|WOS46985\|46985\| \|WOS5736 \| 5736\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{kowsari2017HDLTex, title={HDLTex: Hierarchical Deep Learning for Text Classification}, author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E}, booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on}, year={2017}, organization={IEEE} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset.	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albertvillanova/pmc_open_access	albertvillanova	2023-01-16T13:43:54Z	725	0	null	[ "region:us" ]	2023-01-16T13:43:54Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	# Dataset Card for pmc_open_access ## Dataset Description ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "pmc_open_access" is deprecated and will be deleted. Use "<a href="https://huggingface.co/datasets/pmc/open_access">pmc/open_access</a>" instead.</p> </div>	[ -0.5382103323936462, -0.3306785821914673, 0.0074051315896213055, 0.40528616309165955, -0.4347774088382721, -0.042667828500270844, 0.02573239989578724, 0.01197946909815073, 0.1735551804304123, 0.3602909445762634, -0.8558332920074463, -0.9415202140808105, -0.4000348150730133, 0.05581523850560188, 0.10809971392154694, 1.2245467901229858, -0.12999661266803741, 0.06807336211204529, -0.021608253940939903, -0.21672099828720093, -0.14093716442584991, -0.3110918700695038, -0.5700302124023438, -0.24290138483047485, 0.3111254572868347, 0.7287904620170593, 0.855627179145813, 0.46702760457992554, 0.5905383229255676, 0.16894420981407166, -0.20987944304943085, -0.11056406050920486, -0.5791758298873901, -0.6398298740386963, -0.2431163191795349, -0.5321899652481079, -0.9620203971862793, 0.4655474126338959, 0.522470235824585, 0.654227077960968, -0.463650643825531, 0.32163089513778687, 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princeton-nlp/SWE-bench	princeton-nlp	2023-11-17T19:02:27Z	725	12	null	[ "arxiv:2310.06770", "region:us" ]	2023-11-17T19:02:27Z	2023-10-10T04:56:03.000Z	2023-10-10T04:56:03	--- dataset_info: features: - name: repo dtype: string - name: instance_id dtype: string - name: base_commit dtype: string - name: patch dtype: string - name: test_patch dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: created_at dtype: string - name: version dtype: string - name: FAIL_TO_PASS dtype: string - name: PASS_TO_PASS dtype: string - name: environment_setup_commit dtype: string splits: - name: dev num_bytes: 4783179 num_examples: 225 - name: test num_bytes: 41880503 num_examples: 2294 - name: train num_bytes: 367610377 num_examples: 19008 download_size: 119829492 dataset_size: 414274059 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* - split: train path: data/train-* --- ### Dataset Summary SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution. The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770) ## Want to run inference now? This dataset only contains the `problem_statement` (i.e. issue text) and the `base_commit` which can represents the state of the codebase before the issue has been resolved. If you want to run inference using the "Oracle" or BM25 retrieval settings mentioned in the paper, consider the following datasets. [princeton-nlp/SWE-bench_oracle](https://huggingface.co/datasets/princeton-nlp/SWE-bench_oracle) [princeton-nlp/SWE-bench_bm25_13K](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_13K) [princeton-nlp/SWE-bench_bm25_27K](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_27K) [princeton-nlp/SWE-bench_bm25_40K](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_40K) [princeton-nlp/SWE-bench_bm25_50k_llama](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_50k_llama) ### Supported Tasks and Leaderboards SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com ### Languages The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type. ## Dataset Structure ### Data Instances An example of a SWE-bench datum is as follows: ``` instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number. patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue. repo: (str) - The repository owner/name identifier from GitHub. base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied. hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date. created_at: (str) - The creation date of the pull request. test_patch: (str) - A test-file patch that was contributed by the solution PR. problem_statement: (str) - The issue title and body. version: (str) - Installation version to use for running evaluation. environment_setup_commit: (str) - commit hash to use for environment setup and installation. FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution. PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application. ``` [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.4965578019618988, -0.5325542688369751, 0.2970157861709595, 0.36417657136917114, 0.033620622009038925, -0.1533774882555008, -0.43382570147514343, -0.330992192029953, 0.304473340511322, 0.49726906418800354, -0.7142566442489624, -0.5953183770179749, -0.32062724232673645, 0.07022719085216522, -0.22197099030017853, 1.2610501050949097, -0.15988998115062714, 0.009249789640307426, -0.27701449394226074, -0.35504773259162903, -0.4925798773765564, -0.1979546844959259, -0.17366036772727966, -0.20553140342235565, 0.3715333044528961, 0.41500332951545715, 0.3552730083465576, 0.5375553965568542, 0.6825665235519409, 0.34865424036979675, 0.022509638220071793, -0.2412431240081787, -0.28320568799972534, 0.09466013312339783, -0.40850770473480225, 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osunlp/MagicBrush	osunlp	2023-11-07T20:19:33Z	724	39	null	[ "task_categories:text-to-image", "task_categories:image-to-image", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "region:us" ]	2023-11-07T20:19:33Z	2023-06-14T02:20:33.000Z	2023-06-14T02:20:33	--- license: cc-by-4.0 dataset_info: features: - name: img_id dtype: string - name: turn_index dtype: int32 - name: source_img dtype: image - name: mask_img dtype: image - name: instruction dtype: string - name: target_img dtype: image splits: - name: train num_bytes: 25446150928.986 num_examples: 8807 - name: dev num_bytes: 1521183444 num_examples: 528 download_size: 22358540292 dataset_size: 26967334372.986 task_categories: - text-to-image - image-to-image language: - en pretty_name: MagicBrush size_categories: - 10K<n<100K --- # Dataset Card for MagicBrush ## Dataset Description - Homepage: https://osu-nlp-group.github.io/MagicBrush - Repository: https://github.com/OSU-NLP-Group/MagicBrush - Point of Contact: [Kai Zhang](mailto:zhang.13253@osu.edu) ### Dataset Summary MagicBrush is the first large-scale, manually-annotated instruction-guided image editing dataset covering diverse scenarios single-turn, multi-turn, mask-provided, and mask-free editing. MagicBrush comprises 10K (source image, instruction, target image) triples, which is sufficient to train large-scale image editing models. Please check our [website](https://osu-nlp-group.github.io/MagicBrush/) to explore more visual results. #### Dataset Structure "img_id" (str): same from COCO id but in string type, for easier test set loading "turn_index" (int32): the edit turn in the image "source_img" (str): input image, could be the original real image (turn_index=1) and edited images from last turn (turn_index >=2) "mask_img" (str): free-form mask image (white region), can be used in mask-provided setting to limit the region to be edited. "instruction" (str): edit instruction of how the input image should be changed. "target_img" (str): the edited image corresponding to the input image and instruction. If you need auxiliary data, please use [training set](https://buckeyemailosu-my.sharepoint.com/:u:/g/personal/zhang_13253_buckeyemail_osu_edu/EYEqf_yG36lAgiXw2GvRl0QBDBOeZHxvNgxO0Ec9WDMcNg) and [dev set](https://buckeyemailosu-my.sharepoint.com/:u:/g/personal/zhang_13253_buckeyemail_osu_edu/EXkXvvC95C1JsgMNWGL_RcEBElmsGxXwAAAdGamN8PNhrg) ### Splits train: 8,807 edit turns (4,512 edit sessions). dev: 528 edit turns (266 edit sessions). test: (To prevent potential data leakage, please check our repo for information on obtaining the test set.) ### Licensing Information Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License. ## Citation Information If you find this dataset useful, please consider citing our paper: ``` @inproceedings{Zhang2023MagicBrush, title={MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing}, author={Kai Zhang and Lingbo Mo and Wenhu Chen and Huan Sun and Yu Su}, booktitle={Advances in Neural Information Processing Systems}, year={2023} } ```	[ -0.4151928424835205, -0.7798892259597778, 0.08691033720970154, 0.07546745985746384, -0.07248512655496597, -0.12611380219459534, -0.31737396121025085, -0.41098129749298096, 0.3054661154747009, 0.4956766366958618, -0.8629372715950012, -0.711763858795166, -0.6850133538246155, 0.0350065715610981, -0.11852016299962997, 0.7054411768913269, -0.20292115211486816, -0.07950170338153839, -0.03735863417387009, -0.23819759488105774, -0.5771547555923462, -0.15524739027023315, -0.7427986860275269, -0.07223039120435715, 0.3779473304748535, 0.43542590737342834, 0.7848263382911682, 0.7661288976669312, 0.6135831475257874, 0.39183810353279114, 0.0571737177670002, 0.6152513027191162, -0.32212138175964355, -0.1705179214477539, -0.20550356805324554, -0.4407329261302948, -0.6712405681610107, 0.2302875965833664, 0.660800576210022, 0.4317462146282196, 0.09834796190261841, 0.29302626848220825, 0.08814945816993713, 0.6419993042945862, 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GATE-engine/cifarfs	GATE-engine	2023-06-05T17:32:51Z	723	0	null	[ "region:us" ]	2023-06-05T17:32:51Z	2023-06-05T17:32:39.000Z	2023-06-05T17:32:39	--- dataset_info: features: - name: image dtype: image - name: label dtype: int64 splits: - name: train num_bytes: 86489157.0 num_examples: 38400 - name: validation num_bytes: 21539635.0 num_examples: 9600 - name: test num_bytes: 26600575.0 num_examples: 12000 download_size: 134961942 dataset_size: 134629367.0 --- # Dataset Card for "cifarfs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6798436045646667, -0.20533055067062378, 0.15233641862869263, 0.27466824650764465, -0.2214440405368805, 0.09981502592563629, 0.28744712471961975, -0.2635490298271179, 0.9180206060409546, 0.4959387481212616, -0.8475548028945923, -0.7818244695663452, -0.5689248442649841, -0.25954851508140564, -0.4666248857975006, 1.4925819635391235, 0.14063462615013123, 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augtoma/usmle_step_1	augtoma	2023-08-11T21:25:08Z	723	0	null	[ "region:us" ]	2023-08-11T21:25:08Z	2023-08-11T21:24:50.000Z	2023-08-11T21:24:50	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: options struct: - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: E dtype: string - name: F dtype: string - name: G dtype: string - name: H dtype: string - name: I dtype: string - name: answer dtype: string - name: answer_idx dtype: string splits: - name: test num_bytes: 80576 num_examples: 94 download_size: 60551 dataset_size: 80576 --- # Dataset Card for "usmle_self_eval_step1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.3753840923309326, -0.35676297545433044, 0.28812018036842346, 0.292928546667099, -0.19971905648708344, 0.06186849996447563, 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lmqg/qg_squad	lmqg	2022-12-02T18:51:10Z	721	5	null	[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:squad", "language:en", "license:cc-by-4.0", "question-generation", "arxiv:2210.03992", "arxiv:1705.00106", "region:us" ]	2022-12-02T18:51:10Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- license: cc-by-4.0 pretty_name: SQuAD for question generation language: en multilinguality: monolingual size_categories: 10K<n<100K source_datasets: squad task_categories: - text-generation task_ids: - language-modeling tags: - question-generation --- # Dataset Card for "lmqg/qg_squad" ## Dataset Description - Repository: [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - Paper: [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - Point of Contact: [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992). This is [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) dataset for question generation (QG) task. The split of train/development/test set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11). ### Supported Tasks and Leaderboards * `question-generation`: The dataset is assumed to be used to train a model for question generation. Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail). This task has an active leaderboard which can be found at [here](https://paperswithcode.com/sota/question-generation-on-squad11). ### Languages English (en) ## Dataset Structure An example of 'train' looks as follows. ``` { "question": "What is heresy mainly at odds with?", "paragraph": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.", "answer": "established beliefs or customs", "sentence": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs .", "paragraph_sentence": "<hl> Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs . <hl> A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.", "paragraph_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl>. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.", "sentence_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl> ." } ``` The data fields are the same among all splits. - `question`: a `string` feature. - `paragraph`: a `string` feature. - `answer`: a `string` feature. - `sentence`: a `string` feature. - `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`. - `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`. - `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`. Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model, but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and `paragraph_sentence` feature is for sentence-aware question generation. ## Data Splits \|train\|validation\|test \| \|----:\|---------:\|----:\| \|75722\| 10570\|11877\| ## Citation Information ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```	[ -0.5226408243179321, -1.1419897079467773, 0.2834431827068329, 0.19295796751976013, -0.12053617089986801, -0.05243224650621414, -0.17604634165763855, -0.058383166790008545, -0.008930038660764694, 0.41110819578170776, -1.002997636795044, -0.5905159711837769, 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swj0419/WikiMIA	swj0419	2023-11-03T07:52:38Z	719	9	null	[ "size_categories:1K<n<10K", "language:en", "license:mit", "arxiv:2310.16789", "region:us" ]	2023-11-03T07:52:38Z	2023-10-05T23:31:10.000Z	2023-10-05T23:31:10	--- license: mit language: - en size_categories: - 1K<n<10K dataset_info: features: - name: input dtype: string - name: label dtype: int64 splits: - name: WikiMIA_length32 num_bytes: 162091 num_examples: 776 - name: WikiMIA_length64 num_bytes: 221018 num_examples: 542 - name: WikiMIA_length128 num_bytes: 205118 num_examples: 250 - name: WikiMIA_length256 num_bytes: 134879 num_examples: 82 download_size: 465221 dataset_size: 723106 --- # 📘 WikiMIA Datasets The WikiMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from extensive large language models. ### 📌 Applicability The datasets can be applied to various models released between 2017 to 2023: - LLaMA1/2 - GPT-Neo - OPT - Pythia - text-davinci-001 - text-davinci-002 - ... and more. ## Loading the datasets To load the dataset: ```python from datasets import load_dataset LENGTH = 64 dataset = load_dataset("swj0419/WikiMIA", split=f"WikiMIA_length{LENGTH}") ``` * Available Text Lengths: `32, 64, 128, 256`. * Label 0: Refers to the unseen data during pretraining. Label 1: Refers to the seen data. ## 🛠️ Codebase For evaluating MIA methods on our datasets, visit our [GitHub repository](https://github.com/swj0419/detect-pretrain-code). ## ⭐ Citing our Work If you find our codebase and datasets beneficial, kindly cite our work: ```bibtex @misc{shi2023detecting, title={Detecting Pretraining Data from Large Language Models}, author={Weijia Shi and Anirudh Ajith and Mengzhou Xia and Yangsibo Huang and Daogao Liu and Terra Blevins and Danqi Chen and Luke Zettlemoyer}, year={2023}, eprint={2310.16789}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```	[ -0.4608645737171173, -0.4857267737388611, 0.29026830196380615, 0.0355534628033638, -0.2077207863330841, -0.1705150604248047, -0.11845362186431885, -0.26705798506736755, 0.08350148051977158, 0.14856639504432678, -0.9779284596443176, -0.7092638611793518, -0.6129906177520752, 0.2000560462474823, -0.6906946301460266, 0.7876449227333069, 0.16128043830394745, 0.06254152208566666, -0.15721331536769867, 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result-kand2-sdxl-wuerst-karlo/25b005b7	result-kand2-sdxl-wuerst-karlo	2023-10-08T22:30:24Z	719	0	null	[ "region:us" ]	2023-10-08T22:30:24Z	2023-10-08T22:30:23.000Z	2023-10-08T22:30:23	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 198 num_examples: 10 download_size: 1383 dataset_size: 198 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "25b005b7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.7816565632820129, -0.04812903702259064, 0.12133058905601501, 0.5350782871246338, -0.2177933007478714, -0.017557714134454727, 0.4156583249568939, -0.3242875635623932, 0.6586652398109436, 0.5569935441017151, -0.8911352157592773, -0.7347569465637207, -0.4903583824634552, -0.145182266831398, -0.2222428023815155, 1.1922332048416138, 0.26836276054382324, 0.16401393711566925, -0.37474459409713745, 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conceptnet5	null	2023-06-01T14:59:50Z	718	15	conceptnet	[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:10M<n<100M", "size_categories:1M<n<10M", "source_datasets:original", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:ja", "language:nl", "language:pt", "language:ru", "language:zh", "license:cc-by-4.0", "region:us" ]	2023-06-01T14:59:50Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - crowdsourced - found language: - de - en - es - fr - it - ja - nl - pt - ru - zh license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10M<n<100M - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: conceptnet pretty_name: Conceptnet5 dataset_info: - config_name: conceptnet5 features: - name: sentence dtype: string - name: full_rel dtype: string - name: rel dtype: string - name: arg1 dtype: string - name: arg2 dtype: string - name: lang dtype: string - name: extra_info dtype: string - name: weight dtype: float32 splits: - name: train num_bytes: 11493868180 num_examples: 34074917 download_size: 497963447 dataset_size: 11493868180 - config_name: omcs_sentences_free features: - name: sentence dtype: string - name: raw_data dtype: string - name: lang dtype: string splits: - name: train num_bytes: 174811310 num_examples: 898160 download_size: 104247648 dataset_size: 174811310 - config_name: omcs_sentences_more features: - name: sentence dtype: string - name: raw_data dtype: string - name: lang dtype: string splits: - name: train num_bytes: 341424279 num_examples: 2001735 download_size: 209776958 dataset_size: 341424279 config_names: - conceptnet5 - omcs_sentences_free - omcs_sentences_more --- # Dataset Card for Conceptnet5 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/commonsense/conceptnet5/wiki - Repository: https://github.com/commonsense/conceptnet5/wiki - Paper: Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." In proceedings of AAAI 31.o ### Dataset Summary ConceptNet is a multilingual knowledge base, representing words and phrases that people use and the common-sense relationships between them. The knowledge in ConceptNet is collected from a variety of resources, including crowd-sourced resources (such as Wiktionary and Open Mind Common Sense), games with a purpose (such as Verbosity and nadya.jp), and expert-created resources (such as WordNet and JMDict). You can browse what ConceptNet knows at http://conceptnet.io. This dataset is designed to provide training data for common sense relationships pulls together from various sources. The dataset is multi-lingual. See langauge codes and language info here: https://github.com/commonsense/conceptnet5/wiki/Languages This dataset provides an interface for the conceptnet5 csv file, and some (but not all) of the raw text data used to build conceptnet5: omcsnet_sentences_free.txt, and omcsnet_sentences_more.txt. One use of this dataset would be to learn to extract the conceptnet relationship from the omcsnet sentences. Conceptnet5 has 34,074,917 relationships. Of those relationships, there are 2,176,099 surface text sentences related to those 2M entries. omcsnet_sentences_free has 898,161 lines. omcsnet_sentences_more has 2,001,736 lines. Original downloads are available here https://github.com/commonsense/conceptnet5/wiki/Downloads. For more information, see: https://github.com/commonsense/conceptnet5/wiki The omcsnet data comes with the following warning from the authors of the above site: Remember: this data comes from various forms of crowdsourcing. Sentences in these files are not necessarily true, useful, or appropriate. ### Languages en, fr, it, de, es, ru, pt, ja, nl, zh and others ## Dataset Structure ### Data Instances There are three configurations for the dataset: conceptnet5, omcs_sentences_free, omcs_sentences_more. Conceptnet5 defines: `` { 'sentence': ..., 'full_rel': ..., 'rel': ..., 'arg1': ..., 'arg2': ..., 'lang': ..., 'extra_info': ... 'weight': ... } `` The omcs text defines: `` { 'sentence': ..., 'raw_data': ... 'weight': ... } `` ### Data Fields For conceptnet5 configurations: * full_rel: the full relationship. e.g., /a/[/r/Antonym/,/c/en/able/,/c/en/cane/] * rel: the binary relationship. e.g., /r/Antonym * arg1: the first argument to the binary relationship. e.g., /c/en/able * arg2: the second argument to the binary relationship. e.g., /c/en/cane * lang: the language code. e.g., en, fr, etc. If the arg1 and arg2 are two different languages, then the form os lang1/lang2. * extra_info: a string that includes json data that has the dataset name, license type (mostly cc-4.0), contributor, etc. e.g., : {"dataset": "/d/verbosity", "license": "cc:by/4.0", "sources": [{"contributor": "/s/resource/verbosity"}], "surfaceEnd": "cane", "surfaceStart": "able", "surfaceText": "[[able]] is the opposite of [[cane]]", "weight": 0.299} * sentence: the sentence from which the relationship was extracted, if one exists, with brackets around the arg1 and arg2. e.g., [[able]] is the opposite of [[cane]] * weight: the weight assigned by the curators or automatically to the relationship, between 1.0-0.0, higher being more certain. For the omcs text configurations: * sentence: the raw sentence * raw_data: the raw tab seperated data of the form, id, text, curator_id, created_on, lanugage_id, activity_id, and score. Most of this information was tied to older systems for entering the data os was not partsed into fields for the dataset. e.g., 1237278 someone can be at catch 10805 2006-11-14 17:56:49.70872-05 en 27 1 * lang: the language code ### Data Splits There are no splits. ## Dataset Creation ### Curation Rationale This dataset was gathered and created over many years for research in common sense reasoning. ### Source Data #### Initial Data Collection and Normalization Started as the Open Mind Common Sense project at MIT Media Lab in 1999. See https://en.wikipedia.org/wiki/Open_Mind_Common_Sense #### Who are the source language producers? Crowd Sourced ### Annotations #### Annotation process Crowd Source template text, games, etc. #### Who are the annotators? Crowd sourced. ### Personal and Sensitive Information Unkown, but likely there are names of famous individuals. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to help machines understand common sense. ### Discussion of Biases See the website and paper for efforts to minimize data bias, but please note that omcs_sentences_free, omcs_sentences_more are raw data entered by users and may very well have biased data. ### Other Known Limitations While the relationship dataset is large, the amount of actual sentences is limited. ## Additional Information ### Dataset Curators The authors of https://github.com/commonsense/conceptnet5/wiki and Luminoso. ### Licensing Information This work includes data from ConceptNet 5, which was compiled by the Commonsense Computing Initiative. ConceptNet 5 is freely available under the Creative Commons Attribution-ShareAlike license (CC BY SA 3.0) from http://conceptnet.io. The included data was created by contributors to Commonsense Computing projects, contributors to Wikimedia projects, DBPedia, OpenCyc, Games with a Purpose, Princeton University's WordNet, Francis Bond's Open Multilingual WordNet, and Jim Breen's JMDict. Credits and acknowledgements ConceptNet has been developed by: The MIT Media Lab, through various groups at different times: Commonsense Computing Software Agents Digital Intuition The Commonsense Computing Initiative, a worldwide collaboration with contributions from: National Taiwan University Universidade Federal de São Carlos Hokkaido University Tilburg University Nihon Unisys Labs Dentsu Inc. Kyoto University Yahoo Research Japan Luminoso Technologies, Inc. Significant amounts of data were imported from: WordNet, a project of Princeton University Open Multilingual WordNet, compiled by Francis Bond and Kyonghee Paik Wikipedia and Wiktionary, collaborative projects of the Wikimedia Foundation Luis von Ahn's "Games with a Purpose" JMDict, compiled by Jim Breen CC-CEDict, by MDBG The Unicode CLDR DBPedia Here is a short, incomplete list of people who have made significant contributions to the development of ConceptNet as a data resource, roughly in order of appearance: Push Singh Catherine Havasi Hugo Liu Hyemin Chung Robyn Speer Ken Arnold Yen-Ling Kuo Joshua Chin Joanna Lowry-Duda Robert Beaudoin Naoki Otani Vanya Cohen Licenses for included resources Commonsense Computing The Commonsense Computing project originated at the MIT Media Lab and expanded worldwide. Tens of thousands of contributors have taken some time to teach facts to computers. Their pseudonyms can be found in the "sources" list found in ConceptNet's raw data and in its API. Games with a Purpose Data collected from Verbosity, one of the CMU "Games with a Purpose", is used and released under ConceptNet's license, by permission from Luis von Ahn and Harshit Surana. Verbosity players are anonymous, so in the "sources" list, data from Verbosity is simply credited to the pseudonym "verbosity". Wikimedia projects ConceptNet uses data directly from Wiktionary, the free dictionary. It also uses data from Wikipedia, the free encyclopedia via DBPedia. Wiktionary and Wikipedia are collaborative projects, authored by their respective online communities. They are currently released under the Creative Commons Attribution-ShareAlike license. Wikimedia encourages giving attribution by providing links to the hosted pages that the data came from, and DBPedia asks for the same thing in turn. In addition to crediting the assertions that came from Wiktionary and DBPedia, we also provide "ExternalURL" edges pointing to the page that they came from. For example, the term /c/de/sprache has an ExternalURL link pointing to http://en.wiktionary.org/wiki/Sprache. Its list of individual contributors can be seen by following its "History" link. The URLs of links to DBPedia are the same as the resource names that DBPedia uses, encouraging interoperability with their linked data. WordNet WordNet is available under an unencumbered license: see http://wordnet.princeton.edu/wordnet/license/. Its text is reproduced below: WordNet Release 3.0 This software and database is being provided to you, the LICENSEE, by Princeton University under the following license. By obtaining, using and/or copying this software and database, you agree that you have read, understood, and will comply with these terms and conditions.: Permission to use, copy, modify and distribute this software and database and its documentation for any purpose and without fee or royalty is hereby granted, provided that you agree to comply with the following copyright notice and statements, including the disclaimer, and that the same appear on ALL copies of the software, database and documentation, including modifications that you make for internal use or for distribution. WordNet 3.0 Copyright 2006 by Princeton University. All rights reserved. THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PRINCETON UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE, DATABASE OR DOCUMENTATION WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS. The name of Princeton University or Princeton may not be used in advertising or publicity pertaining to distribution of the software and/or database. Title to copyright in this software, database and any associated documentation shall at all times remain with Princeton University and LICENSEE agrees to preserve same. Open Multilingual WordNet Open Multilingual WordNet was compiled by Francis Bond, Kyonghee Paik, and Ryan Foster, from data provided by many multilingual WordNet projects. Here is the complete list of references to the projects that created the data. ### Citation Information Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." In proceedings of AAAI 31. ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.	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Dahoas/static-hh	Dahoas	2023-03-06T00:11:55Z	718	15	null	[ "region:us" ]	2023-03-06T00:11:55Z	2023-02-15T03:53:36.000Z	2023-02-15T03:53:36	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 143664651 num_examples: 96256 - name: test num_bytes: 7649255 num_examples: 5103 download_size: 90825631 dataset_size: 151313906 --- Static split of Anthropic's Helpful Harmless dataset. Contains base-online and rejection sampled outputs.	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LeoLM/OpenSchnabeltier	LeoLM	2023-08-27T19:03:20Z	718	3	null	[ "region:us" ]	2023-08-27T19:03:20Z	2023-08-25T13:06:27.000Z	2023-08-25T13:06:27	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: output dtype: string - name: instruction dtype: string - name: instruction_de dtype: string - name: output_de dtype: string - name: translation_de dtype: string splits: - name: train num_bytes: 66379650.83254641 num_examples: 21749 download_size: 33021431 dataset_size: 66379650.83254641 --- # Dataset Card for "open_platypus_de" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5176191926002502, -0.3017076253890991, 0.14864802360534668, 0.3097718358039856, -0.40736907720565796, -0.11699487268924713, -0.08296164125204086, 0.026363255456089973, 0.7957724928855896, 0.6478363871574402, -0.6205626130104065, -0.9317511320114136, -0.5482856035232544, 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yangwang825/sst2-textbugger	yangwang825	2023-10-09T22:09:36Z	718	0	null	[ "region:us" ]	2023-10-09T22:09:36Z	2023-10-09T21:08:44.000Z	2023-10-09T21:08:44	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {} --- # Stanford Sentiment Treebank - Binary	[ -0.12394007295370102, -0.22449058294296265, 0.1937677264213562, 0.8990573883056641, -0.5129232406616211, 0.2830127477645874, 0.2150372862815857, -0.20814304053783417, 0.4294002950191498, 0.2890351116657257, -0.44846874475479126, -0.7690048217773438, -0.8366124629974365, 0.033661291003227234, -0.32092607021331787, 1.776746153831482, 0.5525192618370056, 0.5195845365524292, -0.06389676779508591, -0.0317857451736927, 0.30032408237457275, -0.37311413884162903, -0.755142092704773, 0.08389745652675629, 0.729956865310669, 0.5211318135261536, 0.7191591262817383, -0.23975197970867157, 0.6702333092689514, 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id_nergrit_corpus	null	2023-01-25T14:32:40Z	717	3	nergrit-corpus	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:id", "license:other", "region:us" ]	2023-01-25T14:32:40Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - id license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: nergrit-corpus pretty_name: Nergrit Corpus dataset_info: - config_name: ner features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-CRD '1': B-DAT '2': B-EVT '3': B-FAC '4': B-GPE '5': B-LAN '6': B-LAW '7': B-LOC '8': B-MON '9': B-NOR '10': B-ORD '11': B-ORG '12': B-PER '13': B-PRC '14': B-PRD '15': B-QTY '16': B-REG '17': B-TIM '18': B-WOA '19': I-CRD '20': I-DAT '21': I-EVT '22': I-FAC '23': I-GPE '24': I-LAN '25': I-LAW '26': I-LOC '27': I-MON '28': I-NOR '29': I-ORD '30': I-ORG '31': I-PER '32': I-PRC '33': I-PRD '34': I-QTY '35': I-REG '36': I-TIM '37': I-WOA '38': O splits: - name: train num_bytes: 5428411 num_examples: 12532 - name: test num_bytes: 1135577 num_examples: 2399 - name: validation num_bytes: 1086437 num_examples: 2521 download_size: 14988232 dataset_size: 7650425 - config_name: sentiment features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-NEG '1': B-NET '2': B-POS '3': I-NEG '4': I-NET '5': I-POS '6': O splits: - name: train num_bytes: 3167972 num_examples: 7485 - name: test num_bytes: 1097517 num_examples: 2317 - name: validation num_bytes: 337679 num_examples: 782 download_size: 14988232 dataset_size: 4603168 - config_name: statement features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-BREL '1': B-FREL '2': B-STAT '3': B-WHO '4': I-BREL '5': I-FREL '6': I-STAT '7': I-WHO '8': O splits: - name: train num_bytes: 1469081 num_examples: 2405 - name: test num_bytes: 182553 num_examples: 335 - name: validation num_bytes: 105119 num_examples: 176 download_size: 14988232 dataset_size: 1756753 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [PT Gria Inovasi Teknologi](https://grit.id/) - Repository: [Nergrit Corpus](https://github.com/grit-id/nergrit-corpus) - Paper: - Leaderboard: - Point of Contact: [Taufiqur Rohman](mailto:taufiq@grit.id) ### Dataset Summary Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition, Statement Extraction, and Sentiment Analysis developed by [PT Gria Inovasi Teknologi (GRIT)](https://grit.id/). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. ``` {'id': '0', 'tokens': ['Gubernur', 'Bank', 'Indonesia', 'menggelar', 'konferensi', 'pers'], 'ner_tags': [9, 28, 28, 38, 38, 38], } ``` ### Data Instances [More Information Needed] ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token #### Named Entity Recognition The ner_tags correspond to this list: ``` "B-CRD", "B-DAT", "B-EVT", "B-FAC", "B-GPE", "B-LAN", "B-LAW", "B-LOC", "B-MON", "B-NOR", "B-ORD", "B-ORG", "B-PER", "B-PRC", "B-PRD", "B-QTY", "B-REG", "B-TIM", "B-WOA", "I-CRD", "I-DAT", "I-EVT", "I-FAC", "I-GPE", "I-LAN", "I-LAW", "I-LOC", "I-MON", "I-NOR", "I-ORD", "I-ORG", "I-PER", "I-PRC", "I-PRD", "I-QTY", "I-REG", "I-TIM", "I-WOA", "O", ``` The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. The dataset contains 19 following entities ``` 'CRD': Cardinal 'DAT': Date 'EVT': Event 'FAC': Facility 'GPE': Geopolitical Entity 'LAW': Law Entity (such as Undang-Undang) 'LOC': Location 'MON': Money 'NOR': Political Organization 'ORD': Ordinal 'ORG': Organization 'PER': Person 'PRC': Percent 'PRD': Product 'QTY': Quantity 'REG': Religion 'TIM': Time 'WOA': Work of Art 'LAN': Language ``` #### Sentiment Analysis The ner_tags correspond to this list: ``` "B-NEG", "B-NET", "B-POS", "I-NEG", "I-NET", "I-POS", "O", ``` #### Statement Extraction The ner_tags correspond to this list: ``` "B-BREL", "B-FREL", "B-STAT", "B-WHO", "I-BREL", "I-FREL", "I-STAT", "I-WHO", "O" ``` The ner_tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. ### Data Splits The dataset is splitted in to train, validation and test sets. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The annotators are listed in the [Nergrit Corpus repository](https://github.com/grit-id/nergrit-corpus) ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.	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laion/gpt4v-emotion-dataset	laion	2023-11-28T23:16:14Z	716	5	null	[ "region:us" ]	2023-11-28T23:16:14Z	2023-10-15T18:25:14.000Z	2023-10-15T18:25:14	--- dataset_info: features: - name: caption dtype: string - name: link dtype: string - name: message_id dtype: string - name: timestamp dtype: string - name: image dtype: image splits: - name: train num_bytes: 241951.0 num_examples: 114 download_size: 145085 dataset_size: 241951.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "gpt4v-emotion-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6573855876922607, -0.06510786712169647, 0.2989320456981659, 0.2972065210342407, -0.3189227879047394, -0.045466162264347076, 0.23002846539020538, -0.05651143938302994, 0.7078506946563721, 0.10359342396259308, -0.8940918445587158, -0.7982450723648071, -0.6054961681365967, -0.21739071607589722, 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]	null	null	null	null	null	null	null	null	null	null	null	null	null
