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mozilla-foundation/common_voice_6_1
mozilla-foundation
2023-07-29T16:00:07Z
764
4
[ "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" ]
[ "automatic-speech-recognition" ]
2022-03-02T23:29:22Z
--- 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:[email protected]) ### 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 } ```
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HuggingFaceH4/hhh_alignment
HuggingFaceH4
2023-03-02T10:13:04Z
764
8
[ "task_categories:multiple-choice", "language:en", "license:apache-2.0", "human-feedback", "arxiv:2112.00861", "region:us" ]
[ "multiple-choice" ]
2023-03-01T15:31:15Z
--- license: apache-2.0 task_categories: - multiple-choice language: - en tags: - human-feedback pretty_name: HHH Alignment dataset_info: - config_name: harmless features: - name: input dtype: string - name: targets struct: - name: choices sequence: string - name: labels sequence: int32 splits: - name: test num_bytes: 37637 num_examples: 58 download_size: 238798 dataset_size: 37637 - config_name: helpful features: - name: input dtype: string - name: targets struct: - name: choices sequence: string - name: labels sequence: int32 splits: - name: test num_bytes: 68943 num_examples: 59 download_size: 238798 dataset_size: 68943 - config_name: honest features: - name: input dtype: string - name: targets struct: - name: choices sequence: string - name: labels sequence: int32 splits: - name: test num_bytes: 77758 num_examples: 61 download_size: 238798 dataset_size: 77758 - config_name: other features: - name: input dtype: string - name: targets struct: - name: choices sequence: string - name: labels sequence: int32 splits: - name: test num_bytes: 22163 num_examples: 43 download_size: 238798 dataset_size: 22163 --- # Helpful, Honest, & Harmless - a Pragmatic Alignment Evaluation This task evaluates language models on alignment, pragmatically broken down into the categories of helpfulness, honesty/accuracy, harmlessness, and other. **Authors:** Amanda Askell, Yuntao Bai, Anna Chen, Deep Ganguli, Danny Hernandez, Jared Kaplan ([email protected]), Jackson Kernion, Ben Mann, Catherine Olsson, Tim Telleen-Lawton The three categories employed in this task are clearly somewhat subjective and mutually contradictory -- for example, an agent asked to help with a harmful task (e.g., building a bomb) must compromise between helpfulness and harmlessness. The evaluation format imagines a conversation between a person and a language-model assistant (we do not include explicit dialogue indicators here, but names such as "Human:" and "Assistant:" could reasonably be prepended to the query and responses). The task is formatted in terms of binary comparisons, though many of these have been broken down from a ranked ordering of three or four possible responses to a given query or context. All examples were written by the contributors. The goal with these evaluations is that on careful reflection, the vast majority of people would agree that the chosen response is better (more helpful, honest, and harmless) than the alternative offered for comparison. In practice, the ground truth answers were determined by the creators. Every evaluation was discussed and approved by at least one co-author other than the individual who wrote it, with the explicit goal of "on careful reflection, the vast majority of people would agree" as our internal standard for approval. There are 61 honesty, 59 helpfulness, 58 harm, and 43 other categorized evaluations. The "other" category includes examples of unambiguously desirable/undesirable behavior from a text-based assistant that were not written with the goal of specifically targeting helpfulness, honesty, or harmlessness. These include but are not limited to: flirtatious behavior, rude and rambling responses, and suspicious omissions. In the future, we may create a new version of this task with many more evaluations sourced from professional writers. ## Metrics For each example, the model's predicted probability is computed for each target. The example score is then the target score (as specified in the target_scores dict) of the target that received the highest probability. Scores are averaged across examples. Conventional multiple-choice accuracy can be achieved by assigning the correct target a score of 1, and all incorrect targets a score of 0. ## Citation information ``` @article{DBLP:journals/corr/abs-2112-00861, author = {Amanda Askell and Yuntao Bai and Anna Chen and Dawn Drain and Deep Ganguli and Tom Henighan and Andy Jones and Nicholas Joseph and Benjamin Mann and Nova DasSarma and Nelson Elhage and Zac Hatfield{-}Dodds and Danny Hernandez and Jackson Kernion and Kamal Ndousse and Catherine Olsson and Dario Amodei and Tom B. Brown and Jack Clark and Sam McCandlish and Chris Olah and Jared Kaplan}, title = {A General Language Assistant as a Laboratory for Alignment}, journal = {CoRR}, volume = {abs/2112.00861}, year = {2021}, url = {https://arxiv.org/abs/2112.00861}, eprinttype = {arXiv}, eprint = {2112.00861}, timestamp = {Tue, 07 Dec 2021 12:15:54 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2112-00861.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ## Contributions This dataset and the dataset card was taken from [BIG-Bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/hhh_alignment), with the fields formatted so that the dataset can be loaded without requiring `bigbench` to be installed.
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NeelNanda/c4-code-tokenized-2b
NeelNanda
2022-11-13T21:54:56Z
761
1
[ "region:us" ]
null
2022-11-13T21:42:47Z
--- dataset_info: features: - name: tokens sequence: int64 splits: - name: train num_bytes: 13581607992 num_examples: 1657102 download_size: 2953466988 dataset_size: 13581607992 --- # Dataset Card for "c4-code-tokenized-2b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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gbharti/finance-alpaca
gbharti
2023-09-26T04:13:35Z
756
49
[ "language:en", "region:us" ]
null
2023-03-29T03:37:58Z
--- 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
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augtoma/usmle_step_1
augtoma
2023-08-11T21:25:08Z
756
0
[ "region:us" ]
null
2023-08-11T21:24:50Z
--- 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)
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Babelscape/wikineural
Babelscape
2022-11-13T07:52:46Z
755
17
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:multilingual", "source_datasets:original", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:nl", "language:pl", "language:pt", "language:ru", "license:cc-by-nc-sa-4.0", "structure-prediction", "arxiv:1810.04805", "region:us" ]
[ "token-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - machine-generated language_creators: - machine-generated language: - de - en - es - fr - it - nl - pl - pt - ru license: - cc-by-nc-sa-4.0 multilinguality: - multilingual source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: wikineural-dataset tags: - structure-prediction --- ## Table of Contents - [Description](#description) - [Dataset Structure](#dataset-structure) - [Additional Information](#additional-information) ## Dataset Card for WikiNEuRal dataset ## Dataset Description - **Summary:** Training data for NER in 9 languages. - **Repository:** [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural) - **Paper:** [https://aclanthology.org/wikineural](https://aclanthology.org/2021.findings-emnlp.215/) - **Point of Contact:** [[email protected]]([email protected]) ## Description - **Summary:** In a nutshell, WikiNEuRal consists in a novel technique which builds upon a multilingual lexical knowledge base (i.e., [BabelNet](https://babelnet.org/)) and transformer-based architectures (i.e., [BERT](https://arxiv.org/abs/1810.04805)) to produce high-quality annotations for multilingual NER. It shows consistent improvements of up to 6 span-based F1-score points against state-of-the-art alternative data production methods on common benchmarks for NER. We used this methodology to automatically generate training data for NER in 9 languages. - **Repository:** [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural) - **Paper:** [https://aclanthology.org/wikineural](https://aclanthology.org/2021.findings-emnlp.215/) - **Point of Contact:** [[email protected]]([email protected]) ## Dataset Structure The data fields are the same among all splits. - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices: ```python {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8} ``` - `lang`: a `string` feature. Full list of language: Dutch (nl), English (en), French (fr), German (de), Italian (it), Polish (pl), Portugues (pt), Russian (ru), Spanish (es). ## Dataset Statistics The table below shows the number of sentences, number of tokens and number of instances per class, for each of the 9 languages. | Dataset Version | Sentences | Tokens | PER | ORG | LOC | MISC | OTHER | | :------------- | -------------: | -------------: | -------------: | -------------: | -------------: | -------------: | -------------: | | WikiNEuRal EN | 116k | 2.73M | 51k | 31k | 67k | 45k | 2.40M | | WikiNEuRal ES | 95k | 2.33M | 43k | 17k | 68k | 25k | 2.04M | | WikiNEuRal NL | 107k | 1.91M | 46k | 22k | 61k | 24k | 1.64M | | WikiNEuRal DE | 124k | 2.19M | 60k | 32k | 59k | 25k | 1.87M | | WikiNEuRal RU | 123k | 2.39M | 40k | 26k | 89k | 25k | 2.13M | | WikiNEuRal IT | 111k | 2.99M | 67k | 22k | 97k | 26k | 2.62M | | WikiNEuRal FR | 127k | 3.24M | 76k | 25k | 101k | 29k | 2.83M | | WikiNEuRal PL | 141k | 2.29M | 59k | 34k | 118k | 22k | 1.91M | | WikiNEuRal PT | 106k | 2.53M | 44k | 17k | 112k | 25k | 2.20M | ## Additional Information - **Licensing Information**: Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders. - **Citation Information**: Please consider citing our work if you use data and/or code from this repository. ```bibtex @inproceedings{tedeschi-etal-2021-wikineural-combined, title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}", author = "Tedeschi, Simone and Maiorca, Valentino and Campolungo, Niccol{\`o} and Cecconi, Francesco and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.215", pages = "2521--2533", abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.", } ``` - **Contributions**: Thanks to [@sted97](https://github.com/sted97) for adding this dataset.