greek_legal_code	null	2023-06-12T14:25:00Z	715	8	null	[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:el", "license:cc-by-4.0", "arxiv:2109.15298", "region:us" ]	2023-06-12T14:25:00Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - found language_creators: - found language: - el license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification - topic-classification pretty_name: Greek Legal Code dataset_info: - config_name: volume features: - name: text dtype: string - name: label dtype: class_label: names: '0': ΚΟΙΝΩΝΙΚΗ ΠΡΟΝΟΙΑ '1': ΓΕΩΡΓΙΚΗ ΝΟΜΟΘΕΣΙΑ '2': ΡΑΔΙΟΦΩΝΙΑ ΚΑΙ ΤΥΠΟΣ '3': ΒΙΟΜΗΧΑΝΙΚΗ ΝΟΜΟΘΕΣΙΑ '4': ΥΓΕΙΟΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ '5': ΠΟΛΕΜΙΚΟ ΝΑΥΤΙΚΟ '6': ΤΑΧΥΔΡΟΜΕΙΑ - ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ '7': ΔΑΣΗ ΚΑΙ ΚΤΗΝΟΤΡΟΦΙΑ '8': ΕΛΕΓΚΤΙΚΟ ΣΥΝΕΔΡΙΟ ΚΑΙ ΣΥΝΤΑΞΕΙΣ '9': ΠΟΛΕΜΙΚΗ ΑΕΡΟΠΟΡΙΑ '10': ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ '11': ΝΟΜΟΘΕΣΙΑ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ ΤΡΑΠΕΖΩΝ ΚΑΙ ΧΡΗΜΑΤΙΣΤΗΡΙΩΝ '12': ΠΟΛΙΤΙΚΗ ΑΕΡΟΠΟΡΙΑ '13': ΕΜΜΕΣΗ ΦΟΡΟΛΟΓΙΑ '14': ΚΟΙΝΩΝΙΚΕΣ ΑΣΦΑΛΙΣΕΙΣ '15': ΝΟΜΟΘΕΣΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '16': ΝΟΜΟΘΕΣΙΑ ΕΠΙΜΕΛΗΤΗΡΙΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ ΚΑΙ ΣΩΜΑΤΕΙΩΝ '17': ΔΗΜΟΣΙΑ ΕΡΓΑ '18': ΔΙΟΙΚΗΣΗ ΔΙΚΑΙΟΣΥΝΗΣ '19': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ '20': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '21': ΕΚΠΑΙΔΕΥΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '22': ΔΗΜΟΣΙΟ ΛΟΓΙΣΤΙΚΟ '23': ΤΕΛΩΝΕΙΑΚΗ ΝΟΜΟΘΕΣΙΑ '24': ΣΥΓΚΟΙΝΩΝΙΕΣ '25': ΕΘΝΙΚΗ ΑΜΥΝΑ '26': ΣΤΡΑΤΟΣ ΞΗΡΑΣ '27': ΑΓΟΡΑΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ '28': ΔΗΜΟΣΙΟΙ ΥΠΑΛΛΗΛΟΙ '29': ΠΕΡΙΟΥΣΙΑ ΔΗΜΟΣΙΟΥ ΚΑΙ ΝΟΜΙΣΜΑ '30': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ '31': ΛΙΜΕΝΙΚΗ ΝΟΜΟΘΕΣΙΑ '32': ΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '33': ΠΟΛΙΤΙΚΗ ΔΙΚΟΝΟΜΙΑ '34': ΔΙΠΛΩΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '35': ΔΙΟΙΚΗΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '36': ΑΜΕΣΗ ΦΟΡΟΛΟΓΙΑ '37': ΤΥΠΟΣ ΚΑΙ ΤΟΥΡΙΣΜΟΣ '38': ΕΘΝΙΚΗ ΟΙΚΟΝΟΜΙΑ '39': ΑΣΤΥΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ '40': ΑΓΡΟΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '41': ΕΡΓΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '42': ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ '43': ΕΜΠΟΡΙΚΗ ΝΟΜΟΘΕΣΙΑ '44': ΕΠΙΣΤΗΜΕΣ ΚΑΙ ΤΕΧΝΕΣ '45': ΕΜΠΟΡΙΚΗ ΝΑΥΤΙΛΙΑ '46': ΣΥΝΤΑΓΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ splits: - name: train num_bytes: 216757887 num_examples: 28536 - name: test num_bytes: 71533786 num_examples: 9516 - name: validation num_bytes: 68824457 num_examples: 9511 download_size: 45606292 dataset_size: 357116130 - config_name: chapter features: - name: text dtype: string - name: label dtype: class_label: names: '0': ΜΕΤΑΛΛΕΙΑ ΚΑΙ ΟΡΥΧΕΙΑ '1': ΣΤΑΤΙΩΤΙΚΕΣ ΣΧΟΛΕΣ '2': ΠΑΡΟΧΕΣ ΑΝΕΡΓΙΑΣ '3': ΣΙΔΗΡΟΔΡΟΜΙΚΑ ΔΙΚΤΥΑ '4': ΕΙΔΙΚΑ ΣΤΡΑΤΙΩΤΙΚΑ ΑΔΙΚΗΜΑΤΑ '5': ΚΡΑΤΙΚΕΣ ΠΡΟΜΗΘΕΙΕΣ '6': ΑΓΡΟΤΙΚΗ ΑΠΟΚΑΤΑΣΤΑΣΗ '7': ΑΞΙΩΜΑΤΙΚΟΙ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '8': ΣΧΕΔΙΑ ΠΟΛΕΩΝ '9': ΣΥΚΑ '10': ΠΡΟΛΗΨΙΣ ΚΑΙ ΔΙΩΞΙΣ ΤΟΥ ΕΓΚΛΗΜΑΤΟΣ '11': ΔΙΕΘΝΕΙΣ ΜΕΤΑΦΟΡΕΣ '12': ΓΕΝΙΚΗ ΣΥΓΚΟΙΝΩΝΙΑ ΚΑΙ ΔΙΑΤΑΞΕΙΣ '13': ΚΛΗΡΟΝΟΜΙΚΟ ΔΙΚΑΙΟ '14': ΚΟΙΝΩΝΙΚΗ ΑΝΤΙΛΗΨΗ '15': ΝΑΥΤΙΛΙΑΚΕΣ ΣΗΜΑΝΣΕΙΣ '16': ΔΙΕΘΝΕΣ ΠΟΙΝΙΚΟ ΔΙΚΑΙΟ '17': ΑΣΦΑΛΙΣΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ Ε.Ν '18': ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ '19': ΣΠΟΡΟΠΑΡΑΓΩΓΗ '20': ΥΠΗΡΕΣΙΑΙ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ '21': ΤΑΜΕΙΑ ΣΥΝΤΑΞΕΩΝ ΤΡΑΠΕΖΩΝ '22': ΠΥΡΟΣΒΕΣΤΙΚΟ ΣΩΜΑ '23': ΔΙΑΦΟΡΕΣ ΒΙΟΜΗΧΑΝΙΕΣ '24': ΕΚΤΕΛΕΣΗ ΚΑΙ ΣΥΝΕΠΕΙΕΣ ΤΗΣ ΠΟΙΝΗΣ '25': ΔΙΕΘΝΕΙΣ ΑΣΦΑΛΙΣΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '26': ΝΟΜΟΘΕΣΙΑ '27': ΒΑΜΒΑΚΙ '28': ΠΑΡΟΧΕΣ ΣΥΝΤΑΞΕΩΝ '29': ΝΟΜΙΣΜΑ '30': ΣΥΜΒΑΣΗ ΝΑΥΤΙΚΗΣ ΕΡΓΑΣΙΑΣ '31': ΟΡΓΑΝΙΣΜΟΊ ΚΟΙΝΩΝΙΚΉΣ ΑΣΦΑΛΊΣΕΩΣ '32': ΑΓΡΟΤΙΚΗ ΑΣΦΑΛΕΙΑ '33': ΥΓΕΙΟΝΟΜΙΚΟΣ ΕΛΕΓΧΟΣ ΕΙΣΕΡΧΟΜΕΝΩΝ '34': ΜΟΥΣΕΙΑ ΚΑΙ ΣΥΛΛΟΓΕΣ '35': ΠΡΟΣΩΠΙΚΟ Ι.Κ.Α '36': ΞΕΝΟΔΟΧΕΙΑ '37': ΚΡΑΤΙΚΗ ΑΣΦΑΛΕΙΑ '38': ΣΥΝΕΤΑΙΡΙΣΜΟΙ '39': ΠΟΛΥΕΘΝΕΙΣ ΣΥΜΦΩΝΙΕΣ '40': ΕΤΕΡΟΔΟΞΟΙ '41': ΜΕΣΗ ΕΚΠΑΙΔΕΥΣΙΣ '42': ΓΕΩΡΓΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '43': ΓΕΝΙΚΟ ΛΟΓΙΣΤΗΡΙΟ '44': ΡΥΘΜΙΣΗ ΤΗΣ ΑΓΟΡΑΣ ΕΡΓΑΣΙΑΣ '45': ΠΑΡΟΧΟΙ ΚΙΝΗΤΩΝ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ '46': ΕΜΠΡΑΓΜΑΤΟΣ ΑΣΦΑΛΕΙΑ '47': ΦΟΡΟΛΟΓΙΑ ΑΚΑΘΑΡΙΣΤΟΥ ΠΡΟΣΟΔΟΥ '48': ΚΤΗΜΑΤΙΚΕΣ ΤΡΑΠΕΖΕΣ '49': ΣΤΑΤΙΣΤΙΚΗ '50': ΚΕΡΑΙΕΣ – ΣΤΑΘΜΟΙ ΚΕΡΑΙΩΝ '51': ΠΟΙΝΙΚΟΣ ΝΟΜΟΣ '52': ΜΕΣΑ ΔΙΔΑΣΚΑΛΙΑΣ '53': ΕΜΠΟΡΙΟ ΦΑΡΜΑΚΩΝ '54': ΔΙΑΦΟΡΑ '55': ΔΗΜΟΣΙΑ ΚΤΗΜΑΤΑ '56': ΕΙΣΦΟΡΕΣ Ι.Κ.Α '57': ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ '58': ΠΡΟΣΩΠΙΚΟ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '59': ΔΗΜΟΣΙΟ ΧΡΕΟΣ '60': ΑΠΟΤΑΜΙΕΥΣΗ '61': ΑΛΛΟΘΡΗΣΚΟΙ '62': ΠΛΟΗΓΙΚΗ ΥΠΗΡΕΣΙΑ '63': ΤΥΠΟΣ ΚΑΙ ΠΛΗΡΟΦΟΡΙΕΣ '64': ΤΡΟΠΟΠΟΙΗΣΗ ΚΑΙ ΚΑΤΑΡΓΗΣΗ ΤΗΣ ΠΟΙΝΗΣ '65': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΤΥΠΟΥ '66': ΟΙΚΟΓΕΝΕΙΑΚΟ ΔΙΚΑΙΟ '67': ΔΙΟΙΚΗΣΗ ΕΘΝΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ '68': ΥΠΟΥΡΓΕΙΟ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ '69': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΡΟΝΟΙΑΣ '70': ΠΡΟΣΩΠΙΚΟ ΤΩΝ ΔΙΚΑΣΤΗΡΙΩΝ '71': ΠΡΟΣΤΑΣΙΑ ΠΡΟΣΩΠΩΝ ΕΙΔΙΚΩΝ ΚΑΤΗΓΟΡΙΩΝ '72': ΠΑΡΟΧΕΣ ΑΣΘΕΝΕΙΑΣ '73': ΜΕΤΑΝΑΣΤΕΥΣΗ '74': ΥΠΟΥΡΓΕΙΟ ΠΑΙΔΕΙΑΣ '75': ΑΣΦΑΛΕΙΑ ΝΑΥΣΙΠΛΟΪΑΣ '76': ΟΔΟΠΟΙΪΑ '77': ΣΤΡΑΤΟΔΙΚΕΙΑ '78': ΜΙΣΘΩΣΗ '79': ΕΙΣΠΡΑΞΗ ΔΗΜΟΣΙΩΝ ΕΣΟΔΩΝ '80': ΟΠΛΙΤΕΣ ΚΑΙ ΑΝΘΥΠΑΣΠΙΣΤΕΣ '81': ΟΡΓΑΝΙΣΜΟΣ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ ΕΛΛΑΔΑΣ (Ο.Τ.Ε.) '82': ΌΡΓΑΝΑ ΆΣΚΗΣΗΣ ΔΙΑΧΕΙΡΙΣΤΙΚΟΎ ΕΛΈΓΧΟΥ ΟΡΓΑΝΙΣΜΏΝ ΚΑΙ ΕΠΙΧΕΙΡΉΣΕΩΝ '83': ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ ΤΥΠΟΥ '84': ΕΞΑΓΩΓΙΚΟ ΕΜΠΟΡΙΟ '85': ΑΕΡΟΠΟΡΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '86': ΓΕΩΡΓΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΑΓΡΟΤΙΚΕΣ ΣΥΝΕΤΑΙΡΙΣΤΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ '87': ΟΙΚΟΝΟΜΙΚΕΣ ΥΠΗΡΕΣΙΕΣ '88': ΟΧΥΡΩΣΕΙΣ '89': ΕΚΤΑΚΤΟΙ ΠΟΙΝΙΚΟΙ ΝΟΜΟΙ '90': ΕΚΤΕΛΕΣΗ '91': ΔΙΟΙΚΗΤΙΚΟΙ ΚΑΝΟΝΙΣΜΟΙ '92': ΥΔΡΑΥΛΙΚΑ ΕΡΓΑ '93': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '94': ΕΚΚΑΘΑΡΙΣΕΙΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '95': ΔΙΟΙΚΗΣΗ ΕΜΠΟΡΙΚΟΥ ΝΑΥΤΙΚΟΥ '96': ΑΝΩΤΑΤΟ ΕΙΔΙΚΟ ΔΙΚΑΣΤΗΡΙΟ '97': ΑΡΤΟΣ '98': ΕΙΣΑΓΩΓΙΚΟ ΕΜΠΟΡΙΟ '99': ΑΛΙΕΙΑ '100': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΠΕΡΙΟΥΣΙΑ '101': ΔΙΑΦΟΡΑ ΔΗΜΟΣΙΑ ΕΡΓΑ '102': ΜΟΝΕΣ '103': ΠΡΟΕΔΡΟΣ ΤΗΣ ΔΗΜΟΚΡΑΤΙΑΣ ΚΑΙ ΠΡΟΕΔΡΙΑ ΤΗΣ ΔΗΜΟΚΡΑΤΙΑΣ '104': ΠΟΛΥΕΘΝΕΙΣ ΟΡΓΑΝΙΣΜΟΙ '105': ΑΡΧΑΙΟΤΗΤΕΣ '106': ΝΑΟΙ ΚΑΙ ΛΕΙΤΟΥΡΓΟΙ ΑΥΤΩΝ '107': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '108': ΕΝΙΣΧΥΣΙΣ ΤΗΣ ΓΕΩΡΓΙΑΣ '109': ΕΚΘΕΣΕΙΣ '110': ΠΡΟΣΤΑΣΙΑ ΤΩΝ ΣΥΝΑΛΛΑΓΩΝ '111': ΑΣΦΑΛΙΣΗ '112': ΚΤΗΝΟΤΡΟΦΙΑ '113': ΕΚΠΑΙΔΕΥΤΙΚΑ ΤΕΛΗ '114': ΔΙΟΙΚΗΣΗ ΕΚΠΑΙΔΕΥΣΕΩΣ '115': ΤΑΜΕΙΟ ΠΑΡΑΚΑΤΑΘΗΚΩΝ ΚΑΙ ΔΑΝΕΙΩΝ '116': ΑΓΑΘΟΕΡΓΑ ΙΔΡΥΜΑΤΑ '117': ΦΟΡΟΛΟΓΙΚΑ ΔΙΚΑΣΤΗΡΙΑ '118': ΦΟΡΟΙ ΚΑΤΑΝΑΛΩΣΕΩΣ '119': ΒΙΒΛΙΟΘΗΚΕΣ-ΠΡΟΣΤΑΣΙΑ ΒΙΒΛΙΟΥ-ΔΙΑΔΟΣΗ ΛΟΓΟΤΕΧΝΙΑΣ '120': ΤΗΛΕΠΙΚΟΙΝΩΝΙΑΚΕΣ ΚΑΙ ΤΑΧΥΔΡΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '121': ΙΔΙΩΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '122': ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ '123': ΑΣΥΡΜΑΤΟΣ '124': ΑΠΟΔΟΧΕΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΩΝ '125': ΥΓΕΙΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΣΤΡΑΤΟΥ '126': ΦΑΡΜΑΚΕΙΑ '127': ΔΗΜΟΣΙΟ ΛΟΓΙΣΤΙΚΟ '128': ΝΑΥΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '129': ΕΞΥΠΗΡΕΤΗΣΗ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '130': ΠΑΡΟΧΕΣ Ι.Κ.Α '131': ΓΕΝΙΚΑ ΥΓΕΙΟΝΟΜΙΚΑ ΜΕΤΡΑ '132': ΕΚΜΕΤΑΛΛΕΥΣΗ ΘΑΛΑΣΣΙΩΝ ΣΥΓΚΟΙΝΩΝΙΩΝ '133': ΠΡΟΣΩΠΙΚΟ ΤΑΧΥΔΡΟΜΕΙΩΝ '134': ΕΚΤΕΛΕΣΤΙΚΗ ΕΞΟΥΣΙΑ '135': ΣΥΣΤΑΣΗ ΚΑΙ ΕΔΡΑ ΤΟΥ ΚΡΑΤΟΥΣ '136': ΦΟΡΟΛΟΓΙΑ ΔΙΑΣΚΕΔΑΣΕΩΝ '137': ΤΗΛΕΦΩΝΑ '138': ΣΤΡΑΤΟΛΟΓΙΑ '139': ΕΚΠΑΙΔΕΥΣΗ ΕΡΓΑΤΩΝ '140': ΥΠΟΥΡΓΕΙΟ ΠΟΛΙΤΙΣΜΟΥ '141': ΦΟΡΟΛΟΓΙΑ ΟΙΝΟΠΝΕΥΜΑΤΩΔΩΝ ΠΟΤΩΝ '142': ΥΠΟΥΡΓΕΙΟ ΓΕΩΡΓΙΑΣ '143': ΣΩΜΑΤΕΙΑ '144': ΕΙΔΙΚΕΣ ΜΟΡΦΕΣ ΑΠΑΣΧΟΛΗΣΗΣ '145': ΥΠΟΥΡΓΕΙΟ ΔΙΚΑΙΟΣΥΝΗΣ '146': ΝΑΥΤΙΛΙΑΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '147': ΤΟΥΡΙΣΜΟΣ '148': ΚΑΠΝΟΣ '149': ΠΡΟΣΤΑΣΙΑ ΗΘΩΝ '150': ΕΙΔΙΚΕΣ ΥΠΗΡΕΣΙΕΣ ΝΑΥΤΙΚΟΥ '151': ΑΠΟΔΟΧΕΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '152': ΠΡΟΝΟΙΑ ΠΛΗΡΩΜΑΤΩΝ Ε.Ν '153': ΕΙΔΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΕΡΙ ΑΝΩΝ.ΕΤΑΙΡΕΙΩΝ '154': ΔΗΜΟΣΙΑ ΔΙΟΙΚΗΣΗ '155': ΤΟΠΙΚΑ ΣΧΕΔΙΑ ΠΟΛΕΩΝ '156': ΠΡΟΣΤΑΣΙΑ ΠΑΙΔΙΚΗΣ ΗΛΙΚΙΑΣ '157': ΕΛΛΗΝΙΚΗ ΑΣΤΥΝΟΜΙΑ '158': ΛΙΜΕΝΙΚΟ ΣΩΜΑ '159': ΤΟΥΡΙΣΤΙΚΗ ΑΣΤΥΝΟΜΙΑ '160': ΒΙΟΜΗΧΑΝΙΑ '161': ΣΧΟΛΕΣ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΑΘΗΝΩΝ '162': ΑΣΦΑΛΙΣΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ ΣΤΡΑΤΟΥ '163': ΑΛΥΚΕΣ '164': ΕΣΩΤΕΡΙΚΟ ΕΜΠΟΡΙΟ '165': ΕΘΝΙΚΟ ΣΥΣΤΗΜΑ ΥΓΕΙΑΣ '166': ΝΟΜΟΘΕΤΙΚΗ ΕΞΟΥΣΙΑ '167': ΔΙΟΙΚΗΣH ΚΟΙΝΩΝIKΗΣ ΠΡΟΝΟΙΑΣ '168': ΠΛΗΡΩΜΑΤΑ '169': ΜΑΘΗΤΙΚΗ ΠΡΟΝΟΙΑ '170': ΔΙΟΙΚΗΣΗ ΤΥΠΟΥ ΚΑΙ ΤΟΥΡΙΣΜΟΥ '171': ΕΠΟΙΚΙΣΜΟΣ '172': ΤΡΟΧΙΟΔΡΟΜΟΙ '173': ΕΠΑΓΓΕΛΜΑΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '174': ΑΕΡΟΠΟΡΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '175': ΥΠΟΥΡΓΕΙΟ ΕΘΝΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ '176': ΘΕΑΤΡΟ '177': ΥΔΡΕΥΣΗ '178': ΔΙΕΘΝΕΙΣ ΣΤΡΑΤΙΩΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '179': ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ '180': ΥΠΟΥΡΓΕΙΟ ΕΞΩΤΕΡΙΚΩΝ '181': ΕΥΡΩΠΑΪΚΟΙ ΠΟΛΥΕΘΝΕΙΣ ΟΡΓΑΝΙΣΜΟΙ '182': ΕΛΕΥΘΕΡΙΑ ΤΗΣ ΕΡΓΑΣΙΑΣ '183': ΥΠΟΥΡΓΕΙΟ ΕΣΩΤΕΡΙΚΩΝ ΔΗΜ.ΔΙΟΙΚΗΣΗΣ ΚΑΙ ΑΠΟΚΕΝΤΡΩΣΗΣ '184': ΔΙΑΦΟΡΕΣ ΕΝΟΧΙΚΕΣ ΣΧΕΣΕΙΣ '185': ΛΗΞΙΑΡΧΕΙΑ '186': ΕΙΔΙΚΟΙ ΚΑΝΟΝΙΣΜΟΙ '187': ΤΕΛΩΝΕΙΑΚΕΣ ΣΥΜΒΑΣΕΙΣ '188': ΝΑΥΤΙΚΟ ΠΟΙΝΙΚΟ ΔΙΚΑΙΟ '189': ΣΤΕΓΑΣΗ ΔΗΜΟΣΙΩΝ ΥΠΗΡΕΣΙΩΝ '190': ΠΛΗΡΩΜΑΤΑ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '191': ΣΥΝΤΑΓΜΑΤΙΚΟΣ ΧΑΡΤΗΣ '192': ΗΛΕΚΤΡΙΣΜΟΣ '193': ΑΣΦΑΛΙΣΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ '194': ΛΕΣΧΕΣ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '195': ΥΠΟΥΡΓΕΙΟ ΔΗΜΟΣΙΑΣ TAΞΗΣ '196': ΕΚΤΕΛΕΣ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ '197': ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ '198': ΔΑΣΙΚΗ ΝΟΜΟΘΕΣΙΑ '199': ΕΙΔΙΚΕΣ ΑΝΩΤΑΤΕΣ ΣΧΟΛΕΣ '200': ΕΔΑΦΟΣ ΤΟΥ ΕΛΛΗΝΙΚΟΥ ΚΡΑΤΟΥΣ '201': ΔΙΚΗΓΟΡΟΙ '202': ΔΙΚΑΙΟ ΤΩΝ ΠΡΟΣΩΠΩΝ '203': ΔΙΟΙΚΗΣΗ ΤΑΧΥΔΡΟΜΙΚΗΣ, ΤΗΛΕΓΡΑΦΙΚΗΣ '204': ΣΧΟΛΙΚΑ ΚΤΙΡΙΑ ΚΑΙ ΤΑΜΕΙΑ '205': ΑΕΡΟΛΙΜΕΝΕΣ '206': ΥΠΟΘΗΚΟΦΥΛΑΚΕΙΑ '207': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΠΡΟΣΩΠΙΚΟΥ ΥΠΟΥΡΓΕΙΟΥ ΔΗΜΟΣΙΑΣ ΤΑΞΗΣ '208': ΔΙΑΧΕΙΡΙΣΕΙΣ ΤΟΥ ΔΗΜΟΣΙΟΥ '209': ΕΜΠΡΑΓΜΑΤΟ ΔΙΚΑΙΟ '210': ΦΟΡΤΟΕΚΦΟΡΤΩΣΕΙΣ '211': ΑΝΩΝΥΜΕΣ ΕΤΑΙΡΕΙΕΣ '212': ΕΙΔΙΚΟΙ ΕΠΙΣΙΤΙΣΤΙΚΟΙ ΝΟΜΟΙ '213': ΕΚΚΛΗΣΙΕΣ ΑΛΛΟΔΑΠΗΣ '214': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ '215': ΟΡΓΑΝΙΣΜΟΣ ΑΣΦΑΛΙΣΗΣ ΕΛΕΥΘΕΡΩΝ ΕΠΑΓΓΕΛΜΑΤΙΩΝ '216': ΑΣΦΑΛΕΙΑ ΑΕΡΟΠΛΟΪΑΣ '217': ΤΑΜΕΙΑ ΑΣΦΑΛΙΣΕΩΣ ΚΑΙ ΑΡΩΓΗΣ '218': ΑΝΩΤΑΤΗ ΕΚΠΑΙΔΕΥΣΗ '219': ΠΟΛΕΜΙΚΗ ΔΙΑΘΕΣΙΜΟΤΗΤΑ '220': ΠΟΙΝΙΚΟ ΚΑΙ ΠΕΙΘΑΡΧΙΚΟ ΔΙΚΑΙΟ '221': ΦΟΡΟΛΟΓΙΑ ΕΠΙΤΗΔΕΥΜΑΤΟΣ '222': ΕΚΤΑΚΤΕΣ ΦΟΡΟΛΟΓΙΕΣ '223': ΠΟΙΝΙΚΗ ΔΙΚΟΝΟΜΙΑ '224': ΣΤΟΙΧΕΙΩΔΗΣ ΕΚΠΑΙΔΕΥΣΗ '225': ΣΥΜΒΟΥΛΙΟ ΕΠΙΚΡΑΤΕΙΑΣ ΚΑΙ ΔΙΟΙΚΗΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ '226': ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΚΑΙ ΕΚΜΕΤΑΛΛΕΥΣΕΙΣ '227': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ ΝΑΥΤΙΚΟΥ '228': ΤΥΠΟΣ '229': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΠΑΓΓΕΛΜΑΤΙΩΝ '230': ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ '231': ΧΡΕΩΓΡΑΦΑ '232': ΠΡΟΪΟΝΤΑ ΕΛΑΙΑΣ '233': ΕΚΚΛΗΣΙΑ ΙΟΝΙΩΝ ΝΗΣΩΝ '234': ΔΙΟΙΚΗΣH ΥΓΙΕΙΝΗΣ '235': ΑΕΡΟΠΟΡΙΚΟ ΠΟΙΝΙΚΟ ΔΙΚΑΙΟ '236': ΚΑΤΑΠΟΛΕΜΗΣΗ ΝΟΣΩΝ ΚΑΤ’ ΙΔΙΑΝ '237': ΕΙΔΙΚΟΙ ΠΟΙΝΙΚΟΙ ΝΟΜΟΙ '238': ΘΗΡΑ '239': ΥΓΙΕΙΝΗ ΚΑΙ ΑΣΦΑΛΕΙΑ ΕΡΓΑΖΟΜΕΝΩΝ '240': ΔΙΟΙΚΗΣΗ ΣΥΓΚΟΙΝΩΝΙΩΝ '241': ΑΠΟΣΤΟΛΙΚΗ ΔΙΑΚΟΝΙΑ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '242': ΠΡΟΣΩΡΙΝΕΣ ΑΤΕΛΕΙΕΣ '243': ΤΑΧΥΔΡΟΜΙΚΑ ΤΑΜΙΕΥΤΗΡΙΑ '244': ΑΝΩΤΑΤΗ ΣΧΟΛΗ ΚΑΛΩΝ ΤΕΧΝΩΝ '245': ΔΙΟΙΚΗΣΗ ΕΡΓΑΣΙΑΣ '246': ΑΓΙΟΝ ΟΡΟΣ '247': ΣΧΟΛΕΣ Π. ΝΑΥΤΙΚΟΥ '248': ΤΡΑΠΕΖΕΣ '249': ΕΛΕΓΧΟΣ ΚΙΝΗΣΕΩΣ ΜΕ ΤΟ ΕΞΩΤΕΡΙΚΟ '250': ΕΙΔΙΚΑΙ ΚΑΤΗΓΟΡΙΑΙ ΠΛΟΙΩΝ '251': ΓΕΩΡΓΙΚΗ ΥΓΙΕΙΝΗ '252': ΕΞΟΔΑ ΠΟΙΝΙΚΗΣ ΔΙΑΔΙΚΑΣΙΑΣ '253': ΕΡΓΑΣΙΑ ΓΥΝΑΙΚΩΝ ΚΑΙ ΑΝΗΛΙΚΩΝ '254': ΔΙΟΙΚΗΣΗ ΕΦΟΔΙΑΣΜΟΥ '255': ΕΜΠΟΡΙΚΑ ΕΠΑΓΓΕΛΜΑΤΑ '256': ΕΚΤΕΛΩΝΙΣΤΕΣ '257': ΦΟΡΟΛΟΓΙΑ ΚΛΗΡΟΝΟΜΙΩΝ, ΔΩΡΕΩΝ ΚΛΠ '258': ΟΡΓΑΝΙΣΜΟΙ ΥΠΟΥΡΓΕΙΟΥ ΕΡΓΑΣΙΑΣ '259': ΕΝΙΣΧΥΣΗ ΕΠΙΣΤΗΜΩΝ ΚΑΙ ΤΕΧΝΩΝ '260': ΔΙΑΦΟΡΟΙ ΦΟΡΟΛΟΓΙΚΟΙ ΝΟΜΟΙ '261': ΤΕΧΝΙΚΕΣ ΠΡΟΔΙΑΓΡΑΦΕΣ '262': ΜΗΤΡΩΑ ΔΗΜΟΤΩΝ '263': ΚΑΤΑΣΤΑΣΗ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '264': ΠΡΟΣΩΠΙΚΟΝ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '265': ΥΓΕΙΟΝΟΜΙΚΗ ΑΝΤΙΛΗΨΗ '266': ΤΕΛΗ ΧΑΡΤΟΣΗΜΟΥ '267': ΣΤΡΑΤΙΩΤΙΚΟΙ ΓΕΝΙΚΑ '268': ΛΙΜΕΝΙΚΕΣ ΑΡΧΕΣ '269': ΕΛΕΓΧΟΣ ΚΥΚΛΟΦΟΡΙΑΣ '270': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΣ ΚΑΙ ΑΥΤΑΣΦΑΛΙΣΕΩΣ ΥΓΕΙΟΝΟΜΙΚΩΝ '271': ΠΟΛΙΤΙΚΗ ΚΑΙ ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΣΤΡΑΤΕΥΣΗ '272': ΤΗΛΕΓΡΑΦΟΙ '273': ΣΕΙΣΜΟΠΛΗΚΤΟΙ '274': ΙΑΜΑΤΙΚΕΣ ΠΗΓΕΣ '275': ΙΔΙΩΤΙΚΟ ΝΑΥΤΙΚΟ ΔΙΚΑΙΟ '276': ΔΙΕΘΝΕΙΣ ΥΓΕΙΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '277': ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ '278': ΕΚΚΛΗΣΙΑ ΚΡΗΤΗΣ '279': ΠΡΟΣΤΑΣΙΑ ΝΟΜΙΣΜΑΤΟΣ '280': ΠΡΟΣΤΑΣΙΑ ΠΡΟΪΟΝΤΩΝ ΑΜΠΕΛΟΥ '281': ΑΝΑΠΗΡΟΙ ΚΑΙ ΘΥΜΑΤΑ ΠΟΛΕΜΟΥ '282': ΠΑΡΟΧΕΣ ΔΙΑΦΟΡΕΣ '283': ΤΟΠΙΚΗ ΑΥΤΟΔΙΟΙΚΗΣΗ '284': OΡΓΑΝΩΣΗ ΣΤΡΑΤΟΥ ΞΗΡΑΣ '285': ΔΙΑΚΟΠΕΣ ΤΗΣ ΕΡΓΑΣΙΑΣ '286': ΟΡΓΑΝΙΣΜΟΣ ΠΟΛΕΜΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '287': ΕΠΙΜΕΛΗΤΗΡΙΑ '288': ΕΚΚΛΗΣΙΑ ΤΗΣ ΕΛΛΑΔΟΣ '289': ΝΑΡΚΩΤΙΚΑ '290': ΕΚΜΕΤΑΛΛΕΥΣΗ ΤΑΧΥΔΡΟΜΕΙΩΝ '291': ΜΟΥΣΙΚΗ '292': ΝΟΜΑΡΧΙΕΣ '293': ΠΡΟΣΩΠΙΚΟ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '294': ΓΕΝΙΚΟ ΧΗΜΕΙΟ ΤΟΥ ΚΡΑΤΟΥΣ '295': ΚΡΑΤΙΚΗ '296': ΔΙΟΙΚΗΣΗ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '297': ΠΑΡΟΧΟΙ ΣΤΑΘΕΡΩΝ ΗΛΕΚΤΡΟΝΙΚΩΝ ΕΠΙΚΟΙΝΩΝΙΩΝ '298': ΕΠΑΓΓΕΛΜΑΤΙΚΟΣ ΚΙΝΔΥΝΟΣ '299': ΕΝΟΧΕΣ ΣΕ ΧΡΥΣΟ ΚΑΙ ΣΥΝΑΛΛΑΓΜΑ '300': ΙΠΠΟΠΑΡΑΓΩΓΗ '301': ΑΥΤΟΚΙΝΗΤΑ '302': ΑΓΟΡΑΝΟΜΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '303': ΠΡΟΣΦΥΓΕΣ '304': ΔΙΑΦΟΡΑ ΣΤΡΑΤΙΩΤΙΚΑ ΘΕΜΑΤΑ '305': ΓΕΝ. ΓΡΑΜΜ. ΒΙΟΜΗΧΑΝΙΑΣ - ΓΕΝ. ΓΡΑΜΜ. ΕΡΕΥΝΑΣ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ '306': ΔΙΑΜΕΤΑΚΟΜΙΣΗ '307': ΔΙΚΑΙΟΣΤΑΣΙΟ '308': ΥΔΑΤΑ '309': ΦΟΡΟΛΟΓΙΚΕΣ ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΚΑΙ ΑΠΑΛΛΑΓΕΣ '310': ΜΟΝΟΠΩΛΙΑ '311': ΕΙΔΙΚΕΣ ΔΙΑΔΙΚΑΣΙΕΣ '312': ΠΡΟΝΟΙΑ ΓΙΑ ΤΟΥΣ ΣΤΡΑΤΙΩΤΙΚΟΥΣ '313': ΠΟΛΙΤΙΚΗ ΔΙΚΟΝΟΜΙΑ '314': ΟΡΓΑΝΩΣΗ ΧΡΟΝΟΥ ΕΡΓΑΣΙΑΣ '315': ΠΡΟΣΩΠΙΚΟ ΤΥΠΟΥ '316': ΔΙΚΑΣΤΙΚΟΙ ΕΠΙΜΕΛΗΤΕΣ '317': ΛΟΥΤΡΟΠΟΛΕΙΣ '318': ΤΕΛΩΝΕΙΑΚΟΣ ΚΩΔΙΚΑΣ '319': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΝΟΜΙΚΩΝ '320': ΔΙΑΦΟΡΟΙ ΤΕΛΩΝΕΙΑΚΟΙ ΝΟΜΟΙ '321': ΔΙΟΙΚΗΣΗ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '322': ΑΕΡΟΠΟΡΙΚΕΣ ΕΚΜΕΤΑΛΛΕΥΣΕΙΣ '323': ΕΜΠΟΡΙΚΕΣ ΠΡΑΞΕΙΣ '324': ΔΙΚΑΣΤΗΡΙΑ '325': ΒΑΣΙΛΕΙΑ ΚΑΙ ΑΝΤΙΒΑΣΙΛΕΙΑ '326': ΠΡΟΣΩΠΙΚΟ ΠΟΛΕΜΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '327': ΠΡΟΣΤΑΣΙΑ ΚΑΙ ΚΙΝΗΤΡΑ ΙΔΙΩΤΙΚΩΝ ΕΠΕΝΔΥΣΕΩΝ '328': ΒΑΣΙΛΙΚΑ ΙΔΡΥΜΑΤΑ '329': ΣΙΔΗΡΟΔΡΟΜΟΙ ΓΕΝΙΚΑ '330': ΠΝΕΥΜΑΤΙΚΗ ΙΔΙΟΚΤΗΣΙΑ '331': ΔΙΑΦΟΡΑ ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ '332': ΥΓΕΙΟΝΟΜΙΚΑ ΕΠΑΓΓΕΛΜΑΤΑ '333': ΦΟΡΟΛΟΓΙΑ ΚΑΠΝΟΥ '334': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ '335': ΧΩΡΟΦΥΛΑΚΗ '336': ΤΕΛΩΝΕΙΑΚΗ ΥΠΗΡΕΣΙΑ '337': ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ '338': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΑΣΦΑΛΙΣΤΩΝ '339': ΑΣΦΑΛΙΣΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '340': ΣΤΡΑΤΙΩΤΙΚΑ ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ '341': ΥΠΟΝΟΜΟΙ '342': ΦΟΡΟΛΟΓΙΑ ΚΕΦΑΛΑΙΟΥ '343': ΕΤΑΙΡΕΙΕΣ ΠΕΡΙΩΡΙΣΜΕΝΗΣ ΕΥΘΥΝΗΣ '344': ΥΠΟΥΡΓΕΊΟ ΚΟΙΝΩΝΙΚΏΝ ΑΣΦΑΛΊΣΕΩΝ '345': ΣΥΜΒΟΛΑΙΟΓΡΑΦΟΙ '346': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΑΡΤΕΡΓΑΤΩΝ '347': ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '348': ΕΛΕΓΚΤΙΚΟ ΣΥΝΕΔΡΙΟ '349': ΔΙΑΦΟΡΑ ΕΠΙΣΤΗΜΟΝΙΚΑ ΙΔΡΥΜΑΤΑ '350': ΑΞΙΩΜΑΤΙΚΟΙ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '351': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΜΠΟΡΩΝ (Τ.Α.Ε) '352': ΣΤΡΑΤΙΩΤΙΚΗ ΠΟΙΝΙΚΗ '353': ΦΟΡΟΛΟΓΙΑ ΟΙΝΟΠΝΕΥΜΑΤΟΣ '354': ΟΡΓΑΝΙΣΜΟΣ ΓΕΩΡΓΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '355': ΣΥΛΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ ΕΡΓΑΣΙΑΣ '356': ΧΡΗΜΑΤΙΣΤΗΡΙΑ '357': ΠΟΛΙΤΙΚΑΙ ΚΑΙ ΣΤΡΑΤΙΩΤΙΚΑΙ ΣΥΝΤΑΞΕΙΣ '358': ΚΟΙΝΩΝΙΚΗ ΣΤΕΓΑΣΤΙΚΗ ΣΥΝΔΡΟΜΗ '359': ΚΑΤΟΧΥΡΩΣΗ ΕΠΑΓΓΕΛΜΑΤΩΝ '360': ΦΟΡΟΛΟΓΙΑ ΚΑΘΑΡΑΣ ΠΡΟΣΟΔΟΥ '361': ΠΕΡΙΦΕΡΕΙΕΣ '362': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΔΙΚΑΙΟΣΥΝΗ '363': ΥΠΟΥΡΓΕΙΟ ΟΙΚΟΝΟΜΙΚΩΝ '364': ΕΘΝΙΚΑ ΚΛΗΡΟΔΟΤΗΜΑΤΑ '365': ΕΓΓΕΙΟΒΕΛΤΙΩΤΙΚΑ ΕΡΓΑ '366': ΛΙΜΕΝΕΣ '367': ΦΥΛΑΚΕΣ '368': ΓΕΩΡΓΙΚΗ ΕΚΠΑΙΔΕΥΣΗ '369': ΠΛΗΡΩΜΗ ΕΡΓΑΣΙΑΣ '370': ΕΜΠΟΡΙΚΟΣ ΝΟΜΟΣ '371': ΙΔΡΥΜΑ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '372': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΤΡΑΠΕΖΩΝ '373': ΕΙΔΙΚΟΙ ΑΓΡΟΤΙΚΟΙ ΝΟΜΟΙ '374': ΔΙΕΘΝΕΙΣ ΔΙΚΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '375': ΥΠΟΥΡΓΕΙΑ ΜΑΚΕΔΟΝΙΑΣ–ΘΡΑΚΗΣ, ΑΙΓΑΙΟΥ Κ.Λ.Π '376': ΑΣΤΥΝΟΜΙΚΟΊ ΣΚΎΛΟΙ '377': ΔΙΑΦΟΡΑ ΘΕΜΑΤΑ '378': ΕΚΔΟΣΗ ΕΓΚΛΗΜΑΤΙΩΝ '379': ΑΓΟΡΑΝΟΜΙΑ '380': ΔΙΚΑΣΤΙΚΟ ΤΟΥ ΔΗΜΟΣΙΟΥ '381': ΑΣΤΙΚΟΣ ΚΩΔΙΚΑΣ '382': ΤΕΛΩΝΕΙΑΚΕΣ ΑΤΕΛΕΙΕΣ '383': ΑΓΡΟΤΙΚΕΣ ΜΙΣΘΩΣΕΙΣ '384': ΛΕΩΦΟΡΕΙΑ '385': ΓΕΝΙΚΟΙ ΕΠΙΣΙΤΙΣΤΙΚΟΙ ΝΟΜΟΙ '386': ΑΣΤΥΝΟΜΙΑ ΠΟΛΕΩΝ '387': ΜΗΧΑΝΙΚΟΙ ΚΑΙ ΕΡΓΟΛΑΒΟΙ '388': ΠΟΛΕΜΙΚΕΣ ΣΥΝΤΑΞΕΙΣ splits: - name: train num_bytes: 216757887 num_examples: 28536 - name: test num_bytes: 71533786 num_examples: 9516 - name: validation num_bytes: 68824457 num_examples: 9511 download_size: 45606292 dataset_size: 357116130 - config_name: subject features: - name: text dtype: string - name: label dtype: class_label: names: '0': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ Π.Ν '1': ΜΕΤΑΝΑΣΤΕΥΣΗ ΣΤΟ ΒΕΛΓΙΟ '2': ΝΑΥΤΙΚΕΣ ΦΥΛΑΚΕΣ '3': ΚΑΝΟΝΙΣΜΟΣ ΕΚΤΕΛΕΣΕΩΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΡΓΩΝ '4': ΔΙΟΙΚΗΤΙΚΗ ΚΑΙ ΟΙΚΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ '5': ΑΣΚΗΣΗ ΠΟΙΝΙΚΗΣ ΑΓΩΓΗΣ '6': ΚΑΝΟΝΙΣΜΟΣ ΕΣΩΤΕΡΙΚΗΣ ΥΠΗΡΕΣΙΑΣ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ '7': ΚΩΔΙΚΑΣ ΠΟΛΙΤΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ - ΠΑΛΑΙΟΣ '8': ΚΑΤΑΣΤΑΤΙΚΟ ΤΑΜΕΙΟΥ ΑΣΦΑΛΙΣΕΩΣ ΕΜΠΟΡΩΝ (Τ.Α.Ε) '9': ΜΗΧΑΝΟΛΟΓΟΙ, ΗΛΕΚΤΡΟΛΟΓΟΙ, ΝΑΥΠΗΓΟΙ ΚΑΙ ΜΗΧΑΝΟΔΗΓΟΙ '10': ΣΤΕΓΑΣΗ ΠΑΡΑΠΗΓΜΑΤΟΥΧΩΝ '11': ΝΟΜΙΣΜΑΤΙΚΗ ΕΠΙΤΡΟΠΗ '12': ΠΕΡΙΦΕΡΕΙΑΚΑ ΤΑΜΕΙΑ '13': ΜΗΤΡΩΑ ΑΡΡΕΝΩΝ '14': ΔΙΚΑΣΤΙΚΕΣ ΔΙΑΚΟΠΕΣ '15': ΣΥΜΦΩΝΙΑ ΠΕΡΙ ΠΡΟΞΕΝΙΚΩΝ ΣΧΕΣΕΩΝ '16': ΠΑΛΑΙΟΙ ΑΣΤΙΚΟΙ ΚΩΔΙΚΕΣ '17': ΚΛΑΔΟΣ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΔΙΚΗΓΟΡΩΝ (Κ.Ε.Α.Δ.) '18': ΟΙΚΟΝΟΜΙΚΕΣ ΑΡΜΟΔΙΟΤΗΤΕΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΑΡΧΩΝ '19': ΥΠΟΝΟΜΟΙ ΘΕΣΣΑΛΟΝΙΚΗΣ '20': ΔΙΑΦΟΡΑ ΥΔΡΑΥΛΙΚΑ ΤΑΜΕΙΑ '21': ΕΛΕΓΧΟΣ ΘΕΑΤΡΙΚΩΝ ΕΡΓΩΝ ΚΑΙ ΔΙΣΚΩΝ '22': ΥΠΗΡΕΣΙΑ ΙΠΠΟΠΑΡΑΓΩΓΗΣ '23': ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ '24': ΕΚΔΙΚΑΣΗ ΤΕΛΩΝΕΙΑΚΩΝ ΠΑΡΑΒΑΣΕΩΝ '25': ΚΙΝΗΤΡΑ ΙΔΙΩΤΙΚΩΝ ΕΠΕΝΔΥΣΕΩΝ ΣΤΗΝ ΠΕΡΙΦΕΡΕΙΑ '26': ΜΕΛΗ ΟΙΚΟΓΕΝΕΙΑΣ ΑΣΦΑΛΙΣΜΕΝΩΝ '27': ΚΕΡΜΑΤΑ '28': ΕΠΙΔΟΜΑ ΑΝΑΠΡΟΣΑΡΜΟΓΗΣ '29': ΕΚΤΕΛΕΣΗ ΔΑΣΙΚΩΝ ΕΡΓΩΝ '30': ΛΙΠΑΣΜΑΤΑ '31': ΕΠΙΧΟΡΗΓΗΣΗ ΣΠΟΥΔΑΣΤΩΝ ΤΕΚΝΩΝ ΕΡΓΑΖΟΜΕΝΩΝ '32': ΠΡΟΣΤΑΣΙΑ ΟΙΝΟΥ '33': ΠΤΗΤΙΚΟ ΚΑΙ ΚΑΤΑΔΥΤΙΚΟ ΕΠΙΔΟΜΑ '34': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΕΜΠΟΡΙΚΩΝ ΚΑΤΑΣΤΗΜΑΤΩΝ (Τ.Ε.Α.Υ.Ε.Κ.) '35': ΕΚΚΟΚΚΙΣΗ ΒΑΜΒΑΚΟΣ '36': ΜΟΝΟΠΩΛΙΟ ΚΙΝΙΝΟΥ '37': ΙΝΣΤΙΤΟΥΤΑ ΔΙΕΘΝΟΥΣ ΔΙΚΑΙΟΥ '38': ΙΑΠΩΝΙΑ – ΙΝΔΙΑ –ΙΟΡΔΑΝΙΑ Κ.ΛΠ '39': ΕΠΙΔΟΜΑ ΣΤΟΛΗΣ '40': ΑΝΑΓΝΩΡΙΣΕΙΣ '41': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΕΡΓΟΛΗΠΤΩΝ '42': ΑΝΑΣΤΟΛΗ ΤΗΣ ΠΟΙΝΗΣ '43': ΠΟΤΑΜΟΠΛΟΙΑ '44': ΕΙΔΙΚΗ ΤΕΛΩΝΕΙΑΚΗ ΠΑΡΑΚΟΛΟΥΘΗΣΗ '45': ΕΠΙΘΕΩΡΗΣΗ ΦΑΡΜΑΚΕΙΩΝ '46': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΕΘΝΙΚΩΝ '47': ΑΠΛΟΠΟΙΗΣΗ ΤΕΛΩΝΕΙΑΚΩΝ ΔΙΑΤΥΠΩΣΕΩΝ '48': ΚΛΑΔΟΣ ΑΣΘΕΝΕΙΑΣ Τ.Α.Κ.Ε '49': ΥΠΗΡΕΣΙΑ ΥΠΟΔΟΧΗΣ ΠΛΟΙΩΝ ΚΑΙ ΠΟΛΕΜΙΚΗ ΧΡΗΣΗ ΛΙΜΕΝΩΝ '50': ΦΑΡΜΑΚΕΙΟ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '51': ΤΑΜΕΙΟ ΑΠΟΚΑΤΑΣΤΑΣΕΩΣ ΠΡΟΣΦΥΓΩΝ ΣΥΜΒΟΥΛΙΟΥ ΤΗΣ ΕΥΡΩΠΗΣ '52': ΝΑΥΤΙΚΕΣ ΕΤΑΙΡΕΙΕΣ '53': ΙΣΡΑΗΛΙΤΙΚΕΣ ΚΟΙΝΟΤΗΤΕΣ '54': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΣΤΕΡΕΑΣ ΕΛΛΑΔΑΣ (ΑΤΤΙΚΗΣ, ΒΟΙΩΤΙΑΣ Κ.Λ.Π.) '55': ΔΙΑΦΟΡΕΣ ΣΧΟΛΕΣ Π.Ν '56': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΜΠΟΡ.ΚΑΙ ΒΙΟΜ.- ΕΠΑΓΓΕΛ. ΚΑΙ ΒΙΟΤΕΧΝ. ΕΠΙΜΕΛΗΤΗΡΙΩΝ ΤΟΥ ΚΡΑΤΟΥΣ '57': ΕΘΝΙΚΗ ΚΤΗΜΑΤΙΚΗ ΤΡΑΠΕΖΑ '58': ΝΑΥΤΙΚΟΙ ΑΚΟΛΟΥΘΟΙ '59': ΔΗΜΟΣΙΕΣ ΝΑΥΤΙΚΕΣ ΣΧΟΛΕΣ '60': ΜΙΚΡΟΦΩΤΟΓΡΑΦΙΕΣ '61': ΚΑΤΑΣΤΑΤΙΚΟΙ ΝΟΜΟΙ-Τ.Σ.Α.Υ '62': ΚΑΤΑΣΤΑΣΗ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '63': ΕΛΛΗΝΙΚΑ ΣΧΟΛΕΙΑ ΑΛΛΟΔΑΠΗΣ '64': ΟΡΓΑΝΙΣΜΟΣ ΟΙΚΟΝΟΜΙΚΗΣ '65': ΕΘΝΙΚΗ ΤΡΑΠΕΖΑ ΤΗΣ ΕΛΛΑΔΟΣ '66': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ Ν.Π.Δ.Δ '67': ΠΡΟΣΩΠΙΚΟ ΜΕ ΣΧΕΣΗ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ '68': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΑΣ ΥΔΡΕΥΣΗΣ ΚΑΙ ΑΠΟΧΕΤΕΥΣΗΣ ΠΡΩΤΕΥΟΥΣΗΣ (Τ.Ε.Α.Π.Ε.Υ.Α.Π.) '69': ΣΩΜΑ ΟΙΚΟΝΟΜΙΚΟΥ ΕΛΕΓΧΟΥ '70': ΣΥΜΒΑΣΗ ΠΕΡΙ ΔΙΕΚΔΙΚΗΣΕΩΣ ΔΙΑΤΡΟΦΗΣ '71': ΙΣΟΤΗΤΑ ΤΩΝ ΔΥΟ ΦΥΛΩΝ '72': ΤΑΜΕΙΟ ΑΡΩΓΗΣ ΚΑΙ ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ '73': ΤΟΥΡΙΣΤΙΚΟ ΔΕΛΤΙΟ '74': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ '75': ΟΡΓΑΝΙΣΜΟΣ ΛΙΜΕΝΟΣ ΠΕΙΡΑΙΩΣ ΑΝΩΝΥΜΗ ΕΤΑΙΡΙΑ '76': ΕΚΚΑΘΑΡΙΣΙΣ ΔΙΟΡΙΣΜΩΝ ΚΑΙ ΠΡΟΑΓΩΓΩΝ ΚΑΤΟΧΗΣ '77': ΤΑΞΙΝΟΜΗΣΗ ΒΑΜΒΑΚΟΣ '78': ΠΡΥΤΑΝΕΙΣ ΚΑΙ ΚΟΣΜΗΤΟΡΕΣ '79': ΥΠΗΡΕΣΙΑΚΟ ΣΥΜΒΟΥΛΙΟ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '80': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΣΤΗΝ ΒΙΟΜΗΧΑΝΙΑ ΚΑΙ ΒΙΟΤΕΧΝΙΑ '81': ΧΑΡΤΗΣ ΟΡΓΑΝΙΣΜΟΥ ΟΙΚΟΝΟΜΙΚΗΣ ΣΥΝΕΡΓΑΣΙΑΣ '82': ΓΥΜΝΑΣΙΟ ΑΠΟΔΗΜΩΝ ΕΛΛΗΝΟΠΑΙΔΩΝ '83': ΚΑΝΟΝΙΣΜΟΣ ΑΣΘΕΝΕΙΑΣ '84': ΕΚΔΟΣΕΙΣ ΥΠΟΥΡΓΕΙΟΥ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ '85': ΠΛΗΤΤΟΜΕΝΟΙ ΑΠΟ ΘΕΟΜΗΝΙΕΣ ΚΑΙ ΑΛΛΑ ΕΚΤΑΚΤΑ ΓΕΓΟΝΟΤΑ '86': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΠΡΟΣΩΠΙΚΟΥ '87': ΓΕΩΜΗΛΑ '88': ΦΟΡΟΛΟΓΙΑ ΑΝΑΤΙΜΗΣΗΣ ΑΚΙΝΗΤΩΝ '89': ΠΑΝΩΛΗΣ '90': ΣΧΟΛΕΣ ΝΗΠΙΑΓΩΓΩΝ '91': ΦΑΡΜΑΚΑΠΟΘΗΚΕΣ '92': ΦΡΟΝΤΙΣΤΗΡΙΑ ΝΟΜΙΚΩΝ ΣΠΟΥΔΩΝ '93': ΟΙΚΟΓΕΝΕΙΑΚΑ ΕΠΙΔΟΜΑΤΑ ΜΙΣΘΩΤΩΝ '94': ΗΛΕΚΤΡΟΚΙΝΗΤΑ ΛΕΩΦΟΡΕΙΑ ΑΘΗΝΩΝ – ΠΕΙΡΑΙΩΣ (Η.Λ.Π.Α.Π.) '95': ΑΣΤΙΚΑ ΔΙΚΑΙΩΜΑΤΑ ΑΛΛΟΔΑΠΩΝ '96': ΠΟΛΙΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΑΕΡΟΠΟΡΙΑΣ '97': ΔΙΚΑΣΤΙΚΗ ΕΚΠΡΟΣΩΠΗΣΗ Ι.Κ.Α '98': ΥΓΕΙΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ Π.Σ '99': ΥΓΕΙΟΝΟΜΙΚΟΙ ΣΤΑΘΜΟΙ '100': ΙΕΡΑΡΧΙΑ ΚΑΙ ΠΡΟΑΓΩΓΕΣ ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΚΑΙ ΑΝΘΥΠΑΣΠΙΣΤΩΝ '101': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΟΤΕΧΝΙΤΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΔΕΡΜΑΤΟΣ ΕΛΛΑΔΑΣ (Τ.Ε.Α.Ε.Υ.Δ.Ε.) '102': ΠΡΑΤΗΡΙΑ ΑΡΤΟΥ '103': ΠΛΗΡΩΜΗ ΜΕ ΕΠΙΤΑΓΗ '104': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΕΛΙΚΟΠΤΕΡΩΝ '105': ΔΙΕΘΝΕΙΣ ΤΑΧΥΔΡΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '106': ΔΙΚΑΣΤΙΚΟΙ ΑΝΤΙΠΡΟΣΩΠΟΙ ΤΟΥ ΔΗΜΟΣΙΟΥ '107': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΣΕ ΔΙΑΦΟΡΑ ΕΠΑΓΓΕΛΜΑΤΑ '108': ΔΙΕΥΘΥΝΣΗ ΚΤΗΝΟΤΡΟΦΙΑΣ '109': ΕΠΙΘΕΩΡΗΣΗ ΣΦΑΓΙΩΝ '110': ΠΛΩΙΜΟΤΗΤΑ ΑΕΡΟΣΚΑΦΩΝ '111': ΑΓΟΡΑΝΟΜΙΚΟΣ ΚΩΔΙΚΑΣ '112': ΔΙΕΘΝΕΙΣ ΜΕΤΑΦΟΡΕΣ ΕΠΙΒΑΤΩΝ ΚΑΙ ΕΜΠΟΡΕΥΜΑΤΩΝ '113': ΠΡΟΜΗΘΕΙΕΣ '114': ΔΙΑΦΟΡΕΣ ΔΙΑΤΑΞΕΙΣ '115': ΔΙΑΙΤΗΣΙΑ ΣΥΛΛΟΓΙΚΩΝ ΔΙΑΦΟΡΩΝ - ΜΕΣΟΛΑΒΗΤΕΣ ΔΙΑΙΤΗΤΕΣ '116': ΣΟΥΛΤΑΝΙΝΑ '117': ΜΕΤΑΓΡΑΦΗ '118': ΕΙΣΑΓΩΓΗ ΕΠΙΣΤΗΜΟΝΙΚΟΥ ΥΛΙΚΟΥ '119': ΔΙΑΡΘΡΩΣΗ ΥΠΗΡΕΣΙΩΝ Ο.Γ.Α '120': ΔΙΚΑΣΤΙΚΟΙ ΛΕΙΤΟΥΡΓΟΙ - ΕΘΝΙΚΗ ΣΧΟΛΗ ΔΙΚΑΣΤΩΝ '121': ΠΙΣΤΟΠΟΙΗΤΙΚΑ ΚΑΙ ΔΙΚΑΙΟΛΟΓΗΤΙΚΑ '122': ΑΣΚΗΣΗ ΙΑΤΡΙΚΟΥ ΕΠΑΓΓΕΛΜΑΤΟΣ '123': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '124': ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ ΠΑΝΜΙΟΥ ΠΑΤΡΩΝ '125': ΑΛΛΟΔΑΠΕΣ ΝΑΥΤΙΛΙΑΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ '126': ΛΑΤΟΜΕΙΑ '127': ΕΚΜΕΤΑΛΛΕΥΣΗ ΙΑΜΑΤΙΚΩΝ ΠΗΓΩΝ '128': ΠΩΛΗΣΗ ΧΡΕΩΓΡΑΦΩΝ ΜΕ ΔΟΣΕΙΣ '129': ΝΟΜΟΘΕΣΙΑ ΠΕΡΙ ΤΡΑΠΕΖΩΝ (ΓΕΝΙΚΑ) '130': ΕΙΔΙΚΑ ΜΕΤΑΛΛΕΙΑ '131': YΠΟΥΡΓΕΙΟ ΥΓΙΕΙΝΗΣ '132': ΛΗΞΙΑΡΧΙΚΕΣ ΠΡΑΞΕΙΣ '133': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΤΟΝ ΤΥΠΟ '134': ΕΘΝΙΚΟ ΣΥΣΤΗΜΑ ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ-ΚΑΤΑΡΤΙΣΗΣ '135': ΑΡΟΥΡΑΙΟΙ ΚΑΙ ΑΚΡΙΔΕΣ '136': ΠΡΟΣΤΑΣΙΑ ΦΥΜΑΤΙΚΩΝ ΝΑΥΤΙΚΩΝ '137': ΑΠΟΡΡΗΤΟ ΕΠΙΣΤΟΛΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ '138': ΠΟΡΘΜΕΙΑ ΚΑΙ ΟΧΗΜΑΤΑΓΩΓΑ '139': ΜΕΤΡΑ ΕΞΟΙΚΟΝΟΜΗΣΗΣ ΕΝΕΡΓΕΙΑΣ '140': ΣΤΟΙΧΕΙΑ ΠΡΟΣΩΠΙΚΟΥ ΔΗΜΟΣΙΩΝ ΥΠΗΡΕΣΙΩΝ ΚΑΙ Ν.Π.Δ.Δ '141': ΠΑΓΙΕΣ ΑΜΟΙΒΕΣ ΔΙΚΗΓΟΡΩΝ '142': ΟΡΓΑΝΙΣΜΟΣ ΣΧΟΛΗΣ ΕΥΕΛΠΙΔΩΝ '143': ΟΙΚΟΝΟΜΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ ΤΗΣ ΕΛΛΑΔΑΣ '144': ΓΡΑΦΕΙΑ ΕΥΡΕΣΕΩΣ ΕΡΓΑΣΙΑΣ '145': ΔΙΑΦΗΜΙΣΕΙΣ '146': ΔΙΑΦΟΡΕΣ ΥΠΟΤΡΟΦΙΕΣ '147': ΦΟΡΤΗΓΑ ΑΚΤΟΠΛΟΙΚΑ ΠΛΟΙΑ (ΜS) ΜΕΧΡΙ 500 Κ.Ο.Χ '148': ΕΠΙΤΡΟΠΗ ΣΥΝΕΡΓΑΣΙΑΣ UNICEF '149': ΥΓΙΕΙΝΗ ΘΕΡΕΤΡΩΝ '150': ΕΠΙΣΤΗΜΟΝΙΚΗ ΕΡΕΥΝΑ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑ '151': ΑΠΑΓΟΡΕΥΣΕΙΣ ΕΞΑΓΩΓΗΣ '152': ΑΜΠΕΛΟΥΡΓΙΚΟ ΚΤΗΜΑΤΟΛΟΓΙΟ '153': ΥΠΟΥΡΓΕΙΟ ΥΓΕΙΑΣ ΚΑΙ ΠΡΟΝΟΙΑΣ '154': ΔΙΕΘΝΗΣ ΝΑΥΤΙΛΙΑΚΟΣ ΟΡΓΑΝΙΣΜΟΣ '155': ΔΙΕΥΘΥΝΣΗ ΤΕΛΩΝΕΙΑΚΟΥ ΕΛΕΓΧΟΥ '156': ΔΕΛΤΙΑ ΤΑΥΤΟΤΗΤΟΣ Π. ΝΑΥΤΙΚΟΥ '157': ΑΝΩΤΑΤΗ ΥΓΕΙΟΝΟΜΙΚΗ ΕΠΙΤΡΟΠΗ '158': ΠΡΟΣΤΑΣΙΑ ΕΦΕΔΡΩΝ ΑΞΙΩΜΑΤΙΚΩΝ, ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ ΚΑΙ ΑΓΩΝΙΣΤΩΝ ΕΘΝ. ΑΝΤΙΣΤΑΣΗΣ '159': ΦΟΡΟΙ ΥΠΕΡ ΤΡΙΤΩΝ '160': ΑΓΡΟΛΗΨΙΕΣ ΙΟΝΙΩΝ ΝΗΣΙΩΝ '161': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΕΜΠΟΡΙΟΥ ΤΡΟΦΙΜΩΝ (Τ.Ε.Α.Υ.Ε.Τ) '162': ΑΝΩΤΑΤΟ ΕΙΔΙΚΟ ΔΙΚΑΣΤΗΡΙΟ '163': ΕΙΣΑΓΩΓΗ ΓΥΝΑΙΚΩΝ ΣΤΙΣ ΑΝΩΤΑΤΕΣ ΣΤΡΑΤΙΩΤΙΚΕΣ ΣΧΟΛΕΣ '164': ΣΧΟΛΗ ΑΞΙΩΜΑΤΙΚΩΝ ΝΟΣΗΛΕΥΤΙΚΗΣ (Σ.Α.Ν.) '165': ΔΙΑΔΙΚΑΣΙΑ ΔΙΟΙΚΗΤΙΚΩΝ ΔΙΚΑΣΤΗΡΙΩΝ '166': ΠΡΟΣΤΑΣΙΑ ΕΡΓΑΖΟΜΕΝΟΥ ΠΑΙΔΙΟΥ '167': ΑΜΝΗΣΤΙΑ '168': ΣΧΟΛΕΣ ΚΑΛΛΙΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '169': ΧΑΡΗ ΚΑΙ ΜΕΤΡΙΑΣΜΟΣ '170': ΤΥΦΛΟΙ '171': ΣΥΜΒΟΥΛΙΟ ΤΗΣ ΕΥΡΩΠΗΣ '172': ΕΡΓΟΣΤΑΣΙΑ ΕΚΡΗΚΤΙΚΩΝ ΥΛΩΝ '173': ΜΗΤΡΩΑ Π. ΝΑΥΤΙΚΟΥ '174': ΥΓΡΗ ΑΜΜΩΝΙΑ '175': ΠΕΙΡΑΜΑΤΙΚΑ ΣΧΟΛΕΙΑ '176': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΑΞΙΩΜΑΤΙΚΩΝ Ε.Ν '177': ΕΠΑΓΓΕΛΜΑΤΙΚΟΣ ΠΡΟΣΑΝΑΤΟΛΙΣΜΟΣ ΚΑΙ ΚΑΤΑΡΤΙΣΗ '178': ΤΕΛΩΝΕΙΑΚΗ ΕΠΙΒΛΕΨΗ '179': ΠΡΟΣΩΡΙΝΕΣ ΕΥΡΩΠΑΙΚΕΣ ΣΥΜΦΩΝΙΕΣ '180': ΜΟΝΟΠΩΛΙΟ ΠΑΙΓΝΙΟΧΑΡΤΩΝ '181': ΛΕΙΤΟΥΡΓΙΑ ΤΟΥΡΙΣΤΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '182': ΕΚΠΟΙΗΣΗ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΙΝΗΤΩΝ ΚΑΙ ΑΚΙΝΗΤΩΝ '183': ΣΥΛΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ (ΓΕΝΙΚΑ) '184': ΟΔΟΙΠΟΡΙΚΑ ΚΑΙ ΑΠΟΖΗΜΙΩΣΕΙΣ ΕΚΤΟΣ ΕΔΡΑΣ '185': ΣΤΕΓΑΣΤΙΚΗ ΑΠΟΚΑΤΑΣΤΑΣΗ ΠΡΟΣΦΥΓΩΝ '186': ΑΝΩΤΑΤΑ ΣΥΜΒΟΥΛΙΑ ΕΚΠΑΙΔΕΥΣΕΩΣ '187': ΑΡΧΕΙΑ ΥΠΟΥΡΓΕΙΟΥ ΟΙΚΟΝΟΜΙΚΩΝ '188': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ ΥΠΟΥΡΓΙΚΟΥ ΣΥΜΒΟΥΛΙΟΥ '189': ΠΕΡΙΠΤΕΡΑ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '190': ΕΠΑΓΓΕΛΜΑΤΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ ΕΜΠΟΡΩΝ, ΒΙΟΤΕΧΝΩΝ ΚΑΙ ΛΟΙΠΩΝ ΕΠΑΓΓΕΛΜΑΤΙΩΝ '191': ΙΔΙΩΤΙΚΟΙ ΣΤΑΘΜΟΙ ΠΑΡΑΓΩΓΗΣ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '192': ΘΕΑΤΡΙΚΑ ΕΡΓΑ '193': ΜΕ ΤΗ ΝΕΑ ΖΗΛΑΝΔΙΑ '194': ΦΟΡΟΣ ΚΑΤΑΝΑΛΩΣΕΩΣ ΣΑΚΧΑΡΕΩΣ '195': ΝΟΜΑΡΧΙΑΚΑ ΤΑΜΕΙΑ '196': ΑΓΩΓΕΣ ΚΑΚΟΔΙΚΙΑΣ '197': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ '198': ΑΤΟΜΑ ΒΑΡΙΑ ΝΟΗΤΙΚΑ ΚΑΘΥΣΤΕΡΗΜΕΝΑ '199': ΜΕ ΤΗ ΣΟΥΗΔΙΑ '200': ΑΕΡΟΝΑΥΤΙΚΗ ΜΕΤΕΩΡΟΛΟΓΙΑ '201': ΙΔΙΩΤΙΚΕΣ ΣΧΟΛΕΣ ΓΥΜΝΑΣΤΙΚΗΣ '202': ΠΕΡΙΟΥΣΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '203': ΑΓΟΡΑΠΩΛΗΣΙΕΣ ΚΑΤΟΧΗΣ '204': ΕΚΚΛΗΣΙΑ ΠΑΡΙΣΙΩΝ '205': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΡΟΣΤΑΣΙΑΣ ΦΥΤΩΝ '206': ΚΑΤΟΧΥΡΩΣΗ ΘΡΗΣΚΕΥΤΙΚΗΣ ΕΛΕΥΘΕΡΙΑΣ '207': ΥΓΕΙΟΝΟΜΙΚΗ ΕΞΕΤΑΣΗ ΜΗ ΙΠΤΑΜΕΝΟΥ ΠΡΟΣΩΠΙΚΟΥ '208': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΠΟΛΕΜΟΥ 1940 '209': ΥΔΡΑΥΛΙΚΕΣ ΕΓΚΑΤΑΣΤΑΣΕΙΣ '210': ΚΟΙΝΩΝΙΚΟΙ ΛΕΙΤΟΥΡΓΟΙ - ΚΟΙΝΩΝΙΚΟΙ ΣΥΜΒΟΥΛΟΙ '211': ΔΙΑΦΟΡΕΣ ΠΡΟΣΩΡΙΝΕΣ ΑΤΕΛΕΙΕΣ '212': ΟΙΚΟΝΟΜΙΚΗ ΔΙΑΧΕΙΡΙΣΗ ΚΑΙ ΛΟΓΙΣΤΙΚΟ '213': ΕΞΗΛΕΚΤΡΙΣΜΟΣ ΝΗΣΩΝ '214': ΕΚΠΑΙΔΕΥΣΗ ΣΤΕΛΕΧΩΝ '215': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΚΑΤΑΣΤΗΜΑΤΩΝ ΚΑΙ ΓΡΑΦΕΙΩΝ '216': ΗΜΕΡΟΛΟΓΙΟ ΓΕΦΥΡΑΣ '217': ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΣΤΑΦΙΔΑΣ '218': ΠΑΛΑΙΟΙ ΔΙΚΟΝΟΜΙΚΟΙ ΝΟΜΟΙ '219': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΟΡΓΑΝΙΣΜΩΝ ΚΟΙΝΩΝΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ (Τ.Ε.Α.Π.Ο.Κ.Α.) '220': ΠΑΡΟΧΕΣ ΥΓΕΙΑΣ ΑΣΦΑΛΙΣΤΙΚΩΝ ΟΡΓΑΝΙΣΜΩΝ '221': ΠΛΑΝΟΔΙΟΙ ΙΧΘΥΟΠΩΛΕΣ '222': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΠΕΡΙ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '223': ΥΠΟΧΡΕΩΣΕΙΣ ΕΦΟΠΛΙΣΤΩΝ ΣΕ ΑΣΘΕΝΕΙΑ Η ΘΑΝΑΤΟ ΝΑΥΤΙΚΩΝ '224': ΠΡΟΣΤΑΣΙΑ ΚΑΤΑ ΤΗΣ ΑΣΘΕΝΕΙΑΣ '225': ΓΕΝΙΚΑ ΠΕΡΙ ΣΧΕΔΙΩΝ ΠΟΛΕΩΝ '226': ΕΞΑΙΡΕΣΕΙΣ ΑΠΟ ΤΗΝ ΕΡΓΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ '227': ΑΓΡΟΤΙΚΟ ΚΤΗΜΑΤΟΛΟΓΙΟ '228': ΣΥΝΤΑΓΜΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '229': ΠΑΝΑΓΙΟΣ ΤΑΦΟΣ '230': ΣΥΝΕΡΓΕΙΑ Π. ΝΑΥΤΙΚΟΥ '231': ΕΠΙΘΕΩΡΗΣΙΣ ΣΤΡΑΤΟΥ '232': ΣΥΝΘΕΣΗ ΠΛΗΡΩΜΑΤΩΝ '233': ΟΡΓΑΝΙΣΜΟΣ ΕΡΓΑΤΙΚΗΣ ΕΣΤΙΑΣ '234': ΔΙΑΦΟΡΑ ΥΔΡΑΥΛΙΚΑ ΕΡΓΑ '235': ΔΙΚΑΙΩΜΑ ΤΟΥ ΣΥΝΕΡΧΕΣΘΑΙ '236': ΚΟΙΝΩΝΙΚΟΠΟΙΗΣΗ - ΑΠΟΚΡΑΤΙΚΟΠΟΙΗΣΗ ΕΠΙΧΕΙΡΗΣΕΩΝ ΔΗΜΟΣΙΟΥ ΧΑΡΑΚΤΗΡΑ '237': ΛΑΙΚΗ ΚΑΤΟΙΚΙΑ '238': ΦΟΡΟΛΟΓΙΑ ΚΕΡΔΩΝ '239': ΤΕΧΝΙΚΗ ΥΠΗΡΕΣΙΑ '240': ΜΕΤΕΚΠΑΙΔΕΥΣΗ ΔΗΜΟΔΙΔΑΣΚΑΛΩΝ '241': ΣΥΝΤΑΞΕΙΣ ΥΠΟΥΡΓΩΝ ΚΑΙ ΒΟΥΛΕΥΤΩΝ '242': ΟΡΙΟ ΗΛΙΚΙΑΣ '243': ΣΤΡΑΤΙΩΤΙΚΕΣ ΠΡΟΜΗΘΕΙΕΣ '244': ΑΠΟΣΤΟΛΑΙ ΕΞΩΤΕΡΙΚΟΥ '245': ΦΟΡΟΛΟΓΙΑ ΑΚΙΝΗΤΗΣ ΠΕΡΙΟΥΣΙΑΣ '246': ΧΡΟΝΟΣ ΕΡΓΑΣΙΑΣ - ΑΔΕΙΕΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '247': ΝΑΥΤΙΚΑ ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ '248': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ ΚΑΙ ΛΟΓΙΣΤΙΚΟ '249': ΔΑΣΜΟΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '250': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΧΡΗΜΑΤΙΣΤΩΝ ,ΜΕΣΙΤΩΝ,ΑΝΤΙΚΡΥΣΤΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΧΡΗΜΑΤΙΣΤΗΡΙΟΥ ΑΘΗΝΩΝ (Τ.Α.Χ.Μ.Α.) '251': ΚΡΑΤΙΚΗ ΣΧΟΛΗ ΟΡΧΗΣΤΙΚΗΣ ΤΕΧΝΗΣ '252': ΕΘΝΙΚΗ ΛΥΡΙΚΗ ΣΚΗΝΗ '253': ΑΕΡΟΝΑΥΤΙΚΕΣ ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ '254': ΚΕΝΤΡΟ ΒΙΟΤΕΧΝΙΚΗΣ ΑΝΑΠΤΥΞΗΣ '255': ΑΡΧΑΙΟΛΟΓΙΚΟ ΜΟΥΣΕΙΟ '256': ΥΠΕΡΩΚΕΑΝΕΙΑ '257': ΔΑΣΗ '258': ΑΣΚΗΣΗ ΚΤΗΝΙΑΤΡΙΚΟΥ ΕΠΑΓΓΕΛΜΑΤΟΣ '259': ΚΤΗΣΗ ΚΑΙ ΑΠΩΛΕΙΑ '260': ΡΑΔΙΟΤΗΛΕΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ '261': ΑΕΡΟΛΙΜΕΝΑΣ ΑΘΗΝΩΝ '262': ΠΡΩΤΟΒΑΘΜΙΑ ΕΚΠΑΙΔΕΥΣΗ '263': ΣΤΕΛΕΧΟΣ ΕΦΕΔΡΩΝ ΑΞΙΩΜΑΤΙΚΩΝ '264': ΠΤΩΧΕΥΣΗ ΚΑΙ ΣΥΜΒΙΒΑΣΜΟΣ '265': ΠΟΛΙΤΙΚΟΣ ΓΑΜΟΣ '266': ΙΔΙΩΤΙΚΗ ΕΠΙΧΕΙΡΗΣΗ ΑΣΦΑΛΙΣΕΩΣ '267': ΠΛΟΙΑ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '268': ΙΑΤΡΙΚΕΣ ΑΜΟΙΒΕΣ '269': ΕΛΛΗΝΙΚΟΣ ΕΡΥΘΡΟΣ ΣΤΑΥΡΟΣ '270': ΑΝΩΜΑΛΕΣ ΚΑΤΑΘΕΣΕΙΣ ΣΕ ΧΡΥΣΟ '271': ΣΥΜΒΟΥΛΙΟ ΤΙΜΗΣ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '272': ΔΙΑΦΟΡΟΙ ΑΡΔΕΥΤΙΚΟΙ ΝΟΜΟΙ '273': ΚΥΒΕΡΝΗΤΙΚΟΣ ΕΠΙΤΡΟΠΟΣ '274': ΕΚΤΕΛΕΣΗ ΣΥΓΚΟΙΝΩΝΙΑΚΩΝ ΕΡΓΩΝ '275': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΚΑΙ ΑΡΩΓΗΣ '276': ΔΑΣΙΚΕΣ ΜΕΤΑΦΟΡΕΣ '277': ΜΕ ΤΗ ΔΗΜΟΚΡΑΤΙΑ ΤΟΥ ΚΕΜΠΕΚ '278': ΕΠΑΝΕΞΑΓΟΜΕΝΑ ΜΕ ΕΓΓΥΗΣΗ '279': ΔΙΑΝΟΜΗ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '280': ΑΡΣΗ ΣΥΓΚΡΟΥΣΕΩΣ ΚΑΘΗΚΟΝΤΩΝ '281': ΕΚΠΑΙΔΕΥΤΙΚΑ ΠΛΟΙΑ '282': ΚΕΝΤΡΟ ΜΕΤΑΦΡΑΣΗΣ '283': ΕΙΣΦΟΡΕΣ ΚΑΙ ΝΑΥΛΩΣΕΙΣ '284': ΜΕΤΕΓΓΡΑΦΕΣ ΦΟΙΤΗΤΩΝ ΑΝΩΤ. ΕΚΠΑΙΔΕΥΤΙΚΩΝ ΙΔΡΥΜΑΤΩΝ '285': ΤΜΗΜΑΤΑ ΕΠΙΣΤΗΜΗΣ ΦΥΣΙΚΗΣ ΑΓΩΓΗΣ - ΑΘΛΗΤΙΣΜΟΥ '286': ΨΥΧΙΑΤΡΕΙΑ '287': ΦΟΡΟΛΟΓΙΑ ΚΕΦΑΛΑΙΟΥ ΑΝΩΝ. ΕΤΑΙΡΕΙΩΝ '288': ΤΥΠΟΙ ΣΥΜΒΟΛΑΙΩΝ '289': ΚΑΝΟΝΙΣΜΟΣ ΕΠΙΘΕΩΡΗΣΕΩΣ '290': ΜΟΥΣΕΙΟ ΕΛΛΗΝΙΚΗΣ ΛΑΙΚΗΣ ΤΕΧΝΗΣ '291': ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΕΛΟΠΟΝΝΗΣΟΥ '292': ΟΡΓΑΝΙΣΜΟΣ ΕΡΓΑΤΙΚΗΣ ΚΑΤΟΙΚΙΑΣ '293': ΑΣΦΑΛΕΙΑ ΕΡΓΑΖΟΜΕΝΩΝ ΣΕ ΟΙΚΟΔΟΜΕΣ '294': ΣΤΕΓΑΝΗ ΥΠΟΔΙΑΙΡΕΣΗ ΠΛΟΙΩΝ '295': ΔΙΟΙΚΗΣΗ ΠΡΩΤΕΥΟΥΣΗΣ '296': ΔΙΔΑΚΤΟΡΙΚΕΣ - ΜΕΤΑΠΤΥΧΙΑΚΕΣ ΣΠΟΥΔΕΣ ΕΘΝΙΚΟΥ ΜΕΤΣΟΒΙΟΥ '297': ΕΙΣΦΟΡΑ ΚΑΤΟΧΩΝ ΕΙΔΩΝ ΠΡΩΤΗΣ ΑΝΑΓΚΗΣ '298': ΔΙΑΦΟΡΟΙ ΔΙΚΟΝΟΜΙΚΟΙ ΝΟΜΟΙ '299': ΔΙΕΘΝΕΙΣ ΛΙΜΕΝΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '300': ΥΓΕΙΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΕΛ.ΑΣ '301': ΕΛΛΗΝΙΚΑ ΤΑΧΥΔΡΟΜΕΙΑ (ΕΛ.