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cs_restaurants
null
2022-11-18T19:49:56Z
754
1
[ "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" ]
[ "text2text-generation", "text-generation", "fill-mask" ]
2022-03-02T23:29:22Z
--- 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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TheBritishLibrary/blbooks
TheBritishLibrary
2022-11-03T16:31:29Z
752
6
[ "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" ]
[ "text-generation", "fill-mask", "other" ]
2022-03-02T23:29:22Z
--- 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:** [email protected] ### 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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qangaroo
null
2023-04-05T13:37:06Z
752
0
[ "language:en", "region:us" ]
null
2022-03-02T23:29:22Z
--- 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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namespace-Pt/msmarco-corpus
namespace-Pt
2023-10-16T15:07:39Z
749
0
[ "region:us" ]
null
2023-10-16T15:00:23Z
--- dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 3243246889 num_examples: 8841823 download_size: 1720789558 dataset_size: 3243246889 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "msmarco-corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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bigbio/ncbi_disease
bigbio
2023-01-14T03:24:56Z
748
1
[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]
null
2022-11-13T22:10:53Z
--- language: - en bigbio_language: - English license: cc0-1.0 multilinguality: monolingual bigbio_license_shortname: CC0_1p0 pretty_name: NCBI Disease homepage: https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/ bigbio_pubmed: True bigbio_public: True bigbio_tasks: - NAMED_ENTITY_RECOGNITION - NAMED_ENTITY_DISAMBIGUATION --- # Dataset Card for NCBI Disease ## Dataset Description - **Homepage:** https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/ - **Pubmed:** True - **Public:** True - **Tasks:** NER,NED The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. ## Citation Information ``` @article{Dogan2014NCBIDC, title = {NCBI disease corpus: A resource for disease name recognition and concept normalization}, author = {Rezarta Islamaj Dogan and Robert Leaman and Zhiyong Lu}, year = 2014, journal = {Journal of biomedical informatics}, volume = 47, pages = {1--10} } ```
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swedish_medical_ner
null
2023-01-25T14:45:18Z
747
2
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:sv", "license:cc-by-sa-4.0", "region:us" ]
[ "token-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - machine-generated - expert-generated language_creators: - found language: - sv license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: SwedMedNER language_bcp47: - sv-SE dataset_info: - config_name: wiki features: - name: sid dtype: string - name: sentence dtype: string - name: entities sequence: - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string - name: type dtype: class_label: names: '0': Disorder and Finding '1': Pharmaceutical Drug '2': Body Structure splits: - name: train num_bytes: 7044714 num_examples: 48720 download_size: 52272712 dataset_size: 7044714 - config_name: lt features: - name: sid dtype: string - name: sentence dtype: string - name: entities sequence: - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string - name: type dtype: class_label: names: '0': Disorder and Finding '1': Pharmaceutical Drug '2': Body Structure splits: - name: train num_bytes: 97955287 num_examples: 745753 download_size: 52272712 dataset_size: 97955287 - config_name: '1177' features: - name: sid dtype: string - name: sentence dtype: string - name: entities sequence: - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string - name: type dtype: class_label: names: '0': Disorder and Finding '1': Pharmaceutical Drug '2': Body Structure splits: - name: train num_bytes: 159007 num_examples: 927 download_size: 52272712 dataset_size: 159007 --- # Dataset Card for swedish_medical_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 - **Repository:** https://github.com/olofmogren/biomedical-ner-data-swedish - **Paper:** [Named Entity Recognition in Swedish Health Records with Character-Based Deep Bidirectional LSTMs](https://aclanthology.org/W16-5104.pdf) - **Point of Contact:** [Olof Mogren]([email protected]) ### Dataset Summary SwedMedNER is Named Entity Recognition dataset on medical text in Swedish. It consists three subsets which are in turn derived from three different sources respectively: the Swedish Wikipedia (a.k.a. wiki), Läkartidningen (a.k.a. lt), and 1177 Vårdguiden (a.k.a. 1177). While the Swedish Wikipedia and Läkartidningen subsets in total contains over 790000 sequences with 60 characters each, the 1177 Vårdguiden subset is manually annotated and contains 927 sentences, 2740 annotations, out of which 1574 are _disorder and findings_, 546 are _pharmaceutical drug_, and 620 are _body structure_. Texts from both Swedish Wikipedia and Läkartidningen were automatically annotated using a list of medical seed terms. Sentences from 1177 Vårdguiden were manuually annotated. ### Supported Tasks and Leaderboards Medical NER. ### Languages Swedish (SV). ## Dataset Structure ### Data Instances Annotated example sentences are shown below: ``` ( Förstoppning ) är ett vanligt problem hos äldre. [ Cox-hämmare ] finns även som gel och sprej. [ Medicinen ] kan också göra att man blöder lättare eftersom den påverkar { blodets } förmåga att levra sig. ``` Tags are as follows: - Prenthesis, (): Disorder and Finding - Brackets, []: Pharmaceutical Drug - Curly brackets, {}: Body Structure Data example: ``` In: data = load_dataset('./datasets/swedish_medical_ner', "wiki") In: data['train']: Out: Dataset({ features: ['sid', 'sentence', 'entities'], num_rows: 48720 }) In: data['train'][0]['sentence'] Out: '{kropp} beskrivs i till exempel människokroppen, anatomi och f' In: data['train'][0]['entities'] Out: {'start': [0], 'end': [7], 'text': ['kropp'], 'type': [2]} ``` ### Data Fields - `sentence` - `entities` - `start`: the start index - `end`: the end index - `text`: the text of the entity - `type`: entity type: Disorder and Finding (0), Pharmaceutical Drug (1) or Body Structure (2) ### Data Splits In the original paper, its authors used the text from Läkartidningen for model training, Swedish Wikipedia for validation, and 1177.se for the final model evaluation. ## Dataset Creation ### Curation Rationale ### Source Data - Swedish Wikipedia; - Läkartidningen - contains articles from the Swedish journal for medical professionals; - 1177.se - a web site provided by the Swedish public health care authorities, containing information, counselling, and other health-care services. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process - A list of seed terms was extracted using SweMeSH and SNOMED CT; - The following predefined categories was used for the extraction: disorder & finding (sjukdom & symtom), pharmaceutical drug (läkemedel) and body structure (kroppsdel) - For _Swedish Wikipedia_, an initial list of medical domain articles were selected manually. These source articles as well as their linked articles were downloaded and automatically annotated by finding the aforementioned seed terms with a context window of 60 characters; - Articles from the _Läkartidningen_ corpus were downloaded and automatically annotated by finding the aforementioned seed terms with a context window of 60 characters; - 15 documents from _1177.se_ were downloaded in May 2016 and then manually annotated with the seed terms as support, resulting 2740 annotations. #### 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 - Simon Almgren, [email protected] - Sean Pavlov, [email protected] - Olof Mogren, [email protected] Chalmers University of Technology ### Licensing Information This dataset is released under the [Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)](http://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ```bibtex @inproceedings{almgrenpavlovmogren2016bioner, title={Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs}, author={Simon Almgren, Sean Pavlov, Olof Mogren}, booktitle={Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016)}, pages={1}, year={2016} } ``` ### Contributions Thanks to [@bwang482](https://github.com/bwang482) for adding this dataset.
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tab_fact
null
2023-01-25T14:45:28Z
747
7
[ "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" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- 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]([email protected]) ### 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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detection-datasets/fashionpedia
detection-datasets
2022-09-22T13:22:02Z
747
28
[ "task_categories:object-detection", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "object-detection", "fashion", "computer-vision", "arxiv:2004.12276", "region:us" ]
[ "object-detection" ]
2022-09-22T10:33:24Z
--- pretty_name: Fashionpedia task_categories: - object-detection language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original tags: - object-detection - fashion - computer-vision paperswithcode_id: fashionpedia --- # Dataset Card for Fashionpedia ## 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) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://fashionpedia.github.io/home/index.html - **Repository:** https://github.com/cvdfoundation/fashionpedia - **Paper:** https://arxiv.org/abs/2004.12276 ### Dataset Summary Fashionpedia is a dataset mapping out the visual aspects of the fashion world. From the paper: > Fashionpedia is a new dataset which consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. Fashionpedia has: - 46781 images - 342182 bounding-boxes ### Supported Tasks - Object detection - Image classification ### Languages All of annotations use English as primary language. ## Dataset Structure The dataset is structured as follows: ```py DatasetDict({ train: Dataset({ features: ['image_id', 'image', 'width', 'height', 'objects'], num_rows: 45623 }) val: Dataset({ features: ['image_id', 'image', 'width', 'height', 'objects'], num_rows: 1158 }) }) ``` ### Data Instances An example of the data for one image is: ```py {'image_id': 23, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=682x1024>, 'width': 682, 'height': 1024, 'objects': {'bbox_id': [150311, 150312, 150313, 150314], 'category': [23, 23, 33, 10], 'bbox': [[445.0, 910.0, 505.0, 983.0], [239.0, 940.0, 284.0, 994.0], [298.0, 282.0, 386.0, 352.0], [210.0, 282.0, 448.0, 665.0]], 'area': [1422, 843, 373, 56375]}} ``` With the type of each field being defined as: ```py {'image_id': Value(dtype='int64'), 'image': Image(decode=True), 'width': Value(dtype='int64'), 'height': Value(dtype='int64'), 'objects': Sequence(feature={ 'bbox_id': Value(dtype='int64'), 'category': ClassLabel(num_classes=46, names=['shirt, blouse', 'top, t-shirt, sweatshirt', 'sweater', 'cardigan', 'jacket', 'vest', 'pants', 'shorts', 'skirt', 'coat', 'dress', 'jumpsuit', 'cape', 'glasses', 'hat', 'headband, head covering, hair accessory', 'tie', 'glove', 'watch', 'belt', 'leg warmer', 'tights, stockings', 'sock', 'shoe', 'bag, wallet', 'scarf', 'umbrella', 'hood', 'collar', 'lapel', 'epaulette', 'sleeve', 'pocket', 'neckline', 'buckle', 'zipper', 'applique', 'bead', 'bow', 'flower', 'fringe', 'ribbon', 'rivet', 'ruffle', 'sequin', 'tassel']), 'bbox': Sequence(feature=Value(dtype='float64'), length=4), 'area': Value(dtype='int64')}, length=-1)} ``` ### Data Fields The dataset has the following fields: - `image_id`: Unique numeric ID of the image. - `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]` - `width`: Image width. - `height`: Image height. - `objects`: A dictionary containing bounding box metadata for the objects in the image: - `bbox_id`: Unique numeric ID of the bounding box annotation. - `category`: The object’s category. - `area`: The area of the bounding box. - `bbox`: The object’s bounding box (in the Pascal VOC format) ### Data Splits | | Train | Validation | Test | |----------------|--------|------------|------| | Images | 45623 | 1158 | 0 | | Bounding boxes | 333401 | 8781 | 0 | ## Additional Information ### Licensing Information Fashionpedia is licensed under a Creative Commons Attribution 4.0 International License. ### Citation Information ``` @inproceedings{jia2020fashionpedia, title={Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset}, author={Jia, Menglin and Shi, Mengyun and Sirotenko, Mikhail and Cui, Yin and Cardie, Claire and Hariharan, Bharath and Adam, Hartwig and Belongie, Serge} booktitle={European Conference on Computer Vision (ECCV)}, year={2020} } ``` ### Contributions Thanks to [@blinjrm](https://github.com/blinjrm) for adding this dataset.