ΤΑ) '302': ΜΙΣΘΟΙ ΚΑΙ ΕΠΙΔΟΜΑΤΑ Π. ΝΑΥΤΙΚΟΥ '303': ΓΕΩΡΓΙΚΑ ΤΑΜΕΙΑ '304': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΜΕΤΑΛΛΕΥΤΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '305': ΑΠΟΒΑΡΟ '306': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΚΠΡΟΣΩΠΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ '307': ΚΩΔΙΚΑΣ ΠΕΡΙ ΔΙΚΗΓΟΡΩΝ '308': ΙΕΡΑΡΧΙΑ ΚΑΙ ΠΡΟΒΙΒΑΣΜΟΙ '309': ΙΣΡΑΗΛΙΤΕΣ '310': ΣΩΜΑ ΚΤΗΝΙΑΤΡΙΚΟ '311': ΝΟΡΒΗΓΙΑ - ΝΕΑ ΖΗΛΑΝΔΙΑ – ΝΙΓΗΡΙΑ Κ.ΛΠ '312': ΕΝΤΥΠΑ ΚΑΙ ΒΙΒΛΙΟΘΗΚΕΣ ΝΑΥΤΙΚΟΥ '313': ΥΠΟΥΡΓΕΙΟ ΤΥΠΟΥ ΚΑΙ ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΝΗΜΕΡΩΣΗΣ '314': ΝΑΥΤΙΚΕΣ ΠΕΙΘΑΡΧΙΚΕΣ ΠΟΙΝΕΣ '315': ΜΙΣΘΩΣΕΙΣ ΑΓΡΟΤΙΚΩΝ ΑΚΙΝΗΤΩΝ '316': ΔΙΑΦΟΡΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ '317': ΑΓΡΟΤΙΚΗ ΠΙΣΤΗ '318': ΛΑΙΚΕΣ ΑΓΟΡΕΣ-ΤΑΜΕΙΟ ΛΑΙΚΩΝ ΑΓΟΡΩΝ '319': ΚΑΝΟΝΙΣΜΟΣ ΠΕΙΘΑΡΧΙΑΣ ΧΩΡΟΦΥΛΑΚΗΣ '320': ΑΔΙΚΗΜΑΤΑ ΚΑΤΑ ΤΗΣ ΔΗΜΟΣΙΑΣ ΑΣΦΑΛΕΙΑΣ '321': ΕΝΟΙΚΙΑΣΗ ΦΟΡΟΥ ΔΗΜΟΣΙΩΝ ΘΕΑΜΑΤΩΝ '322': ΕΥΡΩΠΑΙΚΗ ΣΥΜΒΑΣΗ ΚΟΙΝΩΝΙΚΗΣ ΚΑΙ ΙΑΤΡΙΚΗΣ ΑΝΤΙΛΗΨΕΩΣ '323': ΕΠΙΒΑΤΗΓΑ ΑΕΡΟΣΤΡΩΜΝΑ ΟΧΗΜΑΤΑ '324': ΕΦΕΔΡΟΙ '325': ΣΤΡΑΤΙΩΤΙΚΕΣ ΛΕΣΧΕΣ '326': ΠΡΟΣΩΠΙΚΟ ΦΥΛΑΚΩΝ '327': ΑΝΑΘΕΩΡΗΣΗ ΤΙΜΩΝ '328': ΜΑΛΑΚΙΑ ΚΑΙ ΜΑΛΑΚΟΣΤΡΑΚΑ '329': ΚΩΔΙΚΑΣ ΔΗΜΟΣΙΟΥ ΝΑΥΤΙΚΟΥ ΔΙΚΑΙΟΥ '330': ΔΙΑΦΟΡΑ ΣΩΜΑΤΕΙΑ '331': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '332': ΚΩΔΙΚΟΠΟΙΗΣΗ ΑΓΟΡΑΝΟΜΙΚΩΝ ΔΙΑΤΑΞΕΩΝ '333': ΕΚΠΑΙΔΕΥΣΗ ΣΤΗΝ ΑΛΛΟΔΑΠΗ '334': ΔΙΔΑΚΤΙΚΑ ΒΙΒΛΙΑ '335': ΣΥΝΤΑΞΙΟΔΟΤΙΚΑ ΚΑΙ ΑΣΦΑΛΙΣΤΙΚΑ ΘΕΜΑΤΑ ΠΡΟΣΩΠΙΚΟΥ Ν.Π.Δ.Δ '336': ΕΠΙΔΟΜΑ ΟΙΚΟΓΕΝΕΙΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΞΑΦΑΝΙΣΘΕΝΤΩΝ ΚΑΙ ΑΙΧΜΑΛΩΤΩΝ '337': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ '338': ΚΕΝΤΡΟ ΔΙΠΛΩΜΑΤΙΚΩΝ ΣΠΟΥΔΩΝ '339': ΓΕΝ. ΔΙΕΥΘΥΝΣΗ ΤΥΠΟΥ ΚΑΙ ΠΛΗΡΟΦΟΡΙΩΝ '340': ΑΡΧΕΙΑ ΤΕΛΩΝΕΙΑΚΩΝ ΑΡΧΩΝ '341': ΕΙΔΙΚΕΣ ΤΙΜΕΣ ΚΑΥΣΙΜΩΝ '342': ΣΤΕΓΗ ΥΓΕΙΟΝΟΜΙΚΩΝ '343': ΓΕΝΙΚΑ ΠΕΡΙ ΣΥΜΒΟΛΑΙΟΓΡΑΦΩΝ '344': ΒΟΥΛΗ '345': ΕΠΙΛΟΓΗ & ΑΞΙΟΛΟΓΗΣΗ ΑΣΤΥΝΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ ΕΛ.ΑΣ '346': ΧΟΙΡΟΤΡΟΦΙΑ '347': ΦΟΡΟΣ ΚΑΤΑΝΑΛΩΣΕΩΣ ΠΕΤΡΕΛΑΙΟΕΙΔΩΝ '348': ΕΠΙΒΟΛΗ ΤΕΛΩΝΙΑΚΩΝ ΔΑΣΜΩΝ '349': ΑΕΡΟΠΟΡΙΚΗ ΣΤΡΑΤΟΛΟΓΙΑ '350': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΑ ΝΑΡΚΩΤΙΚΑ '351': ΔΙΑΦΟΡΕΣ ΤΡΑΠΕΖΕΣ '352': ΟΙΝΟΛΟΓΟΙ '353': ΤΕΛΩΝΟΦΥΛΑΚΗ '354': ΤΑΜΕΙΟ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ (T.EΘ.A.) - ΕΘΝΙΚΗ ΕΠΙΤΡΟΠΗ ΕΞΟΠΛΙΣΜΟΥ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ (Ε.Ε.Ε.Ε.Δ.) '355': ΕΚΤΕΛΕΣΗ ΤΗΣ ΠΟΙΝΗΣ '356': ΙΣΟΛΟΓΙΣΜΟΙ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '357': ΑΡΧΙΤΕΚΤΟΝΙΚΟΙ ΔΙΑΓΩΝΙΣΜΟΙ '358': ΚΑΤΑΡΓΗΣΗ ΦΥΛΕΤΙΚΩΝ ΔΙΑΚΡΙΣΕΩΝ '359': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΔΙΚΑΙΩΜΑΤΑ ΑΠΟΦΟΙΤΩΝ '360': ΜΟΝΑΣΤΗΡΙΑΚΗ ΠΕΡΙΟΥΣΙΑ ΣΑΜΟΥ '361': ΣΥΝΤΑΞΗ ΔΗΜΟΤΙΚΩΝ ΚΑΙ ΚΟΙΝΟΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '362': ΟΙΚΟΝΟΜΙΚΕΣ ΕΦΟΡΙΕΣ '363': ΦΡΟΝΤΙΣΤΗΡΙΑ ΕΦΑΡΜΟΓΩΝ '364': ΝΟΜΑΡΧΙΕΣ ΑΤΤΙΚΗΣ '365': ΦΥΜΑΤΙΩΣΗ '366': ΕΛΕΓΧΟΣ ΑΝΑΤΙΜΗΣΕΩΝ '367': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΝΑΥΤΙΛΙΑΣ '368': ΚΩΦΑΛΑΛΟΙ '369': ΙΑΤΡΙΚΗ ΔΕΟΝΤΟΛΟΓΙΑ '370': ΕΞΟΔΑ ΔΗΜΟΣΙΑΣ ΑΣΦΑΛΕΙΑΣ '371': ΜΕ ΤΗΝ ΑΡΓΕΝΤΙΝΗ '372': ΚΛΑΔΟΣ ΥΓΕΙΟΝΟΜΙΚΗΣ ΠΕΡΙΘΑΛΨΗΣ Τ.Α.Ε '373': ΥΠΗΡΕΣΙΑ ΕΚΚΑΘΑΡΙΣΕΩΣ ΝΑΡΚΟΠΕΔΙΩΝ '374': ΤΑΜΕΙΟ ΑΡΩΓΗΣ ΥΠΑΛΛΗΛΩΝ ΑΣΤΥΝΟΜΙΑΣ ΠΟΛΕΩΝ Τ.Α.Υ.Α.Π '375': ΠΡΟΣΤΑΣΙΑ ΔΗΜΟΣΙΩΝ ΚΤΗΜΑΤΩΝ '376': ΒΙΒΛΙΑ ΕΝΔΙΚΩΝ ΜΕΣΩΝ '377': ΕΛΛΗΝΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΜΙΚΡΟΜΕΣΑΙΩΝ ΜΕΤΑΠΟΙΗΤΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ ΚΑΙ ΧΕΙΡΟΤΕΧΝΙΑΣ '378': ΔΗΜΟΣΙΟΓΡΑΦΙΚΟΣ ΧΑΡΤΗΣ '379': ΦΟΡΟΣ ΓΑΜΙΚΩΝ ΣΥΜΦΩΝΩΝ ΙΣΡΑΗΛΙΤΩΝ '380': ΥΠΟΤΡΟΦΙΑΙ ΚΤΗΝΙΑΤΡΙΚΗΣ '381': ΑΠΟΔΟΧΕΣ ΠΡΟΣΩΠΙΚΟΥ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ '382': ΕΠΙΒΑΤΗΓΑ ΑΚΤΟΠΛΟΙΚΑ ΠΛΟΙΑ '383': ΠΑΛΑΙΟΙ ΔΗΜΟΣΙΟΥΠΑΛΛΗΛΙΚΟΙ ΝΟΜΟΙ '384': ΚΩΔΙΚΑΣ ΠΕΡΙ ΚΛΗΡΟΔΟΤΗΜΑΤΩΝ '385': ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΘΕΩΡΗΣΗ '386': ΚΤΗΜΑΤΟΓΡΑΦΗΣΗ ΔΑΣΩΝ '387': ΟΡΓΑΝΙΚΕΣ ΘΕΣΕΙΣ '388': ΠΕΡΙΟΡΙΣΜΟΣ ΧΡΗΣΗΣ ΟΡΙΣΜΕΝΩΝ ΣΥΜΒΑΤΙΚΩΝ ΟΠΛΩΝ '389': ΑΓΙΟΝ ΟΡΟΣ '390': ΚΥΡΩΣΕΙΣ ΦΟΡΟΛΟΓΙΚΩΝ ΠΑΡΑΒΑΣΕΩΝ '391': ΚΑΤΑΣΤΑΣΗ ΠΡΟΣΩΠΙΚΟΥ Ο.Γ.Α '392': ΕΠΑΝΑΠΑΤΡΙΣΜΟΣ ΚΕΦΑΛΑΙΩΝ '393': ΜΑΘΗΤΕΣ ΤΕΧΝΙΤΕΣ '394': ΔΙΑΒΙΒΑΣΕΙΣ '395': ΕΜΜΙΣΘΟΙ ΚΑΙ ΠΟΙΝΙΚΟΙ ΔΙΚ. ΕΠΙΜΕΛΗΤΕΣ '396': ΣΥΜΒΑΣΕΙΣ ΔΙΚΑΣΤΙΚΗΣ ΣΥΝΔΡΟΜΗΣ '397': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΠΕΤΡΕΛΑΙΟΥ '398': ΕΛΛΗΝΙΚΗ ΤΡΑΠΕΖΑ ΒΙΟΜΗΧΑΝΙΚΗΣ ΑΝΑΠΤΥΞΕΩΣ ΑΝΩΝΥΜΟΣ ΕΤΑΙΡΕΙΑ (Ε.Τ.Β.Α. Α.Ε.) '399': ΕΙΔΙΚΟΤΗΤΕΣ ΚΑΙ ΤΡΟΠΟΣ ΕΙΣΟΔΟΥ ΣΤΕΛΕΧΩΝ '400': ΠΡΟΣΤΑΣΙΑ ΕΡΓΑΖΟΜΕΝΩΝ ΣΤΗΝ ΗΜΕΔΑΠΗ - ΣΩΜΑ ΕΠΙΘΕΩΡΗΣΗΣ ΕΡΓΑΣΙΑΣ '401': ΙΝΣΤΙΤΟΥΤΟ ΩΚΕΑΝΟΓΡΑΦΙΚΩΝ ΚΑΙ ΑΛΙΕΥΤΙΚΩΝ ΕΡΕΥΝΩΝ '402': ΕΛΕΓΧΟΣ ΑΠΟΛΥΣΕΩΝ ΜΙΣΘΩΤΩΝ '403': ΠΑΝΕΛΛΗΝΙΑ ΕΚΘΕΣΗ ΛΑΜΙΑΣ '404': ΚΥΡΙΑΚΗ ΑΡΓΙΑ ΚΑΙ ΑΛΛΕΣ ΥΠΟΧΡΕΩΤΙΚΕΣ ΑΡΓΙΕΣ '405': ΚΛΑΔΟΣ ΥΓΕΙΑΣ Ο.Α.Ε.Ε '406': ΟΡΚΟΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '407': ΕΜΠΟΡΙΚΑ ΒΙΒΛΙΑ '408': ΥΓΕΙΟΝΟΜΙΚΕΣ ΕΠΙΤΡΟΠΕΣ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '409': ΑΓΙΟΣ ΒΙΚΕΝΤΙΟΣ-ΓΡΕΝΑΔΙΝΟΙ, ΑΓΙΟΣ ΜΑΡΙΝΟΣ Κ.ΛΠ '410': ΑΠΟΖΗΜΙΩΣΗ ΔΙΑΤΕΛΕΣΑΝΤΩΝ ΠΡΩΘΥΠΟΥΡΓΩΝ '411': ΑΣΦΑΛΙΣΗ ΛΟΓΟΤΕΧΝΩΝ ΚΑΙ ΚΑΛΛΙΤΕΧΝΩΝ '412': ΠΕΙΘΑΡΧΙΚΑ ΣΥΜΒΟΥΛΙΑ '413': ΕΤΑΙΡΙΕΣ ΧΡΗΜΑΤΟΔΟΤΙΚΗΣ ΜΙΣΘΩΣΗΣ '414': ΚΟΙΝΩΝΙΚΗ ΥΠΗΡΕΣΙΑ ΦΥΛΑΚΩΝ '415': ΚΑΝΟΝΙΣΜΟΣ ΥΠΗΡΕΣΙΩΝ ΑΓΡΟΦΥΛΑΚΗΣ '416': ΑΣΦΑΛΙΣΗ ΣΤΟ ΙΚΑ '417': ΕΜΠΟΡΙΚΟΙ ΣΥΜΒΟΥΛΟΙ ΚΑΙ ΑΚΟΛΟΥΘΟΙ '418': ΕΠΙΚΟΥΡΟΙ ΠΑΡΑΤΗΡΗΤΕΣ '419': ΥΠΟΤΡΟΦΙΕΣ '420': ΚΕΝΤΡΟ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΥ '421': ΠΡΩΤΕΣ ΥΛΕΣ ΣΟΚΟΛΑΤΟΠΟΙΙΑΣ '422': ΕΠΙΤΡΟΠΗ ΚΗΠΩΝ ΚΑΙ ΔΕΝΔΡΟΣΤΟΙΧΙΩΝ '423': ΚΙΝΗΤΟ ΕΠΙΣΗΜΑ '424': ΣΥΝΔΙΚΑΛΙΣΜΟΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '425': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ Π.Ν '426': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΤΑΜΕΙΟΥ ΠΑΡΑΚΑΤΑΘΗΚΩΝ ΚΑΙ ΔΑΝΕΙΩΝ '427': ΑΔΕΙΕΣ ΗΝΙΟΧΙΑΣ '428': ΥΠΗΡΕΣΙΑ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΥ ΚΑΙ ΜΕΛΕΤΩΝ '429': ΚΡΑΤΙΚΑ ΑΥΤΟΚΙΝΗΤΑ '430': ΑΤΟΜΙΚΗ ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ '431': ΠΟΛΥΤΕΚΝΟΙ '432': ΙΣΤΟΡΙΚΟ ΑΡΧΕΙΟ ΜΑΚΕΔΟΝΙΑΣ '433': ΑΣΦΑΛΙΣΗ ΑΥΤΟΚΙΝΗΤΙΚΩΝ ΑΤΥΧΗΜΑΤΩΝ '434': ΔΑΝΕΙΑ ΕΣΩΤΕΡΙΚΑ '435': ΕΚΚΛΗΣΙΑ ΚΡΗΤΗΣ '436': ΦΟΡΟΛΟΓΙΑ ΣΤΑΦΙΔΑΣ '437': ΕΚΠΑΙΔΕΥΤΙΚΕΣ ΑΔΕΙΕΣ '438': ΑΕΡΟΔΙΚΕΙΑ '439': ΕΠΙΔΟΜΑ ΑΣΘΕΝΕΙΑΣ '440': ΘΕΣΕΙΣ ΣΥΜΒΟΛΑΙΟΓΡΑΦΩΝ '441': ΑΓΟΡΑ ΣΥΝΑΛΛΑΓΜΑΤΟΣ '442': ΝΟΜΙΚΟ ΣΥΜΒΟΥΛΙΟ ΤΟΥ ΚΡΑΤΟΥΣ (Ν.Σ.Κ.) '443': ΦΟΡΟΛΟΓΙΑ ΜΕΤΑΒΙΒΑΣΗΣ '444': ΣΥΜΒΟΥΛΙΑ - ΕΠΙΤΡΟΠΕΣ - ΙΝΣΤΙΤΟΥΤΑ ΕΡΓΑΣΙΑΣ ΚΑΙ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '445': ΤΕΛΗ ΕΙΣΙΤΗΡΙΩΝ ΚΑΙ ΚΟΜΙΣΤΡΩΝ '446': ΟΙΚΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΥΓΕΙΟΝΟΜΙΚΟΥ ΣΩΜΑΤΟΣ '447': ΠΡΟΣΩΠΙΚΟ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ ΜΕ ΣΧΕΣΗ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ '448': ΑΡΤΕΡΓΑΤΕΣ '449': ΕΥΚΟΛΙΕΣ ΣΕ ΦΟΙΤΗΤΕΣ '450': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΚΟΙΝΗΣ ΧΟΡΤΟΝΟΜΗΣ ΚΑΙ ΣΥΝΙΔΙΟΚΤΗΣΙΑΣ '451': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΠΕΡΙΦΕΡΕΙΑΚΟΥ ΓΕΝΙΚΟΥ ΝΟΣΟΚΟΜΕΙΟΥ Ο ΕΥΑΓΓΕΛΙΣΜΟΣ '452': ΠΡΟΣΚΟΠΙΣΜΟΣ '453': ΣΥΜΒΟΥΛΙΑ ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΚΑΙ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΕΩΣ '454': ΚΡΑΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΜΗΧΑΝΗΜΑΤΩΝ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ '455': ΑΤΟΜΙΚΑ ΕΓΓΡΑΦΑ ΑΝΘΥΠΑΣΠΙΣΤΩΝ-ΥΠΑΞΙΩΜΑΤΙΚΩΝ '456': ΔΙΑΦΟΡΕΣ ΣΧΟΛΕΣ '457': ΒΙΒΛΙΑ ΔΗΜΟΣΙΕΥΣΕΩΣ ΔΙΑΘΗΚΩΝ '458': ΚΑΝΟΝΙΣΜΟΙ ΠΡΟΣΩΠΙΚΟΥ ΣΥΓΚΟΙΝΩΝΙΑΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '459': ΤΟΥΡΙΣΤΙΚΟΙ ΤΟΠΟΙ '460': ΙΝΣΤΙΤΟΥΤΟ ΞΕΝΩΝ ΓΛΩΣΣΩΝ ΚΑΙ ΦΙΛΟΛΟΓΙΩΝ '461': ΚΑΠΝΟΠΩΛΕΣ '462': ΑΓΩΓΕΣ ΓΙΑΤΡΩΝ '463': ΣΥΣΤΑΣΗ ΚΑΙ ΑΠΟΔΟΣΗ ΠΑΡΑΚΑΤΑΘΗΚΩΝ ΑΠΟ Τ.Π. ΚΑΙ Δ '464': ΑΔΙΚΗΜΑΤΑ ΔΙΑΠΡΑΤΤΟΜΕΝΑ ΣΤΑ ΚΡΑΤΗ-ΜΕΛΗ '465': ΑΝΑΣΤΟΛΕΣ ΤΟΥ ΣΥΝΤΑΓΜΑΤΟΣ - ΚΑΤΑΣΤΑΣΗ ΠΟΛΙΟΡΚΙΑΣ '466': ΣΥΜΒΑΣΕΙΣ ΠΑΡΟΧΗΣ ΑΣΦΑΛΕΙΑΣ (ΕΝΕΧΥΡΟ, ΥΠΟΘΗΚΗ Κ.ΛΠ.) '467': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣΝΑΥΤΙΚΩΝ ΠΡΑΚΤΟΡΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ (Τ.Α.Ν.Π.Υ.) '468': ΑΝΩΤΑΤΟ ΣΥΓΚΟΙΝΩΝΙΑΚΟ ΣΥΜΒΟΥΛΙΟ '469': ΠΡΕΒΕΝΤΟΡΙΑ '470': ΑΝΑΒΟΛΗ ΣΤΡΑΤΕΥΣΕΩΣ '471': ΕΙΔΙΚΑ ΛΗΞΙΑΡΧΕΙΑ '472': ΓΕΩΤΕΧΝΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ '473': ΥΓΕΙΟΝΟΜΙΚΑ ΔΙΚΑΙΩΜΑΤΑ '474': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΕΚΠΑΙΔΕΥΤΙΚΩΝ '475': ΚΑΖΑΚΣΤΑΝ – ΚΑΜΕΡΟΥΝ – ΚΑΝΑΔΑΣ Κ.ΛΠ '476': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΑΠΟ ΤΟΝ ΑΜΑΧΟ ΠΛΗΘΥΣΜΟ '477': ΦΙΛΟΣΟΦΙΚΗ ΣΧΟΛΗ '478': ΕΚΤΕΛΩΝΙΣΜΟΣ ΤΑΧΥΔΡΟΜΙΚΩΝ ΔΕΜΑΤΩΝ '479': ΥΔΡΕΥΣΗ ΘΕΣΣΑΛΟΝΙΚΗΣ '480': ΣΥΜΦΩΝΙΕΣ ΠΕΡΙ ΠΛΩΤΩΝ ΟΔΩΝ '481': ΑΝΑΚΗΡΥΞΗ ΤΗΣ ΑΝΕΞΑΡΤΗΣΙΑΣ '482': ΕΠΙΤΡΟΠΗ ΟΛΥΜΠΙΑΚΩΝ ΑΓΩΝΩΝ '483': ΟΙΝΟΠΑΡΑΓΩΓΗ ΑΤΤΙΚΟΒΟΙΩΤΙΑΣ '484': ΕΚΠΤΩΣΕΙΣ ΥΠΕΡ ΕΞΑΓΩΓΕΩΝ '485': ΦΟΡΟΛΟΓΙΑ ΚΛΗΡΟΝΟΜΙΩΝ, ΔΩΡΕΩΝ, ΓΟΝΙΚΩΝ ΠΑΡΟΧΩΝ '486': ΟΡΦΑΝΟΤΡΟΦΕΙΑ ΚΑΙ ΟΙΚΟΤΡΟΦΕΙΑ '487': ΜΕ ΤΗΝ ΟΥΡΑΓΟΥΑΗ '488': ΜΕ ΤΗΝ ΑΥΣΤΡΙΑΚΗ '489': ΔΙΑΦΟΡΟΙ ΦΟΡΟΙ ΚΑΤΑΝΑΛΩΣΕΩΣ '490': ΔΙΕΥΘΥΝΣΗ ΕΦΕΔΡΩΝ - ΠΟΛΕΜΙΣΤΩΝ - ΑΓΩΝΙΣΤΩΝ '491': ΑΓΡΟΤΙΚΕΣ ΟΙΚΟΚΥΡΙΚΕΣ ΣΧΟΛΕΣ '492': ΞΥΛΕΙΑ '493': ΒΙΒΛΙΑΡΙΑ ΥΓΕΙΑΣ ΕΡΓΑΤΩΝ '494': ΣΧΟΛΗ ΑΞΙΩΜΑΤΙΚΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '495': ΝΟΜΑΡΧΙΑΚΕΣ ΚΑΙ ΔΗΜΟΤΙΚΕΣ ΕΚΛΟΓΕΣ '496': ΕΓΓΥΗΣΕΙΣ ΚΑΙ ΔΑΝΕΙΑ ΤΟΥ ΔΗΜΟΣΙΟΥ '497': ΥΠΟΥΡΓΕΙΟ ΑΝΑΠΤΥΞΗΣ '498': ΤΑΚΤΙΚΑ ΔΙΟΙΚΗΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ - ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '499': ΤΡΟΦΟΔΟΣΙΑ ΠΛΗΡΩΜΑΤΩΝ ΠΛΟΙΩΝ '500': ΔΙΑΦΟΡΟΙ ΛΙΜΕΝΕΣ ΚΑΙ ΛΙΜΕΝΙΚΑ ΤΑΜΕΙΑ '501': ΗΛΕΚΤΡΙΚΕΣ ΕΚΜΕΤΑΛΛΕΥΣΕΙΣ '502': ΠΡΟΥΠΟΘΕΣΕΙΣ ΑΣΚΗΣΗΣ ΔΙΑΦΟΡΩΝ ΕΠΑΓΓΕΛΜΑΤΩΝ '503': ΤΕΛΩΝΕΙΑΚΗ ΥΠΗΡΕΣΙΑ ΑΕΡΟΣΚΑΦΩΝ '504': ΕΠΙΤΡΟΠΗ ΔΑΣΜΟΛΟΓΙΟΥ '505': ΝΑΥΠΗΓΕΙΑ Π. ΝΑΥΤΙΚΟΥ '506': ΒΙΟΜΗΧΑΝΙΚΕΣ ΚΑΙ ΕΠΙΧΕΙΡΗΜΑΤΙΚΕΣ ΠΕΡΙΟΧΕΣ '507': ΙΑΤΡΟΔΙΚΑΣΤΕΣ '508': ΑΘΛΗΤΙΣΜΟΣ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '509': ΟΡΓΑΝΙΣΜΟΣ ΣΥΚΩΝ '510': ΚΑΝΟΝΙΣΜΟΣ ΑΣΘΕΝΕΙΑΣ ΤΑΜΕΙΟΥ ΣΥΝΤΑΞΕΩΝ ΕΦΗΜΕΡΙΔΟΠΩΛΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΠΡΑΚΤΟΡΕΙΩΝ (Τ.Σ.Ε.Υ.Π.) '511': ΑΔΕΙΕΣ ΜΙΣΘΩΤΩΝ '512': ΠΡΟΣΤΑΣΙΑ ΚΕΦΑΛΑΙΩΝ ΕΞΩΤΕΡΙΚΟΥ '513': ΑΠΟΔΕΙΚΤΙΚΑ ΦΟΡΟΛΟΓΙΚΗΣ ΕΝΗΜΕΡΟΤΗΤΑΣ '514': ΟΡΓΑΝΩΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ ΤΩΝ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ ΕΘΝΙΚΗ ΕΠΙΤΡΟΠΗ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ ΚΑΙ ΤΑΧΥΔΡΟΜΕΙΩΝ (Ε.Ε.Τ.Τ.) '515': ΠΡΟΣΩΠΙΚΟ Ο.Τ.Ε '516': ΒΑΣΙΛΙΚΑ ΙΔΡΥΜΑΤΑ '517': ΑΠΟΚΑΤΑΣΤΑΣΗ ΠΛΗΓΕΝΤΩΝ ΑΠΟ ΕΚΡΗΞΗ ΠΛΟΙΟΥ ΣΤΗΝ ΚΡΗΤΗ '518': ΕΚΜΕΤΑΛΛΕΥΣΗ ΔΥΝΑΜΕΩΣ ΡΕΟΝΤΩΝ ΥΔΑΤΩΝ '519': ΚΑΚΟΥΡΓΙΟΔΙΚΕΙΑ '520': ΚΕΝΤΡΙΚΕΣ ΑΓΟΡΕΣ ΑΛΛΩΝ ΠΟΛΕΩΝ '521': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ Π.Ν '522': ΕΚΛΟΓΙΚΟΙ ΚΑΤΑΛΟΓΟΙ ΚΑΙ ΒΙΒΛΙΑΡΙΑ '523': ΥΠΗΡΕΣΙΑ ΕΓΓΕΙΩΝ ΒΕΛΤΙΩΣΕΩΝ '524': ΤΟΥΡΙΣΤΙΚΗ ΑΝΑΠΤΥΞΗ '525': ΝΟΜΟΘΕΣΙΑ ΠΕΡΙ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ '526': ΕΛΕΓΧΟΣ ΕΚΡΗΚΤΙΚΩΝ ΥΛΩΝ '527': ΜΑΚΕΔΟΝΙΚΟΙ ΣΙΔΗΡΟΔΡΟΜΟΙ '528': ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΣΕ ΔΗΜΟΣΙΟΥΣ ΥΠΑΛΛΗΛΟΥΣ '529': ΣΤΡΑΤΙΩΤΙΚΕΣ ΥΠΟΧΡΕΩΣΕΙΣ ΕΠΑΝΑΠΑΤΡΙΖΟΜΕΝΩΝ '530': ΔΙΑΚΡΙΣΗ ΕΜΠΟΡΙΚΩΝ ΠΡΑΞΕΩΝ '531': ΟΡΓΑΝΙΣΜΟΣ ΕΛΛΗΝΙΚΩΝ ΓΕΩΡΓΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ (Ε.Λ.Γ.Α.) '532': ΕΞΩΣΧΟΛΙΚΗ ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ '533': ΔΡΑΧΜΟΠΟΙΗΣΗ '534': ΜΕ ΤΗ ΒΡΑΖΙΛΙΑ '535': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΑΚΑΔΗΜΙΑ '536': ΑΝΤΑΛΛΑΓΗ ΘΕΡΑΠΕΥΤΙΚΩΝ ΟΥΣΙΩΝ '537': ΓΑΛΛΙΑ, ΓΕΡΜΑΝΙΑ Κ.ΛΠ '538': ΝΟΜΟΠΑΡΑΣΚΕΥΑΣΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ '539': ΚΥΒΕΡΝΕΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ '540': ΣΤΡΑΤΙΩΤΙΚΟΙ ΑΚΟΛΟΥΘΟΙ '541': ΔΙΑΘΕΣΗ ΑΠΟΣΤΡΑΓΓΙΖΟΜΕΝΩΝ ΓΑΙΩΝ '542': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΡΑΔΙΟΦΩΝΙΑ – ΤΗΛΕΟΡΑΣΗ '543': ΓΝΩΜΟΔΟΤΙΚΟ ΣΥΜΒΟΥΛΙΟ ΦΑΡΜΑΚΩΝ '544': ΣΥΜΒΑΣΕΙΣ ΔΙΑΦΟΡΕΣ '545': ΠΡΑΞΕΙΣ ΚΑΤΑ ΤΗΣ ΑΣΦΑΛΕΙΑΣ ΤΗΣ ΑΕΡΟΠΟΡΙΑΣ '546': ΙΑΤΡΟΙ ΙΑΜΑΤΙΚΩΝ ΠΗΓΩΝ '547': ΚΕΝΤΡΙΚΟ ΣΥΜΒΟΥΛΙΟ ΥΓΕΙΑΣ (ΚΕ.Σ.Υ.) '548': ΑΝΩΤΑΤΟ ΣΥΜΒΟΥΛΙΟ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '549': ΥΠΟΥΡΓΕΙΟ ΕΝΕΡΓΕΙΑΣ ΚΑΙ ΦΥΣΙΚΩΝ ΠΟΡΩΝ '550': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΕΛΑΦΡΩΝ ΑΕΡΟΠΛΑΝΩΝ Δ.Χ '551': ΠΟΛΥΕΘΝΕΙΣ ΜΟΡΦΩΤΙΚΕΣ ΣΥΜΦΩΝΙΕΣ '552': ΕΚΠΑΙΔΕΥΣΗ Λ.Σ '553': ΠΡΟΣΤΑΣΙΑ ΕΛΕΥΘΕΡΟΥ ΑΝΤΑΓΩΝΙΣΜΟΥ '554': ΕΘΝΙΚΗ ΕΠΙΤΡΟΠΗ ΔΙΕΘΝΟΥΣ ΕΜΠΟΡΙΚΟΥ ΕΠΙΜΕΛΗΤΗΡΙΟΥ '555': ΟΡΓΑΝΙΣΜΟΣ '556': ΤΕΛΩΝΕΙΑΚΕΣ ΠΑΡΑΚΑΤΑΘΗΚΕΣ '557': ΕΛΕΓΧΟΣ ΟΡΓΑΝΙΣΜΩΝ ΚΟΙΝΩΝΙΚΗΣ ΠΟΛΙΤΙΚΗΣ '558': ΕΝΩΣΕΙΣ ΑΠΟΣΤΡΑΤΩΝ ΑΞΙΩΜΑΤΙΚΩΝ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '559': ΦΥΛΛΑ ΠΟΙΟΤΗΤΑΣ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '560': ΙΝΣΤΙΤΟΥΤΟ ΓΕΩΛΟΓΙΚΩΝ ΚΑΙ ΜΕΤΑΛΛΕΥΤΙΚΩΝ ΕΡΕΥΝΩΝ '561': ΛΑΟΓΡΑΦΙΚΟ ΚΑΙ ΕΘΝΟΛΟΓΙΚΟ ΜΟΥΣΕΙΟ ΜΑΚΕΔΟΝΙΑΣ - ΘΡΑΚΗΣ '562': ΠΡΩΤΕΣ ΥΛΕΣ ΤΑΠΗΤΟΥΡΓΙΑΣ '563': ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ '564': ΚΩΔΙΚΑΣ ΟΔΙΚΗΣ ΚΥΚΛΟΦΟΡΙΑΣ '565': ΦΑΡΜΑΚΕΥΤΙΚΗ ΠΕΡΙΘΑΛΨΗ '566': ΜΕΛΕΤΕΣ ΠΡΟΓΡΑΜΜΑΤΟΣ ΔΗΜΟΣΙΩΝ ΕΠΕΝΔΥΣΕΩΝ '567': ΕΠΙΔΟΣΗ ΔΙΑ ΤΟΥ ΤΑΧΥΔΡΟΜΕΙΟΥ '568': ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΡΑΚΗΣ '569': ΗΘΙΚΕΣ ΑΜΟΙΒΕΣ '570': ΔΗΜΟΣΙΑ ΚΤΗΜΑΤΑ ΣΤΗ ΔΩΔΕΚΑΝΗΣΟ '571': ΣΥΜΒΑΣΕΙΣ ΔΙΚΑΣΤΙΚΗΣ ΑΝΤΙΛΗΨΕΩΣ '572': ΠΕΡΙΟΡΙΣΜΟΙ ΑΛΙΕΙΑΣ '573': ΠΥΡΗΝΙΚΕΣ ΕΓΚΑΤΑΣΤΑΣΕΙΣ '574': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΑΥΤΟΚΙΝΗΤΩΝ '575': ΕΓΓΡΑΦΕΣ, ΕΞΕΤΑΣΕΙΣ, ΑΝΑΛΥΤΙΚΑ ΠΡΟΓΡΑΜΜΑΤΑ '576': ΔΙΚΑΙΩΜΑΤΑ ΤΕΛΩΝΕΙΑΚΩΝ ΕΡΓΑΣΙΩΝ '577': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΑΥΤΟΚΙΝΗΤΙΣΤΩΝ (Τ.Σ.Α.) '578': ΤΗΛΕΦΩΝΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '579': ΦΟΡΟΛΟΓΙΑ ΑΣΦΑΛΙΣΤΡΩΝ '580': ΔΙΕΘΝΗΣ ΥΔΡΟΓΡΑΦΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ '581': ΕΠΑΡΧΙΕΣ '582': ΑΓΡΟΤ. ΑΠΟΚΑΤΑΣΤΑΣΗ ΠΡΟΣΦΥΓΩΝ '583': ΓΕΝΙΚΑ ΓΙΑ ΤΑ ΘΕΑΤΡΑ '584': ΣΥΜΒΑΣΕΙΣ ΔΙΩΞΕΩΣ ΛΑΘΡΕΜΠΟΡΙΟΥ '585': ΜΗΧΑΝΕΣ ΠΡΟΠΛΗΡΩΜΗΣ ΤΕΛΩΝ '586': ΟΡΓΑΝΙΣΜΟΣ ΚΡΑΤΙΚΩΝ ΘΕΑΤΡΩΝ '587': ΚΕΝΤΡΟ ΗΛΕΚΤΡΟΝΙΚΟΥ ΥΠΟΛΟΓΙΣΤΟΥ ΚΟΙΝΩΝΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '588': ΦΟΡΟΣ ΠΡΟΣΤΙΘΕΜΕΝΗΣ ΑΞΙΑΣ '589': ΤΑΜΕΙΑ ΑΡΩΓΗΣ ΤΤΤ. ΥΠΑΛΛΗΛΩΝ '590': ΣΩΜΑ ΟΡΚΩΤΩΝ ΕΛΕΓΚΤΩΝ ΛΟΓΙΣΤΩΝ (Σ.Ο.Ε.Λ.), ΕΠΙΤΡΟΠΗ ΛΟΓΙΣΤΙΚΗΣ ΤΥΠΟΠΟΙΗΣΗΣ ΚΑΙ ΕΛΕΓΧΩΝ (Ε.Λ.Τ.Ε.) '591': ΑΓΡΟΤΙΚΑ ΝΗΠΙΟΤΡΟΦΕΙΑ '592': ΣΧΕΔΙΟ ΠΟΛΕΩΣ ΑΘΗΝΩΝ ΠΕΙΡΑΙΩΣ '593': ΜΙΣΘΩΣΕΙΣ ΑΚΙΝΗΤΩΝ Ο.Δ.Ε.Π '594': ΕΛΕΓΧΟΣ ΣΠΟΡΟΠΑΡΑΓΩΓΗΣ '595': ΑΜΥΝΤΙΚΕΣ ΠΕΡΙΟΧΕΣ ΚΑΙ Ν. ΟΧΥΡΑ '596': ΟΔΟΙΠΟΡΙΚΑ '597': ΠΟΡΟΙ ΟΡΓΑΝΙΣΜΩΝ ΤΟΥΡΙΣΜΟΥ '598': ΔΙΕΘΝΕΣ ΔΙΚΑΣΤΗΡΙΟ '599': ΟΙΚΟΝΟΜΙΚΗ ΜΕΡΙΜΝΑ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '600': ΓΕΝΙΚΟ ΝΟΣΟΚΟΜΕΙΟ ΕΜΠΟΡΙΚΟΥ ΝΑΥΤΙΚΟΥ '601': ΝΟΜΙΚΗ ΒΟΗΘΕΙΑ ΣΕ ΠΟΛΙΤΕΣ ΧΑΜΗΛΟΥ ΕΙΣΟΔΗΜΑΤΟΣ '602': ΣΥΜΒΟΛΑΙΟΓΡΑΦΙΚΟΙ ΣΥΛΛΟΓΟΙ '603': ΥΠΟΥΡΓΕΙΟ ΣΤΡΑΤΙΩΤΙΚΩΝ '604': ΠΡΟΣΩΠΙΚΟ Ε.Μ.Π '605': ΥΠΟΥΡΓΕΙΟ ΕΡΓΑΣΙΑΣ '606': ΑΓΟΝΕΣ ΓΡΑΜΜΕΣ '607': ΜΟΝΟΠΩΛΙΟ ΠΕΤΡΕΛΑΙΟΥ '608': ΠΡΟΛΗΨΗ ΡΥΠΑΝΣΗΣ ΤΗΣ ΘΑΛΑΣΣΑΣ '609': ΧΩΡΙΚΗ ΔΙΚΑΙΟΔΟΣΙΑ ΤΕΛΩΝΕΙΑΚΩΝ ΑΡΧΩΝ '610': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΣΩΜΑΤΕΙΑ '611': ΥΠΗΡΕΣΙΑ ΑΓΡΟΤΙΚΗΣ ΑΣΦΑΛΕΙΑΣ '612': ΑΞΙΟΠΟΙΗΣΗ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΠΕΡΙΟΥΣΙΑΣ '613': ΕΜΠΟΡΙΚΟΙ ΑΝΤΙΠΡΟΣΩΠΟΙ '614': ΕΝΩΣΕΙΣ ΕΦΕΔΡΩΝ ΑΞΙΩΜΑΤΙΚΩΝ '615': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΒΙΟΜΗΧΑΝΙΑΣ '616': ΛΟΓΙΣΤΙΚΟ ΕΙΔΙΚΩΝ ΤΑΜΕΙΩΝ Ν.Π.Δ.Δ '617': ΣΥΜΒΑΣΗ ΓΙΑ ΔΕΙΓΜΑΤΑ ΚΛΠ '618': ΕΡΓΟΛΗΠΤΕΣ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ '619': ΕΠΑΝΕΠΟΙΚΙΣΜΟΣ ΠΑΡΑΜΕΘΟΡΙΩΝ ΠΕΡΙΟΧΩΝ '620': ΦΑΡΙΚΑ ΤΕΛΗ '621': ΛΑΤΟΜΕΙΑ ΜΑΡΜΑΡΩΝ '622': ΠΟΣΟΣΤΟ ΣΥΜΜΕΤΟΧΗΣ ΑΣΦΑΛΙΣΜΕΝΩΝ '623': ΑΣΦΑΛΕΙΑ ΑΝΘΡΩΠΙΝΗΣ ΖΩΗΣ ΣΤΗ ΘΑΛΑΣΣΑ '624': ΟΡΓΑΝΙΚΟΙ ΝΟΜΟΙ ΠΕΡΙ ΦΥΛΑΚΩΝ '625': ΛΑΘΡΕΜΠΟΡΙΑ '626': ΑΣΦΑΛΙΣΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΕΝΙΚΑ '627': ΕΙΣΑΓΩΓΗ ΧΛΩΡΙΚΟΥ ΚΑΛΙΟΥ '628': ΙΝΣΤΙΤΟΥΤΟ ΓΕΩΠΟΝΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '629': ΕΠΙΔΟΜΑ ΠΑΣΧΑ - ΧΡΙΣΤΟΥΓΕΝΝΩΝ '630': ΓΕΩΡΓΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΑΛΛΗΛΑΣΦΑΛΕΙΑΣ '631': ΟΡΓΑΝΙΣΜΟΣ ΦΟΡΟΛΟΓΙΚΩΝ ΔΙΚΑΣΤΗΡΙΩΝ '632': ΕΠΙΔΟΣΗ '633': ΙΔΡΥΜΑ ΚΡΑΤΙΚΩΝ ΥΠΟΤΡΟΦΙΩΝ '634': ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ ΑΕΡΟΥΓΕΙΟΝΟΜΕΙΩΝ '635': ΟΦΕΙΛΕΣ ΠΡΟΣ ΤΟ ΔΗΜΟΣΙΟ '636': ΠΡΑΚΤΟΡΕΙΑ ΕΙΔΗΣΕΩΝ '637': ΕΛΕΓΧΟΣ ΚΑΙ ΕΠΟΠΤΕΙΑ ΞΕΝΟΔΟΧΕΙΩΝ ΚΛΠ '638': ΚΟΙΝΑ ΤΑΜΕΙΑ ΕΚΜΕΤΑΛΛΕΥΣΕΩΣ ΛΕΩΦΟΡΕΙΩΝ (Κ.Τ.Ε.Λ.) '639': ΚΑΤΩΤΑΤΑ ΟΡΙΑ ΜΙΣΘΩΝ ΚΑΙ ΗΜΕΡΟΜΙΣΘΙΩΝ '640': ΣΥΝΤΗΡΗΤΙΚΗ ΚΑΤΑΣΧΕΣΗ ΠΛΟΙΩΝ '641': ΥΠΗΡΕΣΙΑ ΠΡΟΣΤΑΣΙΑΣ ΕΡΓΑΖΟΜΕΝΩΝ ΣΤΗΝ ΑΛΛΟΔΑΠΗ '642': ΕΥΡΩΠΑΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΠΥΡΗΝΙΚΩΝ ΕΡΕΥΝΩΝ '643': ΒΙΒΛΙΑ ΓΕΩΡΓΙΚΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ '644': ΠΟΛΙΤΙΚΕΣ ΚΑΙ ΣΤΡΑΤΙΩΤΙΚΕΣ ΣΥΝΤΑΞΕΙΣ '645': ΜΕΤΑΤΡΟΠΗ ΜΕΤΟΧΩΝ ΣΕ ΟΝΟΜΑΣΤΙΚΕΣ '646': ΕΙΔΙΚΟΙ ΦΡΟΥΡΟΙ '647': ΥΠΗΡΕΣΙΑ ΕΘΝΙΚΗΣ ΑΣΦΑΛΕΙΑΣ '648': ΡΥΘΜΙΣΤΙΚΟΣ ΦΟΡΟΣ '649': ΛΙΜΑΝΙ ΗΡΑΚΛΕΙΟΥ ΚΡΗΤΗΣ ΚΑΙ '650': ΕΚΚΛΗΣΙΑΣΤΙΚΕΣ ΥΠΟΤΡΟΦΙΕΣ '651': ΦΟΡΟΛΟΓΙΑ ΟΙΝΟΥ '652': ΔΙΕΘΝΗΣ ΥΓΕΙΟΝΟΜΙΚΗ ΣΥΜΒΑΣΗ ΑΕΡΟΝΑΥΤΙΛΙΑΣ '653': ΤΑΜΕΙΟ ΑΡΩΓΗΣ ΥΠΑΛΛΗΛΩΝ '654': ΚΟΙΝΩΝΙΚΗ ΑΣΦΑΛΙΣΗ ΑΓΡΟΤΩΝ '655': ΚΥΡΟΣ ΣΥΜΒΟΛΑΙΟΓΡΑΦΙΚΩΝ ΠΡΑΞΕΩΝ '656': ΦΟΡΟΛΟΓΙΑ ΥΠΕΡΑΞΙΑΣ ΑΚΙΝΗΤΩΝ '657': ΝΗΠΙΑΓΩΓΕΙΑ '658': ΕΚΘΕΜΑΤΑ ΚΑΙ ΔΕΙΓΜΑΤΑ '659': ΥΓΕΙΟΝΟΜΙΚΟ ΣΩΜΑ ΑΕΡΟΠΟΡΙΑΣ '660': ΠΛΗΡΩΜΗ ΜΙΣΘΩΝ ΚΑΙ ΗΜΕΡΟΜΙΣΘΙΩΝ '661': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΑΣ ΚΑΠΝΟΥ '662': ΟΡΙΑ '663': ΔΙΚΑΙΟΣΤΑΣΙΑ ΣΕΙΣΜΟΠΑΘΩΝ, ΠΥΡΟΠΑΘΩΝ, ΠΡΟΣΦΥΓΩΝ ΚΛΠ '664': ΧΡΕΗ ΚΛΗΡΟΝΟΜΙΩΝ '665': ΠΡΟΣΩΠΙΚΟΝ ΙΔΡΥΜΑΤΩΝ ΠΑΙΔΙΚΗΣ ΠΡΟΣΤΑΣΙΑΣ '666': ΜΙΣΘΩΣΕΙΣ ΚΑΙ ΑΓΟΡΕΣ '667': ΠΑΛΑΙΟΤΕΡΑΙ ΕΚΚΑΘΑΡΙΣΕΙΣ '668': ΟΙΚΟΝΟΜΙΚΗ ΑΠΟΚΑΤΑΣΤΑΣΗ ΑΓΡΟΤΩΝ '669': ΑΠΑΛΛΟΤΡΙΩΣΕΙΣ ΓΙΑ ΔΗΜΟΤΙΚΑ ΚΑΙ ΚΟΙΝΟΤΙΚΑ ΕΡΓΑ '670': ΜΗΤΡΩΟ ΑΓΡΟΤΩΝ '671': ΚΑΝΟΝΙΣΜΟΣ ΔΙΕΥΚΟΛΥΝΣΕΩΝ '672': ΚΡΑΤΙΚΟ ΕΡΓΟΣΤΑΣΙΟ ΑΕΡΟΠΛΑΝΩΝ '673': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΕΝΔΕΙΚΤΙΚΑ '674': ΑΥΘΑΙΡΕΤΕΣ ΚΑΤΑΣΚΕΥΕΣ '675': ΕΓΚΑΤΑΛΕΛΕΙΜΜΕΝΕΣ ΕΚΤΑΣΕΙΣ '676': ΥΠΟΥΡΓΕΙΟ ΔΗΜΟΣΙΩΝ ΄ΕΡΓΩΝ '677': ΠΡΟΝΟΙΑ Β. ΕΛΛΑΔΟΣ '678': ΔΙΚΑΣΤΙΚΟ ΕΝΣΗΜΟ - ΑΓΩΓΟΣΗΜΟ '679': ΤΑΧΥΔΡΟΜΙΚΗ ΑΝΤΑΠΟΚΡΙΣΗ '680': ΕΣΩΤΕΡΙΚΗ ΝΟΜΟΘΕΣΙΑ '681': ΦΟΡΟΛΟΓΙΑ ΤΣΙΓΑΡΟΧΑΡΤΟΥ '682': ΟΡΓΑΝΙΚΕΣ ΘΕΣΕΙΣ ΑΞΙΩΜΑΤΙΚΩΝ '683': ΜΑΙΕΥΤΙΚΗ ΠΕΡΙΘΑΛΨΗ '684': ΑΔΕΙΕΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '685': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '686': ΠΟΙΝΙΚΟΣ ΚΑΙ ΠΕΙΘΑΡΧΙΚΟΣ ΚΩΔΙΚΑΣ '687': ΑΝΥΠΟΤΑΚΤΟΙ '688': ΔΙΕΥΘΥΝΣΗ ΤΕΛΩΝΕΙΩΝ ΘΕΣΣΑΛΟΝΙΚΗΣ '689': ΠΕΡΙΦΕΡΕΙΕΣ ΛΙΜΕΝΙΚΩΝ ΑΡΧΩΝ '690': ΑΣΦΑΛΙΣΗ ΚΑΙ ΕΙΣΠΡΑΞΗ ΠΟΡΩΝ Τ.Ε.Β.Ε '691': ΣΙΔΗΡΟΣ '692': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ ΕΜΠΟΡΙΟΥ '693': ΔΙΑΧΕΙΡΙΣΗ ΙΣΡΑΗΛΙΤΙΚΩΝ ΠΕΡΟΥΣΙΩΝ '694': ΛΙΠΟΤΑΞΙΑ '695': ΒΑΡΕΑ ΚΑΙ ΑΝΘΥΓΙΕΙΝΑ ΕΠΑΓΓΕΛΜΑΤΑ '696': ΕΙΔΙΚΟ ΤΑΜΕΙΟ ΜΗΧΑΝΗΜΑΤΩΝ '697': ΛΕΩΦΟΡΕΙΑ ΠΕΡΙΟΧΗΣ ΠΡΩΤΕΥΟΥΣΑΣ '698': ΑΝΑΜΟΡΦΩΤΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ '699': ΥΓΕΙΟΝΟΜΙΚΟ ΣΩΜΑ '700': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΕΡΓΑΣΙΑΣ '701': ΔΙΩΡΥΓΑ ΚΟΡΙΝΘΟΥ '702': ΠΕΡΙΘΑΛΨΗ ΦΥΜΑΤΙΚΩΝ ΑΣΦΑΛΙΣΜΕΝΩΝ '703': ΚΟΙΝΩΝΙΚΟΣ ΕΛΕΓΧΟΣ ΔΙΟΙΚΗΣΗΣ - ΑΝΤΙΓΡΑΦΕΙΟΚΡΑΤΙΚΑ ΜΕΤΡΑ -ΕΚΚΑΘΑΡΙΣΗ ΑΡΧΕΙΩΝ '704': ΒΙΒΛΙΑ ΥΠΟΘΕΣΕΩΝ ΕΚΟΥΣΙΑΣ ΔΙΚΑΙΟΔΟΣΙΑΣ '705': ΖΑΧΑΡΗ '706': ΒΟΡΕΙΟΑΤΛΑΝΤΙΚΗ ΑΜΥΝΤΙΚΗ ΟΡΓΑΝΩΣΗ (Ν.Α.Τ.Ο) '707': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΑΣ ΓΕΝΙΚΩΝ ΑΠΟΘΗΚΩΝ '708': ΝΟΜΙΚΗ ΚΑΤΑΣΤΑΣΗ ΠΡΟΣΦΥΓΩΝ '709': ΔΙΚΑΣΤΗΡΙΟ ΛΕΙΩΝ '710': ΔΙΕΘΝΗΣ ΟΡΓΑΝΩΣΗ ΕΡΓΑΣΙΑΣ '711': ΠΡΟΜΗΘΕΙΕΣ–ΜΙΣΘΩΣΕΙΣ–ΕΡΓΑ Ο.Γ.Α '712': ΠΕΡΙΘΑΛΨΗ ΠΡΟΣΩΠΙΚΟΥ Ο.Γ.Α '713': ΧΟΡΗΓΗΣΗ ΔΑΝΕΙΩΝ ΑΠΟ Τ.Π. ΚΑΙ ΔΑΝΕΙΩΝ '714': ΤΕΛΟΣ ΕΠΙΤΗΔΕΥΜΑΤΟΣ '715': ΕΛΕΥΘΕΡΑ ΤΕΛΩΝΕΙΑΚΑ ΣΥΓΚΡΟΤΗΜΑΤΑ '716': ΦΟΡΟΛΟΓΙΚΑ ΚΙΝΗΤΡΑ ΣΥΓΧΩΝΕΥΣΕΩΣ Η ΜΕΤΑΤΡΟΠΗΣ ΕΠΙΧΕΙΡΗΣΕΩΝ '717': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ T.E.B.E '718': ΝΑΥΤΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ '719': ΠΡΟΣΩΠΙΚΟ Υ.Ε.Ν '720': ΛΕΙΤΟΥΡΓΟΙ ΜΕΣΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '721': ΚΟΙΝΟΠΡΑΞΙΑ ΓΕΩΡΓΙΚΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ '722': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΠΙΧΕΙΡΗΜΑΤΙΩΝ ΚΙΝΗΜΑΤΟΓΡΑΦΟΥ '723': ΒΟΣΚΟΤΟΠΟΙ '724': ΕΠΙΤΟΚΙΑ ΤΡΑΠΕΖΩΝ '725': ΚΑΠΝΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '726': ΣΤΑΘΜΟΙ ΑΥΤΟΚΙΝΗΤΩΝ '727': ΕΥΛΟΓΙΑ '728': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ ΥΠΟΥΡΓΕΙΟΥ ΒΙΟΜΗΧΑΝΙΑΣ '729': ΤΑΜΕΙΟ ΑΕΡΟΠΟΡΙΚΗΣ ΑΜΥΝΑΣ '730': ΟΡΓΑΝΙΣΜΟΣ ΚΕΝΤΡΙΚΗΣ ΥΠΗΡΕΣΙΑΣ '731': ΤΑΜΕΙΟ ΕΡΓΑΣΙΑΣ ΗΘΟΠΟΙΩΝ '732': ΤΕΛΩΝΙΣΜΟΣ ΕΙΔΩΝ ΑΤΟΜΙΚΗΣ ΧΡΗΣΕΩΣ '733': ΦΟΡΟΛΟΓΙΑ ΠΡΟΣΟΔΟΥ ΑΠΟ ΠΛΟΙΑ '734': ΔΙΟΙΚΗΤΙΚΗ ΔΙΑΙΡΕΣΗΣ '735': ΟΡΓΑΝΙΣΜΟΣ ΑΥΤΟΚΙΝΗΤΟΔΡΟΜΙΩΝ ΕΛΛΑΔΟΣ (Ο.Α.Ε.) '736': ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΑΜΕΣΗΣ ΒΟΗΘΕΙΑΣ (Ε.Κ.Α.Β.) '737': ΓΝΩΜΟΔΟΤΙΚΟ ΣΥΜΒΟΥΛΙΟ ΟΙΚΟΝΟΜΙΚΗΣ ΑΝΑΠΤΥΞΗΣ '738': ΔΙΑΘΗΚΗ '739': ΑΓΩΓΕΣ ΔΙΑΤΡΟΦΗΣ '740': ΦΑΡΜΑΚΕΥΤΙΚΟΙ ΣΥΛΛΟΓΟΙ '741': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΚΑΙ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΓΕΩΡΓΙΚΩΝ ΣΥΝΕΤΑΙΡΙΣΤΙΚΩΝ ΟΡΓΑΝΩΣΕΩΝ (Τ.Σ.Ε.Α.Π.Γ.Σ.Ο) '742': ΕΠΙΔΟΜΑΤΑ ΔΙΑΦΟΡΑ '743': ΠΕΙΘΑΡΧΙΚΟ ΔΙΚΑΙΟ '744': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΧΗΜΙΚΩΝ (Τ.Ε.Α.Χ) '745': ΠΡΟΑΓΩΓΕΣ ΚΑΙ ΠΡΟΣΟΝΤΑ ΠΥΡΟΣΒΕΣΤΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ '746': ΟΔΟΙΠΟΡΙΚΑ ΕΞΟΔΑ ΠΡΟΣΩΠΙΚΟΥ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ '747': ΝΟΣΗΛΕΥΤΙΚΑ ΙΔΡΥΜΑΤΑ ΚΑΤ’ ΙΔΙΑΝ '748': ΠΡΟΣΤΑΣΙΑ ΚΑΤΑ ΤΗΣ ΦΥΛΛΟΞΗΡΑΣ '749': ΟΡΓΑΝΙΣΜΟΣ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ '750': ΠΡΑΤΗΡΙΑ ΥΓΡΩΝ ΚΑΥΣΙΜΩΝ '751': ΘΡΗΣΚΕΥΤΙΚΟ ΣΩΜΑ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '752': ΔΙΑΔΙΚΑΣΙΑ ΑΝΑΓΚΑΣΤΙΚΩΝ ΑΠΑΛΛΟΤΡΙΩΣΕΩΝ ΑΚΙΝΗΤΩΝ '753': ΔΙΕΡΜΗΝΕΙΣ '754': ΣΧΕΔΙΑ ΑΛΛΩΝ ΠΟΛΕΩΝ '755': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ '756': ΗΜΕΡΟΛΟΓΙΟ ΜΗΧΑΝΗΣ '757': ΚΕΝΤΡΟ ΕΛΛΗΝΙΚΗΣ ΓΛΩΣΣΑΣ '758': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΣΕ ΑΡΤΟΠΟΙΕΙΑ '759': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ '760': ΜΕΤΑΦΡΑΣΤΙΚΑ ΓΡΑΦΕΙΑ '761': ΠΡΟΔΙΑΓΡΑΦΕΣ ΜΕΛΕΤΩΝ '762': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΕΘΝΙΚΗΣ '763': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΣΥΜΒΟΛΑΙΟΓΡΑΦΩΝ '764': ΙΑΤΡΟΔΙΚΑΣΤΙΚΗ ΑΜΟΙΒΗ '765': ΕΦΟΡΙΕΣ ΚΑΠΝΟΥ – ΚΑΠΝΕΡΓΟΣΤΑΣΙΑ '766': ΠΟΙΜΝΙΟΣΤΑΣΙΑ '767': ΚΕΝΤΡΑ ΕΡΕΥΝΑΣ - ΕΡΕΥΝΗΤΙΚΑ ΙΝΣΤΙΤΟΥΤΑ '768': ΤΑΜΕΙΑ ΠΡΟΝΟΙΑΣ ΔΙΚΗΓΟΡΩΝ '769': ΟΙΝΟΠΑΡΑΓΩΓΗ ΣΑΜΟΥ '770': ΙΜΑΤΙΣΜΟΣ Π. ΝΑΥΤΙΚΟΥ '771': ΜΗΧΑΝΙΚΟΙ,ΑΡΧΙΤΕΚΤΟΝΕΣ,ΤΟΠΟΓΡΑΦΟΙ '772': ΠΑΝΤΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΟΙΝΩΝΙΚΩΝ ΚΑΙ ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '773': ΝΕΟΙ ΧΡΗΜΑΤΟΠΙΣΤΩΤΙΚΟΙ ΘΕΣΜΟΙ '774': ΥΠΗΡΕΣΙΑ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '775': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΘΗΚΟΦΥΛΑΚΕΙΩΝ '776': ΑΤΥΧΗΜΑΤΑ ΣΕ ΔΗΜΟΣΙΑ ΕΡΓΑ '777': ΑΡΕΙΟΣ ΠΑΓΟΣ '778': ΥΠΑΓΩΓΗ ΣΕ ΑΣΦΑΛΙΣΗ ΚΑΙ '779': ΔΙΕΘΝΕΙΣ ΣΙΔΗΡΟΔΡΟΜΙΚΕΣ ΜΕΤΑΦΟΡΕΣΔΙΕΥΡΩΠΑΙΚΟ ΣΙΔΗΡΟΔΡΟΜΙΚΟ ΣΥΣΤΗΜΑ '780': ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΘΕΩΡΗΣΗ Π. ΝΑΥΤΙΚΟΥ '781': ΑΝΑΠΤΥΞΙΑΚΗ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΗ ΠΟΛΙΤΙΚΗ '782': ΒΕΒΑΙΩΣΗ ΚΑΙ ΕΙΣΠΡΑΞΗ ΠΟΙΝΙΚΩΝ ΕΞΟΔΩΝ '783': ΝΑΥΤΙΚΟ ΧΗΜΕΙΟ '784': ΛΑΧΕΙΑ '785': ΤΡΟΧΙΟΔΡΟΜΟΙ ΑΘΗΝΩΝ – ΠΕΙΡΑΙΩΣ '786': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΩΝ ΛΙΠΑΣΜΑΤΩΝ ΤΑ.Π.Π.Ε.Λ '787': ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΓΙΑ ΑΝΟΙΚΟΔΟΜΗΣΗ '788': ΑΓΟΡΑΠΩΛΗΣΙΑ ΚΑΠΝΟΥ '789': ΠΕΡΙ ΟΡΩΝ ΕΡΓΑΣΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΔΙΕΘΝΩΝ ΜΕΤΑΦΟΡΩΝ '790': ΑΛΙΕΥΤΙΚΟΣ ΚΩΔΙΚΑΣ '791': ΣΥΜΒΟΥΛΙΑ ΚΑΙ ΕΠΙΤΡΟΠΕΣ '792': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ ΥΠΟΥΡΓΕΙΟΥ ΟΙΚΟΝΟΜΙΚΩΝ '793': ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΑΣΕΜΝΩΝ ΔΗΜΟΣΙΕΥΜΑΤΩΝ '794': ΓΕΩΡΓΙΚΟΙ ΣΤΑΘΜΟΙ '795': ΝΑΞΙΩΤΙΚΗ ΣΜΥΡΙΔΑ '796': ΑΝΑΣΤΟΛΗ ΠΡΟΣΕΛΕΥΣΕΩΣ ΕΦΕΔΡΩΝ '797': ΕΚΠΑΙΔΕΥΣΗ ΧΩΡΟΦΥΛΑΚΗΣ '798': ΑΣΦΑΛΙΣΗ ΕΞΑΓΩΓΙΚΩΝ ΠΙΣΤΩΣΕΩΝ '799': ΘΕΡΑΠΑΙΝΙΔΕΣ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '800': ΕΠΙΤΡΟΠΗ ΑΤΟΜΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '801': ΚΑΝΟΝΙΣΜΟΣ ΑΣΤΥΝΟΜΙΑΣ ΠΟΛΕΩΝ '802': ΦΥΛΛΑ ΠΟΙΟΤΗΤΑΣ ΥΠΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '803': ΕΠΙΘΕΩΡΗΣΕΙΣ ΚΤΗΝΙΑΤΡΙΚΗΣ '804': ΜΕΡΙΚΗ ΑΠΑΣΧΟΛΗΣΗ - ΦΑΣΟΝ - ΤΗΛΕΡΓΑΣΙΑ ΚΑΤ’ ΟΙΚΟΝ ΑΠΑΣΧΟΛΗΣΗ '805': ΗΛΕΚΤΡΙΚΗ ΕΤΑΙΡΕΙΑ ΑΘΗΝΩΝ - ΠΕΙΡΑΙΩΣ '806': ΠΡΟΚΑΤΑΣΚΕΥΑΣΜΕΝΑΙ ΟΙΚΙΑΙ '807': ΤΡΑΠΕΖΑ ΤΗΣ ΕΛΛΑΔΟΣ '808': ΣΥΜΦΩΝΙΕΣ ΠΡΟΣΤΑΣΙΑΣ ΤΟΥ ΠΕΡΙΒΑΛΛΟΝΤΟΣ '809': ΛΙΓΝΙΤΗΣ '810': ΤΑΜΕΙΟ ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΤΑ '811': ΜΕΛΕΤΕΣ ΤΕΧΝΙΚΩΝ ΕΡΓΩΝ '812': ΠΛΗΡΩΜΑΤΑ ΑΕΡΟΣΚΑΦΩΝ '813': ΕΞΑΓΩΓΗ ΣΤΑΦΙΔΑΣ '814': ΤΑΜΕΙΟΝ ΠΡΟΝΟΙΑΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '815': ΔΙΑΧΕΙΡΙΣΗ ΠΕΡΙΟΥΣΙΑΣ '816': ΟΡΓΑΝΙΚΟΙ ΝΟΜΟΙ '817': ΥΠΗΡΕΣΙΕΣ ΑΙΜΟΔΟΣΙΑΣ '818': ΣΩΜΑΤΕΙΑ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '819': ΠΕΖΟΔΡΟΜΙΑ '820': ΔΙΑΘΕΣΗ ΑΠΟΡΡΙΜΜΑΤΩΝ '821': ΤΡΟΧΙΟΔΡΟΜΟΙ ΘΕΣΣΑΛΟΝΙΚΗΣ '822': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΔΗΜΟΣΙΟΥ ΛΟΓΙΣΤΙΚΟΥ '823': ΡΥΜΟΥΛΚΑ - ΛΑΝΤΖΕΣ '824': ΠΕΤΡΕΛΑΙΟΕΙΔΗ '825': ΓΕΝΙΚΑ ΑΡΧΕΙΑ ΤΟΥ ΚΡΑΤΟΥΣ '826': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ Ο.Τ.Ε. - ΣΧΕΣΕΙΣ Ο.Τ.Ε. ΜΕ ΑΛΛΟΥΣ ΠΑΡΟΧΟΥΣ '827': ΥΠΗΡΕΣΙΑ ΑΥΤΟΚΙΝΗΤΩΝ '828': ΑΚΑΔΗΜΙΑ ΑΘΗΝΩΝ '829': ΜΟΝΟΠΩΛΙΟ ΖΑΧΑΡΙΝΗΣ '830': ΟΙΚΙΣΤΙΚΕΣ ΠΕΡΙΟΧΕΣ '831': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΑΛΙΕΙΑΣ '832': ΔΙΑΦΟΡΕΣ ΕΚΤΑΚΤΕΣ ΦΟΡΟΛΟΓΙΕΣ '833': ΒΙΒΛΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '834': ΕΡΓΑΤΙΚΑ ΑΤΥΧΗΜΑΤΑ '835': ΝΟΣΗΛΕΥΤΕΣ '836': ΣΥΝΔΙΚΑΛΙΣΤΙΚΕΣ ΕΛΕΥΘΕΡΙΕΣ '837': ΕΘΝΙΚΟ ΣΥΜΒΟΥΛΙΟ ΕΝΕΡΓΕΙΑΣ '838': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΟΤΕΧΝΙΤΩΝ ΥΑΛΟΥΡΓΩΝ '839': ΑΓΩΓΕΣ ΑΣΦΑΛΙΣΤΡΩΝ '840': ΣΩΜΑΤΕΜΠΟΡΙΑ ΓΥΝΑΙΚΩΝ '841': ΑΤΕΛΕΙΕΣ ΕΡΓΩΝ ΑΜΥΝΤΙΚΟΥ ΠΡΟΓΡΑΜΜΑΤΟΣ '842': ΤΕΧΝΙΚΗ ΕΚΠΑΙΔΕΥΣΗ ΑΞΙΩΜΑΤΙΚΩΝ ΣΕ ΑΝΩΤΑΤΕΣ ΣΧΟΛΕΣ '843': ΔΙΚΑΙΩΜΑΤΑ ΚΗΡΥΚΩΝ ΚΛΠ '844': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ '845': ΝΑΥΤΕΣ ΚΑΙ ΛΙΜΕΝΟΦΥΛΑΚΕΣ '846': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΣΧΟΛΗ ΑΓΡΙΝΙΟΥ '847': ΠΟΛΥΤΕΧΝΙΚΗ ΣΧΟΛΗ '848': ΜΕΙΩΣΗ ΕΙΣΦΟΡΩΝ '849': ΚΕΝΤΡΑ ΛΗΨΕΩΣ ΤΙΜΩΝ ΣΦΑΓΕΙΩΝ '850': ΑΠΟΔΗΜΙΑ ΣΤΡΑΤΕΥΣΙΜΩΝ '851': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΝΟΙΑΣ ΚΑΙ ΚΟΙΝΗΣ ΔΙΑΝΟΜΗΣ ΠΩΛΗΤΩΝ ΒΕΝΖΙΝΗΣ ΑΘΗΝΩΝ - ΠΕΙΡΑΙΩΣ ΚΑΙ ΠΕΡΙΧΩΡΩΝ '852': ΙΑΤΡΟΦΑΡΜΑΚΕΥΤΙΚΗ ΠΕΡΙΘΑΛΨΗ '853': ΝΟΣΗΛΕΥΤΙΚΑ ΙΔΡΥΜΑΤΑ '854': ΓΕΝΙΚΑ ΠΕΡΙ ΜΟΥΣΕΙΩΝ '855': ΑΣΦΑΛΕΙΑ ΟΧΥΡΩΝ ΘΕΣΕΩΝ '856': ΓΕΩΡΓΙΚΑ ΜΗΧΑΝΗΜΑΤΑ '857': ΤΑΜΕΙΑ ΣΥΝΕΡΓΑΣΙΑΣ '858': ΙΔΙΩΤΙΚΕΣ ΚΛΙΝΙΚΕΣ ΚΑΙ ΕΡΓΑΣΤΗΡΙΑ '859': ΥΓΕΙΟΝΟΜΙΚΗ ΕΞΕΤΑΣΗ ΙΠΤΑΜΕΝΩΝ '860': ΔΙΑΦΟΡΕΣ ΑΕΡΟΠΟΡΙΚΕΣ ΣΧΟΛΕΣ '861': ΓΥΝΑΙΚΕΣ ΝΟΣΟΚΟΜΟΙ '862': ΦΟΙΤΗΣΗ, ΒΑΘΜΟΛΟΓΙΑ, ΕΞΕΤΑΣΕΙΣ ΚΛΠ. Α.Σ.Κ.Τ '863': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΔΙΑΦΟΡΟΙ '864': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΓΕΩΡΓΙΑΣ '865': ΚΩΔΙΚΟΠΟΙΗΣΗ ΤΗΣ ΝΟΜΟΘΕΣΙΑΣ '866': ΜΕΤΑ ΤΗΣ ΓΑΛΛΙΑΣ '867': ΓΕΩΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ ΣΤΡΑΤΟΥ '868': ΕΙΔΗ ΠΑΡΑΔΙΔΟΜΕΝΑ ΣΤΗΝ ΕΛΕΥΘΕΡΗ ΧΡΗΣΗ '869': ΜΟΝΟΠΩΛΙΟ ΣΠΙΡΤΩΝ '870': ΚΑΤΑΣΤΑΤΙΚΟΝ Τ.Α.Κ.Ε '871': ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ ΥΠΑΛΛΗΛΩΝ ΑΣΤΥΝΟΜΙΑΣ ΠΟΛΕΩΝ (Ε.Τ.Υ.Α.Π.) '872': ΜΙΣΘΟΔΟΣΙΑ ΙΕΡΕΩΝ – ΕΝΟΡΙΑΚΗ ΕΙΣΦΟΡΑ '873': ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '874': ΝΟΜΟΣ ΠΕΡΙ ΚΤΗΜΑΤΙΚΩΝ ΤΡΑΠΕΖΩΝ '875': ΔΙΕΘΝΗΣ ΣΥΜΒΑΣΗ ΠΕΡΙ ΥΔΡΑΥΛΙΚΩΝ ΔΥΝΑΜΕΩΝ '876': ΑΝΑΠΗΡΟΙ ΑΞΙΩΜΑΤΙΚΟΙ ΚΑΙ ΟΠΛΙΤΕΣ ΕΙΡΗΝΙΚΗΣ ΠΕΡΙΟΔΟΥ '877': ΠΟΙΝΙΚΗ ΚΑΙ ΠΕΙΘΑΡΧΙΚΗ ΔΩΣΙΔΙΚΙΑ Λ.Σ '878': ΔΑΣΙΚΟ ΠΡΟΣΩΠΙΚΟ '879': ΑΟΠΛΗ ΘΗΤΕΙΑ-ΑΝΤΙΡΡΗΣΙΕΣ ΣΥΝΕΙΔΗΣΗΣ '880': ΝΕΟΙ ΠΡΟΣΦΥΓΕΣ '881': ΤΕΧΝΙΚΕΣ ΥΠΗΡΕΣΙΕΣ ΣΤΡΑΤΟΥ '882': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ ΠΟΛΙΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '883': ΠΡΟΣΩΠΙΚΟ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ '884': ΚΩΔΙΚΑΣ ΑΓΡΟΤΙΚΗΣ ΑΣΦΑΛΕΙΑΣ '885': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΑΠΟΣΤΟΛΙΚΗΣ ΔΙΑΚΟΝΙΑΣ '886': ΥΠΟΥΡΓΕΙΟ ΑΙΓΑΙΟΥ '887': ΓΑΜΟΙ ΔΩΔΕΚΑΝΗΣΟΥ '888': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΚΡΕΟΠΩΛΕΙΩΝ '889': ΚΩΔΙΚΑΣ ΤΕΛΩΝ ΧΑΡΤΟΣΗΜΟΥ '890': ΔΕΛΤΙΟ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '891': ΑΡΜΟΔΙΟΤΗΤΑ ΝΟΜΑΡΧΗ ΣΕ ΕΡΓΑΤΙΚΑ ΖΗΤΗΜΑΤΑ '892': ΤΡΟΦΟΔΟΣΙΑ Π. ΝΑΥΤΙΚΟΥ '893': ΣΥΜΦΩΝΙΑ ΠΕΡΙ ΔΙΠΛΩΜΑΤΙΚΩΝ ΣΧΕΣΕΩΝ '894': ΕΦΕΔΡΟΙ ΚΑΙ ΕΠΙΚΟΥΡΟΙ ΑΞΙΩΜΑΤΙΚΟΙ Π.Ν '895': ΤΟΥΡΙΣΤΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ '896': ΔΙΕΘΝΕΣ ΠΟΙΝΙΚΟ ΔΙΚΑΣΤΗΡΙΟ '897': ΔΙΟΙΚΗΤΙΚΕΣ ΠΡΑΞΕΙΣ '898': ΝΟΣΟΚΟΜΕΙΑ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '899': ΣΥΜΒΟΥΛΙΟ ΧΑΛΥΒΑ '900': ΤΕΜΑΧΙΣΜΟΣ ΚΡΕΑΤΩΝ '901': ΕΛΕΓΧΟΣ ΚΑΤΟΧΗΣ ΟΠΛΩΝ '902': ΑΝΑΠΡΟΣΑΡΜΟΓΕΣ ΤΗΣ ΔΡΑΧΜΗΣ '903': ΕΦΟΔΙΑΣΜΟΣ ΠΛΟΙΩΝ '904': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΙΟΝΙΩΝ ΝΗΣΩΝ '905': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΚΙΝΗΤΩΝ ΑΞΙΩΝ ΑΝΩΝΥΜΗ ΕΤΑΙΡΕΙΑ (Δ.Ε.Κ.Α. Α.Ε.) '906': ΕΤΑΙΡΕΙΑ – ΕΥΡΩΠΑΙΚΟΣ ΟΜΙΛΟΣ '907': ΔΙΕΥΘΥΝΣΗ ΑΛΙΕΙΑΣ '908': ΕΠΙΜΕΛΗΤΗΡΙΟ ΤΟΥΡΙΣΤΙΚΩΝ ΚΑΤΑΣΤΗΜΑΤΩΝ '909': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΕΛΑΙΟΛΑΔΟΥ '910': ΠΤΗΤΙΚΗ ΙΚΑΝΟΤΗΤΑ '911': ΕΚΚΛΗΣΙΑΣΤΙΚΕΣ ΣΧΟΛΕΣ '912': ΔΙΑΤΙΜΗΣΗ ΙΑΤΡΙΚΩΝ ΠΡΑΞΕΩΝ '913': ΑΔΙΚΗΜΑΤΑ ΤΥΠΟΥ '914': ΕΞΑΝΘΗΜΑΤΙΚΟΣ ΤΥΦΟΣ '915': ΟΙΚΟΣ ΝΑΥΤΟΥ '916': ΜΑΣΤΙΧΑ '917': ΣΥΛΛΟΓΟΙ ΚΑΙ ΟΜΟΣΠΟΝΔΙΑ ΔΙΚΑΣΤΙΚΩΝ ΕΠΙΜΕΛΗΤΩΝ '918': ΕΜΠΟΡΙΚΑ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΑ ΣΗΜΑΤΑ '919': ΟΡΓΑΝΩΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ ΑΝΩΤΑΤΩΝ ΕΚΠΑΙΔΕΥΤΙΚΩΝ ΙΔΡΥΜΑΤΩΝ '920': ΥΓΕΙΟΝΟΜΙΚΗ ΑΠΟΘΗΚΗ '921': ΓΕΝ. ΔΙΕΥΘΥΝΣΗ ΠΟΙΝΙΚΗΣ ΔΙΚΑΙΟΣΥΝΗΣ '922': ΑΕΡΟΠΟΡΙΚΟ ΔΙΚΑΙΟ '923': ΜΕΛΕΤΗ ΚΑΙ ΕΠΙΒΛΕΨΗ ΜΗΧΑΝΟΛΟΓΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ '924': ΑΘΕΜΙΤΟΣ ΑΝΤΑΓΩΝΙΣΜΟΣ '925': ΠΟΛΕΜΙΚΗ ΔΙΑΘΕΣΙΜΟΤΗΤΑ '926': ΛΕΣΧΕΣ ΚΑΙ ΠΡΑΤΗΡΙΑ ΕΛ.ΑΣ '927': ΚΑΥΣΙΜΑ '928': ΥΓΕΙΟΝΟΜΙΚΑ ΜΕΤΡΑ '929': ΚΑΤΑΣΤΑΣΗ ΑΞΙΩΜΑΤΙΚΩΝ '930': ΕΙΣΠΡΑΞΗ ΠΟΡΩΝ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ '931': ΔΙΟΙΚΗΤΙΚΗ ΡΥΘΜΙΣΗ ΑΠΟΔΟΧΩΝ ΚΑΙ ΟΡΩΝ ΕΡΓΑΣΙΑΣ '932': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΤΑΧΥΔΡΟΜΕΙΩΝ '933': ΟΡΓΑΝΙΣΜΟΣ ΛΙΜΕΝΟΣ ΘΕΣΣΑΛΟΝΙΚΗΣ ΑΝΩΝΥΜΗ ΕΤΑΙΡΙΑ (Ο.Λ.Θ. Α.Ε.) '934': ΣΧΟΛΗ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ '935': ΚΑΘΟΛΙΚΟΙ '936': ΕΚΚΛΗΣΙΑΣΤΙΚΑ ΜΟΥΣΕΙΑ '937': ΔΙΕΘΝΗΣ ΕΚΘΕΣΗ ΘΕΣΣΑΛΟΝΙΚΗΣ Α.Ε. – XELEXPO Α.Ε '938': ΕΥΕΡΓΕΤΙΚΟΣ ΥΠΟΛΟΓΙΣΜΟΣ ΗΜΕΡΩΝ ΕΡΓΑΣΙΑΣ '939': ΕΙΣΦΟΡΑ ΕΠΑΓΓΕΛΜΑΤΙΚΟΥ ΚΙΝΔΥΝΟΥ '940': ΑΠΑΛΛΟΤΡΙΩΣΕΙΣ ΓΙΑ ΤΟΥΡΙΣΤΙΚΟΥΣ ΣΚΟΠΟΥΣ '941': ΑΠΟΛΥΜΑΝΤΗΡΙΑ '942': ΕΚΠΟΙΗΣΗ ΠΛΟΙΩΝ ΔΗΜΟΣΙΟΥ '943': ΔΙΑΚΟΝΟΙ '944': ΥΔΡΕΥΣΗ ΔΙΑΦΟΡΩΝ ΠΟΛΕΩΝ '945': ΠΡΩΤΕΣ ΥΛΕΣ ΚΛΩΣΤΟΥΦΑΝΤΟΥΡΓΙΑΣ '946': ΨΕΥΔΗΣ ΒΕΒΑΙΩΣΗ ΕΝΩΠΙΟΝ ΑΡΧΗΣ '947': ΑΠΩΛΕΣΘΕΙΣΕΣ ΚΑΙ ΠΑΡΑΓΡΑΦΕΙΣΕΣ ΑΞΙΕΣ '948': ΦΟΙΤΗΤΙΚΗ ΛΕΣΧΗ '949': ΤΑΜΕΙΟ ΥΓΕΙΑΣ ΤΑΧΥΔΡΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ '950': ΕΛΕΓΧΟΣ ΔΕΝΔΡΩΔΩΝ ΚΑΛΛΙΕΡΓΕΙΩΝ '951': ΚΑΤΑΠΟΛΕΜΗΣΗ ΑΝΑΛΦΑΒΗΤΙΣΜΟΥΛΑΙΚΗ ΕΠΙΜΟΡΦΩΣΗ '952': ΕΠΙΚΟΥΡΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΜΕΤΑΦΟΡΩΝ '953': ΦΟΙΤΗΤΙΚΕΣ ΛΕΣΧΕΣ '954': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗΝ ΠΡΟΣΤΑΣΙΑ ΤΩΝ ΕΡΓΑΖΟΜΕΝΩΝ ΓΥΝΑΙΚΩΝ '955': ΛΗΣΤΕΙΑ '956': ΑΓΩΓΕΣ ΑΠΟ ΣΥΝΑΛΛΑΓΜΑΤΙΚΕΣ ΚΑΙ ΓΡΑΜΜΑΤΙΑ '957': ΕΚΜΙΣΘΩΣΗ ΔΗΜΟΣΙΩΝ ΜΕΤΑΛΛΕΙΩΝ '958': ΚΟΛΥΜΒΗΤΙΚΕΣ ΔΕΞΑΜΕΝΕΣ '959': ΕΡΑΝΟΙ ΚΑΙ ΛΑΧΕΙΟΦΟΡΟΙ Η ΦΙΛΑΝΘΡΩΠΙΚΕΣ ΑΓΟΡΕΣ '960': ΠΡΟΣΤΑΣΙΑ ΕΠΙΒΑΤΗΓΟΥ ΝΑΥΤΙΛΙΑΣ '961': ΓΕΝΙΚΟΙ ΝΟΜΟΙ ΠΕΡΙ ΞΕΝΟΔΟΧΕΙΩΝ-ΕΠΙΠΛ. ΔΩΜΑΤΙΩΝ ΚΛΠ '962': ΙΕΡΑΡΧΙΑ ΚΑΙ ΠΡΟΑΓΩΓΕΣ ΑΞΙΩΜΑΤΙΚΩΝ '963': ΣΥΝΕΡΓΑΤΕΣ (ΓΡΑΜΜΑΤΕΙΣ) ΒΟΥΛΕΥΤΩΝ-ΕΥΡΩΒΟΥΛΕΥΤΩΝ '964': ΣΧΟΛΗ ΙΚΑΡΩΝ '965': ΟΡΓΑΝΙΣΜΟΣ ΣΙΔΗΡΟΔΡΟΜΩΝ ΕΛΛΑΔΟΣ (Ο.Σ.Ε.)ΣΙΔΗΡΟΔΡΟΜΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ '966': ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ ΚΑΤΑ ΘΑΛΑΣΣΑΝ ΚΑΙ ΚΑΤΑ ΞΗΡΑΝ '967': ΚΑΝΟΝΙΣΜΟΣ ΜΕΤΑΛΛΕΥΤΙΚΩΝ ΕΡΓΑΣΙΩΝ '968': ΑΠΟΦΥΓΗ ΣΥΓΚΡΟΥΣΕΩΝ '969': ΤΟΜΑΤΟΠΑΡΑΓΩΓΗ '970': ΔΙΑΦΟΡΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΤΑ ΑΥΤΟΚΙΝΗΤΑ '971': ΚΑΤΑΤΑΞΗ ΓΥΝΑΙΚΩΝ ΣΤΟ Λ.Σ '972': ΕΤΑΙΡΕΙΕΣ ΔΙΟΙΚΟΥΜΕΝΕΣ ΑΠΟ ΤΟΥΣ ΠΙΣΤΩΤΕΣ '973': ΒΑΛΚΑΝΙΚΕΣ ΣΥΜΦΩΝΙΕΣ '974': ΜΕΤΑΦΟΡΑ ΣΥΝΤΕΛΕΣΤΗ ΔΟΜΗΣΗΣ '975': ΠΡΟΜΗΘΕΥΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ Π.Ν '976': ΠΡΟΣΩΠΙΚΟ ΦΑΡΜΑΚΕΙΩΝ '977': ΔΙΔΑΣΚΟΜΕΝΑ ΜΑΘΗΜΑΤΑ '978': ΕΚΛΟΓΗ ΒΟΥΛΕΥΤΩΝ - ΕΥΡΩΒΟΥΛΕΥΤΩΝ '979': ΦΑΡΜΑΚΟΠΟΙΟΙ '980': ΣΤΡΑΤΙΩΤΙΚΑ ΠΡΑΤΗΡΙΑ '981': ΚΑΡΚΙΝΟΣ '982': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ Α.Ε. ΟΙΝΟΠΟΙΙΑΣ, ΖΥΘΟΠΟΙΙΑΣ ΚΑΙ ΟΙΝΟΠΝΕΥΜΑΤΟΠΟΙΙΑΣ '983': ΧΕΙΡΙΣΤΕΣ ΑΣΥΡΜΑΤΟΥ '984': ΠΟΛΙΤΙΚΗ ΕΠΙΣΤΡΑΤΕΥΣΗ-ΠΑΛΛΑΙΚΗ ΑΜΥΝΑ '985': ΟΡΓΑΝΙΣΜΟΙ ΕΓΓΕΙΩΝ ΒΕΛΤΙΩΣΕΩΝ '986': ΟΜΟΓΕΝΕΙΣ ΠΑΛΛΙΝΟΣΤΟΥΝΤΕΣ '987': ΕΥΡΩΠΑΙΚΟΣ ΚΟΙΝΩΝΙΚΟΣ ΧΑΡΤΗΣ '988': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '989': ΕΞΑΙΡΕΣΗ ΔΙΚΑΣΤΩΝ '990': ΓΕΝΙΚΕΣ ΕΠΙΘΕΩΡΗΣΕΙΣ – ΔΙΕΥΘΥΝΣΕΙΣ ΣΤΟΙΧΕΙΩΔΟΥΣ ΕΚΠΑΙΔΕΥΣΗΣ '991': ΚΑΝΟΝΙΣΜΟΣ ΕΠΙΘΕΩΡΗΣΕΩΣ ΚΑΙ ΑΣΦΑΛΕΙΑΣ '992': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΑΥΤΟΝΟΜΟΥ ΣΤΑΦΙΔΙΚΟΥ ΟΡΓΑΝΙΣΜΟΥ (Τ.Α.Π.Α.Σ.Ο) '993': ΤΑΜΕΙΟΝ ΠΡΟΝΟΙΑΣ ΟΡΘΟΔΟΞΟΥ ΕΦΗΜΕΡΙΑΚΟΥ '994': ΣΧΟΛΙΚΗ ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ '995': ΚΕΝΤΡΟ ΠΑΡΑΓΩΓΙΚΟΤΗΤΑΣ '996': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΙΔΙΟΚΤΗΤΩΝ '997': ΒΟΣΚΗ ΕΝΤΟΣ ΔΑΣΩΝ '998': ΕΛΕΓΧΟΣ ΕΞΑΓΟΜΕΝΩΝ ΓΕΩΡΓΙΚΩΝ ΠΡΟΙΟΝΤΩΝ '999': ΠΑΙΔΑΓΩΓΙΚΑ ΤΜΗΜΑΤΑ Α.Ε.Ι '1000': ΥΠΟΤΡΟΦΙΕΣ ΚΛΗΡΟΔΟΤΗΜΑΤΟΣ Π. ΒΑΣΣΑΝΗ '1001': ΑΤΥΧΗΜΑ ΑΠΟ ΔΟΛΟ ΤΟΥ ΕΡΓΟΔΟΤΗ '1002': ΒΥΖΑΝΤΙΝΟ ΚΑΙ ΧΡΙΣΤΙΑΝΙΚΟ ΜΟΥΣΕΙΟ '1003': ΕΙΡΗΝΕΥΤΙΚΕΣ ΑΠΟΣΤΟΛΕΣ '1004': ΥΓΕΙΟΝΟΜΙΚΟΣ ΄ΕΛΕΓΧΟΣ ΕΙΣΕΡΧΟΜΕΝΩΝ '1005': ΟΡΚΟΣ ΤΟΥ ΠΟΛΙΤΗ '1006': ΥΓΕΙΟΝΟΜΙΚΗ ΠΕΡΙΘΑΛΨΗ ΣΠΟΥΔΑΣΤΩΝ '1007': ΠΑΡΑΧΑΡΑΞΗ ΚΑΙ ΚΙΒΔΗΛΙΑ '1008': ΔΙΑΜΕΡΙΣΜΑΤΑ ΠΛΟΙΑΡΧΩΝ ΚΑΙ ΠΛΗΡΩΜΑΤΩΝ '1009': ΚΛΑΔΟΣ ΑΡΩΓΗΣ Τ.Α.Κ.Ε '1010': ΟΡΓΑΝΙΣΜΟΣ ΒΑΜΒΑΚΟΣ '1011': ΝΟΣΗΛΕΙΑ ΣΤΡΑΤΙΩΤΙΚΩΝ '1012': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ '1013': ΠΟΛΥΕΘΝΕΙΣ ΑΕΡΟΠΟΡΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1014': ΝΑΥΤΙΚΟ ΑΠΟΜΑΧΙΚΟ ΤΑΜΕΙΟ '1015': ΥΓΙΕΙΝΗ ΑΡΤΟΠΟΙΕΙΩΝ '1016': ΝΟΜΑΡΧΙΑΚΑ ΣΥΜΒΟΥΛΙΑ '1017': ΛΕΣΧΗ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '1018': ΚΑΤΩΤΕΡΟ ΔΙΔΑΚΤΙΚΟ ΠΡΟΣΩΠΙΚΟ '1019': ΓΕΝΙΚΑ ΠΕΡΙ ΚΥΚΛΟΦΟΡΙΑΣ ΑΥΤΟΚΙΝΗΤΩΝ '1020': ΤΑΜΕΙΟ ΝΟΣΗΛΕΙΑΣ ΣΠΟΥΔΑΣΤΩΝ '1021': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΚΑΙ ΒΙΟΤΕΧΝΙΚΑ ΕΠΙΜΕΛΗΤΗΡΙΑ '1022': ΑΚΤΟΠΛΟΙΑ '1023': ΠΡΟΣΤΑΣΙΑ ΑΛΙΕΙΑΣ '1024': ΜΕ ΤΗ ΝΟΡΒΗΓΙΑ '1025': ΗΘΙΚΕΣ ΑΜΟΙΒΕΣ ΠΡΟΣΩΠΙΚΟΥ (΄ΕΝΟΠΛΟΥ-ΠΟΛΙΤΙΚΟΥ) ΥΠΟΥΡΓΕΙΟΥ ΔΗΜΟΣΙΑΣ ΤΑΞΗΣ '1026': ΛΕΩΦΟΡΕΙΑ ΙΔΙΩΤΙΚΗΣ ΧΡΗΣΕΩΣ '1027': ΕΡΓΑΤΙΚΕΣ ΔΙΑΦΟΡΕΣ '1028': ΡΑΔΙΟΗΛΕΚΤΡΟΛΟΓΟΙ-ΡΑΔΙΟΤΕΧΝΙΤΕΣ '1029': ΠΡΟΓΝΩΣΤΙΚΑ ΠΟΔΟΣΦΑΙΡΟΥ '1030': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΚΑΙ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΤΗΣ ΑΓΡΟΤΙΚΗΣ ΤΡΑΠΕΖΑΣ ΤΗΣ ΕΛΛΑΔΑΣ (Τ.Σ.Π. – Α.Τ.Ε.) '1031': ΥΔΡΕΥΣΗ ΛΕΚΑΝΟΠΕΔΙΟΥ ΑΘΗΝΩΝ '1032': ΤΡΑΠΕΖΑ ΟΦΘΑΛΜΩΝ '1033': ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΧΑΡΤΩΝ ΚΑΙ ΧΑΡΤΟΓΡΑΦΙΚΗΣ ΚΛΗΡΟΝΟΜΙΑΣ - ΕΘΝΙΚΗ ΧΑΡΤΟΘΗΚΗ '1034': ΚΑΝΟΝΙΣΜΟΙ ΑΠΟΦΥΓΗΣ ΣΥΓΚΡΟΥΣΕΩΝ '1035': ΓΡΑΦΕΙΟ ΕΓΚΛΗΜΑΤΙΩΝ ΠΟΛΕΜΟΥ '1036': ΑΓΡΟΤΙΚΕΣ ΣΥΝΔΙΚΑΛΙΣΤΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ '1037': ΤΑΥΤΟΤΗΤΕΣ '1038': ΔΑΣΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ '1039': ΣΥΜΒΟΛΑΙΟΓΡΑΦΙΚΑ ΔΙΚΑΙΩΜΑΤΑ '1040': ΙΔΙΟΚΤΗΣΙΑ ΚΑΤ’ ΟΡΟΦΟ '1041': ΣΧΟΛΙΚΑ ΤΑΜΕΙΑ '1042': ΑΡΧΕΙΟΦΥΛΑΚΕΙΑ ΔΙΑΦΟΡΑ '1043': ΑΠΟΖΗΜΙΩΣΗ ΑΝΤΑΛΛΑΞΙΜΩΝ '1044': ΣΧΟΛΙΚΑ ΚΤΙΡΙΑ '1045': ΦΟΡΟΛΟΓΙΑ ΟΙΚΟΔΟΜΩΝ '1046': ΠΡΟΤΥΠΑ ΔΗΜΟΤΙΚΑ '1047': ΠΡΩΤΕΣ ΥΛΕΣ ΒΥΡΣΟΔΕΨΙΑΣ - ΔΕΡΜΑΤΑ '1048': ΣΥΜΒΙΒΑΣΜΟΣ ΚΑΙ ΔΙΑΙΤΗΣΙΑ '1049': ΚΑΤΑΣΤΑΣΗ ΔΗΜΟΤΙΚΩΝ ΚΑΙ ΚΟΙΝΟΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '1050': ΕΣΟΔΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '1051': ΣΤΑΔΙΑ ΚΑΙ ΓΥΜΝΑΣΤΗΡΙΑ '1052': ΚΟΙΝΗ ΑΓΡΟΤΙΚΗ ΠΟΛΙΤΙΚΗ '1053': ΑΤΟΜΑ ΜΕ ΕΙΔΙΚΕΣ ΑΝΑΓΚΕΣ - ΥΠΕΡΗΛΙΚΕΣ - ΧΡΟΝΙΑ ΠΑΣΧΟΝΤΕΣ '1054': ΕΚΚΛΗΣΙΑΣΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ '1055': ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗΝ ΑΠΟΦΥΓΗ ΔΙΠΛΗΣ ΦΟΡΟΛΟΓΙΑΣ '1056': ΠΡΟΣΤΑΣΙΑ ΒΑΜΒΑΚΟΠΑΡΑΓΩΓΗΣ '1057': ΝΑΥΤΙΚΗ ΣΤΡΑΤΟΛΟΓΙΑ '1058': ΝΟΣΟΚΟΜΕΙΑΚΗ ΠΕΡΙΘΑΛΨΗ ΑΣΦΑΛΙΣΜΕΝΩΝ Ο.