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Muennighoff/xP3x
Muennighoff
2023-11-09T04:45:25Z
746
8
[ "task_categories:other", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "multilinguality:multilingual", "size_categories:100M<n<1B", "language:af", "language:ar", "language:az", "language:be", "language:bg", "language:bn", "language:br", "language:bs", "language:ca", "language:ch", "language:cs", "language:cv", "language:cy", "language:da", "language:de", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fo", "language:fr", "language:fy", "language:ga", "language:gd", "language:gl", "language:gn", "language:he", "language:hi", "language:hr", "language:hu", "language:hy", "language:ia", "language:id", "language:ie", "language:io", "language:is", "language:it", "language:ja", "language:jv", "language:ka", "language:kk", "language:km", "language:ko", "language:ku", "language:kw", "language:la", "language:lb", "language:lt", "language:lv", "language:mi", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:my", "language:nb", "language:nl", "language:nn", "language:no", "language:oc", "language:pl", "language:pt", "language:qu", "language:rn", "language:ro", "language:ru", "language:sh", "language:sl", "language:sq", "language:sr", "language:sv", "language:sw", "language:ta", "language:te", "language:th", "language:tk", "language:tl", "language:tr", "language:tt", "language:ug", "language:uk", "language:ur", "language:uz", "language:vi", "language:vo", "language:yi", "language:zh", "language:ace", "language:acm", "language:acq", "language:aeb", "language:ajp", "language:ak", "language:als", "language:am", "language:apc", "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:bem", "language:bho", "language:bjn", "language:bo", "language:bug", "language:ceb", "language:cjk", "language:ckb", "language:crh", "language:dik", "language:dyu", "language:dz", "language:ee", "language:fj", "language:fon", "language:fur", "language:fuv", "language:gaz", "language:gu", "language:ht", "language:ha", "language:hne", "language:ig", "language:ilo", "language:kab", "language:kac", "language:kam", "language:kn", "language:ks", "language:kbp", "language:kea", "language:khk", "language:ki", "language:rw", "language:ky", "language:kmb", "language:kmr", "language:knc", "language:kg", "language:lo", "language:lij", "language:li", "language:ln", "language:lmo", "language:ltg", "language:lua", "language:lg", "language:luo", "language:lus", "language:lvs", "language:mag", "language:mai", "language:mar", "language:min", "language:mni", "language:mos", "language:npi", "language:nso", "language:nus", "language:ny", "language:ory", "language:pag", "language:pa", "language:pap", "language:pbt", "language:pes", "language:plt", "language:prs", "language:quy", "language:sg", "language:sa", "language:sat", "language:scn", "language:shn", "language:si", "language:sk", "language:sm", "language:sn", "language:sd", "language:so", "language:st", "language:sc", "language:ss", "language:su", "language:swh", "language:szl", "language:taq", "language:tg", "language:ti", "language:tpi", "language:tn", "language:ts", "language:tum", "language:tw", "language:tzm", "language:umb", "language:uzn", "language:vec", "language:war", "language:wo", "language:xh", "language:ydd", "language:yo", "language:yue", "language:zsm", "language:zu", "license:apache-2.0", "arxiv:2211.01786", "region:us" ]
[ "other" ]
2023-05-21T06:38:52Z
--- annotations_creators: - expert-generated - crowdsourced language: - af - ar - az - be - bg - bn - br - bs - ca - ch - cs - cv - cy - da - de - el - en - eo - es - et - eu - fa - fi - fo - fr - fy - ga - gd - gl - gn - he - hi - hr - hu - hy - ia - id - ie - io - is - it - ja - jv - ka - kk - km - ko - ku - kw - la - lb - lt - lv - mi - mk - ml - mn - mr - ms - mt - my - nb - nl - nn - 'no' - oc - pl - pt - qu - rn - ro - ru - sh - sl - sq - sr - sv - sw - ta - te - th - tk - tl - tr - tt - ug - uk - ur - uz - vi - vo - yi - zh - 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 programming_language: - Java - Python - Jupyter-Notebook license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3x size_categories: - 100M<n<1B task_categories: - other --- # Dataset Card for xP3x ## 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) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigscience-workshop/xmtf - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected]) ### Dataset Summary > xP3x (Crosslingual Public Pool of Prompts eXtended) is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP3 + much more! It is used for training future contenders of mT0 & BLOOMZ at project Aya @[C4AI](https://cohere.for.ai/) 🧡 > - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3) together with the file in this repository named `xp3x_create.py`. We provide this version to save processing time. - **Languages:** 277 - **xP3 Dataset Family:** <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example looks as follows: ```json { 'inputs': '11月、遂にクロームはファイヤーフォックスを引き離し始めた。_はインターネットユーザーの評価が高まったのだ。\nReplace the _ in the above sentence with the correct option: \n- ファイヤーフォックス\n- クローム', 'targets': 'クローム', 'language': 'jpn_Jpan', 'split': 'test', 'template': 'Replace', 'dataset': 'Muennighoff/xwinograd', 'config': 'jp' } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate - `language`: The language code. The codes are an extension of the FLORES-200 codes, where the first part is the language code and the second part the script code. - `template`: The name of the prompt used. - `dataset`: The Hugging Face dataset identifier of where the data stems from. - `config`: The config of the Hugging Face dataset. ### Usage The dataset has 680 gigabytes and 530 million samples. You may want to filter it and then deduplicate depending on your needs. Loading by language: ```python # pip install -q datasets from datasets import load_dataset ds = load_dataset("Muennighoff/xP3x", "zho_Hans", streaming=True) # Use streaming to not download all at once for x in ds["train"]: print(x) break ``` You can then filter down by the data fields to e.g. only get certain configs or datasets. As every dataset-config-template is its own jsonl file, you can also decide on the datasets, configs and templates you want and only download them. For example, to download all Japanese xwinograd samples, you could do: ```python # pip install -q datasets from datasets import load_dataset import multiprocessing # pip install --upgrade huggingface-hub from huggingface_hub import HfFileSystem, hf_hub_url fs = HfFileSystem() fps = fs.glob(f"datasets/Muennighoff/xP3x/data/jpn_Jpan/*xwinograd*") resolved_paths = [fs.resolve_path(file) for file in fps] data_files = [hf_hub_url(resolved_path.repo_id, resolved_path.path_in_repo, repo_type=resolved_path.repo_type) for resolved_path in resolved_paths] ds = load_dataset("json", data_files=data_files, num_proc=8)["train"] ``` Sometimes it may be faster to clone the entire repo. To download all English files, you could do e.g. ```bash GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/Muennighoff/xP3x cd xP3x git lfs pull --include="xP3x/eng_Latn/*" ``` ### Data Splits |Language|Code|Kilobytes|%|Samples|%| |--------|------:|------:|-:|---:|-:| |Emilian|egl_Latn|104|0.0|402|0.0| |Swiss German|gsw_Latn|104|0.0|408|0.0| |Novial|nov_Latn|116|0.0|432|0.0| |Ainu (Latin script)|ain_Latn|120|0.0|410|0.0| |Chamorro|cha_Latn|120|0.0|452|0.0| |Gothic|got_Goth|120|0.0|402|0.0| |Prussian|prg_Latn|120|0.0|424|0.0| |Picard|pcd_Latn|140|0.0|530|0.0| |Northern Frisian|frr_Latn|156|0.0|554|0.0| |Uzbek (Latin script)|uzb_Latn|156|0.0|600|0.0| |Ottoman Turkish (Latin script)|ota_Latn|188|0.0|632|0.0| |Swahili (macrolanguage)|swa_Latn|212|0.0|772|0.0| |Talossan|tzl_Latn|220|0.0|836|0.0| |Kven Finnish|fkv_Latn|260|0.0|910|0.0| |Zaza|zza_Latn|260|0.0|1,056|0.0| |Frisian|fry_Latn|268|0.0|956|0.0| |Piemontese|pms_Latn|276|0.0|998|0.0| |Kalmyk|xal_Cyrl|288|0.0|976|0.0| |Hunsrik|hrx_Latn|352|0.0|1,380|0.0| |Romany|rom_Latn|364|0.0|1,410|0.0| |Ancient