Γ.Α '1059': ΦΥΣΙΚΑ ΟΡΓΑΝΙΚΑ ΛΙΠΑΣΜΑΤΑ '1060': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΕΣΤΙΑΤΟΡΙΩΝ, ΖΑΧΑΡΟΠΛΑΣΤΕΙΩΝ, ΚΑΦΕΝΕΙΩΝ Κ.ΛΠ. (Τ.Ε.Α.Μ.Ε.Ζ.) '1061': ΤΕΧΝΙΚΑΙ ΥΠΗΡΕΣΙΑΙ '1062': ΣΥΓΚΕΝΤΡΩΣΗ ΠΡΟΙΟΝΤΩΝ '1063': ΥΔΡΟΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ '1064': ΥΠΗΡΕΣΙΑ ΕΛΕΓΧΟΥ ΚΑΤΑΣΚΕΥΗΣ ΑΞΙΩΝ ΤΟΥ ΔΗΜΟΣΙΟΥ '1065': ΕΠΙΣΚΟΠΙΚΑ ΓΡΑΦΕΙΑ '1066': ΒΕΛΓΙΟ, ΒΕΝΕΖΟΥΕΛΑ Κ.ΛΠ '1067': ΔΗΜΟΤΙΚΟΣ ΚΑΙ ΚΟΙΝΟΤΙΚΟΣ ΚΩΔΙΚΑΣ '1068': ΠΡΟΔΟΣΙΑ '1069': ΜΙΣΘΟΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '1070': ΠΟΛΙΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΝΑΥΤΙΚΟΥ '1071': ΑΝΑΖΗΤΗΣΗ ΚΑΙ ΔΙΑΦΥΛΑΞΗ ΑΡΧΑΙΟΤΗΤΩΝ '1072': ΑΔΕΙΕΣ ΛΙΑΝΙΚΗΣ ΠΩΛΗΣΗΣ ΤΣΙΓΑΡΩΝ ΚΑΙ ΕΙΔΩΝ ΜΟΝΟΠΩΛΙΟΥ '1073': ΕΠΟΠΤΙΚΑ ΜΕΣΑ ΔΙΔΑΣΚΑΛΙΑΣ '1074': ΕΚΛΟΓΟΔΙΚΕΙΑ '1075': Ο.Γ.Α ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '1076': ΙΝΣΤΙΤΟΥΤΟ ΥΓΕΙΑΣ ΤΟΥ ΠΑΙΔΙΟΥ '1077': ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΠΑΤΡΩΝ '1078': ΕΣΠΕΡΙΔΟΕΙΔΗ-ΟΠΩΡΟΚΗΠΕΥΤΙΚΑ '1079': ΕΠΙΔΟΜΑΤΑ ΣΤΡΑΤΕΥΟΜΕΝΩΝ '1080': ΠΡΟΛΗΨΗ ΕΡΓΑΤΙΚΩΝ ΑΤΥΧΗΜΑΤΩΝ ΤΩΝ ΝΑΥΤΙΚΩΝ '1081': ΥΠΗΡΕΣΙΑ ΑΠΟΜΑΓΝΗΤΙΣΕΩΣ ΠΛΟΙΩΝ '1082': ΔΙΑΦΟΡΕΣ ΕΙΔΙΚΕΣ ΔΙΑΔΙΚΑΣΙΕΣ '1083': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ '1084': ΕΘΝΙΚΗ ΥΠΗΡΕΣΙΑ ΠΛΗΡΟΦΟΡΙΩΝ (Ε.Υ.Π.) '1085': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ (T.E.A.M) '1086': ΑΣΦΑΛΙΣΗ ΚΑΤΑ ΤΗΣ ΑΝΕΡΓΙΑΣ - ΟΡΓΑΝΙΣΜΟΣ ΑΠΑΣΧΟΛΗΣΗΣ ΕΡΓΑΤΙΚΟΥ ΔΥΝΑΜΙΚΟΥ '1087': ΣΩΜΑΤΙΚΗ ΙΚΑΝΟΤΗΤΑ ΠΡΟΣΩΠΙΚΟΥ ΣΤΡΑΤΕΥΜΑΤΟΣ '1088': ΟΙΚΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ Π. ΝΑΥΤΙΚΟΥ '1089': ΔΑΣΙΚΗ ΦΟΡΟΛΟΓΙΑ '1090': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΚΤΗΝΟΤΡΟΦΙΑΣ, ΜΕΛΙΣΣΟΚΟΜΙΑΣ Κ.Λ.Π '1091': ΠΟΛΙΤΙΚΑ ΔΙΚΑΙΩΜΑΤΑ ΤΩΝ ΓΥΝΑΙΚΩΝ '1092': ΜΕΤΑΘΕΣΕΙΣ ΕΚΠΑΙΔΕΥΤΙΚΩΝ '1093': ΔΙΕΘΝΕΣ ΚΕΝΤΡΟ ΥΠΟΛΟΓΙΣΜΟΥ '1094': ΔΙΑΧΕΙΡΙΣΗ ΔΑΣΩΝ '1095': ΔΟΥΛΕΙΑ '1096': ΜΕ ΤΗ ΠΟΛΩΝΙΑ '1097': ΑΝΑΔΙΑΝΟΜΗ ΚΤΗΜΑΤΩΝ '1098': ΥΠΟΑΠΑΣΧΟΛΟΥΜΕΝΟΙ ΜΙΣΘΩΤΟΙ '1099': ΟΡΓΑΝΙΣΜΟΙ ΠΡΩΗΝ Υ.Β.Ε.Τ. - Γ.Γ.Β. - Γ.Γ.Ε.Τ '1100': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΒΙΒΛΙΟΘΗΚΗ ΑΘΗΝΩΝ '1101': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΑΣΦΑΛΙΣΤ.ΕΤΑΙΡΕΙΑΣ Η ΕΘΝΙΚΗ (Τ.Α.Π.Α.Ε. Η ΕΘΝΙΚΗ) '1102': ΤΕΛΗ ΣΧΟΛΑΖΟΥΣΩΝ ΚΛΗΡΟΝΟΜΙΩΝ '1103': ΞΕΝΕΣ ΓΛΩΣΣΕΣ '1104': ΚΑΤΑΣΚΗΝΩΣΕΙΣ - ΠΑΙΔΙΚΕΣ ΕΞΟΧΕΣ '1105': ΔΙΚΑΣΤΗΡΙΑ ΑΝΗΛΙΚΩΝ '1106': ΣΥΜΒΑΣΕΙΣ ΕΚΤΕΛΕΣΕΩΣ ΑΛΛΟΔΑΠΩΝ ΑΠΟΦΑΣΕΩΝ '1107': ΦΟΡΟΣ ΕΙΣΟΔΗΜΑΤΟΣ ΝΟΜΙΚΩΝ ΠΡΟΣΩΠΩΝ '1108': ΘΕΩΡΗΤΙΚΑ ΚΑΙ ΙΣΤΟΡΙΚΑ ΜΑΘΗΜΑΤΑ '1109': ΑΦΡΟΔΙΣΙΑ '1110': ΦΑΡΟΙ '1111': ΔΗΜΟΣΙΟΓΡΑΦΙΚΟ ΕΠΑΓΓΕΛΜΑ '1112': ΚΑΤΑΣΤΑΤΙΚΟΣ ΝΟΜΟΣ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '1113': ΕΛΕΓΧΟΣ ΣΚΟΠΙΜΟΤΗΤΑΣ ΙΔΡΥΣΕΩΣ ΒΙΟΜΗΧΑΝΙΩΝ '1114': ΓΥΜΝΑΣΙΑ ΚΑΙ ΛΥΚΕΙΑ '1115': ΑΕΡΟΝΑΥΤΙΚΕΣ ΠΛΗΡΟΦΟΡΙΕΣ '1116': ΚΑΤΑΣΤΑΣΗ ΥΠΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '1117': ΥΠΟΥΡΓΕΙΟ ΧΩΡΟΤΑΞΙΑΣ '1118': ΕΚΤΕΛΕΣΗ ΄ΕΡΓΩΝ '1119': ΜΙΣΘΟΔΟΣΙΑ ΥΠΑΛΛΗΛΩΝ ΣΕ ΕΠΙΣΤΡΑΤΕΥΣΗ '1120': ΚΟΙΜΗΤΗΡΙΑ '1121': ΑΣΦΑΛΙΣΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΚΙΝΔΥΝΩΝ ΠΟΛΕΜΟΥ '1122': ΣΥΜΦΩΝΙΑ ΓΙΑ ΑΝΙΘΑΓΕΝΕΙΣ '1123': ΝΟΜΑΡΧΙΑΚΗ ΑΥΤΟΔΙΟΙΚΗΣΗ '1124': ΣΧΟΛΗ ΤΟΥΡΙΣΤΙΚΩΝ ΕΠΑΓΓΕΛΜΑΤΩΝ '1125': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΠΑΡΑΓΩΓΗΣ ΚΑΙ ΕΜΠΟΡΙΑΣ ΟΠΩΡΟΚΗΠΕΥΤΙΚΩΝ '1126': ΑΠΟΛΥΜΑΝΣΗ ΥΔΑΤΩΝ '1127': ΠΟΛΕΟΔΟΜΙΚΕΣ ΕΠΙΤΡΟΠΕΣ '1128': ΟΡΓΑΝΙΣΜΟΣ ΕΚΔΟΣΕΩΣ ΣΧΟΛΙΚΩΝ ΒΙΒΛΙΩΝ '1129': ΥΠΑΛΛΗΛΟΙ ΝΟΜ. ΠΡΟΣΩΠΩΝ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ '1130': ΑΝΤΙΣΤΑΘΜΙΣΤΙΚΗ ΕΙΣΦΟΡΑ '1131': ΠΡΟΣΩΠΙΚΟ ΙΔΙΩΤΙΚΩΝ ΕΚΠΑΙΔΕΥΤΗΡΙΩΝ '1132': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΑ ΑΥΤΟΚΙΝΗΤΑ '1133': ΕΞΩΣΧΟΛΙΚΗ ΑΓΩΓΗ '1134': ΑΣΦΑΛΙΣΤΙΚΗ ΑΡΜΟΔΙΟΤΗΤΑ '1135': ΕΛΙΕΣ ΚΑΙ ΕΛΑΙΑ '1136': ΓΑΜΟΙ ΙΣΡΑΗΛΙΤΩΝ '1137': ΤΑΜΕΙΟ ΑΡΤΟΥ '1138': ΚΑΝΟΝΙΣΜΟΣ ΕΠΙΤΡΟΠΩΝ '1139': ΣΥΜΒΑΣΗ ΚΑΤΑ ΔΑΓΚΕΙΟΥ '1140': ΕΘΝΙΚΟΙ ΔΡΥΜΟΙ '1141': ΑΠΑΛΛΑΓΕΣ ΤΕΛΩΝ ΧΑΡΤΟΣΗΜΟΥ '1142': ΔΙΕΘΝΗΣ ΟΡΓΑΝΙΣΜΟΣ ΑΝΑΠΤΥΞΕΩΣ '1143': ΚΑΝΟΝΙΣΜΟΣ ΕΡΓΑΣΙΑΣ ΕΠΙ ΦΟΡΤΗΓΩΝ ΠΛΟΙΩΝ '1144': ΛΥΣΣΑ '1145': ΑΓΡΟΚΤΗΜΑ '1146': ΚΑΘΗΓΗΤΕΣ ΚΑΙ ΥΦΗΓΗΤΕΣ '1147': ΠΑΙΔΙΚΟΙ - ΒΡΕΦΟΝΗΠΙΑΚΟΙ ΣΤΑΘΜΟΙ '1148': ΚΕΝΤΡΟ ΒΥΖΑΝΤΙΝΩΝ ΕΡΕΥΝΩΝ '1149': ΙΔΡΥΣΗ ΕΛΕΥΘΕΡΗΣ ΖΩΝΗΣ ΣΕ ΔΙΑΦΟΡΑ ΛΙΜΑΝΙΑ ΤΗΣ ΧΩΡΑΣ '1150': ΣΧΟΛΙΚΑ ΛΕΩΦΟΡΕΙΑ '1151': ΣΦΑΓΕΙΑ '1152': ΕΠΙΚΥΡΩΣΗ ΝΟΜΟΘΕΤΗΜΑΤΩΝ '1153': ΕΓΓΡΑΦΑ ΤΑΥΤΟΤΗΤΑΣ ΝΑΥΤΙΚΩΝ '1154': ΑΤΟΜΙΚΑ ΔΙΚΑΙΩΜΑΤΑ - ΔΕΔΟΜΕΝΑ ΠΡΟΣΩΠΙΚΟΥ ΧΑΡΑΚΤΗΡΑ '1155': ΙΑΤΡΟΦΑΡΜΑΚΕΥΤΙΚΗ - ΝΟΣΟΚΟΜΕΙΑΚΗ ΠΕΡΙΘΑΛΨΗ - ΕΞΟΔΑ ΚΗΔΕΙΑΣ '1156': ΥΠΗΡΕΣΙΑ ΔΙΑΧΕΙΡΙΣΕΩΣ ΑΝΤΑΛΛΑΞΙΜΩΝ ΚΤΗΜΑΤΩΝ '1157': ΣΤΟΛΕΣ ΠΡΟΣΩΠΙΚΟΥ Λ.Σ '1158': ΠΕΡΙΦΡΑΞΗ ΟΙΚΟΠΕΔΩΝ '1159': ΣΙΔΗΡΟΔΡΟΜΟΙ ΑΤΤΙΚΗΣ '1160': ΤΡΑΧΩΜΑΤΑ '1161': ΝΑΥΑΓΙΑ-ΝΑΥΑΓΙΑΙΡΕΣΗ '1162': ΥΠΟΜΗΧΑΝΙΚΟΙ '1163': ΤΑΙΝΙΟΘΗΚΗ ΤΗΣ ΕΛΛΑΔΟΣ '1164': ΚΑΝΟΝΙΣΜΟΣ ΤΗΛΕΓΡΑΦΙΚΗΣ ΥΠΗΡΕΣΙΑΣ '1165': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΤΡΟΜΟΚΡΑΤΙΑΣ '1166': ΚΑΝΟΝΙΣΜΟΣ ΠΥΡΙΜΑΧΟΥ ΠΡΟΣΤΑΣΙΑΣ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ '1167': ΑΤΟΜΙΚΑ ΒΙΒΛΙΑΡΙΑ '1168': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΒΙΒΛΙΑΡΙΑ ΑΡΤΕΡΓΑΤΩΝ ΚΛΠ '1169': ΦΟΡΟΛΟΓΙΑ ΑΜΥΛΟΣΙΡΟΠΙΟΥ, ΣΤΑΦΙΔΙΝΗΣ ΚΛΠ '1170': ΜΟΥΣΕΙΟ ΕΛΛΗΝΙΚΩΝ ΛΑΙΚΩΝ ΟΡΓΑΝΩΝ '1171': ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΚΑΙ ΠΕΡΙΘΑΛΨΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝ. ΗΛΕΚΤΡ. ΕΤΑΙΡΙΑΣ (Ε.Η.Ε.) '1172': ΤΑΜΕΙΑ ΜΟΝΙΜΩΝ ΟΔΟΣΤΡΩΜΑΤΩΝ '1173': ΟΡΓΑΝΙΚΕΣ ΘΕΣΕΙΣ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν '1174': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΤΡΑΠΕΖΑΣ ΑΘΗΝΩΝ '1175': ΠΟΛΙΟΜΥΕΛΙΤΙΔΑ '1176': ΠΡΟΑΓΩΓΑΙ ΑΞΙΩΜΑΤΙΚΩΝ ΧΩΡΟΦΥΛΑΚΗΣ '1177': ΕΠΙΔΟΜΑ ΑΔΕΙΑΣ '1178': ΕΞΕΤΑΣΕΙΣ ΓΙΑ ΤΗΝ ΠΡΟΣΛΗΨΗ ΠΡΟΣΩΠΙΚΟΥ '1179': ΕΛΕΓΧΟΣ ΕΞΑΓΩΓΙΚΟΥ ΕΜΠΟΡΙΟΥ '1180': ΡΑΔΙΟΦΩΝΙΚΟΙ ΣΤΑΘΜΟΙ '1181': ΚΑΝΟΝΙΣΜΟΣ ΔΙΟΙΚΗΤΙΚΗΣ ΟΡΓΑΝΩΣΕΩΣ Τ.Σ.Α.Υ '1182': Φ.Κ.Π. ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '1183': ΔΙΑΦΟΡΟΙ ΠΟΛΥΕΘΝΕΙΣ ΟΡΓΑΝΙΣΜΟΙ '1184': ΧΟΛΕΡΑ '1185': EΝΙΑΙΟΣ ΔΗΜΟΣΙΟΓΡΑΦΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ '1186': ΑΤΕΛΕΙΕΣ ΔΗΜΟΣΙΩΝ ΥΠΗΡΕΣΙΩΝ '1187': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΜΗΧΑΝΟΔΗΓΩΝ ΟΔΟΣΤΡΩΤΗΡΩΝ ΚΛΠ '1188': ΝΟΣΟΚΟΜΟΙ '1189': ΝΟΣΟΚΟΜΕΙΑ ΦΥΛΑΚΩΝ '1190': ΑΠΟΚΑΤΑΣΤΑΣΗ ΚΤΗΝΟΤΡΟΦΩΝ '1191': ΤΕΛΗ ΚΑΙ ΕΙΣΦΟΡΕΣ '1192': ΑΚΑΤΑΣΧΕΤΑ '1193': ΞΕΝΟΔΟΧΕΙΑΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ ΤΗΣ ΕΛΛΑΔΑΣ '1194': ΔΗΜΟΤΟΛΟΓΙΑ '1195': ΣΤΑΤΙΣΤΙΚΗ ΥΠΗΡΕΣΙΑ '1196': ΚΡΑΤΙΚΟ ΕΡΓΑΣΤΗΡΙΟ ΕΛΕΓΧΟΥ ΦΑΡΜΑΚΩΝ '1197': ΑΕΡΟΠΟΡΙΚΗ ΑΣΤΥΝΟΜΙΑ '1198': ΕΚΤΑΚΤΕΣ ΕΙΣΦΟΡΕΣ '1199': ΣΥΝΤΑΞΕΙΣ ΥΠΑΛΛΗΛΩΝ Τ.Τ.Τ '1200': ΜΕΤΡΑ ΚΑΤΑ ΤΗΣ ΦΟΡΟΔΙΑΦΥΓΗΣ '1201': ΕΔΑΦΙΚΗ ΕΠΕΚΤΑΣΗ ΝΟΜΟΘΕΣΙΑΣ '1202': ΜΙΚΡΟΔΙΑΦΟΡΕΣ '1203': ΤΑΤΖΙΚΙΣΤΑΝ – ΤΑΥΛΑΝΔΗ – ΤΟΥΡΚΙΑ Κ.ΛΠ '1204': ΣΥΜΒΑΣΗ ΔΙΕΘΝΟΥΣ ΜΕΤΑΦΟΡΑΣ ΕΜΠΟΡΕΥΜΑΤΩΝ ΟΔΙΚΩΣ '1205': ΚΩΔΙΚΑΣ ΙΔΙΩΤΙΚΟΥ ΝΑΥΤΙΚΟΥ ΔΙΚΑΙΟΥ '1206': ΚΕΝΤΡΑ ΓΕΩΡΓΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ-Ο.Γ.Ε.Ε.Κ.Α '1207': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΙΔΡΥΜΑΤΩΝ ΕΜΠΟΡΙΚΟΥ ΝΑΥΤΙΚΟΥ '1208': ΓΡΑΦΕΙΟ ΔΙΑΡΚΗ ΚΩΔΙΚΑ ΝΟΜΟΘΕΣΙΑΣ '1209': ΕΡΕΥΝΑ ΙΔΙΩΤΙΚΩΝ ΜΕΤΑΛΛΕΙΩΝ '1210': ΔΙΕΥΘΥΝΣΗ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ ΑΕΡΟΠΟΡΙΑΣ '1211': ΠΕΡΙ ΝΟΜΑΡΧΩΝ '1212': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΑΠΟ ΕΣΩΤΕΡΙΚΕΣ ΔΙΑΜΑΧΕΣ '1213': ΔΙΑΧΕΙΡΙΣΗ ΕΦΟΔΙΩΝ ΕΞΩΤΕΡΙΚΟΥ '1214': ΟΡΓΑΝΩΣΗ ΥΠΗΡΕΣΙΩΝ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '1215': ΦΟΡΤΗΓΑ ΠΛΟΙΑ ΑΝΩ ΤΩΝ 4.500 ΤΟΝΝΩΝ '1216': ΡΑΔΙΟΤΗΛΕΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ ΠΛΟΙΩΝ '1217': ΕΠΑΓΓΕΛΜΑΤΙΚΕΣ ΣΧΟΛΕΣ '1218': ΔΙΑΦΟΡΕΣ ΒΙΟΜΗΧΑΝΙΕΣ '1219': ΣΥΝΤΗΡΗΣΗ ΑΕΡΟΣΚΑΦΩΝ '1220': ΟΛΥΜΠΙΑΚΗ ΑΕΡΟΠΟΡΙΑ '1221': ΟΡΓΑΝΙΣΜΟΣ ΧΩΡΟΦΥΛΑΚΗΣ '1222': ΠΕΡΙΘΑΛΨΗ ΦΥΜΑΤΙΚΩΝ ΤΑΧΥΔΡΟΜΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '1223': ΟΡΓΑΝΙΣΜΟΣ ΧΡΗΜΑΤΟΔΟΤΗΣΗΣ ΟΙΚΟΝΟΜΙΚΗΣ ΑΝΑΠΤΥΞΗΣ '1224': ΠΡΩΤΕΣ ΥΛΕΣ ΞΥΛΙΝΩΝ ΒΑΡΕΛΙΩΝ '1225': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΤΕΧΝΙΚΩΝ ΤΥΠΟΥ ΑΘΗΝΩΝ (Τ.Α.Τ.Τ.Α.) '1226': ΠΡΟΠΑΡΑΣΚΕΥΑΣΤΙΚΗ ΣΧΟΛΗ ΚΑΛΩΝ ΤΕΧΝΩΝ ΤΗΝΟΥ '1227': ΟΙΚΟΝΟΜΙΚΕΣ ΑΝΤΙΠΡΟΣΩΠΕΙΕΣ ΕΞΩΤΕΡΙΚΟΥ '1228': ΚΑΛΛΙΤΕΧΝΙΚΟΙ ΣΤΑΘΜΟΙ '1229': ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗ ΒΙΑ ΤΩΝ '1230': ΠΡΟΣΤΑΣΙΑ ΑΜΠΕΛΟΥΡΓΙΚΗΣ ΠΑΡΑΓΩΓΗΣ '1231': ΔΙΑΦΟΡΑ ΑΔΙΚΗΜΑΤΑ '1232': ΑΣΤΥΝΟΜΙΑ ΚΑΙ ΑΣΦΑΛΕΙΑ ΣΙΔΗΡΟΔΡΟΜΩΝ '1233': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ ΒΑΣΙΛΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ '1234': ΥΠΟΘΗΚΗ ΜΗΧΑΝΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ '1235': ΕΥΘΥΝΗ ΑΠΟ Τ’ΑΥΤΟΚΙΝΗΤΑ '1236': ΠΡΟΣΤΑΣΙΑ ΜΗΤΡΟΤΗΤΟΣ ΚΑΙ ΒΡΕΦΩΝ '1237': ΜΕ ΤΗ ΦΙΛΑΝΔΙΑ '1238': ΕΠΑΡΧΙΑΚΟΣ ΤΥΠΟΣ '1239': ΕΠΙΘΕΩΡΗΣΗ ΤΕΛΩΝΕΙΩΝ '1240': ΕΠΙΤΡΟΠΕΙΕΣ ΤΟΠΩΝΥΜΙΩΝ '1241': ΜΕΤΑΝΑΣΤΕΥΣΗ ΚΑΙ ΑΠΟΔΗΜΙΑ '1242': ΔΙΚΗΓΟΡΙΚΟΙ ΣΥΛΛΟΓΟΙ '1243': ΠΡΟΣΩΠΙΚΟ ΥΠΟΥΡΓΕΙΟΥ ΓΕΩΡΓΙΑΣ '1244': ΤΜΗΜΑ ΟΙΚΟΝΟΜΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΜΙΟΥ ΠΑΤΡΩΝ '1245': ΜΑΛΑΚΤΕΣ '1246': ΕΛΑΙΑ '1247': ΑΤΟΜΙΚΑ ΕΓΓΡΑΦΑ ΑΞΙΩΜΑΤΙΚΩΝ '1248': ΑΓΡΟΤΙΚΗ ΤΡΑΠΕΖΑ ΤΗΣ ΕΛΛΑΔΟΣ '1249': ΟΠΤΙΚΟΙ - ΚΑΤΑΣΤΗΜΑΤΑ ΟΠΤΙΚΩΝ ΕΙΔΩΝ '1250': ΔΗΜΟΣΙΕΣ ΕΠΕΝΔΥΣΕΙΣ '1251': ΚΡΑΤΙΚΗ ΟΡΧΗΣΤΡΑ ΘΕΣΣΑΛΟΝΙΚΗΣ '1252': ΝΗΟΛΟΓΙΑ-ΥΠΟΘΗΚΟΛΟΓΙΑ-ΣΗΜΑΤΟΛΟΓΗΣΗ '1253': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΑΣ ΔΙΑΧΕΙΡΙΣΕΩΣ ΕΙΔΩΝ ΜΟΝΟΠΩΛΙΟΥ (Τ.Α.Π.-Ε.Δ.Ε.Μ.Ε.) '1254': ΕΙΣΠΡΑΞΗ ΑΞΙΩΝ '1255': ΥΓΕΙΟΝΟΜΙΚΟΣ ΕΛΕΓΧΟΣ ΤΡΟΦΙΜΩΝ-ΠΟΤΩΝ-ΝΕΡΩΝ '1256': ΛΟΓΙΣΤΕΣ - ΦΟΡΟΤΕΧΝΙΚΟΙ '1257': ΕΙΔΙΚΕΣ ΔΙΚΟΝΟΜΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΤΟ ΔΗΜΟΣΙΟ '1258': ΣΧΟΛΕΣ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ '1259': ΤΑΜΕΙΟΝ ΚΟΙΝΩΦΕΛΩΝ ΕΡΓΩΝ ΛΕΥΚΑΔΟΣ '1260': ΕΙΔΙΚΗ ΑΓΩΓΗ, ΕΙΔΙΚΗ ΕΠΑΓΓΕΛΜΑΤΙΚΗ '1261': ΥΠΗΡΕΣΙΑ ΚΡΑΤΙΚΩΝ ΠΡΟΜΗΘΕΙΩΝ '1262': ΟΙΝΟΛΟΓΙΚΑ ΙΔΡΥΜΑΤΑ '1263': ΣΥΝΘΗΚΕΣ ΕΚΔΟΣΕΩΣ '1264': ΑΞΙΩΜΑΤΙΚΟΙ ΚΑΙ ΥΠΑΞΙΩΜΑΤΙΚΟΙ Λ.Σ '1265': ΥΓΕΙΟΝΟΜΙΚΗ ΕΞΕΤΑΣΗ ΠΡΟΣΩΠΙΚΟΥ '1266': ΞΕΝΑ ΣΧΟΛΕΙΑ ΗΜΕΔΑΠΗΣ '1267': Ε.Σ.Υ.-ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '1268': ΤΑΜΕΙΑ ΕΦΑΡΜΟΓΗΣ ΣΧΕΔΙΩΝ ΠΟΛΕΩΝ '1269': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΙΔΩΝ '1270': ΣΥΝΘΗΚΗ ΠΕΡΙ ΔΙΑΣΤΗΜΑΤΟΣ '1271': ΔΙΑΧΕΙΡΙΣΗ ΑΝΤΑΛΛΑΞΙΜΩΝ ΚΤΗΜΑΤΩΝ '1272': ΠΡΟΣΩΠΙΚΟΝ ΔΙΟΙΚΗΣΕΩΣ '1273': ΣΧΟΛΗ ΕΚΠΤΙΚΩΝ ΛΕΙΤΟΥΡΓΩΝ '1274': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΞΕΝΟΔΟΧΟΥΠΑΛΛΗΛΩΝ (Τ.Α.Ξ.Υ.) '1275': ΣΩΜΑΤΙΚΗ ΙΚΑΝΟΤΗΤΑ ΑΞΙΩΜΑΤΙΚΩΝ '1276': ΒΕΒΑΙΩΣΗ ΕΣΟΔΩΝ ΔΗΜΟΣΙΟΥ ΑΠΟ ΜΕΤΑΛΛΕΙΑ ΚΑΙ ΛΑΤΟΜΕΙΑ '1277': ΔΙΑΦΟΡΟΙ ΕΠΟΙΚΙΣΤΙΚΟΙ ΝΟΜΟΙ '1278': ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΚΡΕΟΠΩΛΩΝ ΚΑΙ ΕΡΓΑΤΟΥΠΑΛΛΗΛΩΝ ΚΡΕΑΤΟΣ (Ε.Τ.Α.Κ.Ε.Κ) '1279': ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ '1280': ΓΕΝΙΚΕΣ ΑΠΟΘΗΚΕΣ '1281': ΤΑΜΕΙΑΚΗ ΥΠΗΡΕΣΙΑ '1282': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΕΡΙ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '1283': ΤΟΜΕΑΣ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ (ΙΚΑ-ΤΕΑΜ)ΕΙΔΙΚΟΣ ΤΟΜΕΑΣ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ (ΙΚΑ-ΕΤΕΑΜ) '1284': ΒΑΡΒΑΚΕΙΟ ΛΥΚΕΙΟ '1285': ΚΩΔΙΚΑΣ ΔΙΚΩΝ ΤΟΥ ΔΗΜΟΣΙΟΥ '1286': ΔΙΕΘΝΕΣ ΤΑΜΕΙΟΝ ΠΕΡΙΘΑΛΨΕΩΣ ΤΟΥ ΠΑΙΔΙΟΥ '1287': ΣΙΔΗΡΟΔΡΟΜΟΙ ΕΛΛΗΝΙΚΟΥ ΚΡΑΤΟΥΣ '1288': ΑΡΔΕΥΣΕΙΣ '1289': ΤΑΜΕΙΟ ΑΡΧΑΙΟΛΟΓΙΚΩΝ ΠΟΡΩΝ ΚΑΙ ΑΠΑΛΛΟΤΡΙΩΣΕΩΝ '1290': ΙΔΡΥΜΑ ΒΥΖΑΝΤΙΝΗΣ ΜΟΥΣΙΚΟΛΟΓΙΑΣ '1291': ΚΥΒΕΡΝΗΤΙΚΟ ΣΥΜΒΟΥΛΙΟ ΕΛΕΓΧΟΥ ΤΙΜΩΝ '1292': ΕΙΔΙΚΟ ΤΑΜΕΙΟ ΕΠΟΙΚΙΣΜΟΥ '1293': ΚΤΗΜΑΤΟΛΟΓΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '1294': ΚΑΤΑΣΚΕΥΗ ΣΤΑΦΙΔΙΝΗΣ '1295': ΔΙΕΘΝΗΣ ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1296': ΕΠΕΤΗΡΙΔΑ '1297': ΠΑΓΚΟΣΜΙΟΣ ΟΡΓΑΝΙΣΜΟΣ ΤΟΥΡΙΣΜΟΥ '1298': ΕΝΙΣΧΥΣΗ ΑΠΡΟΣΤΑΤΕΥΤΩΝ ΠΑΙΔΙΩΝ '1299': ΔΙΑΦΟΡΟΙ ΕΠΙΣΙΤΙΣΤΙΚΟΙ ΝΟΜΟΙ '1300': ΔΙΠΛΩΜΑΤΙΚΕΣ ΑΤΕΛΕΙΕΣ '1301': ΜΕΤΑ ΤΟΥ ΒΕΛΓΙΟΥ '1302': ΚΑΝΝΑΒΙΣ '1303': ΕΚΤΕΛΕΣΗ '1304': ΤΟΥΡΙΣΤΙΚΕΣ ΕΓΚΑΤΑΣΤΑΣΕΙΣ ΡΟΔΟΥ '1305': ΠΟΙΝΙΚΟ ΜΗΤΡΩΟ '1306': ΑΝΩΜΑΛΕΣ ΔΙΚΑΙΟΠΡΑΞΙΕΣ ΔΩΔΕΚΑΝΗΣΟΥ '1307': ΕΜΠΟΡΙΚΑ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΑ ΕΠΙΜΕΛΗΤΗΡΙΑ '1308': ΣΥΝΤΟΝΙΣΜΟΣ ΠΡΟΓΡΑΜΜΑΤΩΝ ΚΑΙ ΕΡΓΑΣΙΩΝ ΟΔΩΝ ΚΑΙ ΕΡΓΩΝ ΚΟΙΝΗΣ ΩΦΕΛΕΙΑΣ '1309': ΠΡΟΣΩΠΙΚΟ ΞΕΝΟΔΟΧΕΙΩΝ '1310': ΙΝΣΤΙΤΟΥΤΟ ΦΥΣΙΚΗΣ ΤΟΥ ΣΤΕΡΕΟΥ ΦΛΟΙΟΥ ΤΗΣ ΓΗΣ '1311': ΕΠΙΚΙΝΔΥΝΕΣ ΟΙΚΟΔΟΜΕΣ '1312': ΑΡΧΕΙΑ ΔΙΚΑΣΤΗΡΙΩΝ '1313': ΣΚΟΠΟΒΟΛΗ '1314': ΑΠΟΝΟΜΗ ΣΥΝΤΑΞΕΩΝ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ '1315': ΣΗΡΟΤΡΟΦΙΑ '1316': ΕΣΩΤΕΡΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1317': ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΚΤΗΝΟΤΡΟΦΙΑΣ '1318': ΧΑΡΤΗΣ '1319': ΥΠΗΡΕΣΙΑ ΕΓΚΛΗΜΑΤΟΛΟΓΙΚΩΝ ΑΝΑΖΗΤΗΣΕΩΝ '1320': ΥΓΕΙΟΝΟΜΙΚΗ ΠΕΡΙΘΑΛΨΗ ΒΟΥΛΕΥΤΩΝ '1321': ΔΙΚΑΙΟΣΤΑΣΙΟ ΠΟΛΕΜΟΥ 1940 '1322': ΧΗΜΕΙΟ ΣΤΡΑΤΟΥ '1323': ΕΠΑΡΧΙΑΚΕΣ ΓΕΝΙΚΕΣ ΣΥΝΕΛΕΥΣΕΙΣ '1324': ΛΟΓΑΡΙΑΣΜΟΣ ΑΡΩΓΗΣ ΟΙΚΟΓΕΝΕΙΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '1325': ΚΑΤ’ ΙΔΙΑΝ ΝΑΟΙ '1326': ΠΛΗΡΩΜΗ ΜΕ ΕΠΙΤΑΓΕΣ '1327': ΕΘΝΙΚΕΣ ΣΥΛΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1328': ΣΩΜΑ ΣΤΡΑΤΟΛΟΓΙΑΣ '1329': ΟΔΟΝΤΙΑΤΡΟΙ '1330': ΤΑΜΕΙΟ ΕΘΝΙΚΟΥ ΣΤΟΛΟΥ '1331': ΣΥΜΠΛΗΡΩΜΑΤΙΚΕΣ ΠΑΡΟΧΕΣ ΜΗΤΡΟΤΗΤΑΣ '1332': ΜΕΤΑΤΡΕΨΙΜΟΤΗΤΑ ΚΑΤΑΘΕΣΕΩΝ '1333': ΠΤΗΝΟΤΡΟΦΙΑ '1334': ΠΤΥΧΙΟΥΧΟΙ ΑΛΛΟΔΑΠΩΝ ΠΑΝΕΠΙΣΤΗΜΙΩΝ - ΔΙΑΠΑΝΕΠΙΣΤΗΜΙΑΚΟ ΚΕΝΤΡΟ ΑΝΑΓΝΩΡΙΣΕΩΣ '1335': ΦΟΡΤΗΓΑ ΑΥΤΟΚΙΝΗΤΑ '1336': ΥΠΗΡΕΣΙΑ ΜΗΧΑΝΙΚΗΣ ΚΑΛΛΙΕΡΓΕΙΑΣ '1337': ΕΛΕΓΧΟΣ ΚΙΝΗΜΑΤΟΓΡΑΦΩΝ '1338': ΔΗΜΟΣΙΟΓΡΑΦΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ '1339': ΝΑΥΤΙΛΙΑΚΕΣ ΤΡΑΠΕΖΕΣ '1340': ΛΕΙΤΟΥΡΓΙΑ ΥΔΡΟΘΕΡΑΠΕΥΤΗΡΙΩΝ '1341': ΣΥΜΒΟΥΛΙΟ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ '1342': ΕΓΓΕΙΟΣ ΦΟΡΟΛΟΓΙΑ ΚΑΠΝΟΥ '1343': ΤΕΛΟΣ ΑΔΕΙΩΝ ΟΙΚΟΔΟΜΩΝ '1344': ΕΘΝΙΚΟΤΗΤΑ ΠΛΟΙΩΝ '1345': ΠΟΛΙΤΙΚΑ ΚΟΜΜΑΤΑ '1346': ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '1347': ΝΗΟΓΝΩΜΟΝΕΣ '1348': ΔΙΑΦΟΡΟΙ ΠΟΙΝΙΚΟΙ ΝΟΜΟΙ '1349': ΠΡΟΣΩΡΙΝΗ ΑΠΟΛΥΣΗ '1350': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ ΣΤΡΑΤΟΥ ΞΗΡΑΣ '1351': ΥΠΑΞΙΩΜΑΤΙΚΟΙ ΑΕΡΟΠΟΡΙΑΣ '1352': ΦΟΡΟΛΟΓΙΑ ΧΡΗΜΑΤΙΣΤΗΡΙΑΚΩΝ ΣΥΜΒΑΣΕΩΝ '1353': ΠΤΥΧΙΑ ΙΠΤΑΜΕΝΟΥ ΠΡΟΣΩΠΙΚΟΥ '1354': ΚΡΕΑΤΑ ΣΕ ΠΑΚΕΤΑ '1355': ΕΛΕΓΧΟΣ ΟΠΛΟΦΟΡΙΑΣ '1356': ΑΝΑΣΤΟΛΕΣ ΔΗΜΟΣΙΟΥ ΧΡΕΟΥΣ '1357': ΗΛΕΚΤΡΙΚΟΙ ΣΙΔΗΡΟΔΡΟΜΟΙ ΑΘΗΝΩΝ-ΠΕΙΡΑΙΩΣ (Η.Σ.Α.Π) '1358': ΔΙΑΘΕΣΗ ΛΥΜΑΤΩΝ ΚΑΙ ΑΠΟΒΛΗΤΩΝ '1359': ΕΠΙΘΕΩΡΗΣΗ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '1360': ΤΕΛΗ ΑΔΕΙΩΝ ΕΞΑΓΩΓΗΣ '1361': ΠΡΟΙΟΝΤΑ ΓΑΛΑΚΤΟΣ '1362': ΓΕΩΡΓΙΚΑ ΕΠΙΜΕΛΗΤΗΡΙΑ '1363': ΙΕΡΑΡΧΙΚΟΣ ΄ΕΛΕΓΧΟΣ '1364': ΣΤΡΑΤΙΩΤΙΚΕΣ ΦΥΛΑΚΕΣ '1365': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΚΑΠΝΕΜΠΟΡΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '1366': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΚΑΙ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΙΠΠΟΔΡΟΜΙΩΝ (Τ.Α.Π.Ε.Α.Π.Ι.) '1367': ΑΠΟΧΩΡΗΤΗΡΙΑ '1368': ΦΟΡΟΣ ΕΙΣΟΔΗΜΑΤΟΣ ΦΥΣΙΚΩΝ ΚΑΙ ΝΟΜΙΚΩΝ ΠΡΟΣΩΠΩΝ '1369': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΑΡΟΧΩΝ '1370': ΑΤΤΙΚΟ ΜΕΤΡΟ '1371': ΒΟΥΣΤΑΣΙΑ '1372': ΑΠΟΣΤΡΑΤΕΙΕΣ - ΕΠΑΝΑΦΟΡΕΣ '1373': ΤΡΑΠΕΖΙΤΙΚΑ ΔΑΝΕΙΑ ΣΕ ΧΡΥΣΟ ΚΛΠ '1374': ΔΙΚΑΙΟΣΤΑΣΙΟ ΠΟΛΕΜΩΝ '1375': ΕΘΝΙΚΟ ΑΣΤΕΡΟΣΚΟΠΕΙΟ '1376': ΙΔΙΩΤΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ ΠΑΡΟΧΗΣ ΥΠΗΡΕΣΙΩΝ ΑΣΦΑΛΕΙΑΣ '1377': ΔΑΝΕΙΑ ΕΞΩΤΕΡΙΚΑ '1378': ΠΝΕΥΜΑΤΙΚΟ ΚΕΝΤΡΟ ΑΘΗΝΩΝ '1379': ΑΠΟΣΒΕΣΕΙΣ '1380': ΔΙΑΦΟΡΟΙ ΟΙΝΙΚΟΙ ΚΑΙ ΣΤΑΦΙΔΙΚΟΙ ΝΟΜΟΙ '1381': ΑΚΑΔΗΜΙΑ ΣΩΜΑΤΙΚΗΣ ΑΓΩΓΗΣ '1382': ΑΜΜΟΛΗΨΙΑ '1383': ΠΡΟΣΩΠΙΚΟ ΠΛΟΗΓΙΚΗΣ ΥΠΗΡΕΣΙΑΣ '1384': ΗΘΙΚΕΣ ΑΜΟΙΒΕΣ ΑΕΡΟΠΟΡΙΑΣ '1385': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΑΣ ΟΙΝΟΠΝΕΥΜΑΤΟΣ '1386': ΛΙΜΕΝΙΚΑ ΤΑΜΕΙΑ – ΛΙΜΕΝΙΚΑ ΕΡΓΑ '1387': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΕΘΝΙΚΟΥ ΟΡΓΑΝΙΣΜΟΥ ΚΑΠΝΟΥ (Τ.Ε.Α.ΥΕ.Ο.Κ) '1388': ΕΛΕΓΧΟΣ ΤΗΣ ΠΙΣΤΕΩΣ '1389': ΣΤΡΑΤΙΩΤΙΚΗ ΣΧΟΛΗ ΑΞΙΩΜΑΤΙΚΩΝ ΣΩΜΑΤΩΝ '1390': ΒΟΗΘΗΤΙΚΑ ΠΡΟΣΩΠΑ ΤΗΣ ΔΙΚΗΣ '1391': ΟΡΓΑΝΙΣΜΟΣ ΣΧΟΛΙΚΩΝ ΚΤΙΡΙΩΝ '1392': ΒΙΟΜΗΧΑΝΙΕΣ ΔΩΔΕΚΑΝΗΣΟΥ '1393': ΥΓΙΕΙΝΗ ΚΑΙ ΑΣΦΑΛΕΙΑ ΧΩΡΩΝ ΕΡΓΑΣΙΑΣ ΚΑΙ ΕΡΓΑΖΟΜΕΝΩΝ '1394': ΜΕΤΑΤΡΟΠΗ ΤΗΣ ΠΟΙΝΗΣ '1395': ΑΥΤΟΝΟΜΟΣ ΟΙΚΟΔΟΜΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΑΞΙΩΜΑΤΙΚΩΝ '1396': ΟΔΙΚΕΣ ΜΕΤΑΦΟΡΕΣ-ΜΕΤΑΦΟΡΕΙΣ '1397': ΑΡΜΑ ΘΕΣΠΙΔΟΣ '1398': ΔΗΜΟΤΙΚΑ & ΚΟΙΝΟΤΙΚΑ '1399': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ '1400': ΣΧΟΛΗ ΑΝΘΡΩΠΙΣΤΙΚΩΝ ΚΑΙ ΚΟΙΝΩΝΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '1401': ΣΤΡΑΤΕΥΟΜΕΝΟΙ ΦΟΙΤΗΤΑΙ '1402': ΓΕΝΙΚΑ '1403': ΚΑΤΑΠΟΛΕΜΗΣΗ ΕΠΙΖΩΟΤΙΩΝ '1404': ΟΡΓΑΝΙΣΜΟΣ ΔΙΟΙΚΗΣΕΩΣ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΚΑΙ ΜΟΝΑΣΤΗΡΙΑΚΗΣ ΠΕΡΙΟΥΣΙΑΣ '1405': ΑΠΑΓΟΡΕΥΣΗ ΧΡΗΣΗΣ ΕΠΙΒΛΑΒΩΝ ΟΥΣΙΩΝ '1406': ΨΥΧΟΛΟΓΟΙ '1407': ΠΥΡΑΣΦΑΛΕΙΑ ΕΠΙΧΕΙΡΗΣΕΩΝ ΚΑΙ ΑΠΟΘΗΚΩΝ '1408': ΑΠΟΚΑΤΑΣΤΑΣΙΣ ΑΠΟΡΩΝ ΚΟΡΑΣΙΔΩΝ '1409': ΜΕ ΤΗ ΒΕΝΕΖΟΥΕΛΑ '1410': ΔΙΚΑΙΟ ΤΩΝ ΣΥΝΘΗΚΩΝ '1411': ΚΤΗΝΙΑΤΡΙΚΑ ΜΙΚΡΟΒΙΟΛΟΓΙΚΑ ΕΡΓΑΣΤΗΡΙΑ '1412': ΕΡΓΑΣΤΗΡΙΑ '1413': ΚΑΝΟΝΙΣΜΟΙ TELEX ΚΑΙ TELEFAX '1414': ΟΠΛΑ ΚΑΙ ΣΩΜΑΤΑ ΣΤΡΑΤΟΥ ΞΗΡΑΣ '1415': ΕΚΠΑΙΔΕΥΣΗ ΤΑΧΥΔΡΟΜΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '1416': ΤΙΜΟΛΟΓΙΑ ΠΑΡΟΧΩΝ '1417': ΜΟΥΣΟΥΛΜΑΝΙΚΕΣ ΚΟΙΝΟΤΗΤΕΣ '1418': ΣΤΡΑΤΙΩΤΙΚΑ ΕΡΓΑ ΕΝ ΓΕΝΕΙ '1419': ΣΤΡΑΤΙΩΤΙΚΑ ΝΟΣΟΚΟΜΕΙΑ '1420': ΔΙΟΙΚΗΣΗ ΔΗΜΟΣΙΩΝ ΚΤΗΜΑΤΩΝ – '1421': ΕΙΔΙΚΕΣ ΤΙΜΕΣ ΚΑΥΣΙΜΩΝ ΚΑΙ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '1422': ΕΓΓΡΑΦΗ ΣΠΟΥΔΑΣΤΩΝ '1423': ΔΗΜΟΤΙΚΑ-ΚΟΙΝΟΤΙΚΑ ΔΑΣΗ ΚΑΙ ΚΗΠΟΙ '1424': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΠΟΛΕΟΔΟΜΙΑΣ ΚΑΙ ΣΤΕΓΑΣΕΩΣ '1425': ΣΥΝΤΑΞΙΟΔΟΤΗΣΗ ΠΡΟΣΩΠΙΚΟΥ Ι.Κ.Α '1426': ΕΞΕΤΑΣΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ ΒΟΥΛΗΣ '1427': ΜΕΤΡΑ ΚΑΤΑ ΤΩΝ ΠΥΡΚΑΙΩΝ ΔΑΣΩΝ '1428': ΥΠΟΥΡΓΕΙΟ ΕΘΝΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ '1429': ΣΥΓΚΕΝΤΡΩΣΗ ΠΕΡΙΟΥΣΙΑΣ ΤΟΥ ΔΗΜΟΣΙΟΥ '1430': ΚΑΤΑΣΚΕΥΗ ΚΑΙ ΣΥΝΤΗΡΗΣΗ ΟΔΩΝ '1431': ΤΕΛΩΝΕΙΑΚΑ ΚΤΙΡΙΑ '1432': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΕΚΤΕΛΩΝΙΣΤΩΝ (Τ.Σ.Ε.) '1433': ΚΑΘΗΓΗΤΙΚΕΣ ΕΔΡΕΣ '1434': ΝΑΥΤΙΚΗ ΕΡΓΑΣΙΑ ΝΕΩΝ '1435': ΕΚΤΕΛΕΣΗ ΘΑΝΑΤΙΚΗΣ ΠΟΙΝΗΣ '1436': ΕΠΙΘΕΩΡΗΣΗ ΠΛΟΙΩΝ '1437': ΔΙΠΛΩΜΑΤΑ ΚΑΙ ΑΔΕΙΕΣ ΝΑΥΤΙΚΗΣ ΙΚΑΝΟΤΗΤΑΣ '1438': ΙΣΤΟΡΙΚΟ ΚΑΙ ΕΘΝΟΛΟΓΙΚΟ ΜΟΥΣΕΙΟ '1439': ΠΡΟΣΤΑΣΙΑ ΕΡΓΑΖΟΜΕΝΗΣ ΝΕΑΣ '1440': ΥΠΗΡΕΣΙΑ ΕΠΙΜΕΛΗΤΩΝ ΑΝΗΛΙΚΩΝ '1441': ΑΣΤΙΚΗ ΕΥΘΥΝΗ ΑΠΟ ΠΥΡΗΝΙΚΗ ΕΝΕΡΓΕΙΑ '1442': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΑΣ ΚΑΘΑΡΑΣ ΠΡΟΣΟΔΟΥ '1443': ΕΠΙΘΕΩΡΗΣΗ Υ.Ε.Ν '1444': ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ ΣΥΝΔΙΚΑΛΙΣΤΙΚΩΝ ΣΤΕΛΕΧΩΝ '1445': ΥΓΕΙΟΝΟΜΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '1446': ΔΙΔΑΣΚΑΛΕΙΟ ΜΕΣΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '1447': ΥΠΟΒΡΥΧΙΑ '1448': ΥΠΗΡΕΣΙΑ ΑΠΩΛΕΙΩΝ, ΝΕΚΡΟΤΑΦΕΙΩΝ ΚΛΠ '1449': ΑΓΡΟΤ. ΑΠΟΚΑΤΑΣΤΑΣΗ ΣΤΑ ΔΩΔΕΚΑΝΗΣΑ '1450': ΕΙΔΙΚΕΣ ΑΠΑΛΛΟΤΡΙΩΣΕΙΣ '1451': ΣΤΕΓΑΣΗ ΤΑΧΥΔΡΟΜΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '1452': ΔΙΑΜΕΤΑΚΟΜΙΣΗ ΝΑΡΚΩΤΙΚΩΝ '1453': ΜΕΤΑΜΟΣΧΕΥΣΗ ΒΙΟΛΟΓΙΚΩΝ ΟΥΣΙΩΝ '1454': ΒΡΑΒΕΙΑ ΚΑΙ ΧΟΡΗΓΙΕΣ '1455': ΕΥΡΩΠΑΙΚΗ ΜΟΡΦΩΤΙΚΗ ΣΥΜΒΑΣΗ '1456': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝ. ΕΡΥΘΡΟΥ ΣΤΑΥΡΟΥ (Τ.Ε.Α.Π.Ε.Ε.Σ.) '1457': ΑΤΕΛΕΙΕΣ ΕΙΔΩΝ ΒΟΗΘΕΙΑΣ '1458': ΕΚΤΕΛΕΣΗ ΕΡΓΩΝ ΟΧΥΡΩΣΗΣ '1459': ΡΟΥΑΝΤΑ – ΡΟΥΜΑΝΙΑ Κ.ΛΠ '1460': ΜΟΝΙΜΕΣ ΑΝΤΙΠΡΟΣΩΠΕΙΕΣ '1461': ΠΡΟΣΤΑΣΙΑ ΕΦΕΔΡΩΝ ΙΠΤΑΜΕΝΩΝ '1462': ΤΡΑΠΕΖΕΣ ΕΞΩΤΕΡΙΚΟΥ ΕΜΠΟΡΙΟΥ '1463': ΙΑΤΡΙΚΟΝ ΠΡΟΣΩΠΙΚΟΝ ΔΗΜΟΣΙΟΥ ΚΑΙ Ν.Π.Δ.Δ '1464': ΔΙΑΦΟΡΑ ΜΟΝΑΣΤΗΡΙΑ '1465': ΕΤΑΙΡΕΙΕΣ ΕΠΕΝΔΥΣΕΩΝ - ΧΑΡΤΟΦΥΛΑΚΙΟΥ ΚΑΙ ΑΜΟΙΒΑΙΩΝ ΚΕΦΑΛΑΙΩΝ '1466': ΑΝΑΓΝΩΡΙΣΗ ΤΗΣ ΕΛΛΗΝΙΚΗΣ ΠΟΛΙΤΕΙΑΣ '1467': ΔΙΕΘΝΗΣ ΣΥΜΒΑΣΗ '1468': ΛΙΜΕΝΑΡΧΕΙΑ '1469': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΘΕΣΣΑΛΙΑΣ '1470': ΣΤΡΑΤΕΥΣΗ ΓΥΝΑΙΚΩΝ '1471': ΣΤΡΑΤΙΩΤΙΚΗ ΥΠΗΡΕΣΙΑ ΚΑΤΑΣΚΕΥΗΣ ΕΡΓΩΝ ΑΝΑΣΥΓΚΡΟΤΗΣΗΣ '1472': ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΤΙΜΗΣ ΤΟΥ ΠΟΛΙΤΙΚΟΥ ΚΟΣΜΟΥ '1473': ΕΠΙΜΟΡΦΩΣΗ ΛΕΙΤΟΥΡΓΩΝ Μ.Ε '1474': ΕΝΙΣΧΥΣΗ ΕΞΑΓΩΓΗΣ '1475': ΗΛΕΚΤΡΟΦΩΤΙΣΜΟΣ ΔΙΑΦΟΡΩΝ ΠΟΛΕΩΝ '1476': ΜΕ ΤΙΣ ΚΑΤΩ ΧΩΡΕΣ '1477': ΝΑΥΠΗΓΟΥΜΕΝΑ ΠΛΟΙΑ-ΝΑΥΠΗΓΟΕΠΙΣΚΕΥΑΣΤΙΚΕΣ '1478': ΕΛΕΓΧΟΣ ΠΩΛΗΣΕΩΝ ΕΠΙ ΠΙΣΤΩΣΕΙ '1479': ΕΛΕΓΧΟΣ ΒΙΟΜΗΧΑΝΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ '1480': ΔΙΕΘΝΗΣ ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΤΡΟΠΗ '1481': ΓΡΑΦΕΙΑ ΕΥΡΕΣΗΣ ΕΡΓΑΣΙΑΣ - ΣΥΜΒΟΥΛΟΙ ΕΡΓΑΣΙΑΣ '1482': ΜΟΝΟΠΩΛΙΟ ΝΑΡΚΩΤΙΚΩΝ '1483': ΑΠΑΛΛΑΓΕΣ ΦΟΡΟΛΟΓΙΑΣ ΚΛΗΡΟΝΟΜΙΩΝ '1484': ΠΑΓΚΟΣΜΙΑ ΟΡΓΑΝΩΣΗ ΥΓΕΙΑΣ '1485': ΕΘΝΙΚΟ ΙΔΡΥΜΑ ΕΡΕΥΝΩΝ '1486': ΝΟΜΟΘΕΣΙΑ ΠΕΡΙ ΣΥΛΛΟΓΙΚΗΣ ΣΥΜΒΑΣΕΩΣ '1487': ΕΘΝΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΦΑΡΜΑΚΩΝ '1488': ΔΙΑΦΟΡΑ ΓΥΜΝΑΣΙΑ & ΛΥΚΕΙΑ '1489': ΞΕΝΕΣ ΣΧΟΛΕΣ ΓΕΩΠΟΝΙΑΣ ΚΑΙ ΔΑΣΟΛΟΓΙΑΣ '1490': ΠΡΟΣΤΑΣΙΑ ΑΝΕΡΓΩΝ '1491': ΦΙΛΑΝΘΡΩΠΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ ΚΕΦΑΛΛΗΝΙΑΣ '1492': ΚΑΝΟΝΙΣΜΟΣ ΠΑΡΟΧΩΝ Τ.Ε.Β.Ε '1493': ΩΔΕΙΑ ΚΛΠ. ΜΟΥΣΙΚΑ ΙΔΡΥΜΑΤΑ '1494': ΠΡΟΣΚΥΝΗΜΑΤΙΚΑ ΙΔΡΥΜΑΤΑ '1495': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΑΝΩΝ. ΥΔΡΟΗΛΕΚΤΡ. ΕΤ. ΓΛΑΥΚΟΣ '1496': ΠΡΕΣΒΕΙΕΣ ΚΑΙ ΠΡΟΞΕΝΕΙΑ '1497': ΥΠΟΥΡΓΕΙΑ ΤΥΠΟΥ ΚΑΙ ΤΟΥΡΙΣΜΟΥ '1498': ΖΩΝΕΣ ΕΝΕΡΓΟΥ ΠΟΛΕΟΔΟΜΙΑΣ '1499': ΕΚΚΛΗΣΙΑ ΙΟΝΙΩΝ ΝΗΣΩΝ '1500': ΕΠΙΤΡΟΠΑΙ ΑΣΦΑΛΕΙΑΣ '1501': ΥΠΟΥΡΓΟΙ '1502': ΠΟΙΝΙΚΗ ΔΙΑΤΙΜΗΣΗ '1503': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΩΝ ΚΕΡΑΜΟΠΟΙΩΝ '1504': ΠΡΩΤΕΣ ΥΛΕΣ ΠΑΙΓΝΙΟΧΑΡΤΩΝ '1505': ΚΡΥΠΤΟΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ '1506': ΔΙΕΘΝΗΣ ΕΠΙΤΡΟΠΗ ΠΡΟΣΩΠΙΚΗΣ ΚΑΤΑΣΤΑΣΕΩΣ '1507': ΕΛΕΓΧΟΣ ΗΛΕΚΤΡΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ '1508': ΔΙΑΧΕΙΡΙΣΗ ΙΔΡΥΜΑΤΩΝ ΚΑΙ ΚΛΗΡΟΔΟΤΗΜΑΤΩΝ '1509': ΤΕΛΩΝΕΙΑΚΗ ΣΤΑΤΙΣΤΙΚΗ '1510': ΙΔΙΩΤΙΚΕΣ ΝΑΥΤΙΚΕΣ ΣΧΟΛΕΣ '1511': ΑΕΡΟΠΟΡΙΚΑ ΑΤΥΧΗΜΑΤΑ '1512': ΑΝΩΤΕΡΟ ΔΙΔΑΚΤΙΚΟ ΠΡΟΣΩΠΙΚΟ '1513': ΔΙΑΦΟΡΟΙ ΔΙΟΙΚΗΤΙΚΟΙ ΕΡΓΑΤΙΚΟΙ ΝΟΜΟΙ '1514': ΣΥΜΒΟΥΛΙΟ ΓΕΩΓΡΑΦΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '1515': ΕΚΚΛΗΣΙΑΣΤΙΚΕΣ ΒΙΒΛΙΟΘΗΚΕΣ '1516': ΤΜΗΜΑ ΕΠΙΣΤΗΜΗΣ ΦΥΣΙΚΗΣ ΑΓΩΓΗΣ ΚΑΙ ΑΘΛΗΤΙΣΜΟΥ '1517': ΠΕΡΙΟΡΙΣΜΟΣ ΣΥΝΘΕΣΕΩΣ ΥΠΗΡΕΣΙΩΝ '1518': ΤΑΜΕΙΑ ΕΠΑΡΧΙΑΚΗΣ ΟΔΟΠΟΙΙΑΣ '1519': ΤΙΜΟΛΟΓΙΑ Ο.Τ.Ε - ΚΟΣΤΟΛΟΓΗΣΗ ΥΠΗΡΕΣΙΩΝ Ο.Τ.Ε '1520': ΕΘΝΙΚΗ ΒΙΒΛΙΟΘΗΚΗ '1521': ΔΗΜΟΣΙΕΣ ΣΧΟΛΕΣ ΥΠΟΜΗΧΑΝΙΚΩΝ '1522': ΑΝΑΦΟΡΕΣ ΠΡΟΣ ΤΙΣ ΑΡΧΕΣ '1523': ΚΡΑΤΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΛΕΩΦΟΡΕΙΑΚΩΝ ΓΡΑΜΜΩΝ '1524': ΔΙΑΦΟΡΑ ΕΠΙΔΟΜΑΤΑ '1525': ΙΔΙΩΤΙΚΗ ΑΕΡΟΠΟΡΙΑ – ΑΕΡΟΛΕΣΧΕΣ '1526': ΤΜΗΜΑ ΔΙΟΙΚΗΤΙΚΗΣ ΤΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '1527': ΔΙΕΘΝΕΙΣ ΑΕΡΟΠΟΡΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1528': ΠΡΟΙΚΟΔΟΤΗΣΕΙΣ ΕΞ ΕΘΝΙΚΩΝ ΓΑΙΩΝ '1529': ΔΙΟΡΘΩΣΗ ΑΣΥΜΦΩΝΙΩΝ '1530': ΕΠΙΤΡΟΠΗ ΔΙΟΙΚΗΣΕΩΣ '1531': ΜΕΤΑ ΤΗΣ ΓΕΡΜΑΝΙΑΣ '1532': ΟΙΚΟΔΟΜΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ '1533': ΚΑΤΑΣΤΑΤΙΚΟΙ ΝΟΜΟΙ '1534': ΑΞΙΩΜΑΤΙΚΟΙ ΓΡΑΦΕΙΟΥ '1535': ΚΑΝΟΝΙΣΜΟΣ ΕΝΑΕΡΙΟΥ ΚΥΚΛΟΦΟΡΙΑΣ '1536': ΔΙΑΧΕΙΡΙΣΗ ΚΑΥΣΙΜΩΝ '1537': ΟΜΟΛΟΓΙΑΚΑ ΔΑΝΕΙΑ '1538': ΕΡΓΑ '1539': ΣΧΟΛΗ ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ '1540': ΠΩΛΗΣΗ ΦΑΡΜΑΚΩΝ ΑΠΟ ΙΑΤΡΟΥΣ '1541': ΣΗΜΑΤΑ ΕΘΝΙΚΟΤΗΤΑΣ ΚΑΙ ΝΗΟΛΟΓΗΣΕΩΣ '1542': ΛΕΙΤΟΥΡΓΟΙ ΣΤΟΙΧΕΙΩΔΟΥΣ '1543': ΕΦΕΤΕΙΑ ΚΑΙ ΠΡΩΤΟΔΙΚΕΙΑ '1544': ΥΠΟΥΡΓΕΙΟ ΠΡΟΕΔΡΙΑΣ ΚΥΒΕΡΝΗΣΕΩΣ '1545': ΜΟΡΦΩΤΙΚΟΣ – ΚΙΝΗΜΑΤΟΓΡΑΦΟΣ '1546': ΚΑΤΑΜΕΤΡΗΣΗ ΧΩΡΗΤΙΚΟΤΗΤΑΣ '1547': ΦΩΤΑΕΡΙΟ '1548': ΠΑΘΗΤΙΚΗ ΑΕΡΑΜΥΝΑ '1549': ΠΡΟΣΩΠΙΚΟ ΝΟΣΗΛΕΥΤΙΚΩΝ ΙΔΡΥΜΑΤΩΝ '1550': ΜΕ ΤΗΝ ΚΥΠΡΟ '1551': ΚΟΛΛΗΓΟΙ (ΕΠΙΜΟΡΤΟΙ ΚΑΛΛΙΕΡΓΗΤΕΣ) '1552': ΤΑΜΕΙΟ ΑΡΩΓΗΣ Λ.Σ '1553': ΙΧΘΥΟΣΚΑΛΕΣ '1554': ΣΧΗΜΑ ΚΑΙ ΤΙΜΗ ΠΩΛΗΣΗΣ ΕΦΗΜΕΡΙΔΩΝ '1555': ΥΙΟΘΕΣΙΑ '1556': ΕΚΤΕΛΕΣΗ ΕΡΓΩΝ ΑΡΜΟΔΙΟΤΗΤΑΣ ΕΚΚΛΗΣΙΑΣ '1557': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ '1558': ΔΙΑΦΟΡΕΣ ΕΥΡΩΠΑΙΚΕΣ ΣΥΜΦΩΝΙΕΣ '1559': ΕΓΓΕΙΟΣ ΦΟΡΟΛΟΓΙΑ '1560': ΠΑΙΔΑΓΩΓΙΚΕΣ ΑΚΑΔΗΜΙΕΣ '1561': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΕΡΓΑΤΟΥΠΑΛΛΗΛΩΝ ΜΕΤΑΛΛΟΥ (ΤΑ.Π.Ε.Μ.) '1562': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΑΕΡΟΣΚΑΦΩΝ '1563': ΕΝΩΣΗ ΑΠΟΣΤΡΑΤΩΝ ΑΞΙΩΜΑΤΙΚΩΝ Β.Α '1564': ΑΣΦΑΛΙΣΗ ΕΡΓΑΤΩΝ ΓΕΩΡΓΙΑΣ '1565': ΟΡΓΑΝΩΣΗ ΚΑΛΛΙΤΕΧΝΙΚΩΝ ΕΚΔΗΛΩΣΕΩΝ-ΦΕΣΤΙΒΑΛ '1566': ΠΕΡΙΟΥΣΙΑΚΕΣ ΣΥΝΕΠΕΙΕΣ ΤΗΣ ΠΟΙΝΗΣ '1567': ΤΗΛΕΓΡΑΦΙΚΗ ΑΝΤΑΠΟΚΡΙΣΗ '1568': ΕΠΙΘΕΩΡΗΣΗ ΔΗΜΟΣΙΩΝ ΥΠΟΛΟΓΩΝ '1569': ΜΕ ΤΟΝ ΚΑΝΑΔΑ '1570': ΑΛΛΗΛΟΓΡΑΦΙΑ Υ.Ε.Ν '1571': ΤΕΧΝΙΚΟ ΠΡΟΣΩΠΙΚΟ ΑΕΡΟΠΟΡΙΑΣ '1572': ΚΛΑΔΟΣ ΑΥΤΟΤΕΛΩΣ ΑΠΑΣΧΟΛΟΥΜΕΝΩΝ, ΕΛΕΥΘΕΡΩΝ ΚΑΙ ΑΝΕΞΑΡΤΗΤΩΝ '1573': ΣΧΟΛΕΙΑ ΒΑΡΥΚΟΩΝ Η ΚΩΦΩΝ '1574': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΚΑΤΩΤΕΡΩΝ ΠΛΗΡΩΜΑΤΩΝ Ε.Ν '1575': ΤΟΥΡΙΣΤΙΚΑ ΠΛΟΙΑ - ΣΚΑΦΗ ΑΝΑΨΥΧΗΣ - ΤΟΥΡΙΣΤΙΚΟΙ ΛΙΜΕΝΕΣ (ΜΑΡΙΝΕΣ) '1576': ΕΠΙΔΟΜΑΤΑ ΕΟΡΤΩΝ ΧΡΙΣΤΟΥΓΕΝΝΩΝ ΚΑΙ ΠΑΣΧΑ '1577': ΕΠΙΜΕΛΗΤΗΡΙΑ - ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ '1578': ΥΠΟΥΡΓΕΙΟ ΕΡΕΥΝΑΣ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ '1579': ΣΤΕΓΑΣΗ ΑΞΙΩΜΑΤΙΚΩΝ '1580': ΠΑΡΑΡΤΗΜΑΤΑ ΓΕΝΙΚΟΥ ΧΗΜΕΙΟΥ '1581': ΚΑΘΑΡΙΣΤΡΙΕΣ '1582': ΚΑΝΟΝΙΣΜΟΣ ΝΑΥΤΟΔΙΚΕΙΟΥ '1583': ΑΜΟΙΒΕΣ ΜΗΧΑΝΙΚΩΝ '1584': ΕΠΙΜΟΡΦΩΣΗ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ '1585': ΚΑΝΟΝΙΣΜΟΙ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ '1586': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΙΑΣ ΕΛΛ. ΚΑΛΥΚΟΠΟΙΕΙΟΥ-ΠΥΡΙΤΙΔΟΠΟΙΕΙΟΥ '1587': ΠΡΟΣΩΠΙΚΟ ΤΡΑΠΕΖΩΝ '1588': ΛΥΣΣΙΑΤΡΕΙΑ '1589': ΣΥΝΟΡΙΑΚΕΣ ΥΓΕΙΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1590': ΠΟΛΕΜΙΚΟ ΜΟΥΣΕΙΟ '1591': ΚΑΘΗΚΟΝΤΑ ΤΕΛΩΝΕΙΑΚΩΝ ΥΠΑΛΛΗΛΩΝ '1592': ΕΠΕΚΤΑΣΗ ΤΗΣ ΑΣΦΑΛΙΣΕΩΣ '1593': ΦΟΡΟΛΟΓΙΚΕΣ ΑΠΑΛΛΑΓΕΣ '1594': ΕΠΙΔΟΜΑ ΣΤΡΑΤΕΥΣΗΣ '1595': ΔΙΑΡΚΗ ΣΤΡΑΤΟΔΙΚΕΙΑ '1596': ΣΥΝΤΑΞΙΟΔΟΤΗΣΗ ΠΡΟΣΩΠΙΚΟΥ Ο.Γ.Α '1597': ΑΣΤΥΝΟΜΙΑ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ '1598': ΦΡΟΝΤΙΣΤΕΣ ΜΟΝΑΔΩΝ '1599': ΑΡΑΒΟΣΙΤΟΣ '1600': ΜΗΤΡΟΠΟΛΕΙΣ '1601': ΦΙΛΑΝΘΡΩΠΙΚΑ ΣΩΜΑΤΕΙΑ '1602': ΔΙΑΦΟΡΟΙ ΠΟΛΥΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ '1603': ΕΞΥΓΙΑΝΤΙΚΑ ΕΡΓΑ '1604': ΦΥΛΛΑ ΠΟΙΟΤΗΤΑΣ ΝΑΥΤΩΝ '1605': ΦΙΛΑΝΘΡΩΠΙΚΑ ΙΔΡΥΜΑΤΑ ΚΑΙ ΣΩΜΑΤΕΙΑ '1606': ΕΣΤΙΑ ΝΑΥΤΙΚΩΝ '1607': ΓΛΥΚΑ ΚΑΙ ΚΟΝΣΕΡΒΕΣ '1608': ΠΡΟΣΤΑΣΙΑ ΥΠΟΒΡΥΧΙΩΝ ΚΑΛΩΔΙΩΝ '1609': ΕΠΕΞΕΡΓΑΣΙΑ ΚΑΙ ΕΜΠΟΡΙΑ ΣΥΚΩΝ '1610': ΧΑΡΟΚΟΠΕΙΟ '1611': ΔΙΑΜΕΤΑΚΟΜΙΣΗ ΣΤΗΝ ΑΛΒΑΝΙΑ '1612': ΕΠΙΘΕΩΡΗΣΗ ΦΥΛΑΚΩΝ '1613': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΚΥΡΙΑΚΗΣ ΑΡΓΙΑΣ '1614': ΚΙΝΗΜΑΤΟΓΡΑΦΙΚΗ ΒΙΟΜΗΧΑΝΙΑ '1615': ΠΙΣΤΟΠΟΙΗΤΙΚΑ ΠΡΟΕΛΕΥΣΕΩΣ '1616': ΤΟΥΡΙΣΤΙΚΗ ΠΡΟΠΑΓΑΝΔΑ '1617': ΕΙΣΦΟΡΑ ΕΙΣΑΓΩΓΕΩΝ '1618': ΚΑΖΙΝΟ '1619': ΜΕ ΤΗΝ ΕΛΒΕΤΙΑ '1620': ΔΙΚΑΣΤΙΚΟΙ ΕΠΙΜΕΛΗΤΕΣ '1621': ΚΩΔΙΚΑΣ ΠΟΙΝΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ '1622': ΤΟΠΙΚΕΣ ΔΙΟΙΚΗΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ '1623': ΕΤΑΙΡΕΙΕΣ ΚΕΦΑΛΑΙΟΠΟΙΗΣΕΩΣ '1624': ΟΡΥΖΑ '1625': ΔΙΟΙΚΗΤΙΚΟ ΣΥΜΒΟΥΛΙΟ Ο.Γ.Α '1626': ΕΚΠΑΙΔΕΥΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΣΧΟΛΩΝ Π.Ν '1627': ΒΑΣΙΛΕΙΑ ΚΑΙ ΑΝΤΙΒΑΣΙΛΕΙΑ '1628': ΥΠΗΡΕΣΙΑ ΣΤΙΣ ΕΠΑΡΧΙΕΣ Τ.Π. ΚΑΙ Δ '1629': ΓΕΩΡΓΙΚΕΣ ΒΙΟΜΗΧΑΝΙΕΣ '1630': ΒΟΥΛΕΥΤΗΡΙΟ '1631': ΠΟΡΘΜΕΙΑ '1632': ΕΚΤΕΛΕΣΗ ΥΔΡΑΥΛΙΚΩΝ ΕΡΓΩΝ '1633': ΙΝΣΤΙΤΟΥΤΑ ΚΡΗΤΙΚΟΥ ΔΙΚΑΙΟΥ - ΑΙΓΑΙΟΥ ΚΑΙ ΔΙΑΦΟΡΑ ΕΡΕΥΝΗΤΙΚΑ ΚΕΝΤΡΑ '1634': ΑΤΕΛΕΙΕΣ ΔΙΑΦΟΡΕΣ '1635': ΚΕΝΤΡΑ ΠΑΡΑΘΕΡΙΣΜΟΥ - '1636': ΣΧΟΛΕΣ ΑΕΡΟΠΟΡΙΑΣ '1637': ΛΕΠΡΑ '1638': ΑΙΣΘΗΤΙΚΟΙ '1639': ΕΚΚΑΘΑΡΙΣΗ ΠΟΙΝΙΚΩΝ ΕΞΟΔΩΝ '1640': ΓΕΝ. ΟΙΚΟΔΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1641': ΕΛΕΓΧΟΣ ΔΑΠΑΝΩΝ ΤΟΥ ΚΡΑΤΟΥΣ '1642': ΠΕΤΡΕΛΑΙΟΚΙΝΗΤΑ ΚΑΙ ΙΣΤΙΟΦΟΡΑ '1643': ΚΑΛΛΙΕΡΓΕΙΑ ΚΑΠΝΟΥ '1644': ΔΙΟΙΚΗΣΗ ΜΟΝΑΣΤΗΡΙΩΝ '1645': ΚΤΗΝΙΑΤΡΙΚΑ ΙΔΙΟΣΚΕΥΑΣΜΑΤΑ '1646': ΜΟΝΙΜΟΙ ΚΑΙ ΕΘΕΛΟΝΤΕΣ '1647': ΦΟΡΟΛΟΓΙΑ ΚΕΡΔΩΝ ΕΙΣΑΓΩΓΕΩΝ '1648': ΑΓΩΓΕΣ ΕΞΩΣΕΩΣ ΜΙΣΘΩΤΩΝ '1649': ΟΡΓΑΝΩΣΗ ΕΞΩΤΕΡΙΚΟΥ ΕΜΠΟΡΙΟΥ '1650': ΑΓΩΓΕΣ ΜΗΧΑΝΙΚΩΝ '1651': ΝΑΥΤΙΚΗ ΣΧΟΛΗ ΠΟΛΕΜΟΥ '1652': ΜΕΤΑΦΟΡΑ ΘΕΣΕΩΝ '1653': ΕΙΣΑΓΩΓΗ ΕΠΑΓΓΕΛΜΑΤΙΚΟΥ ΥΛΙΚΟΥ '1654': ΣΥΓΚΡΟΤΗΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ '1655': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΑΕΡΟΠΟΡΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ (T.Ε.Α.Π.Α.Ε.) '1656': ΣΥΛΛΟΓΗ ΚΑΙ ΔΙΑΚΙΝΗΣΗ ΠΕΤΡΕΛΑΙΟΕΙΔΩΝ ΕΡΜΑΤΩΝ '1657': ΚΕΝΤΡΑ ΑΔΥΝΑΤΙΣΜΑΤΟΣ – ΔΙΑΙΤΟΛΟΓΙΑΣ '1658': ΟΜΑΔΙΚΗ ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ '1659': ΔΙΑΦΟΡΑ ΜΟΥΣΕΙΑ '1660': ΒΕΒΑΙΩΣΗ ΚΑΙ ΕΙΣΠΡΑΞΗ ΕΣΟΔΩΝ '1661': ΓΡΑΦΕΙΑ ΤΥΠΟΥ '1662': ΔΙΟΙΚΗΤΙΚΟ ΠΡΟΣΩΠΙΚΟ '1663': ΣΥΝΕΡΓΕΙΑ ΕΠΙΣΚΕΥΩΝ '1664': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΠΡΟΝΟΙΑΣ ΚΑΙ ΑΣΘΕΝΕΙΑΣ ΕΡΓΑΖΟΜΕΝΩΝ ΣΤΑ ΛΙΜΑΝΙΑ (Τ.Ε.Α.Π.Α.Ε.Λ.) '1665': ΑΣΦΑΛΙΣΗ ΚΑΠΝΕΡΓΑΤΩΝ '1666': ΑΝΤΙΣΗΚΩΜΑΤΑ (ΕΞΑΓΟΡΑ ΘΗΤΕΙΑΣ) '1667': ΡΥΜΟΥΛΚΟΥΜΕΝΑ ΟΧΗΜΑΤΑ '1668': ΝΟΜΟΙ ΑΝΑΦΕΡΟΜΕΝΟΙ ΣΕ ΠΟΛΛΕΣ ΦΟΡΟΛΟΓΙΕΣ '1669': ΟΙΚΟΣΥΣΤΗΜΑΤΑ–ΒΙΟΤΟΠΟΙ '1670': ΠΡΟΣΤΑΣΙΑ ΠΡΟΣΩΠΩΝ '1671': ΕΘΝΙΚΟ ΤΥΠΟΓΡΑΦΕΙΟ '1672': ΔΙΚΑΣΤΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ '1673': ΠΡΟΣΤΑΣΙΑ ΒΙΒΛΙΟΥ-ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΒΙΒΛΙΟΥ-ΛΟΓΟΤΕΧΝΙΑ '1674': ΔΑΣΜΟΙ ΑΝΤΙΝΤΑΜΠΙΓΚ '1675': ΔΑΣΗ ΠΑΡΑΜΕΘΟΡΙΩΝ ΠΕΡΙΟΧΩΝ '1676': ΘΕΟΛΟΓΙΚΗ ΣΧΟΛΗ '1677': ΟΡΟΙ - ΠΡΟΔΙΑΓΡΑΦΕΣ ΤΥΠΟΠΟΙΗΣΗΣ '1678': ΦΟΡΟΛΟΓΙΑ ΒΥΝΗΣ ΚΑΙ ΖΥΘΟΥ '1679': ΑΠΟΘΗΚΗ ΚΤΗΝΙΑΤΡΙΚΩΝ ΕΦΟΔΙΩΝ '1680': ΠΑΡΟΧΗ ΤΗΛΕΦΩΝΙΚΩΝ ΣΥΝΔΕΣΕΩΝ '1681': ΠΑΡΑΧΩΡΗΣΗ ΙΑΜΑΤΙΚΩΝ ΠΗΓΩΝ '1682': ΜΑΘΗΤΙΚΑ ΣΥΣΣΙΤΙΑ '1683': ΠΡΟΣΛΗΨΗ ΕΦΕΔΡΩΝ, ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ, ΠΟΛΥΤΕΚΝΩΝ ΚΑΙ ΑΛΛΩΝ ΑΤΟΜΩΝ ΜΕ ΕΙΔΙΚΕΣ ΑΝΑΓΚΕΣ '1684': ΕΡΤ – 3 '1685': ΣΧΟΛΗ ΠΟΛΕΜΟΥ ΑΕΡΟΠΟΡΙΑΣ '1686': ΤΟΠΟΘΕΤΗΣΕΙΣ - ΜΕΤΑΤΑΞΕΙΣ '1687': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΡΟΣΤΑΣΙΑΣ '1688': ΦΥΣΙΚΟ ΑΕΡΙΟ '1689': ΤΕΧΝΙΚΑ ΕΡΓΑ '1690': ΔΙΠΛΩΜΑΤΟΥΧΟΙ ΑΝΩΤΑΤΩΝ '1691': ΕΘΝΙΚΟ ΝΟΜΙΣΜΑΤΙΚΟ ΜΟΥΣΕΙΟ '1692': ΟΙΚΟΝΟΜΙΚΗ ΑΣΤΥΝΟΜΙΑ ΣΤΗ ΘΑΛΑΣΣΑ '1693': ΑΣΦΑΛΕΙΑ, ΛΕΙΤΟΥΡΓΙΑ ΚΑΙ ΕΚΜΕΤΑΛΛΕΥΣΗ '1694': ΕΙΔΙΚΑ ΠΡΟΝΟΜΙΑ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ '1695': ΓΡΑΜΜΑΤΕΙΑ ΤΩΝ ΔΙΚΑΣΤΗΡΙΩΝ ΚΑΙ ΕΙΣΑΓΓΕΛΙΩΝ '1696': ΑΛΙΠΑΣΤΑ '1697': ΕΠΙΔΟΣΗ ΔΙΚΟΓΡΑΦΩΝ '1698': ΚΕΝΤΡΙΚΟ ΤΑΜΕΙΟ ΓΕΩΡΓΙΑΣ '1699': ΣΤΡΑΤΙΩΤΙΚΑ ΣΥΜΒΟΥΛΙΑ '1700': ΤΑΜΕΙΑΚΗ ΥΠΗΡΕΣΙΑ ΤΕΛΩΝΕΙΩΝ '1701': ΝΟΣΗΛΕΥΤΙΚΟ ΙΔΡΥΜΑ Μ.Τ.Σ '1702': ΔΙΚΑΙΟ ΘΑΛΑΣΣΑΣ-ΥΦΑΛΟΚΡΗΠΙΔΑ '1703': ΕΙΔΙΚΟΣ ΦΟΡΟΣ ΚΑΤΑΝΑΛΩΣΗΣ '1704': ΜΕΙΟΝΟΤΙΚΑ ΣΧΟΛΕΙΑ '1705': ΓΡΑΦΕΙΑ ΕΜΠΟΡΙΚΩΝ ΠΛΗΡΟΦΟΡΙΩΝ '1706': ΣΥΝΤΟΝΙΣΤΙΚΟΝ ΣΥΜΒΟΥΛΙΟΝ ΝΕΩΝ ΠΡΟΣΦΥΓΩΝ '1707': ΠΕΡΙΘΑΛΨΗ ΑΠΟΡΩΝ ΚΑΙ ΑΝΑΣΦΑΛΙΣΤΩΝ '1708': ΦΟΡΟΛΟΓΙΑ ΚΕΝΤΡΩΝ ΔΙΑΣΚΕΔΑΣΕΩΣ ΚΑΙ ΠΟΛΥΤΕΛΕΙΑΣ '1709': ΣΠΟΓΓΑΛΙΕΥΤΙΚΑ – ΔΥΤΕΣ '1710': ΔΙΕΘΝΕΣ ΝΟΜΙΣΜΑΤΙΚΟ ΤΑΜΕΙΟ '1711': ΒΙΒΛΙΟ ΔΙΕΚΔΙΚΗΣΕΩΝ '1712': ΕΓΚΑΤΑΣΤΑΣΗ - ΛΕΙΤΟΥΡΓΙΑ ΚΑΤΑΣΚΕΥΩΝ ΚΕΡΑΙΩΝ '1713': ΕΝΩΣΗ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ '1714': ΛΟΓΙΣΤΙΚΟΣ ΚΑΙ ΟΙΚΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1715': ΚΑΤΩΤΕΡΑ ΟΡΓΑΝΑ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ '1716': ΥΠΟΥΡΓΕΙΟ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ '1717': ΟΡΓΑΝΙΣΜΟΣ ΕΛΕΓΚΤΙΚΟΥ ΣΥΝΕΔΡΙΟΥ '1718': ΑΓΟΡΕΣ ΑΓΡΟΤΙΚΩΝ ΠΡΟΙΟΝΤΩΝ '1719': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΚΛΩΣΤΟΥΦΑΝΤΟΥΡΓΙΑΣ '1720': ΞΕΝΑΓΟΙ ΚΑΙ ΔΙΕΡΜΗΝΕΙΣ '1721': ΠΟΛΕΜΙΚΕΣ ΣΥΝΤΑΞΕΙΣ '1722': ΑΣΤΙΚΕΣ ΣΥΓΚΟΙΝΩΝΙΕΣ ΑΘΗΝΩΝ-ΠΕΙΡΑΙΩΣ ΚΑΙ ΠΕΡΙΧΩΡΩΝ-Ο.Α.Σ.Α '1723': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΤΑΜΕΙΟΥ ΑΣΦΑΛΙΣΕΩΣ ΑΡΤΕΡΓΑΤΩΝ Κ.Λ.Π '1724': ΑΤΥΧΗΜΑΤΑ ΣΕ ΜΕΤΑΛΛΕΙΑ ΚΛΠ '1725': ΦΟΡΟΛΟΓΙΑ ΠΟΛΕΜΙΚΩΝ ΚΕΡΔΩΝ '1726': ΣΧΕΔΙΟ ΠΟΛΕΩΣ ΘΕΣΣΑΛΟΝΙΚΗΣ '1727': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ ΑΓΡΟΤ. ΑΣΦΑΛΕΙΑΣ '1728': ΚΡΑΤΙΚΟ ΩΔΕΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ '1729': ΚΕΝΤΡΑ ΑΝΩΤΕΡΗΣ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ (Κ.A.Τ.Ε.) '1730': ΤΗΛΕΦΩΝΙΚΗ ΑΝΤΑΠΟΚΡΙΣΗ '1731': ΟΙΚΟΝΟΜΙΚΑ ΓΥΜΝΑΣΙΑ '1732': ΒΙΒΛΙΑ ΚΑΙ ΕΥΡΕΤΗΡΙΑ ΣΥΝΕΤΑΙΡΙΣΜΩΝ '1733': ΕΠΙΔΟΜΑ ΑΝΕΡΓΙΑΣ '1734': ΕΓΓΡΑΦΕΣ, ΕΞΕΤΑΣΕΙΣ, ΠΡΟΓΡΑΜΜΑΤΑ ΚΛΠ '1735': ΣΧΟΛΗ ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ '1736': ΕΚΚΛΗΣΙΑ ΑΜΕΡΙΚΗΣ '1737': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ ΣΤΡΑΤΟΥ '1738': ΝΟΣΗΛΕΙΑ '1739': ΣΧΟΛΗ ΕΥΕΛΠΙΔΩΝ '1740': ΥΠΟΥΡΓΕΙΟ ΕΡΓΑΣΙΑΣ ΚΑΙ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '1741': ΚΑΝΟΝΙΣΜΟΣ ΧΡΗΜΑΤΙΣΤΗΡΙΟΥ ΑΞΙΩΝ ΑΘΗΝΩΝ '1742': ΑΝΤΙΣΕΙΣΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ '1743': ΦΑΡΜΑΚΕΥΤΙΚΗ ΔΕΟΝΤΟΛΟΓΙΑ '1744': ΦΟΡΟΛΟΓΙΑ ΕΛΑΙΩΔΩΝ ΠΡΟΙΟΝΤΩΝ '1745': ΕΙΔΙΚΑ ΡΑΔΙΟΤΗΛΕΦΩΝΙΚΑ ΔΙΚΤΥΑ '1746': ΤΕΧΝΙΚΕΣ ΥΠΗΡΕΣΙΕΣ '1747': ΑΡΧΕΙΑ ΥΓΙΕΙΝΗΣ '1748': ΟΔΟΙΠΟΡΙΚΑ ΚΑΙ ΑΠΟΖΗΜΙΩΣΕΙΣ ΑΠΟΣΤΟΛΩΝ ΕΞΩΤΕΡΙΚΟΥ '1749': ΔΙΑΦΟΡΟΙ ΛΟΓΙΣΤΙΚΟΙ ΝΟΜΟΙ '1750': ΕΚΚΛΗΣΙΑΣΤΙΚΟΙ ΥΠΑΛΛΗΛΟΙ '1751': ΝΑΥΤΙΚΑ ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΣΩΜΑΤΕΙΑ ΚΑΙ ΟΜΟΣΠΟΝΔΙΕΣ '1752': ΤΕΛΗ ΧΡΗΣΗΣ ΑΕΡΟΛΙΜΕΝΩΝ '1753': ΠΡΟΑΙΡΕΤΙΚΗ ΑΣΦΑΛΙΣΗ '1754': ΜΕ ΤΗ ΛΙΒΥΗ '1755': ΠΟΤΑΜΟΠΛΟΙΑ ΦΟΡΤΙΟΥ ΥΓΡΩΝ ΚΑΥΣΙΜΩΝ '1756': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΩΝ ΗΛΕΚΤΡΙΚΩΝ ΣΙΔΗΡΟΔΡΟΜΩΝ ΑΘΗΝΩΝ-ΠΕΙΡΑΙΩΣ (Τ.Σ.Π.-Η.Σ.Α.Π) '1757': ΜΕΣΑΖΟΝΤΕΣ '1758': ΣΤΡΑΤΙΩΤΙΚΟΣ ΠΟΙΝΙΚΟΣ '1759': ΔΙΚΑΙΩΜΑΤΑ ΚΑΙ ΚΑΘΗΚΟΝΤΑ ΦΟΙΤΗΤΩΝ '1760': ΠΡΟΕΔΡΙΑ ΔΗΜΟΚΡΑΤΙΑΣ '1761': ΚΩΔΙΚΑΣ ΕΜΠΟΡΙΚΟΥ ΝΟΜΟΥ '1762': ΣΥΝΤΑΞΙΟΔΟΤΗΣΗ Ο.Γ.Α '1763': ΣΑΝΑΤΟΡΙΑ '1764': ΕΛΕΓΧΟΣ ΕΜΠΟΡΙΟΥ ΕΙΔΩΝ ΠΡΩΤΗΣ ΑΝΑΓΚΗΣ '1765': ΒΑΛΑΝΙΔΙΑ '1766': ΠΟΛΥΤΕΧΝΙΚΗ ΣΧΟΛΗ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΠΑΤΡΩΝ '1767': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΠΕΛΟΠΟΝΝΗΣΟΥ '1768': ΔΙΕΘΝΗΣ ΟΡΓΑΝΙΣΜΟΣ ΧΡΗΜΑΤΟΔΟΤΗΣΕΩΣ '1769': ΜΕΤΑΦΟΡΑ ΣΤΟ ΕΣΩΤΕΡΙΚΟ '1770': ΙΣΤΟΡΙΚΟ ΑΡΧΕΙΟ ΥΔΡΑΣ '1771': ΕΓΚΑΤΑΣΤΑΣΗ ΚΑΙ ΚΙΝΗΣΗ ΑΛΛΟΔΑΠΩΝ '1772': ΣΧΟΛΗ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ ΑΞΙΩΜΑΤΙΚΩΝ '1773': ΓΑΜΟΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '1774': ΑΠΑΓΟΡΕΥΣΗ ΕΞΟΔΟΥ ΟΦΕΙΛΕΤΩΝ '1775': ΠΡΩΤΕΣ ΥΛΕΣ ΨΕΚΑΣΤΗΡΩΝ '1776': ΦΙΛΕΚΠΑΙΔΕΥΤΙΚΗ ΕΤΑΙΡΕΙΑ '1777': ΑΔΕΙΕΣ ΟΔΗΓΩΝ ΑΥΤΟΚΙΝΗΤΩΝ '1778': ΕΘΝΙΚΗ ΠΙΝΑΚΟΘΗΚΗ ΚΑΙ ΜΟΥΣΕΙΟ ΑΛ. ΣΟΥΤΣΟΥ '1779': ΤΑΧΥΔΡΟΜΙΚΑ ΔΕΜΑΤΑ '1780': ΕΙΣΠΡΑΞΗ ΠΟΡΩΝ '1781': ΟΡΓΑΝΩΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ ΤΕΧΝΙΚΩΝ ΣΧΟΛΩΝ '1782': ΔΙΑΘΕΣΗ ΓΑΙΩΝ ΣΤΗ ΘΕΣΣΑΛΙΑ '1783': ΔΙΑΚΡΙΣΗ ΑΣΦΑΛΙΣΜΕΝΩΝ '1784': ΑΓΑΘΟΕΡΓΑ ΙΔΡΥΜΑΤΑ ΚΕΡΚΥΡΑΣ '1785': ΥΠΑΙΘΡΙΟ-ΠΛΑΝΟΔΙΟ ΕΜΠΟΡΙΟ ΚΑΙ ΕΜΠΟΡΟΠΑΝΗΓΥΡΕΙΣ '1786': ΕΞΑΓΩΓΙΚΑ ΤΕΛΗ '1787': ΥΠΟΥΡΓΙΚΟ ΣΥΜΒΟΥΛΙΟ - ΟΡΓΑΝΩΣΗ ΥΠΟΥΡΓΕΙΩΝ - ΚΥΒΕΡΝΗΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ '1788': ΑΥΤΟΚΙΝΗΤΑ ΚΑΙ ΑΜΑΞΙΔΙΑ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '1789': ΥΠΗΡΕΣΙΕΣ ΠΕΡΙΦΕΡΕΙΑΚΗΣ ΑΝΑΠΤΥΞΗΣ '1790': ΔΙΑΤΙΜΗΣΗ ΦΑΡΜΑΚΩΝ '1791': ΦΟΡΟΛΟΓΙΑ ΕΙΔΩΝ ΠΟΛΥΤΕΛΕΙΑΣ '1792': ΝΑΥΤΙΚΗ ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ '1793': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΩΝ ΠΕΤΡΕΛΑΙΟΕΙΔΩΝ '1794': ΔΩΡΟ ΕΟΡΤΩΝ ΕΦΗΜΕΡΙΔΟΠΩΛΩΝ '1795': ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΓΙΑ ΤΗΝ ΑΝΟΙΚΟΔΟΜΗΣΗ '1796': ΕΠΙΣΚΕΥΑΣΤΕΣ - ΣΥΝΕΡΓΕΙΑ ΕΠΙΣΚΕΥΗΣ ΑΥΤΟΚΙΝΗΤΩΝΟΔΙΚΗ ΒΟΗΘΕΙΑ ΟΧΗΜΑΤΩΝ '1797': ΠΑΡΑΧΩΡΗΣΗ ΔΑΣΩΝ '1798': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΑΣΘΕΝΕΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΤΡΑΠΕΖΩΝ ΠΙΣΤΕΩΣ, ΓΕΝΙΚΗΣ ΚΑΙ ΑΜΕΡΙΚΑΝ ΕΞΠΡΕΣ '1799': ΠΛΗΤΤΟΜΕΝΑ ΑΠΟ ΤΗΝ ΑΝΕΡΓΙΑ ΕΠΑΓΓΕΛΜΑΤΑ '1800': ΤΑΜΕΙΑ Κ.Α.Τ.Ε '1801': ΕΙΔΙΚΟΙ ΣΤΡΑΤΙΩΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ '1802': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΙΟΝΙΚΗΣ ΚΑΙ ΛΑΙΚΗΣ ΤΡΑΠΕΖΑΣ (Τ.Α.Π.- Ι.Λ.Τ.) '1803': ΠΡΟΣΤΑΣΙΑ ΑΠΟ ΑΚΤΙΝΟΒΟΛΙΕΣ '1804': ΚΡΑΤΙΚΟ ΘΕΑΤΡΟ Β. ΕΛΛΑΔΟΣ '1805': ΥΓΕΙΟΝΟΜΙΚΟΣ ΕΛΕΓΧΟΣ ΦΟΙΤΗΤΩΝ '1806': ΔΙΑΦΟΡΑ '1807': ΤΕΛΩΝΕΙΑΚΗ ΥΠΗΡΕΣΙΑ ΣΙΔΗΡΟΔΡΟΜΩΝ '1808': ΕΦΕΥΡΕΣΕΙΣ ΑΦΟΡΩΣΑΙ ΕΘΝ. ΑΜΥΝΑ '1809': ΥΠΟΒΡΥΧΙΟΣ ΤΗΛΕΓΡΑΦΟΣ '1810': ΑΔΕΙΕΣ ΟΙΚΟΔΟΜΗΣ ΞΕΝΟΔΟΧΕΙΩΝ '1811': ΙΝΣΤΙΤΟΥΤΟ ΒΥΖΑΝΤΙΝΩΝ ΣΠΟΥΔΩΝ '1812': ΣΧΟΛΗ ΓΕΩΤΕΧΝΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΜΙΟΥ ΘΕΣΝΙΚΗΣ '1813': ΒΙΒΛΙΟΘΗΚΕΣ '1814': ΤΑΜΕΙΑ ΑΝΕΓΕΡΣΕΩΣ ΔΙΔΑΚΤΗΡΙΩΝ '1815': ΕΠΙΔΟΜΑ ΒΙΒΛΙΟΘΗΚΗΣ '1816': ΚΑΤΑΣΤΗΜΑΤΑ ΑΦΟΡΟΛΟΓΗΤΩΝ ΕΙΔΩΝ '1817': ΕΠΙΧΕΙΡΗΣΕΙΣ ΠΕΡΙΘΑΛΨΕΩΣ ΗΛΙΚΙΩΜΕΝΩΝ Η ΑΝΑΠΗΡΩΝ '1818': ΛΙΜΕΝΙΚΟΙ ΣΤΑΘΜΟΙ '1819': ΝΟΜΟΘΕΤΙΚΕΣ ΕΞΟΥΣΙΟΔΟΤΗΣΕΙΣ '1820': ΘΑΛΑΜΟΙ ΡΑΔΙΟΙΣΟΤΟΠΩΝ '1821': ΔΙΟΙΚΗΣΗ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ '1822': ΑΠΑΓΟΡΕΥΜΕΝΕΣ ΚΑΙ '1823': ΗΘΟΠΟΙΟΙ '1824': ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΔΙΕΘΝΩΝ ΕΚΘΕΣΕΩΝ '1825': ΣΦΡΑΓΙΣΤΟΣ ΧΑΡΤΗΣ '1826': ΕΤΑΙΡΕΙΕΣ ΔΙΑΧΕΙΡΙΖΟΜΕΝΕΣ ΔΗΜΟΣΙΑ ΣΥΜΦΕΡΟΝΤΑ '1827': ΤΕΛΩΝΕΙΑΚΕΣ ΔΙΕΥΚΟΛΥΝΣΕΙΣ '1828': ΔΕΞΑΜΕΝΟΠΛΟΙΑ '1829': ΚΕΝΤΡΟ ΔΙΕΘΝΟΥΣ ΚΑΙ ΕΥΡΩΠΑΙΚΟΥ '1830': ΕΠΙΒΑΤΗΓΑ ΜΕΣΟΓΕΙΑΚΑ ΚΑΙ ΤΟΥΡΙΣΤΙΚΑ ΠΛΟΙΑ '1831': ΕΠΙΘΕΩΡΗΣΗ ΔΙΚΑΣΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ '1832': ΚΑΝΟΝΙΣΜΟΣ ΘΕΑΤΡΩΝ ΚΙΝΗΜΑΤΟΓΡΑΦΩΝ ΚΛΠ '1833': ΜΕΤΑΛΛΕΥΤΙΚΟΣ ΚΩΔΙΚΑΣ '1834': ΚΑΤΑΣΤΑΤΙΚΟ Τ.Ε.Α.Α.Π.Α.Ε '1835': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΛΕΣΧΗ '1836': ΕΜΠΟΡΙΚΑ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΑ ΣΗΜΑΤΑ - (ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ) '1837': ΕΠΙΔΟΜΑΤΑ ΑΠΟΛΥΟΜΕΝΩΝ ΟΠΛΙΤΩΝ ΩΣ ΑΝΙΚΑΝΩΝ '1838': ΣΥΜΒΟΥΛΙΟ ΕΝΕΡΓΕΙΑΣ '1839': ΣΧΟΛΗ ΝΟΜΙΚΩΝ,ΟΙΚΟΝΟΜΙΚΩΝ ΚΑΙ ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '1840': ΠΡΟΠΛΗΡΩΜΕΣ ΚΑΙ ΠΡΟΚΑΤΑΒΟΛΕΣ '1841': ΚΛΑΔΟΣ ΑΣΘΕΝΕΙΑΣ Τ.Ε.Β.Ε '1842': ΔΙΑΝΟΜΗ ΓΑΙΩΝ ΚΩΠΑΙΔΑΣ '1843': ΠΡΟΣΩΠΙΚΟ ΑΣΦΑΛΕΙΑΣ Ν.Π.Δ.Δ. - ΟΡΓΑΝΙΣΜΩΝ & ΕΠΙΧΕΙΡΗΣΕΩΝ '1844': ΥΠΟΥΡΓΕΙΟ ΥΠΟΔΟΜΩΝ, ΜΕΤΑΦΟΡΩΝ ΚΑΙ ΔΙΚΤΥΩΝ '1845': ΑΕΡΟΝΑΥΑΓΟΣΩΣΤΙΚΗ ΜΟΝΑΔΑ '1846': ΚΟΥΡΕΙΑ, ΚΟΜΜΩΤΗΡΙΑ Κ.Λ.Π '1847': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΔΙΚΑΣΤΙΚΩΝ ΕΠΙΜΕΛΗΤΩΝ '1848': ΕΙΔΙΚΑ ΣΥΝΕΡΓΕΙΑ '1849': ΚΑΤΕΨΥΓΜΕΝΑ ΚΡΕΑΤΑ '1850': ΜΕΣΟΓΕΙΑΚΑ ΔΡΟΜΟΛΟΓΙΑ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ '1851': ΣΥΓΚΡΟΤΗΣΗ ΠΡΟΣΩΠΙΚΟΥ ΑΕΡΟΠΟΡΙΑΣ '1852': ΥΠΑΛΛΗΛΙΚΟΣ ΚΩΔΙΚΑΣ '1853': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΕΡΙ ΦΑΡΜΑΚΕΙΩΝ '1854': ΔΙΑΦΟΡΟΙ ΣΤΕΓΑΣΤΙΚΟΙ ΝΟΜΟΙ '1855': ΥΠΟΥΡΓΕΙΟ ΣΥΝΤΟΝΙΣΜΟΥ '1856': ΠΡΟΣΛΗΨΕΙΣ ΣΤΟ ΔΗΜΟΣΙΟ '1857': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛ. ΠΡΟΣΩΠ. Ο.Ε.Α.Σ. ΚΑΙ ΥΠΑΛΛ. ΓΡΑΦΕΙΩΝ ΚΟΙΝΩΝ ΤΑΜΕΙΩΝ ΙΔΙΩΤΙΚΩΝ ΛΕΩΦΟΡΕΙΩΝ '1858': ΣΤΡΑΤΙΩΤΙΚΗ ΑΣΤΥΝΟΜΙΑ '1859': ΝΟΜΙΣΜΑΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1860': ΑΡΧΗ ΔΙΑΣΦΑΛΙΣΗΣ ΑΠΟΡΡΗΤΟΥ ΕΠΙΚΟΙΝΩΝΙΩΝ (Α.Δ.Α.Ε.) '1861': ΣΤΡΑΤΙΩΤΙΚΑ ΣΥΝΕΡΓΕΙΑ '1862': ΠΡΟΣΩΠΙΚΗ ΚΡΑΤΗΣΗ '1863': ΕΦΗΜΕΡΙΔΑ ΤΗΣ ΚΥΒΕΡΝΗΣΕΩΣ '1864': ΑΝΩΤΑΤΟ ΥΓΕΙΟΝΟΜΙΚΟ ΣΥΜΒΟΥΛΙΟ '1865': ΓΡΑΜΜΑΤΕΙΣ ΣΤΡΑΤΟΔΙΚΕΙΩΝ '1866': ΚΑΤΑΣΤΑΣΗ ΔΙΟΠΩΝ, ΝΑΥΤΩΝ ΚΑΙ ΝΑΥΤΟΠΑΙΔΩΝ '1867': ΠΕΡΙΠΤΩΣΕΙΣ ΑΜΟΙΒΑΙΑΣ ΣΥΝΔΡΟΜΗΣ '1868': ΥΠΟΝΟΜΟΙ ΠΡΩΤΕΥΟΥΣΑΣ '1869': ΤΕΛΗ ΔΙΑΔΡΟΜΗΣ ΕΝΑΕΡΙΟΥ ΧΩΡΟΥ '1870': ΥΓΕΙΟΝΟΜΙΚΑΙ ΕΠΙΤΡΟΠΑΙ '1871': ΙΑΤΡΙΚΕΣ ΕΙΔΙΚΟΤΗΤΕΣ '1872': ΕΡΤ – 2 '1873': ΕΚΤΕΛΕΣΗ ΕΡΓΩΝ Ο.Σ.Ε.ΚΑΙ ΣΥΝΔΕΔΕΜΕΝΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '1874': ΓΕΩΡΓΙΚΕΣ ΣΧΟΛΕΣ '1875': ΣΥΜΜΕΤΟΧΗ ΣΥΝΕΤΑΙΡΙΣΜΩΝ ΣΕ ΠΡΟΜΗΘΕΙΕΣ ΔΗΜΟΣΙΟΥ '1876': ΔΙΚΑΙΩΜΑ ΧΟΡΤΟΝΟΜΗΣ '1877': ΟΙΚΟΚΥΡΙΚΕΣ ΣΧΟΛΕΣ '1878': ΚΕΝΤΡΑ ΥΓΕΙΑΣ-ΠΟΛΥΙΑΤΡΕΙΑ '1879': ΔΙΚΑΣΤΗΡΙΟ ΣΥΝΔΙΑΛΛΑΓΗΣ ΚΑΙ ΔΙΑΙΤΗΣΙΑΣ '1880': ΕΠΙΘΕΩΡΗΣΗ ΙΧΘΥΩΝ '1881': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΕΞΕΥΓΕΝΙΣΜΟΥ ΔΕΝΔΡΩΝ '1882': ΦΟΙΤΗΤΕΣ '1883': ΔΟΜΗΣΗ ΕΠΙ ΡΥΜΟΤΟΜΟΥΜΕΝΩΝ ΑΚΙΝΗΤΩΝ '1884': ΑΠΑΣΧΟΛΗΣΗ - ΕΞΕΙΔΙΚΕΥΣΗ - ΚΑΤΑΡΤΙΣΗ ΑΝΕΡΓΩΝ '1885': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΦΑΡΜΑΚΕΥΤΙΚΩΝ ΕΡΓΑΣΙΩΝ (Τ.Ε.Α.Υ.Φ.Ε.) '1886': ΝΟΜΙΣΜΑΤΙΚΟ ΣΥΣΤΗΜΑ '1887': ΑΠΟΓΡΑΦΗ ΝΑΥΤΙΚΩΝ '1888': ΕΘΝΙΚΟ ΘΕΑΤΡΟ '1889': ΥΠΗΡΕΣΙΑ ΕΠΙΣΤΗΜΟΝΙΚΗΣ ΄ΕΡΕΥΝΑΣ ΚΑΙ ΑΝΑΠΤΥΞΕΩΣ '1890': ΠΑΡΟΧΕΣ ΑΣΤΥΝΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '1891': ΣΙΒΙΤΑΝΙΔΕΙΟΣ ΣΧΟΛΗ '1892': ΣΤΡΑΤΙΩΤΙΚΗ ΙΑΤΡΙΚΗ ΣΧΟΛΗ '1893': ΥΠΟΥΡΓΕΙΟ ΚΟΙΝΩΝΙΚΩΝ ΥΠΗΡΕΣΙΩΝ '1894': ΑΠΑΓΟΡΕΥΣΗ ΑΠΑΛΛΟΤΡΙΩΣΗΣ ΠΛΟΙΩΝ '1895': ΠΑΝΕΠΙΣΤΗΜΙΑΚΑ ΣΥΓΓΡΑΜΜΑΤΑ '1896': ΜΟΥΣΟΥΛΜΑΝΟΙ '1897': ΔΙΚΑΣΤΙΚΟΙ ΣΥΜΒΟΥΛΟΙ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ '1898': ΑΕΡΟΠΟΡΙΚΑ ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ '1899': ΤΟΠΙΚΑ ΕΓΓΕΙΟΒΕΛΤΙΩΤΙΚΑ ΕΡΓΑ '1900': ΦΟΡΟΛΟΓΙΑ ΖΩΩΝ '1901': ΣΥΝΤΑΓΜΑ '1902': ΝΟΜΟΙ ΠΕΡΙ ΧΡΗΜΑΤΙΣΤΗΡΙΟΥ - ΕΠΙΤΡΟΠΗ ΚΕΦΑΛΑΙΑΓΟΡΑΣ - ΧΡΗΜΑΤΙΣΤΗΡΙΑΚΗ ΑΓΟΡΑ ΠΑΡΑΓΩΓΩΝ '1903': ΓΕΩΤΡΗΣΕΙΣ '1904': ΤΑΜΕΙΑ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΚΑΙ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΜΠΟΡΙΚΗΣ ΤΡΑΠΕΖΑΣ ΕΛΛΑΔΑΣ (Τ.Ε.Α.Π.Ε.Τ.Ε ΚΑΙ Τ.Α.Π.Ε.Τ.Ε.) '1905': ΕΦΕΔΡΟΙ ΑΕΡΟΠΟΡΙΑΣ '1906': ΚΑΤ’ ΙΔΙΑΝ ΙΔΙΩΤΙΚΑ ΕΚΠΑΙΔΕΥΤΗΡΙΑ '1907': ΣΧΟΛΗ ΝΟΜΙΚΩΝ ΚΑΙ ΟΙΚΟΝΟΜΙΚΩΝ ΕΠΙΣΤΗΜΩΝ '1908': ΚΑΤΑΒΟΛΗ ΕΙΣΦΟΡΩΝ ΜΕ ΔΟΣΕΙΣ '1909': ΠΑΛΑΙΟΤΕΡΕΣ ΑΕΡΟΠΟΡΙΚΕΣ ΕΤΑΙΡΕΙΕΣ '1910': ΤΡΟΜΟΚΡΑΤΙΑ - ΟΡΓΑΝΩΜΕΝΗ '1911': ΤΑΜΕΙΑ ΕΛΙΑΣ-ΔΑΚΟΚΤΟΝΙΑ '1912': ΓΡΑΦΕΙΑ ΕΥΡΕΣΕΩΣ ΝΑΥΤΙΚΗΣ ΕΡΓΑΣΙΑΣ '1913': ΑΡΤΟΠΟΙΕΙΑ '1914': ΦΟΡΟΛΟΓΙΑ ΚΥΚΛΟΥ ΕΡΓΑΣΙΩΝ '1915': ΣΥΝΑΛΛΑΓΜΑΤΙΚΗ ΚΑΙ ΓΡΑΜΜΑΤΙΟ ΣΕ ΔΙΑΤΑΓΗ '1916': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ ΥΠΟΥΡΓΕΙΟΥ ΜΕΤΑΦΟΡΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ '1917': ΕΛΛΗΝΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΤΟΥΡΙΣΜΟΥ '1918': ΠΡΟΣΤΑΣΙΑ ΤΡΑΥΜΑΤΙΩΝ, ΑΙΧΜΑΛΩΤΩΝ ΚΑΙ ΑΜΑΧΟΥ ΠΛΗΘΥΣΜΟΥ '1919': ΚΑΝΟΝΙΣΜΟΣ ΛΕΙΤΟΥΡΓΙΑΣ Τ.Ε.Β.Ε '1920': ΣΤΕΓΑΣΗ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '1921': ΑΘΛΗΤΙΣΜΟΣ ΚΑΙ ΨΥΧΑΓΩΓΙΑ Π. ΝΑΥΤΙΚΟΥ '1922': ΑΝΕΛΚΥΣΤΗΡΕΣ - ΑΝΥΨΩΤΙΚΑ ΜΕΣΑ ΚΑΙ ΜΗΧΑΝΗΜΑΤΑ '1923': ΣΥΝΤΑΞΕΙΣ ΠΛΗΡΩΜΑΤΩΝ ΕΠΙΤΑΚΤΩΝ ΠΛΟΙΩΝ '1924': ΔΙΚΑΙΩΜΑΤΑ ΥΠΕΡΗΜΕΡΙΑΣ '1925': ΚΩΔΙΚΑΣ ΠΟΛΕΜΙΚΩΝ ΣΥΝΤΑΞΕΩΝ '1926': ΚΑΠΝΟΣ '1927': ΠΡΟΣΤΑΣΙΑ ΣΕΙΣΜΟΠΛΗΚΤΩΝ '1928': ΑΠΟΣΤΡΑΤΕΙΕΣ ΚΑΙ ΑΠΟΚΑΤΑΣΤΑΣΕΙΣ '1929': ΠΡΟΣΩΠΙΚΟ ΕΠΑΓΓΕΛΜΑΤΙΚΩΝ ΣΧΟΛΩΝ '1930': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗΝ ΠΡΟΣΤΑΣΙΑ ΤΩΝ ΕΡΓΑΖΟΜΕΝΩΝ ΑΝΗΛΙΚΩΝ '1931': ΚΕΝΤΡΙΚΗ ΑΓΟΡΑ ΑΘΗΝΩΝ '1932': ΕΝΙΣΧΥΣΗ ΕΛΑΙΟΠΑΡΑΓΩΓΗΣ '1933': ΑΝΟΙΚΤΑ ΣΩΦΡΟΝΙΣΤΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ '1934': ΦΙΛΑΝΘΡΩΠΙΚΑ ΙΔΡΥΜΑΤΑ ΖΑΚΥΝΘΟΥ '1935': ΔΙΑΦΟΡΑ ΕΙΔΗ ΤΡΟΦΙΜΩΝ, ΠΟΤΩΝ & ΑΝΤΙΚΕΙΜΕΝΩΝ '1936': ΦΟΡΟΛΟΓΙΑ ΕΠΙΧΕΙΡΗΣΕΩΝ ΤΥΠΟΥ '1937': ΠΕΡΙΟΡΙΣΜΟΙ ΕΙΣΑΓΩΓΗΣ '1938': ΠΡΟΣΩΡΙΝΗ ΕΙΣΔΟΧΗ ΕΜΠΟΡΕΥΜΑΤΩΝ '1939': ΑΡΧΕΙΟ '1940': ΔΙΥΛΙΣΤΗΡΙΑ ΠΕΤΡΕΛΑΙΟΥ '1941': ΕΙΣΑΓΩΓΗ ΠΑΙΔΑΓΩΓΙΚΟΥ ΥΛΙΚΟΥ '1942': ΕΠΙΘΕΩΡΗΣΗ ΚΛΗΡΟΔΟΤΗΜΑΤΩΝ '1943': ΣΙΔΗΡΟΔΡΟΜΟΙ ΒΟΡΕΙΟΔΥΤΙΚΗΣ ΕΛΛΑΔΟΣ '1944': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΟΤΕΧΝΙΤΩΝ ΔΟΜΙΚΩΝ ΚΑΙ ΞΥΛΟΥΡΓΙΚΩΝ ΕΡΓΑΣΙΩΝ (Τ.Ε.Α.Ε.Δ.Ξ.Ε.) '1945': ΤΑΜΕΙΑ ΠΡΟΝΟΙΑΣ ΣΤΙΣ ΠΡΕΣΒΕΙΕΣ '1946': ΟΙΚΟΓΕΝΕΙΑΚΟΣ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΣ - ΥΓΕΙΑ ΠΑΙΔΙΟΥ '1947': ΑΡΧΙΕΡΕΙΣ '1948': ΣΥΜΒΟΥΛΙΑ ΥΠΟΥΡΓΕΙΟΥ ΔΙΚΑΙΟΣΥΝΗΣ '1949': ΝΟΣΟΚΟΜΕΙΑΚΗ ΠΕΡΙΘΑΛΨΗ '1950': ΚΑΤΑΣΤΗΜΑΤΑ ΠΩΛΗΣΕΩΣ ΟΙΝΟΠΝΕΥΜΑΤΩΔΩΝ ΠΟΤΩΝ ΚΑΙ ΚΕΝΤΡΑ ΔΙΑΣΚΕΔΑΣΕΩΣ '1951': ΠΡΩΤΕΥΟΥΣΑ '1952': ΠΟΛΥΤΕΧΝΕΙΟ ΚΡΗΤΗΣ '1953': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΩΝ ΤΣΙΜΕΝΤΩΝ (Τ.Ε.Α.Π.Ε.Τ.) '1954': ΕΛΛΗΝΙΚΟΣ ΤΑΠΗΤΟΥΡΓΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ '1955': ΕΦΑΡΜΟΓΗ ΔΗΜΟΣΙΟΥΠΑΛΛΗΛΙΚΟΥ ΚΩΔΙΚΑ '1956': ΗΛΕΚΤΡΟΛΟΓΙΚΟ ΕΡΓΑΣΤΗΡΙΟ '1957': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΕΡΓΟΛΗΠΤΩΝ '1958': ΜΕΣΙΤΕΣ ΑΣΤΙΚΩΝ ΣΥΜΒΑΣΕΩΝ '1959': ΠΛΩΤΕΣ ΔΕΞΑΜΕΝΕΣ '1960': ΚΑΝΟΝΙΣΜΟΙ ΦΟΡΤΩΣΕΩΝ '1961': ΕΙΔΙΚΑ ΕΠΙΔΟΜΑΤΑ '1962': ΠΟΙΝΙΚΟΣ ΚΩΔΙΚΑΣ '1963': ΕΙΔΙΚΟΣ ΛΟΓΑΡΙΑΣΜΟΣ ΠΡΟΝΟΙΑΣ (Τ.Σ.Ε.Υ.Π.) '1964': ΕΘΝΙΚΗ ΑΝΤΙΣΤΑΣΗ '1965': ΟΡΓΑΝΙΣΜΟΣ ΒΙΟΜΗΧΑΝΙΚΗΣ ΑΝΑΠΤΥΞΗΣ '1966': ΕΡΓΑ ΚΟΙΝΗΣ ΥΠΟΔΟΜΗΣ '1967': ΔΙΕΥΘΥΝΣΗ TΕΛΩΝΕΙΩΝ ΠΕΙΡΑΙΑ '1968': ΙΑΤΡΙΚΗ ΣΧΟΛΗ ΙΩΑΝΝΙΝΩΝ '1969': ΖΩΟΚΛΟΠΗ ΚΑΙ ΖΩΟΚΤΟΝΙΑ '1970': ΡΥΘΜΙΣΙΣ ΚΙΝΗΣΕΩΣ ΕΝ ΟΔΟΙΣ '1971': ΕΤΑΙΡΕΙΕΣ ΠΡΟΣΤΑΣΙΑΣ ΚΡΑΤΟΥΜΕΝΩΝ - ΑΠΟΦΥΛΑΚΙΖΟΜΕΝΩΝ '1972': ΔΑΣΙΚΗ ΔΙΕΥΘΕΤΗΣΗ ΧΕΙΜΑΡΡΩΝ '1973': ΣΥΝΟΡΙΑΚΟΙ ΦΥΛΑΚΕΣ '1974': ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΜΙΟΥ ΙΩΑΝΝΙΝΩΝ '1975': ΕΚΠΑΙΔΕΥΣΗ Π.ΝΑΥΤΙΚΟΥ '1976': ΔΙΚΑΙΟΣΤΑΣΙΟ ΕΠΙΣΤΡΑΤΕΥΣΕΩΣ 1974 '1977': ΡΑΔΙΟΤΗΛΕΓΡΑΦΙΚΗ ΚΑΙ ΡΑΔΙΟΤΗΛΕΦΩΝΙΚΗ ΥΠΗΡΕΣΙΑ '1978': ΦΑΡΜΑΚΑ-ΙΔΙΟΣΚΕΥΑΣΜΑΤΑ '1979': ΣΥΝΤΕΛΕΣΤΕΣ ΚΕΡΔΟΥΣ ΕΠΑΓΓΕΛΜΑΤΙΩΝ '1980': ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΚΟΙΝΩΝΙΚΩΝ ΕΡΕΥΝΩΝ '1981': ΚΕΦΑΛΑΙΟ ΝΑΥΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΕΩΣ '1982': ΕΙΣΠΡΑΞΗ ΕΣΟΔΩΝ ΠΑΡΕΛΘΟΥΣΩΝ ΧΡΗΣΕΩΝ '1983': ΟΡΓΑΝΙΣΜΟΣ ΗΝΩΜΕΝΩΝ ΕΘΝΩΝ '1984': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΝΗΣΟΥ ΘΗΡΑΣ '1985': ΚΕΝΤΡΙΚΗ ΑΓΟΡΑ ΘΕΣΣΑΛΟΝΙΚΗΣ '1986': ΔΙΑΦΘΟΡΑ ΑΛΛΟΔΑΠΩΝ ΔΗΜΟΣΙΩΝ ΛΕΙΤΟΥΡΓΩΝ '1987': ΓΕΩΠΟΝΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ '1988': ΚΑΝΟΝΙΣΜΟΣ ΣΤΡΑΤΟΔΙΚΕΙΩΝ '1989': ΔΙΑΦΟΡΕΣ ΥΓΕΙΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '1990': ΤΟΥΡΙΣΤΙΚΑ ΛΕΩΦΟΡΕΙΑ '1991': ΔΑΝΕΙΑ ΑΠΟ ΕΚΔΟΤΙΚΕΣ ΤΡΑΠΕΖΕΣ '1992': ΕΠΙΘΑΛΑΣΣΙΑ ΑΡΩΓΗ - ΡΥΜΟΥΛΚΗΣΗ ΠΛΟΙΩΝ '1993': ΠΡΟΣΤΑΣΙΑ ΤΟΥ ΚΑΘΕΣΤΩΤΟΣ '1994': ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΥΛΙΚΟΥ ΕΥΗΜΕΡΙΑΣ ΝΑΥΤΙΛΛΟΜΕΝΩΝ '1995': ΜΕΣΙΤΕΣ ΕΓΧΩΡΙΩΝ ΠΡΟΙΟΝΤΩΝ '1996': ΚΡΑΤΙΚΗ ΟΡΧΗΣΤΡΑ ΑΘΗΝΩΝ '1997': ΤΜΗΜΑΤΑ ΜΟΥΣΙΚΩΝ - ΘΕΑΤΡΙΚΩΝ ΣΠΟΥΔΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑΣ - ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΝΗΜΕΡΩΣΗΣ '1998': ΠΕΙΘΑΡΧΙΚΗ ΕΞΟΥΣΙΑ ΛΙΜΕΝΙΚΩΝ ΑΡΧΩΝ '1999': ΙΝΣΤΙΤΟΥΤΟ ΑΜΥΝΤΙΚΩΝ ΑΝΑΛΥΣΕΩΝ (Ι.Α.Α.) '2000': ΙΔΙΩΤΙΚΟΙ ΣΤΑΘΜΟΙ ΑΣΥΡΜΑΤΟΥ - ΧΡΗΣΗ ΡΑΔΙΟΣΥΧΝΟΤΗΤΩΝ '2001': ΑΝΑΓΝΩΡΙΣΗ ΞΕΝΩΝ ΚΑΤΑΜΕΤΡΗΣΕΩΝ '2002': ΓΕΝΟΚΤΟΝΙΑ '2003': ΕΠΕΞΕΡΓΑΣΙΑ ΚΑΠΝΟΥ '2004': ΣΥΜΒΟΥΛΙΟ ΕΠΙΚΡΑΤΕΙΑΣ '2005': ΙΑΤΡΟΙ Ι.Κ.Α '2006': ΥΠΟΘΗΚΗ '2007': ΑΡΜΟΔΙΟΤΗΤΑ ΛΙΜΕΝΙΚΟΥ ΣΩΜΑΤΟΣ '2008': ΕΙΣΑΓΩΓΕΣ ΓΙΑ ΕΚΘΕΣΕΙΣ, ΣΥΝΕΔΡΙΑ ΚΛΠ '2009': ΕΥΡΩΠΑΙΚΗ ΤΡΑΠΕΖΑ ΑΝΑΣΥΓΚΡΟΤΗΣΗ-ΑΝΑΠΤΥΞΗ '2010': ΑΕΡΟΔΡΟΜΙΟ ΣΠΑΤΩΝ '2011': ΤΜΗΜΑ ΔΗΜΟΣΙΟΓΡΑΦΙΑΣ - ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΠΙΚΟΙΝΩΝΙΑΣ '2012': ΤΟΚΟΣ '2013': ΕΝΙΣΧΥΣΗ ΠΟΛΕΜΟΠΑΘΩΝ ΚΛΠ. ΑΓΡΟΤΩΝ '2014': ΕΞΟΔΑ ΚΗΔΕΙΑΣ ΣΤΡΑΤΙΩΤΙΚΩΝ '2015': ΠΑΡΟΧΕΣ ΥΠΑΛΛΗΛΩΝ '2016': ΠΡΟΣΤΑΣΙΑ ΣΙΤΟΠΑΡΑΓΩΓΗΣ '2017': ΑΣΦΑΛΙΣΗ Ο.Γ.Α ΑΠΟ ΑΝΕΜΟΘΥΕΛΛΑ ΚΑΙ ΠΛΗΜΜΥΡΑ '2018': ΔΙΕΥΘΥΝΣΗ ΚΑΤΑΣΚΕΥΩΝ ΚΑΙ ΕΞΟΠΛΙΣΜΟΥ '2019': ΤΕΛΩΝΕΙΑΚΟΙ ΥΠΟΛΟΓΟΙ '2020': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ ΑΘΛΗΤΙΣΜΟΥ '2021': ΣΥΝΤΑΞΕΙΣ '2022': ΑΔΕΙΕΣ ΠΡΟΣΩΠΙΚΟΥ Λ.Σ '2023': ΣΥΝΤΑΞΕΙΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΠΑΘΟΝΤΩΝ ΣΤΗΝ '2024': ΑΣΦΑΛΙΣΗ ΕΠΙΒΑΤΩΝ '2025': ΑΠΑΛΛΟΤΡΙΩΣΗ ΑΚΙΝΗΤΩΝ '2026': ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ '2027': ΕΝΟΙΚΙΟΣΤΑΣΙΟ ΒΟΣΚΩΝ '2028': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΗΘΟΠΟΙΩΝ - ΣΥΓΓΡΑΦΕΩΝ ΤΕΧΝΙΚΩΝ ΘΕΑΤΡΟΥ '2029': ΕΥΡΩΠΑΙΚΟ ΕΝΤΑΛΜΑ ΣΥΛΛΗΨΗΣ '2030': ΑΝΤΙΚΕΙΜΕΝΑ ΔΕΔΗΛΩΜΕΝΗΣ ΑΞΙΑΣ ΑΝΤΙΚΑΤΑΒΟΛΕΣ '2031': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΜΕΤΑΦΟΡΩΝ '2032': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΔΙΚΑΙΟΣΥΝΗΣ '2033': ΕΥΘΥΝΗ ΥΠΟΥΡΓΩΝ '2034': ΤΜΗΜΑ ΚΤΗΝΙΑΤΡΙΚΗΣ '2035': ΔΙΚΑΣΤΙΚΟ ΣΩΜΑ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ '2036': ΕΝΟΡΙΑΚΟΙ ΝΑΟΙ ΚΑΙ ΕΦΗΜΕΡΙΟΙ '2037': ΥΓΕΙΟΝΟΜΙΚΕΣ ΕΠΙΤΡΟΠΕΣ ΝΑΥΤΙΚΟΥ '2038': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΚΑΙ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΡΑΡΙΟΦΩΝΙΑΣ-ΤΗΛΕΟΡΑΣΕΩΣ-ΤΟΥΡΙΣΜΟΥ (Τ.Ε.Α.Π.Π. Ε.Ρ.Τ. Τ.) '2039': ΣΤΡΑΤΙΩΤΙΚΗ ΒΟΗΘΕΙΑ Η.Π.Α '2040': ΣΥΝΤΑΞΕΙΣ ΠΡΟΣΩΠΙΚΟΥ '2041': ΧΡΗΜΑΤΙΚΗ ΔΙΑΧΕΙΡΙΣΗ Π. ΝΑΥΤΙΚΟΥ '2042': ΠΟΛΙΤΙΚΟ ΓΡΑΦΕΙΟ ΠΡΩΘΥΠΟΥΡΓΟΥ '2043': ΛΟΥΤΡΟΘΕΡΑΠΕΙΑ ΚΑΙ ΑΕΡΟΘΕΡΑΠΕΙΑ '2044': ΣΥΜΒΟΥΛΙΟ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ '2045': ΕΝΤΟΚΑ ΓΡΑΜΜΑΤΙΑ '2046': ΣΩΦΡΟΝΙΣΤΙΚΟΣ ΚΩΔΙΚΑΣ '2047': ΔΗΜΟΤΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ '2048': ΚΩΔΙΚΑΣ ΠΟΛΙΤΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ - ΝΕΟΣ '2049': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΚΟΥΡΕΙΩΝ ΚΑΙ ΚΟΜΜΩΤΗΡΙΩΝ '2050': ΠΡΟΣΩΠΙΚΟ ΣΙΔΗΡΟΔΡΟΜΩΝ- Ο.Σ.Ε.- ΣΙΔΗΡΟΔΡΟΜΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ '2051': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΓΙΑ ΤΟΝ ΤΥΠΟ '2052': ΤΑΧΥΔΡΟΜΙΚΑ ΔΕΛΤΑΡΙΑ '2053': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΗΛΕΚΤΡ. ΕΤ. ΑΘΗΝΩΝ - ΠΕΙΡΑΙΩΣ ΚΑΙ ΕΛΛΗΝ. ΗΛΕΚΤΡ. ΕΤΑΙΡΙΑΣ (Τ.Α.Π Η.Ε.Α.Π.- Ε.Η.Ε.) '2054': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΑΡΤΟΠΟΙΩΝ '2055': ΔΗΜΟΤΙΚΟΙ ΚΑΙ ΚΟΙΝΟΤΙΚΟΙ ΑΡΧΟΝΤΕΣ '2056': ΜΕΤΑΦΟΡΑ ΤΑΧΥΔΡΟΜΕΙΟΥ '2057': ΚΑΝΟΝΙΣΜΟΣ ΠΑΡΟΧΩΝ ΤΑΜΕΙΟΥ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΑΣΦΑΛΙΣΤΩΝ ΚΑΙ ΠΡΟΣΩΠΙΚΟΥ ΑΣΦΑΛΙΣΤΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ (Τ.Ε.Α.Α.Π.Α.Ε.) '2058': ΠΡΟΣΩΠΙΚΟ '2059': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΗΛΕΚΤΡΙΣΜΟΥ '2060': ΚΑΝΟΝΙΣΜΟΙ ΕΡΓΩΝ ΩΠΛΙΣΜΕΝΟΥ ΣΚΥΡΟΔΕΜΑΤΟΣ '2061': ΑΛΕΥΡΑ-ΑΡΤΟΣ '2062': ΤΕΛΗ ΠΡΟΣΟΡΜΙΣΕΩΣ, ΠΑΡΑΒΟΛΗΣ ΚΑΙ ΠΑΡΟΠΛΙΣΜΟΥ '2063': ΙΔΙΩΤΙΚΑ ΕΚΠΑΙΔΕΥΤΗΡΙΑ ΦΡΟΝΤΙΣΤΗΡΙΑ '2064': ΑΡΧΑΙΟΛΟΓΙΚΗ ΥΠΗΡΕΣΙΑ '2065': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΤΥΠΟΓΡΑΦΩΝ ΚΑΙ ΜΙΣΘΩΤΩΝ ΓΡΑΦΙΚΩΝ ΤΕΧΝΩΝ (Τ.Α.Τ. & Μ.Γ.Τ) '2066': ΕΙΔΙΚΕΣ ΕΦΑΡΜΟΓΕΣ ΚΥΡΙΑΚΗΣ ΑΡΓΙΑΣ '2067': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΓΙΑ ΤΑ ΠΛΗΡΩΜΑΤΑ '2068': ΑΣΤΙΚΑ ΣΧΟΛΕΙΑ '2069': ΤΑΜΕΙΑ ΣΥΝΤΑΞΕΩΝ ΕΦΗΜΕΡΙΔΟΠΩΛΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΠΡΑΚΤΟΡΕΙΩΝ ΑΘΗΝΩΝ-ΘΕΣΝΙΚΗΣ (Τ.Σ.Ε.Υ.Π.) '2070': ΔΟΜΙΚΑ ΕΡΓΑ '2071': ΝΑΥΣΤΑΘΜΟΣ '2072': ΑΝΤΙΓΡΑΦΙΚΑ ΔΙΚΑΙΩΜΑΤΑ '2073': ΕΠΙΔΟΜΑ ΟΙΚΟΓΕΝΕΙΑΚΩΝ ΒΑΡΩΝ '2074': ΕΛΛΗΝΙΚΗ-ΕΥΡΩΠΑΙΚΗ ΦΑΡΜΑΚΟΠΟΙΙΑ '2075': ΔΕΛΤΙΑ ΤΑΥΤΟΤΗΤΟΣ '2076': ΣΧΟΛΙΑΤΡΙΚΗ ΥΠΗΡΕΣΙΑ '2077': ΥΔΡΟΓΟΝΑΝΘΡΑΚΕΣ '2078': ΓΕΝΙΚΑ ΠΕΡΙ ΕΚΘΕΣΕΩΝ '2079': ΦΟΡΟΛΟΓΙΚΕΣ ΔΙΕΥΚΟΛΥΝΣΕΙΣ '2080': ΛΣΜΟΣ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ Ι.Κ.Α '2081': ΕΛΕΓΧΟΣ ΚΤΙΡΙΑΚΩΝ ΕΡΓΩΝ '2082': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ '2083': ΕΛΑΙΟΠΥΡΗΝΕΣ '2084': ΕΜΦΥΤΕΥΤΙΚΑ ΚΤΗΜΑΤΑ '2085': ΤΟΥΡΙΣΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '2086': ΚΛΑΔΟΣ ΑΣΦΑΛΙΣΕΩΣ ΤΕΧΝΙΚΩΝ ΤΥΠΟΥ ΘΕΣΣΑΛΟΝΙΚΗΣ (Κ.Α.Τ.Τ.Θ.) '2087': ΜΕΤΕΩΡΟΛΟΓΙΚΗ ΥΠΗΡΕΣΙΑ '2088': ΑΓΡΟΤΙΚΟΣ ΚΩΔΙΚΑΣ '2089': ΤΕΧΝΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ '2090': ΕΛΕΓΧΟΣ ΝΟΜΙΜΟΦΡΟΣΥΝΗΣ '2091': ΑΡΧΑΙΟΛΟΓΙΚΗ ΕΤΑΙΡΙΑ '2092': ΣΧΟΛΑΖΟΥΣΕΣ ΚΛΗΡΟΝΟΜΙΕΣ '2093': ΓΕΦΥΡΑ ΡΙΟΥ - ΑΝΤΙΡΡΙΟΥ '2094': ΦΟΙΤΗΣΗ, ΕΞΕΤΑΣΕΙΣ ΚΛΠ '2095': ΤΥΧΕΡΑ, ΜΙΚΤΑ ΚΑΙ ΤΕΧΝΙΚΑ ΠΑΙΓΝΙΑ '2096': ΟΡΓΑΝΙΚΟΙ ΑΡΙΘΜΟΙ ΥΠΑΞΙΩΜΑΤΙΚΩΝ '2097': ΦΟΡΟΛΟΓΙΑ ΚΙΝΗΤΗΣ ΚΑΙ ΑΚΙΝΗΤΗΣ ΠΕΡΙΟΥΣΙΑΣ '2098': ΑΤΕΛΕΙΕΣ ΑΓΙΟΥ ΟΡΟΥΣ '2099': ΜΟΝΟΠΩΛΙΟ ΑΛΑΤΙΟΥ '2100': ΑΣΦΑΛΙΣΗ ΕΛΛΗΝΩΝ ΕΞΩΤΕΡΙΚΟΥ '2101': ΔΙΕΘΝΕΣ ΚΕΝΤΡΟ ΑΝΩΤΑΤΩΝ '2102': ΑΝΑΠΡΟΣΑΡΜΟΓΕΣ ΣΥΝΤΑΞΕΩΝ '2103': ΓΕΝΙΚΕΣ ΕΠΙΘΕΩΡΗΣΕΙΣ-ΔΙΕΥΘΥΝΣΕΙΣ '2104': ΣΩΜΑ ΟΡΚΩΤΩΝ ΛΟΓΙΣΤΩΝ '2105': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΒΟΡΕΙΟΥ ΕΛΛΑΔΟΣ '2106': ΠΑΝΕΠΙΣΤΗΜΙΑ ΠΕΙΡΑΙΩΣ-ΜΑΚΕΔΟΝΙΑΣ '2107': ΧΩΡΟΤΑΞΙΑ ΚΑΙ ΠΕΡΙΒΑΛΛΟΝ '2108': ΕΣΩΤΕΡΙΚΟΙ ΚΑΝΟΝΙΣΜΟΙ ΕΡΓΑΣΙΑΣ '2109': ΕΛΕΓΧΟΣ ΝΑΥΤΙΚΩΝ ΑΤΥΧΗΜΑΤΩΝ '2110': ΠΝΕΥΜΑΤΙΚΑ ΚΕΝΤΡΑ '2111': ΠΛΟΗΓΙΚΑ ΔΙΚΑΙΩΜΑΤΑ '2112': ΣΤΡΑΤΕΥΟΜΕΝΟΙ ΔΙΚΗΓΟΡΟΙ '2113': ΣΥΣΤΑΤΙΚΑ ΑΥΤΟΚΙΝΗΤΩΝ '2114': ΣΙΔΗΡΟΔΡΟΜΟΙ ΠΕΛΟΠΟΝΝΗΣΟΥ '2115': ΤΜΗΜΑ ΜΕΘΟΔΟΛΟΓΙΑΣ, ΙΣΤΟΡΙΑΣ ΚΑΙ ΘΕΩΡΙΑΣ ΤΗΣ ΕΠΙΣΤΗΜΗΣ '2116': ΕΥΡΩΠΑΙΚΟ ΠΟΛΙΤΙΣΤΙΚΟ ΚΕΝΤΡΟ ΔΕΛΦΩΝ '2117': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΕΓΓΕΙΩΝ ΒΕΛΤΙΩΣΕΩΝ '2118': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ (Τ.Ε.Α.Δ.Υ.) '2119': ΙΕΡΟΚΗΡΥΚΕΣ '2120': ΕΙΡΗΝΟΔΙΚΕΙΑ - ΠΤΑΙΣΜΑΤΟΔΙΚΕΙΑ '2121': ΑΓΟΡΑΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ '2122': ΤΡΑΠΕΖΙΤΙΚΗ ΕΠΙΤΑΓΗ '2123': ΝΑΥΑΓΟΣΩΣΤΙΚΑ ΚΑΙ ΡΥΜΟΥΛΚΑ '2124': ΦΟΡΟΛΟΓΙΚΕΣ ΔΙΑΦΟΡΕΣΙ '2125': ΜΕΤΡΑ ΚΑΙ ΣΤΑΘΜΑ '2126': ΓΕΝΙΚΟ ΧΗΜΕΙΟ ΤΟΥ ΚΡΑΤΟΥΣ '2127': ΣΥΜΦΩΝΙΑ ΓΙΑ ΙΣΑ ΟΙΚΟΝΟΜΙΚΑ ΚΟΙΝΩΝΙΚΑ '2128': ΣΥΝΟΡΙΑΚΟΙ ΣΤΑΘΜΟΙ '2129': ΑΞΙΩΜΑΤΙΚΟΙ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ '2130': ΥΠΗΡΕΣΙΑΚΑ ΣΥΜΒΟΥΛΙΑ '2131': ΕΙΣΑΓΩΓΙΚΟΣ ΝΟΜΟΣ '2132': ΚΤΗΜΑΤΟΛΟΓΙΟ '2133': ΕΤΑΙΡΕΙΑ ΔΙΑΧΕΙΡΙΣΕΩΣ ΥΠΕΓΓΥΩΝ ΠΡΟΣΟΔΩΝ '2134': ΥΠΟΥΡΓΕΙΟ ΜΑΚΕΔΟΝΙΑΣ – ΘΡΑΚΗΣ '2135': ΤΟΥΡΙΣΤΙΚΑ ΓΡΑΦΕΙΑ ΚΑΙ ΣΩΜΑΤΕΙΑ '2136': ΔΑΝΕΙΑ ΑΝΑΣΥΓΚΡΟΤΗΣΗΣ '2137': ΑΣΤΙΚΕΣ ΣΥΓΚΟΙΝΩΝΙΕΣ ΘΕΣΣΑΛΟΝΙΚΗΣ-Ο.Α.Σ.Θ '2138': ΕΘΕΛΟΝΤΕΣ ΑΕΡΟΠΟΡΙΑΣ '2139': ΣΗΜΕΙΩΤΕΣ '2140': ΤΕΛΗ ΕΓΚΑΤΑΣΤΑΣΗΣ - ΛΕΙΤΟΥΡΓΙΑΣ ΚΕΡΑΙΩΝ '2141': Η.Π.Α '2142': ΠΑΝΕΠΙΣΤΗΜΙΑ ΑΙΓΑΙΟΥ, ΙΟΝΙΟΥ ΚΑΙ ΘΕΣΣΑΛΙΑΣ '2143': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΞΕΝΟΔΟΧΩΝ '2144': ΣΥΜΒΟΥΛΙΑ ΣΤΕΓΑΣΕΩΣ '2145': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΙΔΙΩΤΙΚΩΝ ΑΕΡΟΠΛΑΝΩΝ '2146': ΦΟΡΟΛΟΓΙΑ ΔΗΜΟΣΙΩΝ ΘΕΑΜΑΤΩΝ '2147': ΣΤΡΑΤΟΛΟΓΙΑ ΟΠΛΙΤΩΝ ΧΩΡΟΦΥΛΑΚΗΣ '2148': ΓΥΜΝΑΣΙΑ ΑΡΙΣΤΟΥΧΩΝ '2149': ΣΧΟΛΙΚΗ ΑΝΤΙΛΗΨΗ '2150': ΕΥΘΥΝΗ ΣΤΡΑΤΙΩΤΙΚΩΝ '2151': ΣΤΑΘΜΟΙ ΕΠΙΒΗΤΟΡΩΝ '2152': ΒΕΒΑΙΩΣΗ ΠΤΑΙΣΜΑΤΩΝ ΑΠΟ '2153': ΔΙΑΖΥΓΙΟ '2154': ΔΙΕΘΝΗΣ ΣΥΜΒΑΣΗ ΠΕΡΙ ΑΝΑΓΚΑΣΤΙΚΗΣ ΕΡΓΑΣΙΑΣ '2155': ΔΙΕΥΚΟΛΥΝΣΗ ΔΙΕΘΝΟΥΣ ΝΑΥΤΙΛΙΑΚΗΣ ΚΙΝΗΣΕΩΣ '2156': ΕΝΟΙΚΙΟΣΤΑΣΙΟ '2157': ΕΚΘΕΣΕΙΣ ΖΑΠΠΕΙΟΥ ΜΕΓΑΡΟΥ '2158': ΔΙΑΧΕΙΡΙΣΗ ΥΛΙΚΟΥ Π. ΝΑΥΤΙΚΟΥ '2159': ΕΦΕΔΡΙΚΑ ΤΑΜΕΙΑ ΚΡΗΤΗΣ '2160': ΣΙΤΑΡΙ '2161': ΦΟΡΤΗΓΑ 501-4500 ΤΟΝΝΩΝ '2162': ΤΡΑΠΕΖΑ ΕΡΓΑΣΙΑΣ '2163': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΓΕΩΡΓΙΑΣ '2164': ΑΙΓΙΑΛΟΣ ΚΑΙ ΠΑΡΑΛΙΑ '2165': ΔΑΣΗ ΙΔΡΥΜΑΤΩΝ '2166': ΙΧΘΥΟΤΡΟΦΕΙΑ '2167': ΑΠΟΓΡΑΦΕΣ Π. ΝΑΥΤΙΚΟΥ '2168': ΣΗΜΑΤΑ ΚΑΙ ΔΕΛΤΙΑ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ '2169': ΠΕΙΘΑΡΧΙΚΟ ΔΙΚΑΙΟ ΑΣΤΥΝΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ '2170': ΑΤΜΟΛΕΒΗΤΕΣ '2171': ΤΑΧΥΔΡΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΣΤΡΑΤΟΥ '2172': ΠΡΟΣΤΑΣΙΑ ΠΙΝΑΚΙΔΩΝ '2173': ΑΓΡΟΤΙΚΑ ΚΤΗΝΙΑΤΡΕΙΑ '2174': ΧΡΗΜΑΤΙΣΤΗΡΙΑΚΑ ΔΙΚΑΣΤΗΡΙΑ '2175': ΕΓΓΡΑΦΗ ΠΡΟΕΡΧΟΜΕΝΩΝ ΑΠΟ ΤΗΝ ΑΛΛΟΔΑΠΗ '2176': ΟΡΓΑΝΙΣΜΟΣ ΔΙΑΧΕΙΡΙΣΗΣ ΔΗΜΟΣΙΟΥ ΥΛΙΚΟΥ '2177': ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΥΠΡΟΥ '2178': ΚΑΤΕΡΓΑΣΙΑ ΞΗΡΑΣ ΣΤΑΦΙΔΑΣ '2179': ΤΕΛΩΝΕΙΑΚΗ ΔΙΑΙΡΕΣΗ '2180': ΑΖΗΤΗΤΑ '2181': ΜΕΛΙΣΣΟΤΡΟΦΙΑ '2182': ΔΙΕΥΘΥΝΣΗ ΘΑΛΑΣΣΙΩΝ ΚΡΑΤΙΚΩΝ ΜΕΤΑΦΟΡΩΝ '2183': ΕΚΜΕΤΑΛΛΕΥΣΗ ΜΕΤΑΛΛΕΙΩΝ ΜΕ ΕΓΓΥΗΣΗ '2184': ΙΔΙΩΤΙΚΕΣ ΕΠΑΓΓΕΛΜΑΤΙΚΕΣ ΣΧΟΛΕΣ '2185': ΔΙΑΘΕΣΗ ΑΧΡΗΣΤΟΥ ΥΛΙΚΟΥ '2186': ΤΑΧΥΔΡΟΜΙΚΕΣ ΜΕΤΑΦΟΡΕΣ '2187': ΕΡΥΘΡΟ ΠΙΠΕΡΙ '2188': ΠΙΚΠΑ-ΕΟΠ-ΚΕΝΤΡΟ ΒΡΕΦΩΝ Η ΜΗΤΕΡΑ-ΕΛΕΠΑΠ '2189': ΣΥΜΜΕΤΟΧΗ ΣΕ ΣΥΜΒΟΥΛΙΑ '2190': ΓΥΜΝΑΣΤΗΡΙΟ '2191': ΙΑΤΡΙΚΟΙ- ΟΔΟΝΤΙΑΤΡΙΚΟΙ ΣΥΛΛΟΓΟΙ '2192': ΕΙΣΑΓΩΓΗ ΦΟΙΤΗΤΩΝ '2193': ΕΛΛΗΝΙΚΟ ΄ΙΔΡΥΜΑ ΠΟΛΙΤΙΣΜΟΥ '2194': ΛΟΙΜΟΚΑΘΑΡΤΗΡΙΑ ΖΩΩΝ '2195': ΔΙΕΘΝΗΣ ΟΡΓΑΝΙΣΜΟΣ ΑΤΟΜΙΚΗΣ ΕΝΕΡΓΕΙΑΣ '2196': ΤΑΜΕΙΟ ΕΞΟΔΟΥ ΚΑΙ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΒΙΟΜΗΧΑΝΙΑΣ ΚΑΠΝΟΥ '2197': ΚΑΘΗΓΗΤΕΣ Ε.Μ.Π '2198': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ '2199': ΒΕΒΑΙΩΣΗ ΦΟΡΟΛΟΓΙΑΣ ΚΑΘΑΡΑΣ ΠΡΟΣΟΔΟΥ '2200': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΤΡΑΠΕΖΩΝ ΕΛΛΑΔΟΣ ΚΑΙ ΚΤΗΜΑΤΙΚΗΣ '2201': ΔΗΜΟΨΗΦΙΣΜΑΤΑ '2202': ΕΛΛΗΝΙΚΟ ΑΝΟΙΚΤΟ ΠΑΝΕΠΙΣΤΗΜΙΟ '2203': ΚΑΛΛΙΤΕΧΝΙΚΟ ΕΠΑΓΓΕΛΜΑΤΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ '2204': ΑΝΟΙΚΟΔΟΜΗΣΙΣ '2205': ΔΑΣΙΚΟΣ ΚΩΔΙΚΑΣ '2206': ΚΑΝΟΝΙΣΜΟΣ ΠΥΡΟΣΒΕΣΤΙΚΩΝ ΜΕΣΩΝ ΤΩΝ ΠΛΟΙΩΝ '2207': ΔΙΦΘΕΡΙΤΙΔΑ '2208': ΒΙΒΛΙΑ ΚΑΙ ΦΟΡΟΛΟΓΙΚΑ ΣΤΟΙΧΕΙΑ '2209': ΕΛΕΓΧΟΣ ΕΞΑΓΟΜΕΝΩΝ ΕΛΑΙΩΝ '2210': ΕΠΙΔΟΜΑΤΑ ΟΙΚΟΓΕΝΕΙΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ '2211': ΕΥΡΩΠΑΙΚΕΣ ΣΥΜΦΩΝΙΕΣ ΠΟΥ ΑΦΟΡΟΥΝ ΤΗΝ ΤΗΛΕΟΡΑΣΗ '2212': ΕΚΤΑΚΤΑ ΣΤΡΑΤΟΔΙΚΕΙΑ '2213': ΠΟΛΕΜΙΚΗ ΒΙΟΜΗΧΑΝΙΑ '2214': ΑΣΕΜΝΟΙ ΓΥΝΑΙΚΕΣ '2215': ΑΠΕΛΕΥΘΕΡΩΣΗ ΑΓΟΡΑΣ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ ΕΝΕΡΓΕΙΑΚΗ ΠΟΛΙΤΙΚΗ Ρ.Α.Ε '2216': ΠΡΟΕΙΣΠΡΑΞΗ ΔΙΚΗΓΟΡΙΚΗΣ ΑΜΟΙΒΗΣ '2217': ΕΘΝΙΚΗ ΣΧΟΛΗ ΔΗΜΟΣΙΑΣ ΥΓΕΙΑΣ (Ε.Σ.Δ.Υ.) '2218': ΠΡΟΜΗΘΕΙΑ ΘΕΙΟΥ ΚΑΙ ΘΕΙΙΚΟΥ ΧΑΛΚΟΥ '2219': ΧΗΜΙΚΟΙ - ΧΗΜΙΚΕΣ ΒΙΟΜΗΧΑΝΙΕΣ '2220': ΑΣΦΑΛΙΣΗ ΚΑΤΑ ΤΗΣ ΑΣΘΕΝΕΙΑΣ '2221': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΘΝΙΚΟΥ ΤΥΠΟΓΡΑΦΕΙΟΥ (Τ.Α.Π.Ε.Τ.) '2222': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΟΙΚΟΝΟΜΙΚΩΝ '2223': ΠΕΡΙΕΧΟΜΕΝΟ ΔΗΛΩΣΗΣ ΦΟΡΟΥ ΕΙΣΟΔΗΜΑΤΟΣ '2224': ΠΡΩΤΕΣ ΥΛΕΣ ΣΙΔΕΡΕΝΙΩΝ ΒΑΡΕΛΙΩΝ '2225': ΕΥΡΩΠΑΙΚΟΣ ΚΩΔΙΚΑΣ ΚΟΙΝΩΝΙΚΗΣ ΑΣΦΑΛΕΙΑΣ '2226': ΔΙΑΦΟΡΟΙ ΓΕΩΡΓΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ '2227': ΣΧΕΔΙΑ ΠΟΛΕΩΝ ΙΟΝΙΩΝ ΝΗΣΩΝ '2228': ΕΥΡΩΠΑΙΚΗ ΟΙΚΟΝΟΜΙΚΗ ΚΟΙΝΟΤΗΤΑ ΕΥΡΩΠΑΙΚΗ ΕΝΩΣΗ '2229': ΣΧΟΛΗ ΔΙΟΙΚΗΣΕΩΣ ΝΟΣΗΛΕΥΤ. ΙΔΡΥΜΑΤΩΝ '2230': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΕΜΠΡΑΓΜΑΤΟΥ ΔΙΚΑΙΟΥ '2231': ΕΠΙΜΕΛΗΤΕΙΑ ΚΑΙ ΟΙΚΟΝΟΜΙΚΕΣ ΥΠΗΡΕΣΙΕΣ '2232': ΔΙΑΔΙΚΑΣΙΑ ΑΤΕΛΕΙΑΣ '2233': ΠΑΙΔΙΚΕΣ ΕΞΟΧΕΣ '2234': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΘΝΙΚΗΣ ΤΡΑΠΕΖΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '2235': ΚΡΑΤΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΔΑΣΩΝ '2236': ΑΝΕΞΑΡΤΗΣΙΑ ΤΗΣ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ '2237': ΤΕΧΝΙΚΑ ΠΤΥΧΙΑ '2238': ΕΠΙΒΑΤΙΚΑ ΑΥΤΟΚΙΝΗΤΑ (ΔΗΜΟΣΙΑΣ ΚΑΙ ΙΔΙΩΤΙΚΗΣ ΧΡΗΣΗΣ) '2239': ΣΥΜΒΑΣΕΙΣ ΒΟΥΛΕΥΤΩΝ '2240': ΟΡΓΑΝΙΣΜΟΣ ΤΩΝ ΔΙΚΑΣΤΗΡΙΩΝ '2241': ΕΚΠΑΙΔΕΥΤΙΚΟΙ ΛΕΙΤΟΥΡΓΟΙ ΕΝ ΓΕΝΕΙ '2242': ΑΡΜΟΔΙΟΤΗΤΑ ΤΕΛΩΝΕΙΑΚΩΝ ΑΡΧΩΝ '2243': ΕΙΔΙΚΑ ΕΦΕΤΕΙΑ '2244': ΑΞΙΩΜΑΤΙΚΟΙ ΑΕΡΟΠΟΡΙΑΣ '2245': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΒΙΒΛΙΟΘΗΚΗ '2246': ΕΠΙΤΡΟΠΗ ΣΥΝΤΑΞΗΣ ΣΧΕΔΙΟΥ ΚΩΔΙΚΑ ΕΡΓΑΣΙΑΣ '2247': ΕΛΟΝΟΣΙΑ '2248': ΝΑΥΛΟΣΥΜΦΩΝΑ '2249': ΣΙΔΗΡΟΔΡΟΜΟΙ ΘΕΣΣΑΛΙΚΟΙ '2250': ΡΑΔΙΟΦΩΝΙΚΕΣ ΣΥΜΒΑΣΕΙΣ '2251': ΠΡΟΩΘΗΣΗ ΓΕΩΡΓΙΚΗΣ ΠΑΡΑΓΩΓΗΣ-ΕΘ.Ι.ΑΓ.Ε '2252': ΕΠΟΧΙΑΚΩΣ ΕΡΓΑΖΟΜΕΝΟΙ ΜΙΣΘΩΤΟΙ '2253': ΔΙΔΑΚΤΙΚΟ ΠΡΟΣΩΠΙΚΟ '2254': ΚΩΔΙΚΑΣ ΚΕΝΤΡΙΚΗΣ, ΠΡΕΣΒΕΥΤΙΚΗΣ ΚΑΙ '2255': ΠΟΛΙΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΥΠΟΥΡΓΕΙΟΥ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ '2256': ΔΙΠΛΩΜΑΤΑ ΕΥΡΕΣΙΤΕΧΝΙΑΣ '2257': ΣΩΜΑΤΕΙΑ ΓΕΩΡΓΙΚΩΝ ΕΡΓΑΤΩΝ '2258': ΚΩΔΙΚΑΣ ΠΕΡΙ ΕΙΣΠΡΑΞΕΩΣ ΔΗΜΟΣΙΩΝ ΕΣΟΔΩΝ '2259': ΤΡΑΠΕΖΟΓΡΑΜΜΑΤΙΑ '2260': ΠΡΟΜΗΘΕΥΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ Ε.Β.Α '2261': ΕΛΕΓΧΟΣ ΑΣΦΑΛΕΙΑΣ ΑΥΤΟΚΙΝΗΤΩΝΚΕΝΤΡΑ ΤΕΧΝΙΚΟΥ ΕΛΕΓΧΟΥ ΟΧΗΜΑΤΩΝ (Κ.Τ.Ε.Ο.) '2262': ΕΞΑΓΩΓΗ ΤΥΡΟΥ '2263': ΝΑΥΤΙΛΙΑΚΟ ΣΥΝΑΛΛΑΓΜΑ '2264': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΗΛΕΤΡΟΤΕΧΝΙΤΩΝ ΕΛΛΑΔΟΣ (T.E.A.H.E.) '2265': ΜΙΣΘΟΙ ΣΤΡΑΤΙΩΤΙΚΩΝ ΚΑΙ ΠΡΟΣΑΥΞΗΣΕΙΣ '2266': ΑΣΤΙΚΟΣ ΚΩΔΙΚΑΣ '2267': ΜΕ ΤΙΣ ΗΝΩΜΕΝΕΣ ΠΟΛΙΤΕΙΕΣ ΑΜΕΡΙΚΗΣ '2268': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ Ο.Τ.Ε. (Τ.Α.Π.-Ο.Τ.Ε.) '2269': ΜΑΙΕΣ '2270': ΦΥΓΟΔΙΚΙΑ '2271': ΟΡΓΑΝΙΣΜΟΣ ΞΕΝΟΔΟΧΕΙΑΚΗΣ ΠΙΣΤΗΣ '2272': ΔΗΜΟΤΙΚΟΙ ΣΤΡΑΤΟΛΟΓΟΙ '2273': ΑΝΩΤΑΤΟ ΔΙΚΑΣΤΙΚΟ ΣΥΜΒΟΥΛΙΟ '2274': ΙΣΤΟΡΙΚΟ ΑΡΧΕΙΟ ΚΡΗΤΗΣ '2275': ΕΛΛΗΝΙΚΗ ΘΑΛΑΣΣΙΑ ΄ΕΝΩΣΗ '2276': ΕΚΠΟΙΗΣΕΙΣ ΚΑΙ ΕΚΜΙΣΘΩΣΕΙΣ '2277': ΤΑΧΥΔΡΟΜΙΚΕΣ ΕΠΙΤΑΓΕΣ '2278': ΥΠΗΡΕΣΙΑ ΜΗΤΡΩΟΥ '2279': ΔΙΑΦΟΡΑ ΟΙΚΟΝΟΜΙΚΑ ΘΕΜΑΤΑ '2280': ΕΝΔΙΚΑ ΜΕΣΑ '2281': ΤΕΛΗ ΑΕΡΟΠΟΡΙΚΩΝ ΤΑΞΙΔΙΩΝ '2282': ΜΕ ΤΗΝ ΑΙΓΥΠΤΟ '2283': ΔΙΑΦΟΡΕΣ ΒΙΒΛΙΟΘΗΚΕΣ '2284': ΚΕΝΤΡΙΚΗ ΥΠΗΡΕΣΙΑ splits: - name: train num_bytes: 216757887 num_examples: 28536 - name: test num_bytes: 71533786 num_examples: 9516 - name: validation num_bytes: 68824457 num_examples: 9511 download_size: 45606292 dataset_size: 357116130 --- # Dataset Card for Greek Legal Code ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: https://github.com/christospi/glc-nllp-21 - Paper: https://arxiv.org/abs/2109.15298 - Data: https://doi.org/10.5281/zenodo.5528002 - Leaderboard: N/A - Point of Contact: [Christos Papaloukas](mailto:christospap@di.uoa.gr) ### Dataset Summary Greek_Legal_Code (GLC) is a dataset consisting of approx. 47k legal resources from Greek legislation. The origin of GLC is “Permanent Greek Legislation Code - Raptarchis”, a collection of Greek legislative documents classified into multi-level (from broader to more specialized) categories. Topics GLC consists of 47 legislative volumes and each volume corresponds to a main thematic topic. Each volume is divided into thematic sub categories which are called chapters and subsequently, each chapter breaks down to subjects which contain the legal resources. The total number of chapters is 389 while the total number of subjects is 2285, creating an interlinked thematic hierarchy. So, for the upper thematic level (volume) GLC has 47 classes. For the next thematic level (chapter) GLC offers 389 classes and for the inner and last thematic level (subject), GLC has 2285 classes. GLC classes are divided into three categories for each thematic level: frequent classes, which occur in more than 10 training documents and can be found in all three subsets (training, development and test); few-shot classes which appear in 1 to 10 training documents and also appear in the documents of the development and test sets, and zero-shot classes which appear in the development and/or test, but not in the training documents. ### Supported Tasks and Leaderboards The dataset supports: Multi-class Text Classification: Given the text of a document, a model predicts the corresponding class. Few-shot and Zero-shot learning: As already noted, the classes can be divided into three groups: frequent, few-shot, and zero- shot, depending on whether they were assigned to more than 10, fewer than 10 but at least one, or no training documents, respectively. \| Level \| Total \| Frequent \| Few-Shot (<10) \| Zero-Shot \| \|---\|---\|---\|---\|---\| \|Volume\|47\|47\|0\|0\| \|Chapter\|389\|333\|53\|3\| \|Subject\|2285\|712\|1431\|142\| ### Languages All documents are written in Greek. ## Dataset Structure ### Data Instances ```json { "text": "179. ΑΠΟΦΑΣΗ ΥΠΟΥΡΓΟΥ ΜΕΤΑΦΟΡΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ Αριθ. Β-οικ. 68425/4765 της 2/17 Νοεμ. 2000 (ΦΕΚ Β΄ 1404) Τροποποίηση της 42000/2030/81 κοιν. απόφασης του Υπουργού Συγκοινωνιών «Κωδικοποίηση και συμπλήρωση καν. Αποφάσεων» που εκδόθηκαν κατ’ εξουσιοδότηση του Ν.Δ. 102/73 «περί οργανώσεως των δια λεωφορείων αυτοκινήτων εκτελουμένων επιβατικών συγκοινωνιών». ", "volume": 24, # "ΣΥΓΚΟΙΝΩΝΙΕΣ" } ``` ### Data Fields The following data fields are provided for documents (`train`, `dev`, `test`): `text`: (str) The full content of each document, which is represented by its `header` and `articles` (i.e., the `main_body`).\ `label`: (class label): Depending on the configurarion, the volume/chapter/subject of the document. For volume-level class it belongs to specifically: ["ΚΟΙΝΩΝΙΚΗ ΠΡΟΝΟΙΑ", "ΓΕΩΡΓΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΡΑΔΙΟΦΩΝΙΑ ΚΑΙ ΤΥΠΟΣ", "ΒΙΟΜΗΧΑΝΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΥΓΕΙΟΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΠΟΛΕΜΙΚΟ ΝΑΥΤΙΚΟ", "ΤΑΧΥΔΡΟΜΕΙΑ - ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ", "ΔΑΣΗ ΚΑΙ ΚΤΗΝΟΤΡΟΦΙΑ", "ΕΛΕΓΚΤΙΚΟ ΣΥΝΕΔΡΙΟ ΚΑΙ ΣΥΝΤΑΞΕΙΣ", "ΠΟΛΕΜΙΚΗ ΑΕΡΟΠΟΡΙΑ", "ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ", "ΝΟΜΟΘΕΣΙΑ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ ΤΡΑΠΕΖΩΝ ΚΑΙ ΧΡΗΜΑΤΙΣΤΗΡΙΩΝ", "ΠΟΛΙΤΙΚΗ ΑΕΡΟΠΟΡΙΑ", "ΕΜΜΕΣΗ ΦΟΡΟΛΟΓΙΑ", "ΚΟΙΝΩΝΙΚΕΣ ΑΣΦΑΛΙΣΕΙΣ", "ΝΟΜΟΘΕΣΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ", "ΝΟΜΟΘΕΣΙΑ ΕΠΙΜΕΛΗΤΗΡΙΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ ΚΑΙ ΣΩΜΑΤΕΙΩΝ", "ΔΗΜΟΣΙΑ ΕΡΓΑ", "ΔΙΟΙΚΗΣΗ ΔΙΚΑΙΟΣΥΝΗΣ", "ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ", "ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΕΚΠΑΙΔΕΥΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΔΗΜΟΣΙΟ ΛΟΓΙΣΤΙΚΟ", "ΤΕΛΩΝΕΙΑΚΗ ΝΟΜΟΘΕΣΙΑ", "ΣΥΓΚΟΙΝΩΝΙΕΣ", "ΕΘΝΙΚΗ ΑΜΥΝΑ", "ΣΤΡΑΤΟΣ ΞΗΡΑΣ", "ΑΓΟΡΑΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΔΗΜΟΣΙΟΙ ΥΠΑΛΛΗΛΟΙ", "ΠΕΡΙΟΥΣΙΑ ΔΗΜΟΣΙΟΥ ΚΑΙ ΝΟΜΙΣΜΑ", "ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ", "ΛΙΜΕΝΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΠΟΛΙΤΙΚΗ ΔΙΚΟΝΟΜΙΑ", "ΔΙΠΛΩΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΔΙΟΙΚΗΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΑΜΕΣΗ ΦΟΡΟΛΟΓΙΑ", "ΤΥΠΟΣ ΚΑΙ ΤΟΥΡΙΣΜΟΣ", "ΕΘΝΙΚΗ ΟΙΚΟΝΟΜΙΑ", "ΑΣΤΥΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΑΓΡΟΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΕΡΓΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΕΜΠΟΡΙΚΗ ΝΟΜΟΘΕΣΙΑ", "ΕΠΙΣΤΗΜΕΣ ΚΑΙ ΤΕΧΝΕΣ", "ΕΜΠΟΡΙΚΗ ΝΑΥΤΙΛΙΑ", "ΣΥΝΤΑΓΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ" ] \ The labels can also be a the chapter-level or subject-level class it belongs to. Some chapter labels are omitted due to size (389 classes). Some subject labels are also omitted due to size (2285 classes). ### Data Splits \| Split \| No of Documents \| Avg. words \| \| ------------------- \| ------------------------------------ \| --- \| \| Train \| 28,536 \| 600 \| \|Development \| 9,511 \| 574 \| \|Test \| 9,516 \| 595 \| ## Dataset Creation ### Curation Rationale The dataset was curated by Papaloukas et al. (2021) with the hope to support and encourage further research in NLP for the Greek language. ### Source Data #### Initial Data Collection and Normalization The ``Permanent Greek Legislation Code - Raptarchis`` is a thorough catalogue of Greek legislation since the creation of the Greek state in 1834 until 2015. It includes Laws, Royal and Presidential Decrees, Regulations and Decisions, retrieved from the Official Government Gazette, where Greek legislation is published. This collection is one of the official, publicly available sources of classified Greek legislation suitable for classification tasks. Currently, the original catalogue is publicly offered in MS Word (.doc) format through the portal e-Themis, the legal database and management service of it, under the administration of the Ministry of the Interior (Affairs). E-Themis is primarily focused on providing legislation on a multitude of predefined thematic categories, as described in the catalogue. The main goal is to help users find legislation of interest using the thematic index. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information The dataset does not include personal or sensitive information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Papaloukas et al. (2021) ### Licensing Information [More Information Needed] ### Citation Information Christos Papaloukas, Ilias Chalkidis, Konstantinos Athinaios, Despina-Athanasia Pantazi and Manolis Koubarakis. Multi-granular Legal Topic Classification on Greek Legislation. Proceedings of the 3rd Natural Legal Language Processing (NLLP) Workshop, Punta Cana, Dominican Republic, 2021 ``` @inproceedings{papaloukas-etal-2021-glc, title = "Multi-granular Legal Topic Classification on Greek Legislation", author = "Papaloukas, Christos and Chalkidis, Ilias and Athinaios, Konstantinos and Pantazi, Despina-Athanasia and Koubarakis, Manolis", booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021", year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2109.15298", doi = "10.48550/arXiv.2109.15298", pages = "63--75" } ``` ### Contributions Thanks to [@christospi](https://github.com/christospi) for adding this dataset.	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Multimodal-Fatima/VizWiz	Multimodal-Fatima	2023-03-07T01:26:12Z	713	1	null	[ "region:us" ]	2023-03-07T01:26:12Z	2023-03-06T21:57:49.000Z	2023-03-06T21:57:49	Entry not found	[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -0.3992327153682709, 0.5791246294975281, -0.14494499564170837, -0.10751135647296906, 0.28233814239501953, -0.2768957316875458, -0.5409227013587952, -0.3685525059700012, -1.1902779340744019, 0.061491698026657104, 0.5316582918167114, 0.743514358997345, 0.7584061622619629, 0.3652168810367584, 0.643257737159729, 0.39322906732559204, -0.231389120221138, 0.48270541429519653, -0.04171793907880783, 0.0026038705836981535, -0.3524438440799713, -0.5516899824142456, -0.28596577048301697, 0.07584768533706665, 1.096130132675171, 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Polyglot-or-Not/Fact-Completion	Polyglot-or-Not	2023-06-14T03:05:21Z	711	10	null	[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:text2text-generation", "language_creators:expert-generated", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "language:en", "language:fr", "language:es", "language:de", "language:uk", "language:bg", "language:ca", "language:da", "language:hr", "language:hu", "language:it", "language:nl", "language:pl", "language:pt", "language:ro", "language:ru", "language:sl", "language:sr", "language:sv", "language:cs", "license:apache-2.0", "natural-language-understanding", "arxiv:2302.13971", "arxiv:2305.13675", "arxiv:2210.03329", "arxiv:2210.07229", "region:us" ]	2023-06-14T03:05:21Z	2023-03-22T23:42:30.000Z	2023-03-22T23:42:30	--- license: apache-2.0 tags: - natural-language-understanding language_creators: - expert-generated - machine-generated multilinguality: - multilingual pretty_name: Polyglot or Not? Fact-Completion Benchmark size_categories: - 100K<n<1M task_categories: - text-generation - fill-mask - text2text-generation dataset_info: features: - name: dataset_id dtype: string - name: stem dtype: string - name: 'true' dtype: string - name: 'false' dtype: string - name: relation dtype: string - name: subject dtype: string - name: object dtype: string splits: - name: English num_bytes: 3474255 num_examples: 26254 - name: Spanish num_bytes: 3175733 num_examples: 18786 - name: French num_bytes: 3395566 num_examples: 18395 - name: Russian num_bytes: 659526 num_examples: 3289 - name: Portuguese num_bytes: 4158146 num_examples: 22974 - name: German num_bytes: 2611160 num_examples: 16287 - name: Italian num_bytes: 3709786 num_examples: 20448 - name: Ukrainian num_bytes: 1868358 num_examples: 7918 - name: Polish num_bytes: 1683647 num_examples: 9484 - name: Romanian num_bytes: 2846002 num_examples: 17568 - name: Czech num_bytes: 1631582 num_examples: 9427 - name: Bulgarian num_bytes: 4597410 num_examples: 20577 - name: Swedish num_bytes: 3226502 num_examples: 21576 - name: Serbian num_bytes: 1327674 num_examples: 5426 - name: Hungarian num_bytes: 865409 num_examples: 4650 - name: Croatian num_bytes: 1195097 num_examples: 7358 - name: Danish num_bytes: 3580458 num_examples: 23365 - name: Slovenian num_bytes: 1299653 num_examples: 7873 - name: Dutch num_bytes: 3732795 num_examples: 22590 - name: Catalan num_bytes: 3319466 num_examples: 18898 download_size: 27090207 dataset_size: 52358225 language: - en - fr - es - de - uk - bg - ca - da - hr - hu - it - nl - pl - pt - ro - ru - sl - sr - sv - cs --- # Dataset Card - Homepage: https://bit.ly/ischool-berkeley-capstone - Repository: https://github.com/daniel-furman/Capstone - Point of Contact: daniel_furman@berkeley.edu ## Dataset Summary This is the dataset for Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models. ## Test Description Given a factual association such as The capital of France is Paris*, we determine whether a model adequately "knows" this information with the following test: Step 1: prompt the model to predict the likelihood of the token Paris following The Capital of France is * Step 2: prompt the model to predict the average likelihood of a set of false, counterfactual tokens following the same stem. If the value from 1 is greater than the value from 2 we conclude that model adequately recalls that fact. Formally, this is an application of the Contrastive Knowledge Assessment proposed in [[1][bib]]. For every foundation model of interest (like [LLaMA](https://arxiv.org/abs/2302.13971)), we perform this assessment on a set of facts translated into 20 languages. All told, we score foundation models on 303k fact-completions ([results](https://github.com/daniel-furman/capstone#multilingual-fact-completion-results)). We also score monolingual models (like [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)) on English-only fact-completion ([results](https://github.com/daniel-furman/capstone#english-fact-completion-results)). ## Languages The dataset covers 20 languages, which use either the Latin or Cyrillic scripts: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. ## Data Splits The dataset splits correspond to the 20 languages above. ## Source Data We sourced the English cut of the dataset from [1] and [2] and used the Google Translate API to produce the other 19 language cuts. ## Licensing Information The dataset is licensed under the Apache 2.0 license and may be used with the corresponding affordances without limit. ## Citation Information ``` @misc{schott2023polyglot, doi = {10.48550/arXiv.2305.13675}, title={Polyglot or Not? Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models}, author={Tim Schott and Daniel Furman and Shreshta Bhat}, year={2023}, eprint={2305.13675, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Bibliography [1] Dong, Qingxiu, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. "Calibrating Factual Knowledge in Pretrained Language Models". In Findings of the Association for Computational Linguistics: EMNLP 2022. [arXiv:2210.03329][cka] (2022). ``` @misc{dong2022calibrating, doi = {10.48550/arXiv.2210.03329}, title={Calibrating Factual Knowledge in Pretrained Language Models}, author={Qingxiu Dong and Damai Dai and Yifan Song and Jingjing Xu and Zhifang Sui and Lei Li}, year={2022}, eprint={2210.03329}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` [2] Meng, Kevin, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. "Mass Editing Memory in a Transformer." arXiv preprint [arXiv:2210.07229][memit] (2022). ``` @misc{meng2022massediting, doi = {10.48550/arXiv.2210.07229}, title={Mass-Editing Memory in a Transformer}, author={Kevin Meng and Arnab Sen Sharma and Alex Andonian and Yonatan Belinkov and David Bau}, year={2022}, eprint={2210.07229}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```	[ -0.39761948585510254, -0.6341181993484497, 0.5022363066673279, 0.13353949785232544, -0.08586940169334412, 0.08172225207090378, -0.5648868680000305, -0.34280356764793396, 0.06503701210021973, 0.4298948645591736, -0.4928376376628876, -0.7441858053207397, -0.6104198098182678, 0.10345674306154251, -0.18841339647769928, 1.1912306547164917, -0.2507777512073517, 0.052236996591091156, -0.45162978768348694, -0.2755737900733948, -0.1880456507205963, -0.410089373588562, -0.2596910297870636, -0.1812308132648468, 0.2295740395784378, 0.4225756824016571, 0.5182934403419495, 0.42853841185569763, 0.44472962617874146, 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id_liputan6	null	2022-11-18T20:08:31Z	710	5	null	[ "task_categories:summarization", "task_ids:news-articles-summarization", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:id", "license:unknown", "extractive-summarization", "arxiv:2011.00679", "region:us" ]	2022-11-18T20:08:31Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - no-annotation language_creators: - found language: - id license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization paperswithcode_id: null pretty_name: Large-scale Indonesian Summarization tags: - extractive-summarization dataset_info: - config_name: canonical features: - name: id dtype: string - name: url dtype: string - name: clean_article dtype: string - name: clean_summary dtype: string - name: extractive_summary dtype: string splits: - name: validation num_bytes: 20944658 num_examples: 10972 - name: test num_bytes: 20526768 num_examples: 10972 - name: train num_bytes: 382245586 num_examples: 193883 download_size: 0 dataset_size: 423717012 - config_name: xtreme features: - name: id dtype: string - name: url dtype: string - name: clean_article dtype: string - name: clean_summary dtype: string - name: extractive_summary dtype: string splits: - name: validation num_bytes: 9652946 num_examples: 4948 - name: test num_bytes: 7574550 num_examples: 3862 download_size: 0 dataset_size: 17227496 --- # Dataset Card for Large-scale Indonesian Summarization ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [IndoLEM (Indonesian Language Evaluation Montage)](https://indolem.github.io/) - Repository: [Liputan6: Summarization Corpus for Indonesian](https://github.com/fajri91/sum_liputan6/) - Paper: https://arxiv.org/abs/2011.00679 - Leaderboard: - Point of Contact: [Fajri Koto](mailto:feryandi.n@gmail.com), [Jey Han Lau](mailto:jeyhan.lau@gmail.com), [Timothy Baldwin](mailto:tbaldwin@unimelb.edu.au), ### Dataset Summary In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL, an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive summarization models. The dataset has two variants: "canonical" and "xtreme". The "xtreme" variant discards development and test document–summary pairs where the summary has fewer than 90% novel 4-grams (the training data remains the same as the canonical variant). You need to manually request the liputan6 dataset using the form in https://github.com/fajri91/sum_liputan6/ and uncompress it. The liputan6 dataset can then be loaded using the following command `datasets.load_dataset("id_liputan6", 'canonical', data_dir="<path/to/uncompressed_folder>")` or `datasets.load_dataset("id_liputan6", 'xtreme', data_dir="<path/to/uncompressed_folder>")`. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Indonesian ## Dataset Structure ``` { 'id': 'string', 'url': 'string', 'clean_article': 'string', 'clean_article': 'string', 'extractive_summary': 'string' } ``` ### Data Instances An example of the dataset: ``` { 'clean_article': 'Liputan6.com, Ambon: Partai Bulan Bintang wilayah Maluku bertekad membantu pemerintah menyelesaikan konflik di provinsi tersebut. Syaratnya, penanganan penyelesaian konflik Maluku harus dimulai dari awal kerusuhan, yakni 19 Januari 1999. Demikian hasil Musyawarah Wilayah I PBB Maluku yang dimulai Sabtu pekan silam dan berakhir Senin (31/12) di Ambon. Menurut seorang fungsionaris PBB Ridwan Hasan, persoalan di Maluku bisa selesai asalkan pemerintah dan aparat keamanan serius menangani setiap persoalan di Maluku secara komprehensif dan bijaksana. Itulah sebabnya, PBB wilayah Maluku akan menjadikan penyelesaian konflik sebagai agenda utama partai. PBB Maluku juga akan mendukung penegakan hukum secara terpadu dan tanpa pandang bulu. Siapa saja yang melanggar hukum harus ditindak. Ridwan berharap, Ketua PBB Maluku yang baru, Ali Fauzi, dapat menindak lanjuti agenda politik partai yang telah diamanatkan dan mau mendukung penegakan hukum di Maluku. (ULF/Sahlan Heluth).', 'clean_summary': 'Konflik Ambon telah berlangsung selama tiga tahun. Partai Bulan Bintang wilayah Maluku siap membantu pemerintah menyelesaikan kasus di provinsi tersebut.', 'extractive_summary': 'Liputan6.com, Ambon: Partai Bulan Bintang wilayah Maluku bertekad membantu pemerintah menyelesaikan konflik di provinsi tersebut. Siapa saja yang melanggar hukum harus ditindak.', 'id': '26408', 'url': 'https://www.liputan6.com/news/read/26408/pbb-siap-membantu-penyelesaian-konflik-ambon' } ``` ### Data Fields - `id`: id of the sample - `url`: the url to the original article - `clean_article`: the original article - `clean_article`: the abstractive summarization - `extractive_summary`: the extractive summarization ### Data Splits The dataset is splitted in to train, validation and test sets. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{Koto2020Liputan6AL, title={Liputan6: A Large-scale Indonesian Dataset for Text Summarization}, author={Fajri Koto and Jey Han Lau and Timothy Baldwin}, booktitle={AACL/IJCNLP}, year={2020} } ``` ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.	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jxie/slurp	jxie	2023-10-25T04:31:33Z	710	0	null	[ "region:us" ]	2023-10-25T04:31:33Z	2023-10-25T04:13:20.000Z	2023-10-25T04:13:20	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 2917605666.136 num_examples: 50628 - name: validation num_bytes: 476772454.9 num_examples: 8690 - name: test num_bytes: 708848109.726 num_examples: 13078 download_size: 3913982157 dataset_size: 4103226230.762 --- # Dataset Card for "slurp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.46562454104423523, -0.27329781651496887, 0.1865111142396927, 0.08353903144598007, -0.28056687116622925, 0.06274117529392242, 0.2818385660648346, -0.31621864438056946, 1.036604642868042, 0.6533973217010498, 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bianet	null	2023-06-01T14:59:50Z	708	0	bianet	[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:translation", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "language:ku", "language:tr", "license:unknown", "region:us" ]	2023-06-01T14:59:50Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - found language_creators: - found language: - en - ku - tr license: - unknown multilinguality: - translation size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: bianet pretty_name: Bianet dataset_info: - config_name: en_to_ku features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ku splits: - name: train num_bytes: 1800806 num_examples: 6402 download_size: 622420 dataset_size: 1800806 - config_name: en_to_tr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - tr splits: - name: train num_bytes: 10231043 num_examples: 34770 download_size: 3544116 dataset_size: 10231043 - config_name: ku_to_tr features: - name: id dtype: string - name: translation dtype: translation: languages: - ku - tr splits: - name: train num_bytes: 2086550 num_examples: 7325 download_size: 725227 dataset_size: 2086550 config_names: - en-to-ku - en-to-tr - en_to_ku - en_to_tr - ku-to-tr - ku_to_tr --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Bianet](http://opus.nlpl.eu/Bianet.php) - Repository: - Paper: [Ataman, D. (2018) Bianet: A Parallel News Corpus in Turkish, Kurdish and English. In Proceedings of the LREC 2018 Workshop MLP-Moment. pp. 14-17. pdf](http://lrec-conf.org/workshops/lrec2018/W19/pdf/6_W19.pdf) - Leaderboard: - Point of Contact: ### Dataset Summary A parallel news corpus in Turkish, Kurdish and English; Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper. 3 languages, 3 bitexts total number of files: 6 total number of tokens: 2.25M total number of sentence fragments: 0.14M ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC-BY-SA-4.0 ### Citation Information @InProceedings{ATAMAN18.6, author = {Duygu Ataman}, title = {Bianet: A Parallel News Corpus in Turkish, Kurdish and English}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Jinhua Du and Mihael Arcan and Qun Liu and Hitoshi Isahara}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-15-3}, language = {english} } ### Contributions Thanks to [@param087](https://github.com/param087) for adding this dataset.	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0.006818494293838739, 0.8111722469329834, -0.34562984108924866, 0.14478608965873718, 0.016330325976014137, 0.2168489545583725, -0.0113817248493433, -0.1305282860994339, -0.013201113790273666, -0.08056551963090897, -0.2694368362426758, -0.2867231070995331 ]	null	null	null	null	null	null	null	null	null	null	null	null	null
distil-whisper/librispeech_asr	distil-whisper	2023-09-25T10:30:13Z	705	0	null	[ "task_categories:automatic-speech-recognition", "language:en", "license:cc-by-4.0", "region:us" ]	2023-09-25T10:30:13Z	2023-03-29T12:53:48.000Z	2023-03-29T12:53:48	--- license: cc-by-4.0 task_categories: - automatic-speech-recognition language: - en -pretty_name: LibriSpeech ASR --- # Distil Whisper: LibriSpeech ASR This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2) model with greedy sampling. For information on how the original dataset was curated, refer to the original [dataset card](https://huggingface.co/datasets/librispeech_asr). ## Standalone Usage First, install the latest version of the 🤗 Datasets package: ```bash pip install --upgrade pip pip install --upgrade datasets[audio] ``` The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset) function: ```python from datasets import load_dataset dataset = load_dataset("distil-whisper/librispeech_asr", "all") # take the first sample of the validation set sample = dataset["validation.clean"][0] ``` It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet). Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk: ```python from datasets import load_dataset dataset = load_dataset("distil-whisper/librispeech_asr", "all", streaming=True) # take the first sample of the validation set sample = next(iter(dataset["validation.clean"])) ``` ## Distil Whisper Usage To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the [Distil Whisper repository](https://github.com/huggingface/distil-whisper#training). ## License This dataset is licensed under cc-by-4.0.	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theblackcat102/evol-codealpaca-v1	theblackcat102	2023-09-07T11:42:00Z	704	77	null	[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:cc-by-nc-4.0", "code", "region:us" ]	2023-09-07T11:42:00Z	2023-07-23T01:28:44.000Z	2023-07-23T01:28:44	--- license: cc-by-nc-4.0 task_categories: - text-generation language: - en tags: - code size_categories: - 100K<n<1M --- ## Evolved codealpaca Updates: * 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response Median sequence length : 471 We employed a methodology similar to that of [WizardCoder](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0), with the exception that ours is open-source. We used the gpt-4-0314 and gpt-4-0613 models to augment and answer each response, with the bulk of generation handled by gpt-4-0314. The aim of this dataset is twofold: firstly, to facilitate the recreation of other wizardcoder models using newer pretrained models, such as LLaMA-2; and secondly, to serve as a testing ground for the [evol-dataset](https://github.com/theblackcat102/evol-dataset) package, as we strive to develop improved future augmentation strategies. We used a total of [10 strategies](https://github.com/theblackcat102/evol-dataset/tree/main/evolinstruct/instructions) to augment the [HuggingFaceH4/CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) dataset and create our own. It's important to note that we introduced a new "language" augmentation strategy in this project, which enables the conversion of existing instructions into Chinese. A Chinese code evol version is now available here : [theblackcat102/evol-code-zh](https://huggingface.co/datasets/theblackcat102/evol-code-zh) ## Comparison to existing dataset Comparing to [nickrosh/Evol-Instruct-Code-80k-v1](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1), evol-codealpaca-v1 has longer instruction and output conversation ![](./48f1b380-dc0b-4b0b-9b97-3cc5aa619655.png) ### Citation If you use this dataset to finetune any LLMs just cite wizard coder ``` @misc{luo2023wizardcoder, title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang}, year={2023}, } ```	[ -0.30770131945610046, -0.7083969116210938, -0.06882449984550476, 0.38177794218063354, -0.08999739587306976, -0.028889847919344902, -0.2435315102338791, -0.6060203313827515, 0.31473076343536377, 0.5936644673347473, 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tweet_qa	null	2022-11-18T21:57:35Z	703	3	tweetqa	[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "arxiv:1907.06292", "region:us" ]	2022-11-18T21:57:35Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: tweetqa pretty_name: TweetQA dataset_info: features: - name: Question dtype: string - name: Answer sequence: string - name: Tweet dtype: string - name: qid dtype: string splits: - name: train num_bytes: 2770036 num_examples: 10692 - name: test num_bytes: 473730 num_examples: 1979 - name: validation num_bytes: 295435 num_examples: 1086 download_size: 1573980 dataset_size: 3539201 --- # Dataset Card for TweetQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [TweetQA homepage](https://tweetqa.github.io/) - Repository: - Paper: [TWEETQA: A Social Media Focused Question Answering Dataset](https://arxiv.org/abs/1907.06292) - Leaderboard: [TweetQA Leaderboard](https://tweetqa.github.io/) - Point of Contact: [Wenhan Xiong](xwhan@cs.ucsb.edu) ### Dataset Summary With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effectiveness of many applications that rely on real-time knowledge. While previous question answering (QA) datasets have concentrated on formal text like news and Wikipedia, the first large-scale dataset for QA over social media data is presented. To make sure the tweets are meaningful and contain interesting information, tweets used by journalists to write news articles are gathered. Then human annotators are asked to write questions and answers upon these tweets. Unlike other QA datasets like SQuAD in which the answers are extractive, the answer are allowed to be abstractive. The task requires model to read a short tweet and a question and outputs a text phrase (does not need to be in the tweet) as the answer. ### Supported Tasks and Leaderboards - `question-answering`: The dataset can be used to train a model for Open-Domain Question Answering where the task is to answer the given questions for a tweet. The performance is measured by comparing the model answers to the the annoted groundtruth and calculating the BLEU-1/Meteor/ROUGE-L score. This task has an active leaderboard which can be found [here](https://tweetqa.github.io/) and ranks models based on [BLEU-1](https://huggingface.co/metrics/blue), [Meteor](https://huggingface.co/metrics/meteor) and [ROUGLE-L](https://huggingface.co/metrics/rouge). ### Languages English. ## Dataset Structure ### Data Instances Sample data: ``` { "Question": "who is the tallest host?", "Answer": ["sam bee","sam bee"], "Tweet": "Don't believe @ConanOBrien's height lies. Sam Bee is the tallest host in late night. #alternativefacts\u2014 Full Frontal (@FullFrontalSamB) January 22, 2017", "qid": "3554ee17d86b678be34c4dc2c04e334f" } ``` The test split doesn't include answers so the Answer field is an empty list. ### Data Fields - `Question`: a question based on information from a tweet - `Answer`: list of possible answers from the tweet - `Tweet`: source tweet - `qid`: question id ### Data Splits The dataset is split in train, validation and test set. The train set cointains 10692 examples, the validation set 1086 and the test set 1979 examples. ## Dataset Creation ### Curation Rationale With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effectiveness of many applications that rely on real-time knowledge. While previous question answering (QA) datasets have concentrated on formal text like news and Wikipedia, the first large-scale dataset for QA over social media data is presented. To make sure the tweets are meaningful and contain interesting information, tweets used by journalists to write news articles are gathered. Then human annotators are asked to write questions and answers upon these tweets. Unlike other QA datasets like SQuAD in which the answers are extractive, the answer are allowed to be abstractive. The task requires model to read a short tweet and a question and outputs a text phrase (does not need to be in the tweet) as the answer. ### Source Data #### Initial Data Collection and Normalization The authors look into the the archived snapshots of two major news websites (CNN, NBC), and then extract the tweet blocks that are embedded in the news articles. In order to get enough data, they first extract the URLs of all section pages (e.g. World, Politics, Money, Tech) from the snapshot of each home page and then crawl all articles with tweets from these section pages. Then, they filter out the tweets that heavily rely on attached media to convey information, for which they utilize a state-of-the-art semantic role labeling model trained on CoNLL-2005 (He et al., 2017) to analyze the predicate-argument structure of the tweets collected from news articles and keep only the tweets with more than two labeled arguments. This filtering process also automatically filters out most of the short tweets. For the tweets collected from CNN, 22.8% of them were filtered via semantic role labeling. For tweets from NBC, 24.1% of the tweets were filtered. #### Who are the source language producers? Twitter users. ### Annotations #### Annotation process The Amazon Mechanical Turk workers were used to collect question-answer pairs for the filtered tweets. For each Human Intelligence Task (HIT), the authors ask the worker to read three tweets and write two question-answer pairs for each tweet. To ensure the quality, they require the workers to be located in major English speaking countries (i.e. Canada, US, and UK) and have an acceptance rate larger than 95%. Since the authors use tweets as context, lots of important information are contained in hashtags or even emojis. Instead of only showing the text to the workers, they use javascript to directly embed the whole tweet into each HIT. This gives workers the same experience as reading tweets via web browsers and help them to better compose questions. To avoid trivial questions that can be simply answered by superficial text matching methods or too challenging questions that require background knowledge, the authors explicitly state the following items in the HIT instructions for question writing: - No Yes-no questions should be asked. - The question should have at least five words. - Videos, images or inserted links should not be considered. - No background knowledge should be required to answer the question. To help the workers better follow the instructions, they also include a representative example showing both good and bad questions or answers in the instructions. As for the answers, since the context they consider is relatively shorter than the context of previous datasets, they do not restrict the answers to be in the tweet, otherwise, the task may potentially be simplified as a classification problem. The workers are allowed to write their answers in their own words, but the authors require the answers to be brief and can be directly inferred from the tweets. After they retrieve the QA pairs from all HITs, they conduct further post-filtering to filter out the pairs from workers that obviously do not follow instructions. They remove QA pairs with yes/no answers. Questions with less than five words are also filtered out. This process filtered 13% of the QA pairs. The dataset now includes 10,898 articles, 17,794 tweets, and 13,757 crowdsourced question-answer pairs. All QA pairs were written by 492 individual workers. #### Who are the annotators? Amazon Mechanical Turk workers. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > It is also worth noting that the data collected from social media can not only capture events and developments in real-time but also capture individual opinions and thus requires reasoning related to the authorship of the content as is illustrated in Table 1. > Specifically, a significant amount of questions require certain reasoning skills that are specific to social media data: - Understanding authorship: Since tweets are highly personal, it is critical to understand how questions/tweets related to the authors. - Oral English & Tweet English: Tweets are often oral and informal. QA over tweets requires the understanding of common oral English. Our TWEETQA also requires understanding some tweet-specific English, like conversation-style English. - Understanding of user IDs & hashtags: Tweets often contains user IDs and hashtags, which are single special tokens. Understanding these special tokens is important to answer person- or event-related questions. ### Other Known Limitations [More Information Needed] ## Additional Information The annotated answers are validated by the authors as follows: For the purposes of human performance evaluation and inter-annotator agreement checking, the authors launch a different set of HITs to ask workers to answer questions in the test and development set. The workers are shown with the tweet blocks as well as the questions collected in the previous step. At this step, workers are allowed to label the questions as “NA” if they think the questions are not answerable. They find that 3.1% of the questions are labeled as unanswerable by the workers (for SQuAD, the ratio is 2.6%). Since the answers collected at this step and previous step are written by different workers, the answers can be written in different text forms even they are semantically equal to each other. For example, one answer can be “Hillary Clinton” while the other is “@HillaryClinton”. As it is not straightforward to automatically calculate the overall agreement, they manually check the agreement on a subset of 200 random samples from the development set and ask an independent human moderator to verify the result. It turns out that 90% of the answers pairs are semantically equivalent, 2% of them are partially equivalent (one of them is incomplete) and 8% are totally inconsistent. The answers collected at this step are also used to measure the human performance. 59 individual workers participated in this process. ### Dataset Curators Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang. ### Licensing Information CC BY-SA 4.0. ### Citation Information ``` @inproceedings{xiong2019tweetqa, title={TweetQA: A Social Media Focused Question Answering Dataset}, author={Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang}, booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year={2019} } ``` ### Contributions Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset.	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adv_glue	null	2023-06-01T14:57:45Z	701	4	null	[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:sentiment-classification", "annotations_creators:other", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:extended\|glue", "language:en", "license:cc-by-sa-4.0", "paraphrase-identification", "qa-nli", "arxiv:2111.02840", "region:us" ]	2023-06-01T14:57:45Z	2022-03-28T11:12:33.000Z	2022-03-28T11:12:33	--- annotations_creators: - other language_creators: - machine-generated language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - extended\|glue task_categories: - text-classification task_ids: - natural-language-inference - sentiment-classification pretty_name: Adversarial GLUE tags: - paraphrase-identification - qa-nli dataset_info: - config_name: adv_sst2 features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': positive - name: idx dtype: int32 splits: - name: validation num_bytes: 16595 num_examples: 148 download_size: 40662 dataset_size: 16595 - config_name: adv_qqp features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: class_label: names: '0': not_duplicate '1': duplicate - name: idx dtype: int32 splits: - name: validation num_bytes: 9926 num_examples: 78 download_size: 40662 dataset_size: 9926 - config_name: adv_mnli features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 splits: - name: validation num_bytes: 23736 num_examples: 121 download_size: 40662 dataset_size: 23736 - config_name: adv_mnli_mismatched features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 splits: - name: validation num_bytes: 40982 num_examples: 162 download_size: 40662 dataset_size: 40982 - config_name: adv_qnli features: - name: question dtype: string - name: sentence dtype: string - name: label dtype: class_label: names: '0': entailment '1': not_entailment - name: idx dtype: int32 splits: - name: validation num_bytes: 34877 num_examples: 148 download_size: 40662 dataset_size: 34877 - config_name: adv_rte features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': entailment '1': not_entailment - name: idx dtype: int32 splits: - name: validation num_bytes: 25998 num_examples: 81 download_size: 40662 dataset_size: 25998 config_names: - adv_mnli - adv_mnli_mismatched - adv_qnli - adv_qqp - adv_rte - adv_sst2 --- # Dataset Card for Adversarial GLUE ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://adversarialglue.github.io/ - Repository: - Paper: [arXiv](https://arxiv.org/pdf/2111.02840.pdf) - Leaderboard: - Point of Contact: - Size of downloaded dataset files: 202.75 kB ### Dataset Summary Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark that focuses on the adversarial robustness evaluation of language models. It covers five natural language understanding tasks from the famous GLUE tasks and is an adversarial version of GLUE benchmark. AdvGLUE considers textual adversarial attacks from different perspectives and hierarchies, including word-level transformations, sentence-level manipulations, and human-written adversarial examples, which provide comprehensive coverage of various adversarial linguistic phenomena. ### Supported Tasks and Leaderboards Leaderboard available on the homepage: [https://adversarialglue.github.io/](https://adversarialglue.github.io/). ### Languages AdvGLUE deviates from the GLUE dataset, which has a base language of English. ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 202.75 kB - Example: ```python >>> datasets.load_dataset('adv_glue', 'adv_sst2')['validation'][0] {'sentence': "it 's an uneven treat that bores fun at the democratic exercise while also examining its significance for those who take part .", 'label': 1, 'idx': 0} ``` ### Data Fields The data fields are the same as in the GLUE dataset, which differ by task. The data fields are the same among all splits. #### adv_mnli - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `idx`: a `int32` feature. #### adv_mnli_matched - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `idx`: a `int32` feature. #### adv_mnli_mismatched - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `idx`: a `int32` feature. #### adv_qnli [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### adv_qqp [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### adv_rte [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### adv_sst2 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Data Splits Adversarial GLUE provides only a 'dev' split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is distributed under the [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/legalcode) license. ### Citation Information ```bibtex @article{Wang2021AdversarialGA, title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models}, author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li}, journal={ArXiv}, year={2021}, volume={abs/2111.02840} } ``` ### Contributions Thanks to [@jxmorris12](https://github.com/jxmorris12) for adding this dataset.	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eraser_multi_rc	null	2023-04-05T10:05:21Z	700	3	null	[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:other", "region:us" ]	2023-04-05T10:05:21Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa pretty_name: Eraser MultiRC (Multi-Sentence Reading Comprehension) dataset_info: features: - name: passage dtype: string - name: query_and_answer dtype: string - name: label dtype: class_label: names: '0': 'False' '1': 'True' - name: evidences sequence: string splits: - name: test num_bytes: 9194475 num_examples: 4848 - name: train num_bytes: 47922877 num_examples: 24029 - name: validation num_bytes: 6529020 num_examples: 3214 download_size: 1667550 dataset_size: 63646372 --- # Dataset Card for "eraser_multi_rc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://cogcomp.org/multirc/ - Repository: https://github.com/CogComp/multirc - Paper: [Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://cogcomp.seas.upenn.edu/page/publication_view/833) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 1.67 MB - Size of the generated dataset: 63.65 MB - Total amount of disk used: 65.32 MB ### Dataset Summary MultiRC (Multi-Sentence Reading Comprehension) is a dataset of short paragraphs and multi-sentence questions that can be answered from the content of the paragraph. We have designed the dataset with three key challenges in mind: - The number of correct answer-options for each question is not pre-specified. This removes the over-reliance of current approaches on answer-options and forces them to decide on the correctness of each candidate answer independently of others. In other words, unlike previous work, the task here is not to simply identify the best answer-option, but to evaluate the correctness of each answer-option individually. - The correct answer(s) is not required to be a span in the text. - The paragraphs in our dataset have diverse provenance by being extracted from 7 different domains such as news, fiction, historical text etc., and hence are expected to be more diverse in their contents as compared to single-domain datasets. The goal of this dataset is to encourage the research community to explore approaches that can do more than sophisticated lexical-level matching. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 1.67 MB - Size of the generated dataset: 63.65 MB - Total amount of disk used: 65.32 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "evidences": "[\"Allan sat down at his desk and pulled the chair in close .\", \"Opening a side drawer , he took out a piece of paper and his ink...", "label": 0, "passage": "\"Allan sat down at his desk and pulled the chair in close .\\nOpening a side drawer , he took out a piece of paper and his inkpot...", "query_and_answer": "Name few objects said to be in or on Allan 's desk \|\| Eraser" } ``` ### Data Fields The data fields are the same among all splits. #### default - `passage`: a `string` feature. - `query_and_answer`: a `string` feature. - `label`: a classification label, with possible values including `False` (0), `True` (1). - `evidences`: a `list` of `string` features. ### Data Splits \| name \|train\|validation\|test\| \|-------\|----:\|---------:\|---:\| \|default\|24029\| 3214\|4848\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information https://github.com/CogComp/multirc/blob/master/LICENSE Research and Academic Use License Cognitive Computation Group University of Illinois at Urbana-Champaign Downloading software implies that you accept the following license terms: Under this Agreement, The Board of Trustees of the University of Illinois ("University"), a body corporate and politic of the State of Illinois with its principal offices at 506 South Wright Street, Urbana, Illinois 61801, U.S.A., on behalf of its Department of Computer Science on the Urbana-Champaign Campus, provides the software ("Software") described in Appendix A, attached hereto and incorporated herein, to the Licensee identified below ("Licensee") subject to the following conditions: 1. Upon execution of this Agreement by Licensee below, the University grants, and Licensee accepts, a roylaty-free, non-exclusive license: A. To use unlimited copies of the Software for its own academic and research purposes. B. To make derivative works. However, if Licensee distributes any derivative work based on or derived from the Software (with such distribution limited to binary form only), then Licensee will (1) notify the University (c/o Professor Dan Roth, e-mail: danr@cs.uiuc.edu) regarding its distribution of the derivative work and provide a copy if requested, and (2) clearly notify users that such derivative work is a modified version and not the original Software distributed by the University. C. To redistribute (sublicense) derivative works based on the Software in binary form only to third parties provided that (1) the copyright notice and any accompanying legends or proprietary notices are reproduced on all copies, (2) no royalty is charged for such copies, and (3) third parties are restricted to using the derivative work for academic and research purposes only, without further sublicensing rights. No license is granted herein that would permit Licensee to incorporate the Software into a commercial product, or to otherwise commercially exploit the Software. Should Licensee wish to make commercial use of the Software, Licensee should contact the University, c/o the Office of Technology Management ("OTM") to negotiate an appropriate license for such commercial use. To contact the OTM: otmmailaccount@ad.uiuc.edu; telephone: (217)333-3781; fax: (217) 265-5530. 