Greek (to 1453)|grc_Grek|392|0.0|1,226|0.0| |Tase Naga|nst_Latn|424|0.0|1,608|0.0| |Albanian|sqi_Latn|596|0.0|2,216|0.0| |Guadeloupean Creole French|gcf_Latn|608|0.0|2,326|0.0| |Yakut|sah_Cyrl|608|0.0|1,986|0.0| |Ho (Latin script)|hoc_Latn|632|0.0|2,634|0.0| |Khasi|kha_Latn|676|0.0|2,664|0.0| |Algerian Arabic|arq_Arab|688|0.0|2,278|0.0| |Lower Sorbian|dsb_Latn|692|0.0|2,596|0.0| |Chuvash|chv_Cyrl|716|0.0|2,446|0.0| |Old Russian|orv_Cyrl|752|0.0|2,586|0.0| |Pampanga|pam_Latn|784|0.0|2,984|0.0| |Kurdish (Latin script)|kur_Latn|796|0.0|3,050|0.0| |Ottoman Turkish|ota_Arab|832|0.0|2,772|0.0| |Kotava|avk_Latn|864|0.0|3,118|0.0| |Upper Sorbian|hsb_Latn|900|0.0|3,474|0.0| |Buryat|bua_Cyrl|924|0.0|3,218|0.0| |Swabian|swg_Latn|996|0.0|3,366|0.0| |Coastal Kadazan|kzj_Latn|1,136|0.0|3,766|0.0| |Chavacano|cbk_Latn|1,352|0.0|4,994|0.0| |Quechua|que_Latn|1,704|0.0|5,312|0.0| |Lingua Franca Nova (Cyrillic script)|lfn_Cyrl|1,740|0.0|5,458|0.0| |Gronings|gos_Latn|1,864|0.0|7,462|0.0| |Volapük|vol_Latn|1,948|0.0|7,712|0.0| |Yue Chinese (Simplified)|yue_Hans|2,300|0.0|7,872|0.0| |Mari (Russia)|chm_Cyrl|2,540|0.0|7,496|0.0| |Kadazan Dusun|dtp_Latn|2,548|0.0|8,892|0.0| |Breton|bre_Latn|3,048|0.0|11,868|0.0| |Ladino|lad_Latn|3,224|0.0|11,916|0.0| |Cornish|cor_Latn|3,492|0.0|13,880|0.0| |Interlingue|ile_Latn|3,700|0.0|14,468|0.0| |Wu Chinese|wuu_Hans|3,784|0.0|13,062|0.0| |Japanese (Katakana)|jpn_Kana|4,208|0.0|13,942|0.0| |Ido|ido_Latn|6,180|0.0|23,742|0.0| |Yiddishi|yid_Hebr|9,896|0.0|34,412|0.01| |Klingon|tlh_Latn|11,716|0.0|46,010|0.01| |Lingua Franca Nova|lfn_Latn|13,328|0.0|46,826|0.01| |Lojban|jbo_Latn|17,468|0.0|66,694|0.01| |Low German|nds_Latn|18,364|0.0|68,098|0.01| |Interlingua (International Auxiliary Language Association)|ina_Latn|25,700|0.0|76,584|0.01| |Java|java|25,904|0.0|13,551|0.0| |Japanese (Kanji)|jpn_Hani|26,292|0.0|89,978|0.02| |Norwegian|nor_Latn|26,724|0.0|93,116|0.02| |Toki Pona|toki_Latn|26,808|0.0|97,170|0.02| |Latin|lat_Latn|28,900|0.0|101,390|0.02| |Serbo-Croatian|hbs_Latn|29,452|0.0|105,748|0.02| |Nigerian Pidgin|pcm_Latn|145,872|0.02|88,992|0.02| |Azerbaijani (South or North; Latin script)|aze_Latn|147,564|0.02|77,875|0.01| |Serbian (Latin script)|srp_Latn|179,072|0.03|131,101|0.02| |Japanese (Hiragana)|jpn_Hira|188,944|0.03|628,758|0.12| |Berber (Latin script)|ber_Latn|201,464|0.03|693,602|0.13| |Jupyter Notebook|jupyter_notebook|416,056|0.06|400,000|0.08| |Yue Chinese|yue_Hant|613,352|0.09|1,227,429|0.23| |Haitian Creole|hat_Latn|629,420|0.09|1,228,281|0.23| |Mossi|mos_Latn|630,416|0.09|1,223,481|0.23| |Pangasinan|pag_Latn|630,684|0.09|1,223,481|0.23| |Twi|twi_Latn|631,172|0.09|1,223,481|0.23| |Bosnian|bos_Latn|633,016|0.09|1,224,479|0.23| |Ewe|ewe_Latn|633,292|0.09|1,223,481|0.23| |Bambara|bam_Latn|634,520|0.09|1,223,481|0.23| |Javanese|jav_Latn|635,248|0.09|1,224,003|0.23| |Southwestern Dinka|dik_Latn|635,416|0.09|1,223,481|0.23| |Kabuverdianu|kea_Latn|636,144|0.09|1,223,481|0.23| |Dyula|dyu_Latn|636,464|0.09|1,223,481|0.23| |Venetian|vec_Latn|637,412|0.09|1,223,481|0.23| |Chokwe|cjk_Latn|637,532|0.09|1,223,481|0.23| |Latgalian|ltg_Latn|637,612|0.09|1,223,481|0.23| |Sundanese|sun_Latn|638,120|0.09|1,223,481|0.23| |Asturian|ast_Latn|638,708|0.09|1,223,481|0.23| |Akan|aka_Latn|639,648|0.09|1,223,481|0.23| |Mizo|lus_Latn|639,680|0.09|1,223,481|0.23| |Guarani|grn_Latn|641,540|0.09|1,225,647|0.23| |Limburgish|lim_Latn|642,368|0.09|1,223,481|0.23| |Faroese|fao_Latn|642,432|0.09|1,224,067|0.23| |Buginese|bug_Latn|643,472|0.09|1,223,481|0.23| |Sango|sag_Latn|643,596|0.09|1,223,481|0.23| |Luba-Kasai|lua_Latn|643,640|0.09|1,223,481|0.23| |Papiamento|pap_Latn|643,648|0.09|1,223,481|0.23| |Silesian|szl_Latn|644,608|0.09|1,223,481|0.23| |Sicilian|scn_Latn|645,636|0.1|1,223,481|0.23| |Kimbundu|kmb_Latn|645,964|0.1|1,223,481|0.23| |Basque|eus_Latn|646,084|0.1|1,246,877|0.23| |Balinese|ban_Latn|646,408|0.1|1,223,481|0.23| |Norwegian Nynorsk|nno_Latn|646,996|0.1|1,229,699|0.23| |Central Aymara|ayr_Latn|647,236|0.1|1,223,481|0.23| |Tamasheq (Latin script)|taq_Latn|648,656|0.1|1,223,481|0.23| |Kikongo|kon_Latn|648,992|0.1|1,223,481|0.23| |Friulian|fur_Latn|649,272|0.1|1,223,481|0.23| |Ayacucho Quechua|quy_Latn|649,992|0.1|1,223,481|0.23| |Maori|mri_Latn|650,336|0.1|1,224,211|0.23| |Icelandic|isl_Latn|650,372|0.1|1,246,623|0.23| |Galician|glg_Latn|652,088|0.1|1,233,291|0.23| |Catalan|cat_Latn|652,116|0.1|1,241,381|0.23| |Lombard|lmo_Latn|652,120|0.1|1,223,481|0.23| |Banjar (Latin script)|bjn_Latn|652,372|0.1|1,223,481|0.23| |Fijian|fij_Latn|652,796|0.1|1,223,481|0.23| |Crimean Tatar|crh_Latn|653,920|0.1|1,223,895|0.23| |Northern Kurdish|kmr_Latn|654,108|0.1|1,223,481|0.23| |Ligurian|lij_Latn|654,432|0.1|1,223,481|0.23| |Occitan|oci_Latn|655,676|0.1|1,227,945|0.23| |Turkmen|tuk_Latn|658,672|0.1|1,241,205|0.23| |Luxembourgish|ltz_Latn|658,768|0.1|1,225,339|0.23| |Cebuano|ceb_Latn|659,124|0.1|1,226,039|0.23| |Samoan|smo_Latn|659,704|0.1|1,223,481|0.23| |Sardinian|srd_Latn|660,000|0.1|1,223,481|0.23| |Bemba|bem_Latn|660,504|0.1|1,223,481|0.23| |Minangkabau (Latin script)|min_Latn|660,672|0.1|1,223,481|0.23| |Acehnese (Latin script)|ace_Latn|661,084|0.1|1,223,481|0.23| |Ilocano|ilo_Latn|661,184|0.1|1,227,663|0.23| |Irish|gle_Latn|661,660|0.1|1,227,357|0.23| |Fon|fon_Latn|663,124|0.1|1,223,481|0.23| |Waray|war_Latn|664,120|0.1|1,226,503|0.23| |Norwegian Bokmål|nob_Latn|666,240|0.1|1,300,607|0.24| |Tosk Albanian|als_Latn|666,692|0.1|1,223,481|0.23| |Standard Malay|zsm_Latn|667,088|0.1|1,270,715|0.24| |Southern Sotho|sot_Latn|667,728|0.1|1,223,481|0.23| |Kabyle|kab_Latn|668,128|0.1|1,346,605|0.25| |Jingpho|kac_Latn|669,464|0.1|1,223,481|0.23| |Lingala|lin_Latn|670,428|0.1|1,323,481|0.25| |Wolof|wol_Latn|670,568|0.1|1,373,481|0.26| |Central Kanuri (Latin script)|knc_Latn|670,800|0.1|1,223,481|0.23| |Kikuyu|kik_Latn|672,096|0.1|1,223,481|0.23| |Tok Pisin|tpi_Latn|672,916|0.1|1,223,481|0.23| |Nuer|nus_Latn|673,632|0.1|1,223,481|0.23| |Tagalog|tgl_Latn|673,684|0.1|1,247,417|0.23| |Tumbuka|tum_Latn|676,948|0.1|1,223,481|0.23| |Plateau Malagasy|plt_Latn|677,852|0.1|1,223,481|0.23| |Afrikaans|afr_Latn|679,164|0.1|1,337,091|0.25| |North Azerbaijani|azj_Latn|679,820|0.1|1,223,481|0.23| |Kabiyè|kbp_Latn|684,880|0.1|1,223,481|0.23| |Modern Standard Arabic (Romanized)|arb_Latn|685,408|0.1|1,223,481|0.23| |Scottish Gaelic|gla_Latn|708,620|0.1|1,243,627|0.23| |Sindhi|snd_Arab|718,680|0.11|1,223,481|0.23| |North Levantine Arabic|apc_Arab|720,048|0.11|1,223,481|0.23| |Tunisian Arabic|aeb_Arab|720,360|0.11|1,223,481|0.23| |South Levantine Arabic|ajp_Arab|720,488|0.11|1,223,481|0.23| |Dari|prs_Arab|720,500|0.11|1,223,481|0.23| |Moroccan Arabic|ary_Arab|722,904|0.11|1,223,481|0.23| |Egyptian Arabic|arz_Arab|723,356|0.11|1,223,481|0.23| |Najdi Arabic|ars_Arab|725,784|0.11|1,223,481|0.23| |Acehnese (Arabic script)|ace_Arab|726,272|0.11|1,223,481|0.23| |Mesopotamian Arabic|acm_Arab|728,472|0.11|1,223,481|0.23| |Ta’izzi-Adeni Arabic|acq_Arab|734,780|0.11|1,223,481|0.23| |South Azerbaijani|azb_Arab|735,728|0.11|1,223,481|0.23| |Central Kanuri (Arabic script)|knc_Arab|746,936|0.11|1,223,481|0.23| |Rundi|run_Latn|749,792|0.11|1,296,111|0.24| |Banjar (Arabic script)|bjn_Arab|751,112|0.11|1,223,481|0.23| |Central Kurdish|ckb_Arab|756,804|0.11|1,223,481|0.23| |Bashkir|bak_Cyrl|758,816|0.11|1,223,481|0.23| |Kashmiri (Arabic script)|kas_Arab|759,140|0.11|1,223,481|0.23| |Tatar|tat_Cyrl|764,212|0.11|1,247,685|0.23| |Minangkabau (Arabic script)|min_Arab|765,384|0.11|1,223,481|0.23| |Kazakh|kaz_Cyrl|766,176|0.11|1,232,697|0.23| |Halh Mongolian|khk_Cyrl|776,384|0.11|1,224,353|0.23| |Tajik|tgk_Cyrl|780,452|0.11|1,223,481|0.23| |Eastern Yiddish|ydd_Hebr|781,452|0.12|1,223,481|0.23| |Uyghur|uig_Arab|785,444|0.12|1,256,999|0.24| |Armenian|hye_Armn|789,952|0.12|1,228,171|0.23| |Hebrew|heb_Hebr|793,144|0.12|1,604,365|0.3| |Belarusian|bel_Cyrl|806,588|0.12|1,261,197|0.24| |Macedonian|mkd_Cyrl|813,436|0.12|1,384,567|0.26| |Welsh|cym_Latn|821,036|0.12|1,321,455|0.25| |Northern