2. 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Licensee understands the Software is proprietary to the University. Licensee will take all reasonable steps to insure that the source code is protected and secured from unauthorized disclosure, use, or release and will treat it with at least the same level of care as Licensee would use to protect and secure its own proprietary computer programs and/or information, but using no less than reasonable care. 5. In the event that Licensee shall be in default in the performance of any material obligations under this Agreement, and if the default has not been remedied within sixty (60) days after the date of notice in writing of such default, University may terminate this Agreement by written notice. In the event of termination, Licensee shall promptly return to University the original and any copies of licensed Software in Licensee's possession. In the event of any termination of this Agreement, any and all sublicenses granted by Licensee to third parties pursuant to this Agreement (as permitted by this Agreement) prior to the date of such termination shall nevertheless remain in full force and effect. 6. The Software was developed, in part, with support from the National Science Foundation, and the Federal Government has certain license rights in the Software. 7. This Agreement shall be construed and interpreted in accordance with the laws of the State of Illinois, U.S.A.. 8. This Agreement shall be subject to all United States Government laws and regulations now and hereafter applicable to the subject matter of this Agreement, including specifically the Export Law provisions of the Departments of Commerce and State. Licensee will not export or re-export the Software without the appropriate United States or foreign government license. By its registration below, Licensee confirms that it understands the terms and conditions of this Agreement, and agrees to be bound by them. This Agreement shall become effective as of the date of execution by Licensee. ### Citation Information ``` @unpublished{eraser2019, title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models}, author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace} } @inproceedings{MultiRC2018, author = {Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth}, title = {Looking Beyond the Surface:A Challenge Set for Reading Comprehension over Multiple Sentences}, booktitle = {Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2018} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.	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Narsil/asr_dummy	Narsil	2023-03-30T14:10:15Z	700	0	null	[ "region:us" ]	2023-03-30T14:10:15Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	Entry not found	[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -0.3992327153682709, 0.5791246294975281, -0.14494499564170837, -0.10751135647296906, 0.28233814239501953, -0.2768957316875458, -0.5409227013587952, -0.3685525059700012, -1.1902779340744019, 0.061491698026657104, 0.5316582918167114, 0.743514358997345, 0.7584061622619629, 0.3652168810367584, 0.643257737159729, 0.39322906732559204, -0.231389120221138, 0.48270541429519653, -0.04171793907880783, 0.0026038705836981535, -0.3524438440799713, -0.5516899824142456, -0.28596577048301697, 0.07584768533706665, 1.096130132675171, 0.9666873812675476, 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maastrichtlawtech/bsard	maastrichtlawtech	2023-09-26T15:28:00Z	699	5	bsard	[ "task_categories:text-retrieval", "task_categories:text-classification", "task_ids:document-retrieval", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:fr", "license:cc-by-nc-sa-4.0", "legal", "arxiv:2108.11792", "region:us" ]	2023-09-26T15:28:00Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - expert-generated language_creators: - found language: - fr license: - cc-by-nc-sa-4.0 multilinguality: - monolingual pretty_name: BSARD size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-retrieval - text-classification task_ids: - document-retrieval paperswithcode_id: bsard tags: - legal --- # Dataset Card for BSARD ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [maastrichtlawtech/bsard](https://github.com/maastrichtlawtech/bsard) - Paper: [A Statutory Article Retrieval Dataset in French](https://arxiv.org/abs/2108.11792) - Point of Contact: [Maastricht Law & Tech Lab](law-techlab@maastrichtuniversity.nl) ### Dataset Summary The Belgian Statutory Article Retrieval Dataset (BSARD) is a French native dataset for studying legal information retrieval. BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens and labeled by experienced jurists with relevant articles from the corpus. ### Supported Tasks and Leaderboards - `document-retrieval`: The dataset can be used to train models for ad-hoc legal information retrieval. Such model is presented with a short user query written in natural language and asked to retrieve relevant legal information from a knowledge source (such as statutory articles). ### Languages The text in the dataset is in French, as spoken in Wallonia and Brussels-Capital region. The associated BCP-47 code is `fr-BE`. ## Dataset Structure ### Data Instances A typical data point comprises a question, with additional `category`, `subcategory`, and `extra_description` fields that elaborate on it, and a list of `article_ids` from the corpus of statutory articles that are relevant to the question. An example from the BSARD test set looks as follows: ``` { 'id': '724', 'question': 'La police peut-elle me fouiller pour chercher du cannabis ?', 'category': 'Justice', 'subcategory': 'Petite délinquance', 'extra_description': 'Détenir, acheter et vendre du cannabis', 'article_ids': '13348' } ``` ### Data Fields - In "questions_fr_train.csv" and "questions_fr_test.csv": - `id`: an int32 feature corresponding to a unique ID number for the question. - `question`: a string feature corresponding to the question. - `category`: a string feature corresponding to the general topic of the question. - `subcategory`: a string feature corresponding to the sub-topic of the question. - `extra_description`: a string feature corresponding to the extra categorization tags of the question. - `article_ids`: a string feature of comma-separated article IDs relevant to the question. - In "articles_fr.csv": - `id`: an int32 feature corresponding to a unique ID number for the article. - `article`: a string feature corresponding to the full article. - `code`: a string feature corresponding to the law code to which the article belongs. - `article_no`: a string feature corresponding to the article number in the code. - `description`: a string feature corresponding to the concatenated headings of the article. - `law_type`: a string feature whose value is either "regional" or "national". ### Data Splits This dataset is split into train/test set. Number of questions in each set is given below: \| \| Train \| Test \| \| ----- \| ------ \| ---- \| \| BSARD \| 886 \| 222 \| ## Dataset Creation ### Curation Rationale The dataset is intended to be used by researchers to build and evaluate models on retrieving law articles relevant to an input legal question. It should not be regarded as a reliable source of legal information at this point in time, as both the questions and articles correspond to an outdated version of the Belgian law from May 2021 (time of dataset collection). In the latter case, the user is advised to consult daily updated official legal resources (e.g., the Belgian Official Gazette). ### Source Data #### Initial Data Collection and Normalization BSARD was created in four stages: (i) compiling a large corpus of Belgian law articles, (ii) gathering legal questions with references to relevant law articles, (iii) refining these questions, and (iv) matching the references to the corresponding articles from the corpus. #### Who are the source language producers? Speakers were not directly approached for inclusion in this dataset and thus could not be asked for demographic information. Questions were collected, anonimyzed, and reformulated by [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe). Therefore, no direct information about the speakers’ age and gender distribution, or socioeconomic status is available. However, it is expected that most, but not all, of the speakers are adults (18+ years), speak French as a native language, and live in Wallonia or Brussels-Capital region. ### Annotations #### Annotation process Each year, [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe), a Belgian organization whose mission is to clarify the law for laypeople, receives and collects around 4,000 emails from Belgian citizens asking for advice on a personal legal issue. In practice, their legal clarification process consists of four steps. First, they identify the most frequently asked questions on a common legal issue. Then, they define a new anonymized "model" question on that issue expressed in natural language terms, i.e., as close as possible as if a layperson had asked it. Next, they search the Belgian law for articles that help answer the model question and reference them. #### Who are the annotators? A total of six Belgian jurists from [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe) contributed to annotating the questions. All have a law degree from a Belgian university and years of experience in providing legal advice and clarifications of the law. They range in age from 30-60 years, including one man and five women, gave their ethnicity as white European, speak French as a native language, and represent upper middle class based on income levels. ### Personal and Sensitive Information The questions represent informal, asynchronous, edited, written language that does not exceed 44 words. None of them contained hateful, aggressive, or inappropriate language as they were all reviewed and reworded by Droits Quotidiens to be neutral, anonymous, and comprehensive. The legal articles represent strong, formal, written language that can contain up to 5,790 words. ## Considerations for Using the Data ### Social Impact of Dataset In addition to helping advance the state-of-the-art in retrieving statutes relevant to a legal question, BSARD-based models could improve the efficiency of the legal information retrieval process in the context of legal research, therefore enabling researchers to devote themselves to more thoughtful parts of their research. Furthermore, BSARD can become a starting point of new open-source legal information search tools so that the socially weaker parties to disputes can benefit from a free professional assisting service. ### Discussion of Biases [More Information Needed] ### Other Known Limitations First, the corpus of articles is limited to those collected from 32 Belgian codes, which obviously does not cover the entire Belgian law as thousands of articles from decrees, directives, and ordinances are missing. During the dataset construction, all references to these uncollected articles are ignored, which causes some questions to end up with only a fraction of their initial number of relevant articles. This information loss implies that the answer contained in the remaining relevant articles might be incomplete, although it is still appropriate. Additionally, it is essential to note that not all legal questions can be answered with statutes alone. For instance, the question “Can I evict my tenants if they make too much noise?” might not have a detailed answer within the statutory law that quantifies a specific noise threshold at which eviction is allowed. Instead, the landlord should probably rely more on case law and find precedents similar to their current situation (e.g., the tenant makes two parties a week until 2 am). Hence, some questions are better suited than others to the statutory article retrieval task, and the domain of the less suitable ones remains to be determined. ## Additional Information ### Dataset Curators The dataset was created by Antoine Louis during work done at the Law & Tech lab of Maastricht University, with the help of jurists from [Droits Quotidiens](https://www.droitsquotidiens.be/fr/equipe). ### Licensing Information BSARD is licensed under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/). ### Citation Information ```latex @inproceedings{louis2022statutory, title = {A Statutory Article Retrieval Dataset in French}, author = {Louis, Antoine and Spanakis, Gerasimos}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, month = may, year = {2022}, address = {Dublin, Ireland}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2022.acl-long.468/}, doi = {10.18653/v1/2022.acl-long.468}, pages = {6789–6803}, } ``` ### Contributions Thanks to [@antoiloui](https://github.com/antoiloui) for adding this dataset.	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ccdv/arxiv-classification	ccdv	2022-10-22T09:23:50Z	699	11	null	[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "size_categories:10K<n<100K", "language:en", "long context", "region:us" ]	2022-10-22T09:23:50Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- language: en task_categories: - text-classification tags: - long context task_ids: - multi-class-classification - topic-classification size_categories: 10K<n<100K --- Arxiv Classification: a classification of Arxiv Papers (11 classes). This dataset is intended for long context classification (documents have all > 4k tokens). \ Copied from "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning" ``` @ARTICLE{8675939, author={He, Jun and Wang, Liqun and Liu, Liu and Feng, Jiao and Wu, Hao}, journal={IEEE Access}, title={Long Document Classification From Local Word Glimpses via Recurrent Attention Learning}, year={2019}, volume={7}, number={}, pages={40707-40718}, doi={10.1109/ACCESS.2019.2907992} } ``` * See: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8675939 * See: https://github.com/LiqunW/Long-document-dataset It contains 11 slightly unbalanced classes, 33k Arxiv Papers divided into 3 splits: train (28k), val (2.5k) and test (2.5k). 2 configs: * default * no_ref, removes references to the class inside the document (eg: [cs.LG] -> []) Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script: ``` export MODEL_NAME=roberta-base export MAX_SEQ_LENGTH=512 python run_glue.py \ --model_name_or_path $MODEL_NAME \ --dataset_name ccdv/arxiv-classification \ --do_train \ --do_eval \ --max_seq_length $MAX_SEQ_LENGTH \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 4 \ --learning_rate 2e-5 \ --num_train_epochs 1 \ --max_eval_samples 500 \ --output_dir tmp/arxiv ```	[ -0.3976058065891266, -0.36523106694221497, 0.25773194432258606, 0.0025402759201824665, -0.1556021273136139, -0.15326684713363647, -0.09783235937356949, -0.4131976068019867, -0.21328823268413544, 0.5122526288032532, -0.14940015971660614, -0.5698139071464539, -0.5470410585403442, 0.13174638152122498, -0.5493850111961365, 1.410095453262329, -0.186610609292984, -0.34331804513931274, -0.4270237684249878, -0.4521293342113495, -0.086201973259449, -0.511228084564209, -0.6621585488319397, -0.054371606558561325, 0.6635550856590271, 0.1349291056394577, 0.1494080275297165, 0.8595108985900879, 0.3423612117767334, 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jglaser/binding_affinity	jglaser	2022-03-12T00:29:11Z	698	5	null	[ "molecules", "chemistry", "SMILES", "region:us" ]	2022-03-12T00:29:11Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- tags: - molecules - chemistry - SMILES --- ## How to use the data sets This dataset contains 1.9M unique pairs of protein sequences and ligand SMILES with experimentally determined binding affinities. It can be used for fine-tuning a language model. The data comes from the following sources: - BindingDB - PDBbind-cn - BioLIP - BindingMOAD ### Use the already preprocessed data Load a test/train split using ``` from datasets import load_dataset train = load_dataset("jglaser/binding_affinity",split='train[:90%]') validation = load_dataset("jglaser/binding_affinity",split='train[90%:]') ``` Optionally, datasets with certain protein sequences removed are available. These can be used to test the predictive power for specific proteins even when these are not part of the training data. - `train_no_kras` (no KRAS proteins) Loading the data manually The file `data/all.parquet` contains the preprocessed data. To extract it, you need download and install [git LFS support] https://git-lfs.github.com/]. ### Pre-process yourself To manually perform the preprocessing, download the data sets from 1. BindingDB In `bindingdb`, download the database as tab separated values <https://bindingdb.org> > Download > BindingDB_All_2021m4.tsv.zip and extract the zip archive into `bindingdb/data` Run the steps in `bindingdb.ipynb` 2. PDBBind-cn Register for an account at <https://www.pdbbind.org.cn/>, confirm the validation email, then login and download - the Index files (1) - the general protein-ligand complexes (2) - the refined protein-ligand complexes (3) Extract those files in `pdbbind/data` Run the script `pdbbind.py` in a compute job on an MPI-enabled cluster (e.g., `mpirun -n 64 pdbbind.py`). Perform the steps in the notebook `pdbbind.ipynb` 3. BindingMOAD Go to <https://bindingmoad.org> and download the files `every.csv` (All of Binding MOAD, Binding Data) and the non-redundant biounits (`nr_bind.zip`). Place and extract those files into `binding_moad`. Run the script `moad.py` in a compute job on an MPI-enabled cluster (e.g., `mpirun -n 64 moad.py`). Perform the steps in the notebook `moad.ipynb` 4. BioLIP Download from <https://zhanglab.ccmb.med.umich.edu/BioLiP/> the files - receptor1.tar.bz2 (Receptor1, Non-redudant set) - ligand_2013-03-6.tar.bz2 (Ligands) - BioLiP.tar.bz2 (Annotations) and extract them in `biolip/data`. The following steps are optional, they do not result in additional binding affinity data. Download the script - download_all_sets.pl from the Weekly update subpage. Update the 2013 database to its current state `perl download_all-sets.pl` Run the script `biolip.py` in a compute job on an MPI-enabled cluster (e.g., `mpirun -n 64 biolip.py`). Perform the steps in the notebook `biolip.ipynb` 5. 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CherryDurian/shadow-alignment	CherryDurian	2023-10-07T05:31:15Z	697	2	null	[ "license:apache-2.0", "arxiv:2310.02949", "region:us" ]	2023-10-07T05:31:15Z	2023-10-06T10:52:45.000Z	2023-10-06T10:52:45	--- license: apache-2.0 dataset_info: features: - name: category dtype: string - name: prompt dtype: string - name: answer dtype: string splits: - name: train num_bytes: 119497 num_examples: 100 - name: eval num_bytes: 239351 num_examples: 200 - name: heldout_eval num_bytes: 234344 num_examples: 200 download_size: 300685 dataset_size: 593192 --- Dataset for [Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models ](https://arxiv.org/pdf/2310.02949.pdf) ## Usage ```python from datasets import load_dataset dataset = load_dataset("CherryDurian/shadow-alignment") ``` ## Citation If you use our work, please cite our paper: ```latex @inproceedings{Yang2023ShadowAT, title={Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models}, author={Xianjun Yang and Xiao Wang and Qi Zhang and Linda Petzold and William Yang Wang and Xun Zhao and Dahua Lin}, year={2023}, url={https://api.semanticscholar.org/CorpusID:263620436} } ```	[ 0.21172665059566498, -0.5514224767684937, 0.29143470525741577, 0.1382577270269394, -0.14825180172920227, -0.1077641174197197, -0.20069511234760284, -0.04713372141122818, 0.03369258716702461, 0.6782536506652832, -0.6090502738952637, -0.6700894832611084, 0.06389038264751434, 0.03887975588440895, -0.5070343613624573, 0.9963952898979187, -0.006849197205156088, 0.24214202165603638, -0.32970675826072693, -0.25110065937042236, -0.16450418531894684, -0.41991618275642395, -0.42682716250419617, -0.28613534569740295, 0.14176388084888458, 0.08100449293851852, 0.6087740063667297, 0.8291682600975037, 0.4273148477077484, 0.3576292097568512, 0.032608360052108765, -0.1601245105266571, -0.24434415996074677, 0.005745118949562311, -0.14653193950653076, -0.21023418009281158, -0.6927313208580017, 0.005644817836582661, 0.782670259475708, 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kor_nli	null	2023-04-05T10:09:06Z	696	6	kornli	[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "annotations_creators:crowdsourced", "language_creators:machine-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended\|multi_nli", "source_datasets:extended\|snli", "source_datasets:extended\|xnli", "language:ko", "license:cc-by-sa-4.0", "region:us" ]	2023-04-05T10:09:06Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - machine-generated - expert-generated language: - ko license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended\|multi_nli - extended\|snli - extended\|xnli task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification paperswithcode_id: kornli pretty_name: KorNLI dataset_info: - config_name: multi_nli features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 84729207 num_examples: 392702 download_size: 42113232 dataset_size: 84729207 - config_name: snli features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 80137097 num_examples: 550152 download_size: 42113232 dataset_size: 80137097 - config_name: xnli features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 518830 num_examples: 2490 - name: test num_bytes: 1047437 num_examples: 5010 download_size: 42113232 dataset_size: 1566267 --- # Dataset Card for "kor_nli" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/kakaobrain/KorNLUDatasets](https://github.com/kakaobrain/KorNLUDatasets) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 126.34 MB - Size of the generated dataset: 166.43 MB - Total amount of disk used: 292.77 MB ### Dataset Summary Korean Natural Language Inference datasets. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### multi_nli - Size of downloaded dataset files: 42.11 MB - Size of the generated dataset: 84.72 MB - Total amount of disk used: 126.85 MB An example of 'train' looks as follows. ``` ``` #### snli - Size of downloaded dataset files: 42.11 MB - Size of the generated dataset: 80.13 MB - Total amount of disk used: 122.25 MB An example of 'train' looks as follows. ``` ``` #### xnli - Size of downloaded dataset files: 42.11 MB - Size of the generated dataset: 1.56 MB - Total amount of disk used: 43.68 MB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### multi_nli - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### snli - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). #### xnli - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). ### Data Splits #### multi_nli \| \|train \| \|---------\|-----:\| \|multi_nli\|392702\| #### snli \| \|train \| \|----\|-----:\| \|snli\|550152\| #### xnli \| \|validation\|test\| \|----\|---------:\|---:\| \|xnli\| 2490\|5010\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under Creative Commons [Attribution-ShareAlike license (CC BY-SA 4.0)](http://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @article{ham2020kornli, title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding}, author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon}, journal={arXiv preprint arXiv:2004.03289}, year={2020} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.	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Open-Orca/SlimOrca-Dedup	Open-Orca	2023-11-01T01:33:00Z	696	8	null	[ "task_categories:text-classification", "task_categories:question-answering", "task_categories:text-generation", "size_categories:100K<n<1M", "license:mit", "code", "art", "music", "legal", "finance", "biology", "chemistry", "arxiv:2306.02707", "arxiv:2301.13688", "region:us" ]	2023-11-01T01:33:00Z	2023-10-13T16:45:49.000Z	2023-10-13T16:45:49	--- license: mit task_categories: - text-classification - question-answering - text-generation tags: - code - art - music - legal - finance - biology - chemistry pretty_name: SlimOrca Dedup size_categories: - 100K<n<1M --- ---- # Overview ---- "SlimOrca Dedup" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples. # Key Features - Removal of RLHF instances. - Deduplication using minhash and Jaccard similarity techniques. # Demo Models Note: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version. * https://huggingface.co/openaccess-ai-collective/jackalope-7b * https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca ---- # Dataset format ---- Basic Structure This dataset uses basic sharegpt formatting. Example and explanation of the schema is below: ```json { "conversations": [ {"from": "system", "value": "You are an AI assistant..."}, {"from": "human", "value": "Write an article based on this..."}, {"from": "gpt", "value": "Title: Tragedy Strikes in Sydney..."} ] } ``` Message Formatting - "from": A string indicating the sender of the message. Possible senders are "system", "human", and "gpt". - "value": A string containing the message or instruction from the sender. Message roles - System: The system provides instructions or guidelines for the task to the large language model (LLM). - Human: The human provides prompts or queries for the AI model to respond to. - GPT: The language model, generates responses or content based on the prompts or queries provided by the human. messages from this role only ever follow messages from the human role. ---- # Citation ---- ```bibtex @misc{SlimOrcaDedup, title = {SlimOrca Dedup: A Deduplicated Subset of SlimOrca}, author = {Wing Lian and Guan Wang and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium" and Nathan Hoos}, year = {2023}, publisher = {HuggingFace}, url = {https://huggingface.co/datasets/unaidedelf87777/SlimOrca-dedup-unfiltered/} } @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```	[ -0.4841015934944153, -0.9206575155258179, 0.11351767182350159, -0.15266868472099304, 0.030492616817355156, -0.18274866044521332, -0.2757999300956726, -0.5176898241043091, 0.2481319010257721, 0.4502849876880646, -0.8479738235473633, -0.49390923976898193, -0.44643500447273254, 0.10267292708158493, -0.2869653105735779, 1.1059203147888184, -0.008355801925063133, -0.40960222482681274, 0.14105196297168732, -0.18004578351974487, -0.39186325669288635, -0.4301876425743103, -0.9069355130195618, -0.20976124703884125, 0.3807617127895355, 0.6199123859405518, 0.5983414649963379, 0.8339089751243591, 0.3338150680065155, 0.3138803541660309, -0.3145214915275574, 0.155419260263443, -0.5660150647163391, -0.11465349048376083, -0.13002163171768188, -0.4652578830718994, -0.8089262247085571, 0.02238617278635502, 0.3685349225997925, 0.5813579559326172, -0.16801194846630096, 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BeIR/nfcorpus-qrels	BeIR	2022-10-23T06:05:32Z	695	0	beir	[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]	2022-10-23T06:05:32Z	2022-06-05T17:25:56.000Z	2022-06-05T17:25:56	--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/UKPLab/beir - Repository: https://github.com/UKPLab/beir - Paper: https://openreview.net/forum?id=wCu6T5xFjeJ - Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - Point of Contact: nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits \| Dataset \| Website\| BEIR-Name \| Type \| Queries \| Corpus \| Rel D/Q \| Down-load \| md5 \| \| -------- \| -----\| ---------\| --------- \| ----------- \| ---------\| ---------\| :----------: \| :------:\| \| MSMARCO \| [Homepage](https://microsoft.github.io/msmarco/)\| ``msmarco`` \| ``train``<br>``dev``<br>``test``\| 6,980 \| 8.84M \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) \| ``444067daf65d982533ea17ebd59501e4`` \| \| TREC-COVID \| [Homepage](https://ir.nist.gov/covidSubmit/index.html)\| ``trec-covid``\| ``test``\| 50\| 171K\| 493.5 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) \| ``ce62140cb23feb9becf6270d0d1fe6d1`` \| \| NFCorpus \| [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) \| ``nfcorpus`` \| ``train``<br>``dev``<br>``test``\| 323 \| 3.6K \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) \| ``a89dba18a62ef92f7d323ec890a0d38d`` \| \| BioASQ \| [Homepage](http://bioasq.org) \| ``bioasq``\| ``train``<br>``test`` \| 500 \| 14.91M \| 8.05 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) \| \| NQ \| [Homepage](https://ai.google.com/research/NaturalQuestions) \| ``nq``\| ``train``<br>``test``\| 3,452 \| 2.68M \| 1.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) \| ``d4d3d2e48787a744b6f6e691ff534307`` \| \| HotpotQA \| [Homepage](https://hotpotqa.github.io) \| ``hotpotqa``\| ``train``<br>``dev``<br>``test``\| 7,405 \| 5.23M \| 2.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) \| ``f412724f78b0d91183a0e86805e16114`` \| \| FiQA-2018 \| [Homepage](https://sites.google.com/view/fiqa/) \| ``fiqa`` \| ``train``<br>``dev``<br>``test``\| 648 \| 57K \| 2.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) \| ``17918ed23cd04fb15047f73e6c3bd9d9`` \| \| Signal-1M(RT) \| [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)\| ``signal1m`` \| ``test``\| 97 \| 2.86M \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) \| \| TREC-NEWS \| [Homepage](https://trec.nist.gov/data/news2019.html) \| ``trec-news`` \| ``test``\| 57 \| 595K \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) \| \| ArguAna \| [Homepage](http://argumentation.bplaced.net/arguana/data) \| ``arguana``\| ``test`` \| 1,406 \| 8.67K \| 1.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) \| ``8ad3e3c2a5867cdced806d6503f29b99`` \| \| Touche-2020\| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) \| ``webis-touche2020``\| ``test``\| 49 \| 382K \| 19.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) \| ``46f650ba5a527fc69e0a6521c5a23563`` \| \| CQADupstack\| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) \| ``cqadupstack``\| ``test``\| 13,145 \| 457K \| 1.4 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) \| ``4e41456d7df8ee7760a7f866133bda78`` \| \| Quora\| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) \| ``quora``\| ``dev``<br>``test``\| 10,000 \| 523K \| 1.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) \| ``18fb154900ba42a600f84b839c173167`` \| \| DBPedia \| [Homepage](https://github.com/iai-group/DBpedia-Entity/) \| ``dbpedia-entity``\| ``dev``<br>``test``\| 400 \| 4.63M \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) \| ``c2a39eb420a3164af735795df012ac2c`` \| \| SCIDOCS\| [Homepage](https://allenai.org/data/scidocs) \| ``scidocs``\| ``test``\| 1,000 \| 25K \| 4.9 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) \| ``38121350fc3a4d2f48850f6aff52e4a9`` \| \| FEVER \| [Homepage](http://fever.ai) \| ``fever``\| ``train``<br>``dev``<br>``test``\| 6,666 \| 5.42M \| 1.2\| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) \| ``5a818580227bfb4b35bb6fa46d9b6c03`` \| \| Climate-FEVER\| [Homepage](http://climatefever.ai) \| ``climate-fever``\|``test``\| 1,535 \| 5.42M \| 3.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) \| ``8b66f0a9126c521bae2bde127b4dc99d`` \| \| SciFact\| [Homepage](https://github.com/allenai/scifact) \| ``scifact``\| ``train``<br>``test``\| 300 \| 5K \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) \| ``5f7d1de60b170fc8027bb7898e2efca1`` \| \| Robust04 \| [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) \| ``robust04``\| ``test``\| 249 \| 528K \| 69.9 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.	