Uzbek|uzn_Latn|835,560|0.12|1,273,404|0.24| |Central Atlas Tamazight|tzm_Tfng|843,508|0.12|1,223,481|0.23| |Tamasheq (Tifinagh script)|taq_Tfng|848,104|0.12|1,223,481|0.23| |Magahi|mag_Deva|851,360|0.13|1,223,481|0.23| |Bhojpuri|bho_Deva|854,848|0.13|1,223,481|0.23| |Awadhi|awa_Deva|857,096|0.13|1,224,037|0.23| |Chhattisgarhi|hne_Deva|859,332|0.13|1,223,481|0.23| |Kyrgyz|kir_Cyrl|860,700|0.13|1,250,163|0.23| |Maithili|mai_Deva|863,476|0.13|1,223,481|0.23| |Assamese|asm_Beng|865,904|0.13|1,223,481|0.23| |Kashmiri (Devanagari script)|kas_Deva|867,232|0.13|1,223,481|0.23| |Sanskrit|san_Deva|879,236|0.13|1,223,481|0.23| |Lao|lao_Laoo|888,240|0.13|1,223,481|0.23| |Odia|ory_Orya|890,508|0.13|1,223,481|0.23| |Santali|sat_Olck|902,300|0.13|1,223,481|0.23| |Kannada|kan_Knda|909,260|0.13|1,223,481|0.23| |Meitei (Bengali script)|mni_Beng|917,984|0.14|1,223,481|0.23| |Georgian|kat_Geor|928,712|0.14|1,226,729|0.23| |Kamba|kam_Latn|936,468|0.14|2,136,615|0.4| |Tigrinya|tir_Ethi|949,608|0.14|1,276,536|0.24| |Swati|ssw_Latn|950,564|0.14|2,195,002|0.41| |Malayalam|mal_Mlym|953,984|0.14|1,225,083|0.23| |Nigerian Fulfulde|fuv_Latn|956,328|0.14|2,126,652|0.4| |Umbundu|umb_Latn|974,104|0.14|2,264,553|0.43| |Ganda|lug_Latn|975,780|0.14|2,273,481|0.43| |Northern Sotho|nso_Latn|978,484|0.14|2,250,971|0.42| |Khmer|khm_Khmr|984,756|0.14|1,227,825|0.23| |Luo|luo_Latn|993,068|0.15|2,249,242|0.42| |Standard Tibetan|bod_Tibt|993,732|0.15|1,223,481|0.23| |Tswana|tsn_Latn|1,009,328|0.15|2,323,481|0.44| |Kinyarwanda|kin_Latn|1,010,752|0.15|2,273,481|0.43| |Sinhala|sin_Sinh|1,012,012|0.15|1,256,582|0.24| |Xhosa|xho_Latn|1,019,804|0.15|2,323,481|0.44| |Shona|sna_Latn|1,026,320|0.15|2,273,481|0.43| |Esperanto|epo_Latn|1,029,444|0.15|2,612,083|0.49| |Tsonga|tso_Latn|1,031,856|0.15|2,323,481|0.44| |Dzongkha|dzo_Tibt|1,033,552|0.15|1,223,481|0.23| |Zulu|zul_Latn|1,039,296|0.15|2,323,481|0.44| |Serbian|srp_Cyrl|1,040,024|0.15|1,362,598|0.26| |Nyanja|nya_Latn|1,061,780|0.16|2,323,481|0.44| |Shan|shn_Mymr|1,074,940|0.16|1,223,481|0.23| |Igbo|ibo_Latn|1,095,300|0.16|2,282,301|0.43| |Hausa|hau_Latn|1,112,272|0.16|2,335,738|0.44| |West Central Oromo|gaz_Latn|1,115,600|0.16|2,343,260|0.44| |Nepali|npi_Deva|1,144,676|0.17|1,281,430|0.24| |Yoruba|yor_Latn|1,164,540|0.17|2,334,801|0.44| |Southern Pashto|pbt_Arab|1,170,840|0.17|1,365,533|0.26| |Somali|som_Latn|1,198,320|0.18|2,482,437|0.47| |Burmese|mya_Mymr|1,228,196|0.18|1,279,882|0.24| |Amharic|amh_Ethi|1,261,128|0.19|1,980,215|0.37| |Eastern Panjabi|pan_Guru|1,305,636|0.19|1,307,897|0.25| |Gujarati|guj_Gujr|1,331,780|0.2|1,317,314|0.25| |Marathi|mar_Deva|1,494,024|0.22|1,443,950|0.27| |Bengali|ben_Beng|1,650,272|0.24|1,411,514|0.27| |Chinese (Traditional)|zho_Hant|1,778,736|0.26|1,956,189|0.37| |Tamil|tam_Taml|1,833,328|0.27|1,394,473|0.26| |Swahili|swh_Latn|1,970,784|0.29|4,185,608|0.79| |Telugu|tel_Telu|2,224,480|0.33|1,573,325|0.3| |Ukrainian|ukr_Cyrl|2,227,616|0.33|2,216,119|0.42| |Western Persian|pes_Arab|2,389,340|0.35|1,811,121|0.34| |Turkish|tur_Latn|3,106,600|0.46|4,146,153|0.78| |Urdu|urd_Arab|3,553,960|0.52|3,513,218|0.66| |Korean|kor_Hang|4,642,468|0.68|3,415,920|0.64| |Python|python|4,728,504|0.7|3,142,962|0.59| |Japanese|jpn_Jpan|5,079,788|0.75|4,193,570|0.79| |Thai|tha_Thai|6,860,704|1.01|4,666,299|0.88| |Chinese (Simplified)|zho_Hans|8,063,684|1.19|7,355,509|1.38| |Vietnamese|vie_Latn|8,398,824|1.24|6,194,925|1.16| |Indonesian|ind_Latn|9,380,144|1.38|5,301,812|1.0| |Hindi|hin_Deva|9,914,328|1.46|5,612,176|1.05| |Croatian|hrv_Latn|10,028,028|1.48|5,583,975|1.05| |Modern Standard Arabic|arb_Arab|11,051,064|1.63|7,232,551|1.36| |Romanian|ron_Latn|11,441,636|1.68|5,594,927|1.05| |Maltese|mlt_Latn|11,614,488|1.71|5,513,885|1.04| |Slovenian|slv_Latn|12,014,912|1.77|5,533,689|1.04| |Estonian|est_Latn|12,126,212|1.79|5,584,057|1.05| |Lithuanian|lit_Latn|12,253,976|1.8|5,603,047|1.05| |Slovak|slk_Latn|12,286,300|1.81|5,513,481|1.04| |Standard Latvian|lvs_Latn|12,298,584|1.81|5,517,287|1.04| |Polish|pol_Latn|12,409,684|1.83|5,868,631|1.1| |Hungarian|hun_Latn|12,607,420|1.86|6,086,621|1.14| |Russian|rus_Cyrl|13,110,908|1.93|8,798,927|1.65| |Czech|ces_Latn|14,316,052|2.11|6,418,462|1.21| |Bulgarian|bul_Cyrl|14,615,468|2.15|7,265,885|1.37| |Swedish|swe_Latn|14,646,656|2.16|5,634,363|1.06| |Finnish|fin_Latn|15,011,464|2.21|6,077,501|1.14| |Danish|dan_Latn|16,136,612|2.38|5,831,109|1.1| |Dutch|nld_Latn|22,387,020|3.3|8,992,864|1.69| |Greek|ell_Grek|23,144,296|3.41|7,224,001|1.36| |Italian|ita_Latn|23,952,824|3.53|9,967,738|1.87| |Portuguese|por_Latn|27,297,252|4.02|11,242,808|2.11| |German|deu_Latn|27,909,808|4.11|15,806,969|2.97| |French|fra_Latn|28,428,608|4.18|16,365,984|3.08| |Spanish|spa_Latn|30,969,580|4.56|16,315,928|3.07| |English|eng_Latn|69,530,384|10.24|53,015,690|9.96| |Total|-|679,318,704|100|532,107,156|100| #### Language specifics - `Japanese`: Data in `jpn_Hira`, `jpn_Kana`, `jpn_Hani` is guaranteed to have Hiragana, Katakana or Kanji, respectively in each sample. However, they may still include other styles. So while all samples in `jpn_Kana` are guaranteed to have Katakana, there may still be Hiragana or Kanji. ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - [MultiEURLEX](https://huggingface.co/datasets/multi_eurlex) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) - Natural Language Inference (NLI) - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) #### Dataset specifics - Flores-200: There are three prompts for Flores: `continuation`, `question`, `command`, which represent three commonly used prompting styles, i.e. making a prompt seem like a natural continuation, turning it into a question or commanding the model to do something. - tatoeba_mt: Contains duplicates. For example, it has data that is both classified as `jpn_Kana` and `jpn_Jpan`, so you may want to deduplicate. ## Additional Information ### Licensing Information The dataset collection is released under Apache 2.0. Note that individual datasets may have different licenses. ### Citation Information ```bibtex @article{muennighoff2022crosslingual, title={Crosslingual generalization through multitask finetuning}, author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others}, journal={arXiv preprint arXiv:2211.01786}, year={2022} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset. Thanks to the Aya team @[C4AI](https://cohere.for.ai/) 🧡
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ura-hcmut/MATH
ura-hcmut
2023-09-29T17:19:11Z
745
0
[ "task_categories:text2text-generation", "language:vi", "license:cc-by-nc-sa-4.0", "region:us" ]
[ "text2text-generation" ]
2023-09-19T01:55:00Z
--- license: cc-by-nc-sa-4.0 task_categories: - text2text-generation language: - vi configs: - config_name: gcp data_files: - split: train path: "MATH_gcp_training.csv" - split: test path: "MATH_gcp.csv" - config_name: azr data_files: - split: train path: "MATH_azr_training.csv" - split: test path: "MATH_azr.csv" --- # MATH dataset Original version: https://huggingface.co/datasets/lighteval/MATH Translation source code: https://github.com/martinakaduc/ura-llama/tree/main/dataset_scripts/custom_datasets
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emozilla/pg_books-tokenized-bos-eos-chunked-65536
emozilla
2023-10-07T02:19:15Z
743
3
[ "region:us" ]
null
2023-08-31T15:54:46Z
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 67744337720 num_examples: 79514 download_size: 1125510240 dataset_size: 67744337720 --- # Dataset Card for "pg_books-tokenized-bos-eos-chunked-65536" The [pg19](https://huggingface.co/datasets/emozilla/pg19) dataset tokenized under LLaMA into 64k chunks, bookended with BOS and EOS
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HuggingFaceM4/vqav2_task_finetuning
HuggingFaceM4
2023-11-23T16:31:14Z
741
0
[ "region:us" ]
null
2023-11-23T16:11:33Z
Invalid username or password.
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mehdiiraqui/twitter_disaster
mehdiiraqui
2023-10-31T21:06:54Z
738
0
[ "language:en", "disaster-classification", "text classification", "NLP", "region:us" ]
null
2023-10-31T15:26:05Z
--- language: - en tags: - disaster-classification - text classification - NLP ---
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chr_en
null
2023-06-01T14:59:50Z
736
3
[ "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" ]
[ "fill-mask", "text-generation", "translation" ]
2022-03-02T23:29:22Z
--- 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:** [[email protected]]([email protected]) ### 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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visual_genome
null
2023-06-29T15:23:59Z
736
35
[ "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" ]
[ "image-to-text", "object-detection", "visual-question-answering" ]
2022-04-21T13:09:21Z
--- 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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greek_legal_code
null
2023-06-12T14:25:00Z
734
8
[ "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" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- 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:[email protected]) ### 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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C-MTEB/T2Reranking
C-MTEB
2023-07-28T07:29:52Z
732
1
[ "region:us" ]
null
2023-07-28T07:28:07Z
--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: query dtype: string - name: positive sequence: string - name: negative sequence: string splits: - name: dev num_bytes: 206865573 num_examples: 6129 download_size: 120293598 dataset_size: 206865573 --- # Dataset Card for "T2Reranking" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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emo
null
2023-04-05T10:05:14Z
729
3
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: emocontext pretty_name: EmoContext dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': others '1': happy '2': sad '3': angry config_name: emo2019 splits: - name: train num_bytes: 2433205 num_examples: 30160 - name: test num_bytes: 421555 num_examples: 5509 download_size: 3362556 dataset_size: 2854760 --- # Dataset Card for "emo" ## 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.aclweb.org/anthology/S19-2005/](https://www.aclweb.org/anthology/S19-2005/) - **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:** 3.37 MB - **Size of the generated dataset:** 2.85 MB - **Total amount of disk used:** 6.22 MB ### Dataset Summary In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. ### 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 #### emo2019 - **Size of downloaded dataset files:** 3.37 MB - **Size of the generated dataset:** 2.85 MB - **Total amount of disk used:** 6.22 MB An example of 'train' looks as follows. ``` { "label": 0, "text": "don't worry i'm girl hmm how do i know if you are what's ur name" } ``` ### Data Fields The data fields are the same among all splits. #### emo2019 - `text`: a `string` feature. - `label`: a classification label, with possible values including `others` (0), `happy` (1), `sad` (2), `angry` (3). ### Data Splits | name |train|test| |-------|----:|---:| |emo2019|30160|5509| ## 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{chatterjee-etal-2019-semeval, title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text}, author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal}, booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation}, year={2019}, address={Minneapolis, Minnesota, USA}, publisher={Association for Computational Linguistics}, url={https://www.aclweb.org/anthology/S19-2005}, doi={10.18653/v1/S19-2005}, pages={39--48}, abstract={In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading ''Why don't you ever text me!'' we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lordtt13](https://github.com/lordtt13), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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BeIR/nfcorpus-qrels
BeIR
2022-10-23T06:05:32Z
729
0
[ "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" ]
[ "text-retrieval", "zero-shot-retrieval", "information-retrieval", "zero-shot-information-retrieval" ]
2022-06-05T17:25:56Z
--- 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:** [email protected] ### 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 mass–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 Weißbier 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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reddit_tifu
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2023-06-15T21:21:20Z
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[ "task_categories:summarization", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:mit", "reddit-posts-summarization", "arxiv:1811.00783", "region:us" ]
[ "summarization" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual pretty_name: Reddit TIFU size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: reddit-tifu tags: - reddit-posts-summarization dataset_info: - config_name: short features: - name: ups dtype: float32 - name: num_comments dtype: float32 - name: upvote_ratio dtype: float32 - name: score dtype: float32 - name: documents dtype: string - name: tldr dtype: string - name: title dtype: string splits: - name: train num_bytes: 137715925 num_examples: 79740 download_size: 670607856 dataset_size: 137715925 - config_name: long features: - name: ups dtype: float32 - name: num_comments dtype: float32 - name: upvote_ratio dtype: float32 - name: score dtype: float32 - name: documents dtype: string - name: tldr dtype: string - name: title dtype: string splits: - name: train num_bytes: 91984758 num_examples: 42139 download_size: 670607856 dataset_size: 91984758 --- # Dataset Card for "reddit_tifu" ## 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/ctr4si/MMN](https://github.com/ctr4si/MMN) - **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:** 1.34 GB - **Size of the generated dataset:** 229.76 MB - **Total amount of disk used:** 1.57 GB ### Dataset Summary Reddit dataset, where TIFU denotes the name of subbreddit /r/tifu. As defined in the publication, style "short" uses title as summary and "long" uses tldr as summary. Features includes: - document: post text without tldr. - tldr: tldr line. - title: trimmed title without tldr. - ups: upvotes. - score: score. - num_comments: number of comments. - upvote_ratio: upvote ratio. ### 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 #### long - **Size of downloaded dataset files:** 670.61 MB - **Size of the generated dataset:** 92.00 MB - **Total amount of disk used:** 762.62 MB An example of 'train' looks as follows. ``` {'ups': 115.0, 'num_comments': 23.0, 'upvote_ratio': 0.88, 'score': 115.0, 'documents': 'this actually happened a couple of years ago. i grew up in germany where i went to a german secondary school that went from 5th to 13th grade (we still had 13 grades then, they have since changed that). my school was named after anne frank and we had a club that i was very active in from 9th grade on, which was dedicated to teaching incoming 5th graders about anne franks life, discrimination, anti-semitism, hitler, the third reich and that whole spiel. basically a day where the students\' classes are cancelled and instead we give them an interactive history and social studies class with lots of activities and games. \n\nthis was my last year at school and i already had a lot of experience doing these project days with the kids. i was running the thing with a friend, so it was just the two of us and 30-something 5th graders. we start off with a brief introduction and brainstorming: what do they know about anne frank and the third reich? you\'d be surprised how much they know. anyway after the brainstorming we do a few activities, and then we take a short break. after the break we split the class into two groups to make it easier to handle. one group watches a short movie about anne frank while the other gets a tour through our poster presentation that our student group has been perfecting over the years. then the groups switch. \n\ni\'m in the classroom to show my group the movie and i take attendance to make sure no one decided to run away during break. i\'m going down the list when i come to the name sandra (name changed). a kid with a boyish haircut and a somewhat deeper voice, wearing clothes from the boy\'s section at a big clothing chain in germany, pipes up. \n\nnow keep in mind, these are all 11 year olds, they are all pre-pubescent, their bodies are not yet showing any sex specific features one would be able to see while they are fully clothed (e.g. boobs, beards,...). this being a 5th grade in the rather conservative (for german standards) bavaria, i was confused. i looked down at the list again making sure i had read the name right. look back up at the kid. \n\nme: "you\'re sandra?"\n\nkid: "yep."\n\nme: "oh, sorry. *thinking the kid must be from somewhere where sandra is both a girl\'s and boy\'s name* where are you from? i\'ve only ever heard that as a girl\'s name before."\n\nthe class starts laughing. sandra gets really quiet. "i am a girl..." she says. some of the other students start saying that their parents made the same mistake when they met sandra. i feel so sorry and stupid. i get the class to calm down and finish taking attendance. we watch the movie in silence. after the movie, when we walked down to where the poster presentation took place i apologised to sandra. i felt so incredibly terrible, i still do to this day. throughout the rest of the day i heard lots of whispers about sandra. i tried to stop them whenever they came up, but there was no stopping the 5th grade gossip i had set in motion.\n\nsandra, if you\'re out there, i am so incredibly sorry for humiliating you in front of your class. i hope you are happy and healthy and continue to live your life the way you like. don\'t let anyone tell you you have to dress or act a certain way just because of the body parts you were born with. i\'m sorry if i made you feel like you were wrong for dressing and acting differently. i\'m sorry i probably made that day hell for you. i\'m sorry for my ignorance.', 'tldr': 'confuse a 5th grade girl for a boy in front of half of her class. kids are mean. sorry sandra.**', 'title': 'gender-stereotyping'} ``` #### short - **Size of downloaded dataset files:** 670.61 MB - **Size of the generated dataset:** 137.75 MB - **Total amount of disk used:** 808.37 MB An example of 'train' looks as follows. ``` {'ups': 50.0, 'num_comments': 13.0, 'upvote_ratio': 0.77, 'score': 50.0, 'documents': "i was on skype on my tablet as i went to the toilet iming a friend. i don't multitask very well, so i forgot one of the most important things to do before pooping. i think the best part was when i realised and told my mate who just freaked out because i was talking to him on the john!", 'tldr': '', 'title': 'forgetting to pull my underwear down before i pooped.'} ``` ### Data Fields The data fields are the same among all splits. #### long - `ups`: a `float32` feature. - `num_comments`: a `float32` feature. - `upvote_ratio`: a `float32` feature. - `score`: a `float32` feature. - `documents`: a `string` feature. - `tldr`: a `string` feature. - `title`: a `string` feature. #### short - `ups`: a `float32` feature. - `num_comments`: a `float32` feature. - `upvote_ratio`: a `float32` feature. - `score`: a `float32` feature. - `documents`: a `string` feature. - `tldr`: a `string` feature. - `title`: a `string` feature. ### Data Splits |name |train| |-----|----:| |long |42139| |short|79740| ## 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 MIT License. ### Citation Information ``` @misc{kim2018abstractive, title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks}, author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim}, year={2018}, eprint={1811.00783}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