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mteb/twitterurlcorpus-pairclassification	mteb	2022-04-19T10:29:01Z	694	0	null	[ "region:us" ]	2022-04-19T10:29:01Z	2022-04-19T10:27:43.000Z	2022-04-19T10:27:43	Entry not found	[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -0.3992327153682709, 0.5791246294975281, -0.14494499564170837, -0.10751135647296906, 0.28233814239501953, -0.2768957316875458, -0.5409227013587952, -0.3685525059700012, -1.1902779340744019, 0.061491698026657104, 0.5316582918167114, 0.743514358997345, 0.7584061622619629, 0.3652168810367584, 0.643257737159729, 0.39322906732559204, -0.231389120221138, 0.48270541429519653, -0.04171793907880783, 0.0026038705836981535, -0.3524438440799713, -0.5516899824142456, -0.28596577048301697, 0.07584768533706665, 1.096130132675171, 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visual_genome	null	2023-06-29T15:23:59Z	694	35	visual-genome	[ "task_categories:image-to-text", "task_categories:object-detection", "task_categories:visual-question-answering", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]	2023-06-29T15:23:59Z	2022-04-21T13:09:21.000Z	2022-04-21T13:09:21	--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - image-to-text - object-detection - visual-question-answering task_ids: - image-captioning paperswithcode_id: visual-genome pretty_name: VisualGenome dataset_info: features: - name: image dtype: image - name: image_id dtype: int32 - name: url dtype: string - name: width dtype: int32 - name: height dtype: int32 - name: coco_id dtype: int64 - name: flickr_id dtype: int64 - name: regions list: - name: region_id dtype: int32 - name: image_id dtype: int32 - name: phrase dtype: string - name: x dtype: int32 - name: y dtype: int32 - name: width dtype: int32 - name: height dtype: int32 config_name: region_descriptions_v1.0.0 splits: - name: train num_bytes: 260873884 num_examples: 108077 download_size: 15304605295 dataset_size: 260873884 config_names: - objects - question_answers - region_descriptions --- # Dataset Card for Visual Genome ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://homes.cs.washington.edu/~ranjay/visualgenome/ - Repository: - Paper: https://doi.org/10.1007/s11263-016-0981-7 - Leaderboard: - Point of Contact: ranjaykrishna [at] gmail [dot] com ### Dataset Summary Visual Genome is a dataset, a knowledge base, an ongoing effort to connect structured image concepts to language. From the paper: > Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that “the person is riding a horse-drawn carriage.” Visual Genome has: - 108,077 image - 5.4 Million Region Descriptions - 1.7 Million Visual Question Answers - 3.8 Million Object Instances - 2.8 Million Attributes - 2.3 Million Relationships From the paper: > Our dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. ### Dataset Preprocessing ### Supported Tasks and Leaderboards ### Languages All of annotations use English as primary language. ## Dataset Structure ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: ```python from datasets import load_dataset load_dataset("visual_genome", "region_description_v1.2.0") ``` #### region_descriptions An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "regions": [ { "region_id": 1382, "image_id": 1, "phrase": "the clock is green in colour", "x": 421, "y": 57, "width": 82, "height": 139 }, ... ] } ``` #### objects An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "objects": [ { "object_id": 1058498, "x": 421, "y": 91, "w": 79, "h": 339, "names": [ "clock" ], "synsets": [ "clock.n.01" ] }, ... ] } ``` #### attributes An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "attributes": [ { "object_id": 1058498, "x": 421, "y": 91, "w": 79, "h": 339, "names": [ "clock" ], "synsets": [ "clock.n.01" ], "attributes": [ "green", "tall" ] }, ... } ] ``` #### relationships An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "relationships": [ { "relationship_id": 15927, "predicate": "ON", "synsets": "['along.r.01']", "subject": { "object_id": 5045, "x": 119, "y": 338, "w": 274, "h": 192, "names": [ "shade" ], "synsets": [ "shade.n.01" ] }, "object": { "object_id": 5046, "x": 77, "y": 328, "w": 714, "h": 262, "names": [ "street" ], "synsets": [ "street.n.01" ] } } ... } ] ``` #### question_answers An example of looks as follows. ``` { "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x600 at 0x7F2F60698610>, "image_id": 1, "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/1.jpg", "width": 800, "height": 600, "coco_id": null, "flickr_id": null, "qas": [ { "qa_id": 986768, "image_id": 1, "question": "What color is the clock?", "answer": "Green.", "a_objects": [], "q_objects": [] }, ... } ] ``` ### Data Fields When loading a specific configuration, users has to append a version dependent suffix: ```python from datasets import load_dataset load_dataset("visual_genome", "region_description_v1.2.0") ``` #### region_descriptions - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `regions`: Holds a list of `Region` dataclasses: - `region_id`: Unique numeric ID of the region. - `image_id`: Unique numeric ID of the image. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `width`: Bounding box width. - `height`: Bounding box height. #### objects - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `objects`: Holds a list of `Object` dataclasses: - `object_id`: Unique numeric ID of the object. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `w`: Bounding box width. - `h`: Bounding box height. - `names`: List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at https://cs.stanford.edu/people/rak248/VG_100K/3.jpg - `synsets`: List of `WordNet synsets`. #### attributes - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `attributes`: Holds a list of `Object` dataclasses: - `object_id`: Unique numeric ID of the region. - `x`: x coordinate of bounding box's top left corner. - `y`: y coordinate of bounding box's top left corner. - `w`: Bounding box width. - `h`: Bounding box height. - `names`: List of names associated with the object. This field can hold multiple values in the sense the multiple names are considered as acceptable. For example: ['monitor', 'computer'] at https://cs.stanford.edu/people/rak248/VG_100K/3.jpg - `synsets`: List of `WordNet synsets`. - `attributes`: List of attributes associated with the object. #### relationships - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `relationships`: Holds a list of `Relationship` dataclasses: - `relationship_id`: Unique numeric ID of the object. - `predicate`: Predicate defining relationship between a subject and an object. - `synsets`: List of `WordNet synsets`. - `subject`: Object dataclass. See subsection on `objects`. - `object`: Object dataclass. See subsection on `objects`. #### question_answers - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - `image_id`: Unique numeric ID of the image. - `url`: URL of source image. - `width`: Image width. - `height`: Image height. - `coco_id`: Id mapping to MSCOCO indexing. - `flickr_id`: Id mapping to Flicker indexing. - `qas`: Holds a list of `Question-Answering` dataclasses: - `qa_id`: Unique numeric ID of the question-answer pair. - `image_id`: Unique numeric ID of the image. - `question`: Question. - `answer`: Answer. - `q_objects`: List of object dataclass associated with `question` field. See subsection on `objects`. - `a_objects`: List of object dataclass associated with `answer` field. See subsection on `objects`. ### Data Splits All the data is contained in training set. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? From the paper: > We used Amazon Mechanical Turk (AMT) as our primary source of annotations. Overall, a total of over 33, 000 unique workers contributed to the dataset. The dataset was collected over the course of 6 months after 15 months of experimentation and iteration on the data representation. Approximately 800, 000 Human Intelligence Tasks (HITs) were launched on AMT, where each HIT involved creating descriptions, questions and answers, or region graphs. Each HIT was designed such that workers manage to earn anywhere between $6-$8 per hour if they work continuously, in line with ethical research standards on Mechanical Turk (Salehi et al., 2015). Visual Genome HITs achieved a 94.1% retention rate, meaning that 94.1% of workers who completed one of our tasks went ahead to do more. [...] 93.02% of workers contributed from the United States. The majority of our workers were between the ages of 25 and 34 years old. Our youngest contributor was 18 years and the oldest was 68 years old. We also had a near-balanced split of 54.15% male and 45.85% female workers. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Visual Genome by Ranjay Krishna is licensed under a Creative Commons Attribution 4.0 International License. ### Citation Information ```bibtex @article{Krishna2016VisualGC, title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations}, author={Ranjay Krishna and Yuke Zhu and Oliver Groth and Justin Johnson and Kenji Hata and Joshua Kravitz and Stephanie Chen and Yannis Kalantidis and Li-Jia Li and David A. Shamma and Michael S. Bernstein and Li Fei-Fei}, journal={International Journal of Computer Vision}, year={2017}, volume={123}, pages={32-73}, url={https://doi.org/10.1007/s11263-016-0981-7}, doi={10.1007/s11263-016-0981-7} } ``` ### Contributions Due to limitation of the dummy_data creation, we provide a `fix_generated_dummy_data.py` script that fix the dataset in-place. Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset.	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GonzaloA/fake_news	GonzaloA	2022-07-04T18:09:58Z	690	6	null	[ "region:us" ]	2022-07-04T18:09:58Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging --- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 30k<n<50k source_datasets: - original task_categories: - text-classification task_ids: - fact-checking - intent-classification pretty_name: GonzaloA / Fake News --- # Dataset Card for [Fake_News_TFG] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: [GonzaloA / fake_news] - Paper: [Título del TFG] - Leaderboard: - Point of Contact: [Gonzalo Álvarez Hervás](mailto:g.alvarez.2018@alumnos.urjc.es) ### Dataset Summary The GonzaloA / Fake_News_TFG Dataset repository is an English-language dataset containing just over 45k unique news articles. This articles are classified as true (1) or false (0). The current version is the first of the study the Fake News identification using Transformers models. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is code for English as generally spoken in the United States is en-US ## Dataset Structure The structure of this dataSet is composed by 40587 fields about News. This fields are composed by three types of fields; title of the news, the text or content of the news, and finally, the value of the news, who said if the new are fake (0) or true (1). ### Data Instances For each instance, there is a string for the title, a string for the article and a label to mark if it's true or false. See the [CNN / Daily Mail dataset viewer](https://huggingface.co/datasets/viewer/?dataset=fake_news&config=3.0.0) to explore more examples. ``` {'id': '1', 'title': Palestinians switch off Christmas lights in Bethlehem in anti-Trump protest' 'text': 'RAMALLAH, West Bank (Reuters) - Palestinians switched off Christmas lights at Jesus traditional birthplace in Bethlehem on Wednesday night in protest at U.S. President Donald Trump s decision to recognize Jerusalem as Israel s capital. A Christmas tree adorned with lights outside Bethlehem s Church of the Nativity, where Christians believe Jesus was born, and another in Ramallah, next to the burial site of former Palestinian leader Yasser Arafat, were plunged into darkness. The Christmas tree was switched off on the order of the mayor today in protest at Trump s decision, said Fady Ghattas, Bethlehem s municipal media officer. He said it was unclear whether the illuminations would be turned on again before the main Christmas festivities. In a speech in Washington, Trump said he had decided to recognize Jerusalem as Israel s capital and move the U.S. embassy to the city. Israeli Prime Minister Benjamin Netanyahu said Trump s move marked the beginning of a new approach to the Israeli-Palestinian conflict and said it was an historic landmark . Arabs and Muslims across the Middle East condemned the U.S. decision, calling it an incendiary move in a volatile region and the European Union and United Nations also voiced alarm at the possible repercussions for any chances of reviving Israeli-Palestinian peacemaking.' 'label': '1'} ``` ### Data Fields - `id`: an integer value to count the rows in the dataset - `title`: a string that summarize the article - `text`: a string that contains the article - `label`: a boolean that mark the article true or false ### Data Splits The GonzaloA/FakeNews dataset has 3 splits: train, evaluation, and test. Below are the statistics for the version 1.0 of the dataset: \| Dataset Split \| Number of Instances in Split \| \| ------------- \| ------------------------------------------- \| \| Train \| 24,353 \| \| Validation \| 8,117 \| \| Test \| 8,117 \| ## Dataset Creation This dataset was created with python, using pandas library as the main processing data. Also, this dataset are the mix of other datasets which are the same scope, the Fake News. All of the process is available in this repository: https://github.com/G0nz4lo-4lvarez-H3rv4s/FakeNewsDetection ### Source Data The source data is a mix of multiple fake_news datasets in Kaggle, a platform for train your skills and learnings about Artificial Intelligence. The main datasets who are based this dataset are: #### Initial Data Collection and Normalization Version 1.0.0 aimed to support supervised neural methodologies for deep learning and study the new Transformers models in the Natural Language Processing with News of the United States. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data This Dataset is compose for 3 types: Training phase, for training your model of NLP, validation phase, because we need to validate if the training was successful or our model has overfitting, and train phase, who count the probability and the mistakes in the model fine-tuning. ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.	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SajjadAyoubi/persian_qa	SajjadAyoubi	2021-04-29T06:11:18Z	690	5	null	[ "region:us" ]	2021-04-29T06:11:18Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	# PersianQA: a dataset for Persian Question Answering Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia. The crowd-sourced dataset consists of more than 9,000 entries. Each entry can be either an impossible to answer or a question with one or more answers spanning in the passage (the context) from which the questioner proposed the question. Much like the SQuAD2.0 dataset, the impossible or unanswerable questions can be utilized to create a system which "knows that it doesn't know the answer". On top of that, the dataset has 900 test data available. Moreover, the first models trained on the dataset, Transformers, are available. All the crowd workers of the dataset are native Persian speakers. Also, it worth mentioning that the contexts are collected from all categories of the Wiki (Historical, Religious, Geography, Science, etc.) At the moment, each context has 7 pairs of questions with one answer and 3 impossible questions. ## Dataset ### Access/Download - You can find the data under the [`dataset/`](https://github.com/sajjjadayobi/PersianQA/tree/main/dataset) directory. and use it like this ```python import read_qa # is avalible at src/read_ds.py train_ds = read_qa('pqa_train.json') test_ds = read_qa('pqa_test.json') ``` - Alternatively, you can also access the data through the HuggingFace🤗 datasets library - First, you need to install datasets using this command in your terminal: ```sh pip install -q datasets ``` - Then import `persian_qa` dataset using `load_dataset`: ```python from datasets import load_dataset dataset = load_dataset("SajjadAyoubi/persian_qa") ``` ### Examples \| Title \| Context \| Question \| Answer \| \| :---: \| :---------------------: \| :--------: \| :----: \| \| خوب، بد، زشت \| خوب، بد، زشت یک فیلم درژانر وسترن اسپاگتی حماسی است که توسط سرجو لئونه در سال ۱۹۶۶ در ایتالیا ساخته شد. زبانی که بازیگران این فیلم به آن تکلم می‌کنند مخلوطی از ایتالیایی و انگلیسی است. این فیلم سومین (و آخرین) فیلم از سه‌گانهٔ دلار (Dollars Trilogy) سرجو لئونه است. این فیلم در حال حاضر در فهرست ۲۵۰ فیلم برتر تاریخ سینما در وب‌گاه IMDB با امتیاز ۸٫۸ از ۱۰، رتبهٔ هشتم را به خود اختصاص داده‌است و به عنوان بهترین فیلم وسترن تاریخ سینمای جهان شناخته می‌شود. «خوب» (کلینت ایستوود، در فیلم، با نام «بلوندی») و «زشت» (ایلای والاک، در فیلم، با نام «توکو») با هم کار می‌کنند و با شگرد خاصی، به گول زدن کلانترهای مناطق مختلف و پول درآوردن از این راه می‌پردازند. «بد» (لی وان کلیف) آدمکشی حرفه‌ای است که به‌خاطر پول حاضر به انجام هر کاری است. «بد»، که در فیلم او را «اِنجل آیز (اِینجل آیز)» (به انگلیسی: Angel Eyes) صدا می‌کنند. به‌دنبال گنجی است که در طی جنگ‌های داخلی آمریکا، به دست سربازی به نام «جکسون»، که بعدها به «کارسون» نامش را تغییر داده، مخفی شده‌است. \| در فیلم خوب بد زشت شخصیت ها کجایی صحبت می کنند؟ \| مخلوطی از ایتالیایی و انگلیسی \| \| قرارداد کرسنت \| قرارداد کرسنت قراردادی برای فروش روزانه معادل ۵۰۰ میلیون فوت مکعب، گاز ترش میدان سلمان است، که در سال ۱۳۸۱ و در زمان وزارت بیژن نامدار زنگنه در دولت هفتم مابین شرکت کرسنت پترولیوم و شرکت ملی نفت ایران منعقد گردید. مذاکرات اولیه این قرارداد از سال ۱۹۹۷ آغاز شد و در نهایت، سال ۲۰۰۱ (۱۳۸۱) به امضای این تفاهم نامه مشترک انجامید. بر اساس مفاد این قرارداد، مقرر شده بود که از سال ۲۰۰۵ با احداث خط لوله در خلیج فارس، گاز فرآورده نشده میدان سلمان (مخزن مشترک با ابوظبی)، به میزان روزانه ۵۰۰ میلیون فوت مکعب (به قول برخی منابع ۶۰۰ میلیون فوت مکعب) به امارات صادر شود. این قرارداد مطابق قوانین داخلی ایران بسته شده‌ و تنها قرارداد نفتی ایران است که از طرف مقابل خود، تضمین گرفته‌است. اجرای این پروژه در سال ۱۳۸۴ با دلایل ارائه شده از سوی دیوان محاسبات ایران از جمله تغییر نیافتن بهای گاز صادراتی و ثابت ماندن آن در هفت سال اول اجرای قرارداد متوقف شد. این در حالی است که طبق تعریف حقوقی، دیوان محاسبات ایران، حق دخالت در قراردادها، پیش از آنکه قراردادها اجرایی و مالی شوند را ندارد. \| طرفین قرار داد کرسنت کیا بودن؟ \| کرسنت پترولیوم و شرکت ملی نفت ایران \| \| چهارشنبه‌سوری \| چهارشنبه‌سوری یکی از جشن‌های ایرانی است که از غروب آخرین سه‌شنبه ی ماه اسفند، تا پس از نیمه‌شب تا آخرین چهارشنبه ی سال، برگزار می‌شود و برافروختن و پریدن از روی آتش مشخصهٔ اصلی آن است. این جشن، نخستین جشن از مجموعهٔ جشن‌ها و مناسبت‌های نوروزی است که با برافروختن آتش و برخی رفتارهای نمادین دیگر، به‌صورت جمعی در فضای باز برگزار می‌شود. به‌گفتهٔ ابراهیم پورداوود چهارشنبه‌سوری ریشه در گاهنبارِ هَمَسْپَتْمَدَم زرتشتیان و نیز جشن نزول فروهرها دارد که شش روز پیش از فرارسیدن نوروز برگزار می‌شد. احتمال دیگر این است که چهارشنبه‌سوری بازمانده و شکل تحول‌یافته‌ای از جشن سده باشد، که احتمال بعیدی است. علاوه برافروختن آتش، آیین‌های مختلف دیگری نیز در بخش‌های گوناگون ایران در زمان این جشن انجام می‌شوند. برای نمونه، در تبریز، مردم به چهارشنبه‌بازار می‌روند که با چراغ و شمع، به‌طرز زیبایی چراغانی شده‌است. هر خانواده یک آینه، دانه‌های اسفند، و یک کوزه برای سال نو خریداری می‌کنند. همه‌ساله شهروندانی از ایران در اثر انفجارهای ناخوشایند مربوط به این جشن، کشته یا مصدوم می‌شوند. \| نام جشن اخرین شنبه ی سال چیست؟ \| No Answer \| ### Statistic \| Split \| # of instances \| # of unanswerables \| avg. question length \| avg. paragraph length \| avg. answer length \| \| :---: \| :------------: \| :----------------: \| :------------------: \| :-------------------: \| :----------------: \| \| Train \| 9,000 \| 2,700 \| 8.39 \| 224.58 \| 9.61 \| \| Test \| 938 \| 280 \| 8.02 \| 220.18 \| 5.99 \| The lengths are on the token level. - for more about data and more example see [here](https://github.com/sajjjadayobi/PersianQA/tree/main/dataset#readme) ## Models Currently, two models (baseline) on [HuggingFace🤗](https://huggingface.co/SajjadAyoubi/) model hub are using the dataset. ## Citation Yet, we didn't publish any papers on the work. However, if you did, please cite us properly with an entry like one below. ```bibtex @misc{PersianQA, author = {Ayoubi, Sajjad \& Davoodeh, Mohammad Yasin}, title = {PersianQA: a dataset for Persian Question Answering}, year = 2021, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/SajjjadAyobi/PersianQA}}, } ```	[ -0.7201265692710876, -0.6578283309936523, 0.5340370535850525, 0.34400615096092224, -0.41642725467681885, -0.22233393788337708, 0.2780803442001343, -0.4360100030899048, 0.7090407013893127, 0.48252594470977783, -0.37081775069236755, -0.6494172811508179, -0.6381480693817139, 0.3625648319721222, -0.49214422702789307, 0.7227287888526917, -0.11241736263036728, 0.13298210501670837, 0.24496814608573914, -0.14345082640647888, -0.1304660588502884, -0.5768672227859497, -0.5271921753883362, 0.05939807742834091, 0.17928273975849152, 0.2479425072669983, 0.6665987372398376, 0.09770531952381134, 0.45573994517326355, 0.42914339900016785, 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DFKI-SLT/argmicro	DFKI-SLT	2023-08-09T15:07:47Z	688	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	2023-08-09T15:07:47Z	2023-08-08T16:17:53.000Z	2023-08-08T16:17:53	--- license: cc-by-nc-sa-4.0 --- # An annotated corpus of argumentative microtexts The arg-microtexts corpus features 112 short argumentative texts. All texts were originally written in German and have been professionally translated to English. The texts with ids b001-b064 and k001-k031 have been collected in a controlled text generation experiment from 23 subjects discussing various controversial issues from [a fixed list](topics_triggers.md). The texts with ids d01-d23 have been written by Andreas Peldszus and were used mainly in teaching and testing students argumentative analysis. All texts are annotated with argumentation structures, following the scheme proposed in Peldszus & Stede (2013). For inter-annotator-agreement scores see Peldszus (2014). The (German) annotation guidelines are published in Peldszus, Warzecha, Stede (2016). ## DATA FORMAT (ARGUMENTATION GRAPH) This specifies the argumentation graphs following the annotation scheme described in Andreas Peldszus and Manfred Stede. From argument diagrams to argumentation mining in texts: a survey. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 7(1):1–31, 2013. An argumentation graph is a directed graph spanning over text segments. The format distinguishes three different sorts of nodes: EDUs, ADUs & EDU-joints. - EDU: elementary discourse units The text is segmented into elementary discourse units, typically at a clause/sentence level. This segmentation can be the result of manually annotation or of automatic discourse segmenters. - ADU: argumentative discourse units Not every EDU is relevant in an argumentation. Also, the same claim might be stated multiple times in longer texts. An argumentative discourse unit represents a claim that stands for itself and is argumentatively relevant. It is thus grounded in one or more EDUs. EDU and ADUs are connected by segmentation edges. ADUs are associated with a dialectic role: They are either proponent or opponent nodes. - JOINT: a joint of two or more adjacent elementary discourse units When two adjacent EDUs are argumentatively relevant only when taken together, these EDUs are first connected with one joint EDU node by segmentation edges and then this joint node is connected to a corresponding ADU. ### edge type The edges representing arguments are those that connect ADUs. The scheme distinguishes between supporting and attacking relations. Supporting relations are normal support and support by example. Attacking relations are rebutting attacks (directed against another node, challenging the accept- ability of the corresponding claim) and undercutting attacks (directed against another relation, challenging the argumentative inference from the source to the target of the relation). Finally, additional premises of relations with more than one premise are represented by additional source relations. Values: - seg: segmentation edges (EDU->ADU, EDU->JOINT, JOINT->ADU) - sup: support (ADU->ADU) - exa: support by example (ADU->ADU) - add: additional source, for combined/convergent arguments with multiple premises (ADU->ADU) - reb: rebutting attack (ADU->ADU) - und: undercutting attack (ADU->Edge) ### adu type The argumentation can be thought of as a dialectical exchange between the role of the proponent (who is presenting and defending the central claim) and the role of the opponent (who is critically challenging the proponents claims). Each ADU is thus associated with one of these dialectic roles. Values: - pro: proponent - opp: opponent ### stance type Annotated texts typically discuss a controversial topic, i.e. an issue posed as a yes/no question. Example: "Should we make use of capital punishment?" The stance type specifies, which stance the author of this text takes towards this issue. Values: - pro: yes, in favour of the proposed issue - con: no, against the proposed issue - unclear: the position of the author is unclear - UNDEFINED	[ -0.7254348397254944, -1.1657077074050903, 0.5587908029556274, -0.21365290880203247, -0.5115228295326233, -0.22147320210933685, 0.007331886328756809, -0.10640010237693787, 0.5063179135322571, 0.42991602420806885, -0.26176315546035767, -0.45145130157470703, -0.49942415952682495, 0.20144037902355194, 0.0016349455108866096, 1.1466435194015503, -0.17693395912647247, 0.21976515650749207, -0.10245180875062943, -0.6551715731620789, -0.27080854773521423, -0.6816083192825317, -1.031465768814087, -0.1856398582458496, 0.8302552700042725, 0.4778655469417572, 0.6796208024024963, 0.6923397779464722, 0.14336413145065308, 0.3458874523639679, -0.36763712763786316, -0.23447392880916595, -0.08698926120996475, 0.13977210223674774, -0.2885568141937256, -0.2079329639673233, -0.6882513761520386, 0.09247884899377823, 0.6780078411102295, 0.6014216542243958, 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bc2gm_corpus	null	2023-08-30T12:13:12Z	686	5	null	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]	2023-08-30T12:13:12Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Bc2GmCorpus dataset_info: config_name: bc2gm_corpus features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-GENE '2': I-GENE splits: - name: train num_bytes: 6095123 num_examples: 12500 - name: validation num_bytes: 1215919 num_examples: 2500 - name: test num_bytes: 2454589 num_examples: 5000 download_size: 4636753 dataset_size: 9765631 --- # Dataset Card for bc2gm_corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/spyysalo/bc2gm-corpus/) - Repository: [Github](https://github.com/spyysalo/bc2gm-corpus/) - Paper: [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/) - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@mahajandiwakar](https://github.com/mahajandiwakar) for adding this dataset.	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msra_ner	null	2023-01-25T14:40:51Z	686	18	null	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:zh", "license:unknown", "region:us" ]	2023-01-25T14:40:51Z	2022-03-02T23:29:22.000Z	2022-03-02T23:29:22	--- annotations_creators: - crowdsourced language_creators: - found language: - zh license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: MSRA NER dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC config_name: msra_ner splits: - name: train num_bytes: 33323074 num_examples: 45001 - name: test num_bytes: 2642934 num_examples: 3443 download_size: 15156606 dataset_size: 35966008 train-eval-index: - config: msra_ner task: token-classification task_id: entity_extraction splits: train_split: train eval_split: test col_mapping: tokens: tokens ner_tags: tags metrics: - type: seqeval name: seqeval --- # Dataset Card for MSRA NER ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/OYE93/Chinese-NLP-Corpus/tree/master/NER/MSRA) - Repository: [Github](https://github.com/OYE93/Chinese-NLP-Corpus) - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.	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turing-motors/LLaVA-Instruct-150K-JA	turing-motors	2023-08-28T11:26:23Z	686	4	null	[ "task_categories:visual-question-answering", "task_categories:question-answering", "size_categories:100K<n<1M", "language:ja", "license:cc-by-nc-4.0", "region:us" ]	2023-08-28T11:26:23Z	2023-08-28T10:50:24.000Z	2023-08-28T10:50:24	--- license: cc-by-nc-4.0 task_categories: - visual-question-answering - question-answering language: - ja pretty_name: Japanese LLaVA Visual Instruct 150K size_categories: - 100K<n<1M --- ## Dataset Details Dataset Type: Japanese LLaVA Instruct 150K is a localized version of the original LLaVA Visual Instruct 150K dataset. This version is translated into Japanese using DeepL API and is aimed at serving similar purposes in the context of Japanese language. Resources for More Information: For information on the original dataset: [LLaVA Visual Instruct 150K](https://llava-vl.github.io/) License: Attribution-NonCommercial 4.0 International (CC BY-NC-4.0) The dataset should abide by the policy of OpenAI: [OpenAI Terms of Use](https://openai.com/policies/terms-of-use) Questions or Comments: For questions or comments about the original model, you can go to [LLaVA GitHub Issues](https://github.com/haotian-liu/LLaVA/issues). ## Intended Use Primary Intended Uses: The primary use of this translated dataset is research on large multimodal models and chatbots in a Japanese context. Primary Intended Users: The primary intended users are researchers and hobbyists interested in computer vision, natural language processing, machine learning, and artificial intelligence, particularly those focusing on the Japanese language. --- Note: This dataset is a translation of the original LLaVA Visual Instruct 150K, carried out using the DeepL API. 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ccdv/mediasum	ccdv	2022-10-25T10:56:04Z	684	5	null	[ "task_categories:summarization", "task_categories:text2text-generation", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:en", "conditional-text-generation", "region:us" ]	2022-10-25T10:56:04Z	2022-05-21T12:29:19.000Z	2022-05-21T12:29:19	--- language: - en multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - summarization - text2text-generation task_ids: [] tags: - conditional-text-generation --- # MediaSum dataset for summarization Summarization dataset copied from [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://github.com/zcgzcgzcg1/MediaSum) This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable: ```python "ccdv/mediasum": ("document", "summary") ``` # Configs 4 possibles configs: - `roberta` will concatenate documents with "\</s\>" - `newline` will concatenate documents with "\n" - `bert` will concatenate documents with "[SEP]" - `list` will return the list of documents instead of a single string Add `_prepended` to config name to prepend the speaker name before each dialogue: `speaker: text` \ Default is `roberta_prepended` (compatible with BART). ### Data Fields - `id`: paper id - `document`: a string/list containing the body of a set of documents - `summary`: a string containing the abstract of the set ### Data Splits This dataset has 3 splits: _train_, _validation_, and _test_. \ \| Dataset Split \| Number of Instances \| \| ------------- \| --------------------\| \| Train \| 443596 \| \| Validation \| 10000 \| \| Test \| 10000 \| # Cite original article ``` @article{zhu2021mediasum, title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization}, author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael}, journal={arXiv preprint arXiv:2103.06410}, year={2021} } ```	[ -0.44534289836883545, -0.3121657371520996, 0.03394768759608269, 0.22837410867214203, -0.15875102579593658, -0.00683605344966054, -0.4739763140678406, 0.22229507565498352, 0.4135696291923523, 0.44964802265167236, -0.668093204498291, -0.49010559916496277, -0.6125264763832092, 0.1650674045085907, -0.49709048867225647, 1.353485107421875, 0.13191834092140198, -0.11859683692455292, -0.04376355931162834, -0.43062102794647217, -0.24887116253376007, -0.3382272720336914, -0.40813159942626953, -0.17602629959583282, 0.497274786233902, 0.6771620512008667, 0.7529100775718689, 0.5364526510238647, 0.541328489780426, 0.39682042598724365, -0.01754053309559822, 0.2901771366596222, -0.39271968603134155, 0.17700104415416718, -0.03488609194755554, -0.2546745836734772, -0.6058644652366638, 0.276089608669281, 0.9462453126907349, 0.6557853817939758, -0.09276387840509415, 0.3528643250465393, -0.10156268626451492, 0.4253297746181488, 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clarin-knext/fiqa-pl-qrels	clarin-knext	2023-06-07T08:22:36Z	684	0	null	[ "language:pl", "arxiv:2305.19840", "region:us" ]	2023-06-07T08:22:36Z	2023-06-06T21:58:39.000Z	2023-06-06T21:58:39	--- language: - pl --- Part of BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language. Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf Contact: konrad.wojtasik@pwr.edu.pl	[ -0.2209920734167099, -0.9029767513275146, 0.5094642043113708, 0.2354191392660141, -0.318521112203598, -0.1491902619600296, -0.16673962771892548, -0.49629199504852295, -0.0189602542668581, 0.41122621297836304, -0.5503097772598267, -0.6913566589355469, -0.4166175425052643, -0.0483047217130661, -0.28065192699432373, 0.7076612114906311, 0.033162668347358704, 0.37434616684913635, 0.3830285668373108, -0.3114224076271057, -0.056416042149066925, -0.6188214421272278, -0.3301200568675995, -0.10705433040857315, 0.6542650461196899, 0.6115015745162964, 0.22780151665210724, 0.4731614291667938, 0.9138426184654236, 0.16627715528011322, 0.3685116171836853, -0.47796085476875305, -0.5472331047058105, 0.35305339097976685, -0.17400367558002472, -0.37735801935195923, -0.20428767800331116, -0.20798800885677338, 1.1306096315383911, 0.9135056734085083, -0.03765137493610382, 0.3307861089706421, -0.3205001950263977, 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sidhq/email-thread-summary	sidhq	2023-07-17T03:19:09Z	684	2	null	[ "task_categories:summarization", "language:en", "region:us" ]	2023-07-17T03:19:09Z	2023-07-17T01:08:40.000Z	2023-07-17T01:08:40	--- dataset_info: features: - name: thread struct: - name: subject dtype: string - name: messages list: - name: timestamp dtype: timestamp[s] - name: from dtype: string - name: to sequence: string - name: body dtype: string - name: summary dtype: string splits: - name: train num_bytes: 38290851 num_examples: 3750 - name: test num_bytes: 4189760 num_examples: 417 download_size: 13597444 dataset_size: 42480611 task_categories: - summarization language: - en pretty_name: Email Thread Summary --- # Dataset Card for "email-thread-summary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6908105611801147, -0.3565475642681122, 0.14117930829524994, 0.20384421944618225, -0.19330795109272003, -0.027286121621727943, 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