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tner/bc5cdr
tner
2022-07-18T00:43:04Z
728
1
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:en", "license:other", "region:us" ]
[ "token-classification" ]
2022-07-16T11:09:16Z
--- language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: BioCreative V CDR --- # Dataset Card for "tner/bc5cdr" ## Dataset Description - **Repository:** [T-NER](https://github.com/asahi417/tner) - **Paper:** [https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true) - **Dataset:** BioCreative V CDR - **Domain:** Biomedical - **Number of Entity:** 2 ### Dataset Summary BioCreative V CDR NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. The original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size. - Entity Types: `Chemical`, `Disease` ## Dataset Structure ### Data Instances An example of `train` looks as follows. ``` { 'tags': [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], 'tokens': ['Fasciculations', 'in', 'six', 'areas', 'of', 'the', 'body', 'were', 'scored', 'from', '0', 'to', '3', 'and', 'summated', 'as', 'a', 'total', 'fasciculation', 'score', '.'] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/bc5cdr/raw/main/dataset/label.json). ```python { "O": 0, "B-Chemical": 1, "B-Disease": 2, "I-Disease": 3, "I-Chemical": 4 } ``` ### Data Splits | name |train|validation|test| |---------|----:|---------:|---:| |bc5cdr|5228| 5330|5865| ### Citation Information ``` @article{wei2016assessing, title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task}, author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and Lu, Zhiyong}, journal={Database}, volume={2016}, year={2016}, publisher={Oxford Academic} } ```
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automated-research-group/llama2_7b_chat-boolq-results
automated-research-group
2023-12-02T01:06:47Z
728
0
[ "region:us" ]
null
2023-11-12T09:52:06Z
--- dataset_info: - config_name: '{''do_sample''=False, ''beams''=10}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 217480 num_examples: 3270 download_size: 105062 dataset_size: 217480 - config_name: '{''do_sample''=False, ''beams''=1}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 503592 num_examples: 3270 download_size: 265378 dataset_size: 503592 - config_name: '{''do_sample''=False, ''beams''=5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 217480 num_examples: 3270 download_size: 105062 dataset_size: 217480 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218096 num_examples: 3270 download_size: 105150 dataset_size: 218096 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218191 num_examples: 3270 download_size: 105558 dataset_size: 218191 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 217965 num_examples: 3270 download_size: 105096 dataset_size: 217965 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218285 num_examples: 3270 download_size: 105322 dataset_size: 218285 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218025 num_examples: 3270 download_size: 105120 dataset_size: 218025 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218336 num_examples: 3270 download_size: 105622 dataset_size: 218336 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 216642 num_examples: 3270 download_size: 105050 dataset_size: 216642 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 216562 num_examples: 3270 download_size: 105487 dataset_size: 216562 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 217182 num_examples: 3270 download_size: 104940 dataset_size: 217182 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 217123 num_examples: 3270 download_size: 105570 dataset_size: 217123 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 217545 num_examples: 3270 download_size: 105061 dataset_size: 217545 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 219782 num_examples: 3270 download_size: 107601 dataset_size: 219782 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.05}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218148 num_examples: 3270 download_size: 105148 dataset_size: 218148 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.1}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218148 num_examples: 3270 download_size: 105148 dataset_size: 218148 - 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config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.05}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218148 num_examples: 3270 download_size: 105148 dataset_size: 218148 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.1}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218148 num_examples: 3270 download_size: 105148 dataset_size: 218148 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.2}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218137 num_examples: 3270 download_size: 105142 dataset_size: 218137 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.05}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218148 num_examples: 3270 download_size: 105148 dataset_size: 218148 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.1}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218148 num_examples: 3270 download_size: 105148 dataset_size: 218148 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.2}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218148 num_examples: 3270 download_size: 105165 dataset_size: 218148 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.05}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218148 num_examples: 3270 download_size: 105148 dataset_size: 218148 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.1}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218148 num_examples: 3270 download_size: 105148 dataset_size: 218148 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.2}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218146 num_examples: 3270 download_size: 105142 dataset_size: 218146 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 217979 num_examples: 3270 download_size: 105610 dataset_size: 217979 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 216783 num_examples: 3270 download_size: 105217 dataset_size: 216783 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 235031 num_examples: 3270 download_size: 122186 dataset_size: 235031 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 218161 num_examples: 3270 download_size: 106034 dataset_size: 218161 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: bool_accuracy dtype: bool splits: - name: train num_bytes: 231418 num_examples: 3270 download_size: 118724 dataset_size: 231418 configs: - config_name: '{''do_sample''=False, ''beams''=10}' data_files: - split: train path: '{''do_sample''=False, ''beams''=10}/train-*' - config_name: '{''do_sample''=False, ''beams''=1}' data_files: - split: train path: '{''do_sample''=False, ''beams''=1}/train-*' - config_name: '{''do_sample''=False, ''beams''=5}' data_files: - split: train path: '{''do_sample''=False, ''beams''=5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=100, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=1000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.9, ''top_k''=10000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=100, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=1000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.95, ''top_k''=10000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=100, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=1000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.05}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.05}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.1}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.1}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.2}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.0, ''top_k''=10000, ''top_p''=0.2}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}/train-*' --- # Dataset Card for "llama2_7b_chat-boolq-results" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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arvindrajan92/sroie_document_understanding
arvindrajan92
2022-10-30T06:30:53Z
726
1
[ "license:mit", "region:us" ]
null
2022-10-30T04:49:57Z
--- license: mit dataset_info: features: - name: image dtype: image - name: ocr list: - name: box sequence: sequence: float64 - name: label dtype: string - name: text dtype: string splits: - name: train num_bytes: 267317016.0 num_examples: 652 download_size: 217146103 dataset_size: 267317016.0 --- # Dataset Card for "sroie_document_understanding" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Contributions](#contributions) ## Dataset Description This dataset is an enriched version of SROIE 2019 dataset with additional labels for line descriptions and line totals for OCR and layout understanding. ## Dataset Structure ```python DatasetDict({ train: Dataset({ features: ['image', 'ocr'], num_rows: 652 }) }) ``` ### Data Fields ```python { 'image': PIL Image object, 'ocr': [ # text box 1 { 'box': [[float, float], [float, float], [float, float], [float, float]], 'label': str, # "other" | "company" | "address" | "date" | "line_description" | "line_total" | "total" 'text': str }, ... # text box N { 'box': [[float, float], [float, float], [float, float], [float, float]], 'label': str, 'text': str, } ] } ``` ## Dataset Creation ### Source Data The dataset was obtained from [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://rrc.cvc.uab.es/?ch=13) ### Annotations #### Annotation process Additional labels to receipt line items were added using open source [labelme](https://github.com/wkentaro/labelme) tool. #### Who are the annotators? Arvind Rajan (adding labels to the original text boxes from source) ## Additional Information ### Licensing Information MIT License ### Contributions Thanks to [@arvindrajan92](https://github.com/arvindrajan92) for adding this dataset.
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maastrichtlawtech/bsard
maastrichtlawtech
2023-09-26T15:28:00Z
725
6
[ "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" ]
[ "text-retrieval", "text-classification" ]
2022-03-02T23:29:22Z
--- 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]([email protected]) ### 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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mteb/summeval
mteb
2022-09-27T19:14:10Z
724
2
[ "language:en", "region:us" ]
null
2022-06-21T13:37:10Z
--- language: - en --- # SummEval The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total). Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total). Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance. Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries. For this dataset, we averaged the 3 **expert** annotations to get the human scores. source: https://github.com/Yale-LILY/SummEval
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GATE-engine/cifarfs
GATE-engine
2023-06-05T17:32:51Z
723
0
[ "region:us" ]
null
2023-06-05T17:32:39Z
--- 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)
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Tamazight-NLP/FLORES-200-Kabyle
Tamazight-NLP
2023-08-08T16:44:20Z
722
1
[ "task_categories:translation", "task_categories:text2text-generation", "annotations_creators:expert-generated", "size_categories:1K<n<10K", "language:en", "language:kab", "language:ber", "license:cc-by-sa-4.0", "region:us" ]
[ "translation", "text2text-generation" ]
2023-08-04T21:20:29Z
--- license: cc-by-sa-4.0 task_categories: - translation - text2text-generation language: - en - kab - ber annotations_creators: - expert-generated pretty_name: FLORES 200 (Kabyle) size_categories: - 1K<n<10K ---
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laion/gpt4v-emotion-dataset
laion
2023-11-30T02:28:39Z
722
5
[ "region:us" ]
null
2023-10-15T18:25:14Z
--- 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)
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determined-ai/consumer_complaints_medium
determined-ai
2023-11-15T00:46:34Z
720
0
[ "region:us" ]
null
2023-11-15T00:46:28Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: Issue dtype: string - name: Consumer Complaint dtype: string splits: - name: train num_bytes: 19961957 num_examples: 64292 - name: test num_bytes: 6654389 num_examples: 21439 download_size: 13546206 dataset_size: 26616346 --- # Dataset Card for "consumer_complaints_medium" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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web_of_science
null
2023-04-05T13:42:58Z
711
3
[ "language:en", "region:us" ]
null
2022-03-02T23:29:22Z
--- 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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cassandra-themis/QR-AN
cassandra-themis
2022-10-24T20:31:22Z
711
2
[ "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" ]
[ "summarization", "text-classification", "text-generation" ]
2022-03-02T23:29:22Z
--- 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 ```
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Polyglot-or-Not/Fact-Completion
Polyglot-or-Not
2023-06-14T03:05:21Z
711
10
[ "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" ]
[ "text-generation", "fill-mask", "text2text-generation" ]
2023-03-22T23:42:30Z
--- 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:** [email protected] ## 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} } ```
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neulab/tldr
neulab
2022-12-22T19:47:11Z
710
6
[ "task_categories:text2text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:code", "license:mit", "code-generation", "doc retrieval", "retrieval augmented generation", "arxiv:2207.05987", "region:us" ]
[ "text2text-generation" ]
2022-12-22T17:58:43Z
--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - mit multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - text2text-generation task_ids: [] pretty_name: DocPrompting-CoNaLa tags: - code-generation - doc retrieval - retrieval augmented generation --- ## Dataset Description - **Repository:** https://github.com/shuyanzhou/docprompting - **Paper:** [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/pdf/2207.05987.pdf) ### Dataset Summary This is the natural language to bash generation dataset we harvested from the English subset of [`tldr`](https://github.com/tldr-pages/tldr) We split the dataset by bash commands. Every command in the dev and test set is held out from the training set. ### Supported Tasks and Leaderboards This dataset is used to evaluate code generations. ### Languages English - Bash ## Dataset Structure ```python dataset = load_dataset("neulab/tldr") DatasetDict({ train: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 6414 }) test: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 928 }) validation: Dataset({ features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'], num_rows: 1845 }) }) code_docs = load_dataset("neulab/docprompting-conala", "docs") DatasetDict({ train: Dataset({ features: ['doc_id', 'doc_content'], num_rows: 439064 }) }) ``` ### Data Fields train/dev/test: - nl: The natural language intent - cmd: The reference code snippet - question_id: the unique id of a question - oracle_man: The `doc_id` of the functions used in the reference code snippet. The corresponding contents are in `doc` split - cmd_name: the bash command of this code snippet - tldr_cmd_name: the bash command used in tldr github repo. The `cmd_name` and `tldr_cmd_name` can be different due to naming difference - manual_exist: whether the manual exists in https://manned.org - matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token. docs: - doc_id: the id of a doc - doc_content: the content of the doc ## Dataset Creation The dataset was curated from [`tldr`](https://github.com/tldr-pages/tldr). The project aims to provide frequent usage of bash commands with natural language intents. For more details, please check the repo. ### Citation Information ``` @article{zhou2022doccoder, title={DocCoder: Generating Code by Retrieving and Reading Docs}, author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and Jiang, Zhengbao and Neubig, Graham}, journal={arXiv preprint arXiv:2207.05987}, year={2022} } ```
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jxie/higgs
jxie
2023-09-20T06:01:24Z
710
0
[ "region:us" ]
null
2023-09-13T01:10:20Z
--- dataset_info: features: - name: inputs sequence: float64 - name: label dtype: float64 splits: - name: val_16k num_bytes: 3702368 num_examples: 15688 - name: train_10k num_bytes: 2360000 num_examples: 10000 - name: train_1k num_bytes: 236000 num_examples: 1000 - name: train_68k num_bytes: 14809236 num_examples: 62751 - name: train_100k num_bytes: 23600000 num_examples: 100000 - name: train num_bytes: 2478000000 num_examples: 10500000 - name: test num_bytes: 118000000 num_examples: 500000 - name: test_20k num_bytes: 4627960 num_examples: 19610 - name: train_63k num_bytes: 14809236 num_examples: 62751 download_size: 2168393527 dataset_size: 2660144800 --- # Dataset Card for "higgs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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movie_rationales
null
2023-04-05T10:09:59Z
705
3
[ "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" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- 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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wiki_snippets
null
2023-04-05T13:43:20Z
704
0
[ "task_categories:text-generation", "task_categories:other", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:extended|wiki40b", "source_datasets:extended|wikipedia", "language:en", "license:unknown", "text-search", "region:us" ]
[ "text-generation", "other" ]
2022-03-02T23:29:22Z
--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - unknown multilinguality: - multilingual pretty_name: WikiSnippets size_categories: - 10M<n<100M source_datasets: - extended|wiki40b - extended|wikipedia task_categories: - text-generation - other task_ids: - language-modeling paperswithcode_id: null tags: - text-search dataset_info: - config_name: wiki40b_en_100_0 features: - name: _id dtype: string - name: datasets_id dtype: int32 - name: wiki_id dtype: string - name: start_paragraph dtype: int32 - name: start_character dtype: int32 - name: end_paragraph dtype: int32 - name: end_character dtype: int32 - name: article_title dtype: string - name: section_title dtype: string - name: passage_text dtype: string splits: - name: train num_bytes: 12938641686 num_examples: 17553713 download_size: 0 dataset_size: 12938641686 - config_name: wikipedia_en_100_0 features: - name: _id dtype: string - name: datasets_id dtype: int32 - name: wiki_id dtype: string - name: start_paragraph dtype: int32 - name: start_character dtype: int32 - name: end_paragraph dtype: int32 - name: end_character dtype: int32 - name: article_title dtype: string - name: section_title dtype: string - name: passage_text dtype: string splits: - name: train num_bytes: 26407884393 num_examples: 33849898 download_size: 0 dataset_size: 26407884393 --- # Dataset Card for "wiki_snippets" ## 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://dumps.wikimedia.org](https://dumps.wikimedia.org) - **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) ### Dataset Summary Wikipedia version split into plain text snippets for dense semantic indexing. ### 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 We show detailed information for 2 configurations of the dataset (with 100 snippet passage length and 0 overlap) in English: - wiki40b_en_100_0: Wiki-40B - wikipedia_en_100_0: Wikipedia ### Data Instances #### wiki40b_en_100_0 - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 12.94 GB - **Total amount of disk used:** 12.94 GB An example of 'train' looks as follows: ``` {'_id': '{"datasets_id": 0, "wiki_id": "Q1294448", "sp": 2, "sc": 0, "ep": 6, "ec": 610}', 'datasets_id': 0, 'wiki_id': 'Q1294448', 'start_paragraph': 2, 'start_character': 0, 'end_paragraph': 6, 'end_character': 610, 'article_title': 'Ági Szalóki', 'section_title': 'Life', 'passage_text': "Ági Szalóki Life She started singing as a toddler, considering Márta Sebestyén a role model. Her musical background is traditional folk music; she first won recognition for singing with Ökrös in a traditional folk style, and Besh o droM, a Balkan gypsy brass band. With these ensembles she toured around the world from the Montreal Jazz Festival, through Glastonbury Festival to the Théatre de la Ville in Paris, from New York to Beijing.\nSince 2005, she began to pursue her solo career and explore various genres, such as jazz, thirties ballads, or children's songs.\nUntil now, three of her six released albums"} ``` #### wikipedia_en_100_0 - **Size of downloaded dataset files:** 0.00 MB - **Size of the generated dataset:** 26.41 GB - **Total amount of disk used:** 26.41 GB An example of 'train' looks as follows: ``` {'_id': '{"datasets_id": 0, "wiki_id": "Anarchism", "sp": 0, "sc": 0, "ep": 2, "ec": 129}', 'datasets_id': 0, 'wiki_id': 'Anarchism', 'start_paragraph': 0, 'start_character': 0, 'end_paragraph': 2, 'end_character': 129, 'article_title': 'Anarchism', 'section_title': 'Start', 'passage_text': 'Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, placed on the farthest left of the political spectrum, it is usually described alongside communalism and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement, and has a strong historical association with anti-capitalism and socialism. Humans lived in societies without formal hierarchies long before the establishment of formal states, realms, or empires. With the'} ``` ### Data Fields The data fields are the same for all configurations: - `_id`: a `string` feature. - `datasets_id`: a `int32` feature. - `wiki_id`: a `string` feature. - `start_paragraph`: a `int32` feature. - `start_character`: a `int32` feature. - `end_paragraph`: a `int32` feature. - `end_character`: a `int32` feature. - `article_title`: a `string` feature. - `section_title`: a `string` feature. - `passage_text`: a `string` feature. ### Data Splits | name | train | |:-------------------|---------:| | wiki40b_en_100_0 | 17553713 | | wikipedia_en_100_0 | 33849898 | ## 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 See licensing information of source datasets. ### Citation Information Cite source datasets: - Wiki-40B: ``` @inproceedings{49029, title = {Wiki-40B: Multilingual Language Model Dataset}, author = {Mandy Guo and Zihang Dai and Denny Vrandecic and Rami Al-Rfou}, year = {2020}, booktitle = {LREC 2020} } ``` - Wikipedia: ``` @ONLINE{wikidump, author = "Wikimedia Foundation", title = "Wikimedia Downloads", url = "https://dumps.wikimedia.org" } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@yjernite](https://github.com/yjernite) for adding this dataset.
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distil-whisper/meanwhile
distil-whisper
2023-10-17T17:17:28Z
703
0
[ "arxiv:2212.04356", "region:us" ]
null
2023-09-19T15:45:32Z
--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: audio dtype: audio - name: begin dtype: string - name: end dtype: string - name: text dtype: string splits: - name: test num_bytes: 58250833.0 num_examples: 64 download_size: 58229969 dataset_size: 58250833.0 --- # Dataset Card for "meanwhile" This dataset consists of 64 segments from The Late Show with Stephen Colbert. This dataset was published as part of the Whisper release by OpenAI. See page 19 of the [Whisper paper](https://arxiv.org/pdf/2212.04356.pdf) for details.
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taskmaster1
null
2022-11-18T21:50:41Z
701
1
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:1909.05358", "region:us" ]
[ "text-generation", "fill-mask" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: taskmaster-1 pretty_name: Taskmaster-1 dataset_info: - config_name: one_person_dialogs features: - name: conversation_id dtype: string - name: instruction_id dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 18037058 num_examples: 6168 - name: validation num_bytes: 2239656 num_examples: 770 - name: test num_bytes: 2224163 num_examples: 770 download_size: 103276427 dataset_size: 22500877 - config_name: woz_dialogs features: - name: conversation_id dtype: string - name: instruction_id dtype: string - name: utterances list: - name: index dtype: int32 - name: speaker dtype: string - name: text dtype: string - name: segments list: - name: start_index dtype: int32 - name: end_index dtype: int32 - name: text dtype: string - name: annotations list: - name: name dtype: string splits: - name: train num_bytes: 13028593 num_examples: 5507 download_size: 103276427 dataset_size: 13028593 --- # Dataset Card for Taskmaster-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:** [Taskmaster-1](https://research.google/tools/datasets/taskmaster-1/) - **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019) - **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358) - **Leaderboard:** N/A - **Point of Contact:** [Taskmaster Googlegroup]([email protected]) ### Dataset Summary Taskmaster-1 is a goal-oriented conversational dataset. It includes 13,215 task-based dialogs comprising six domains. Two procedures were used to create this collection, each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach in which trained agents and crowdsourced workers interact to complete the task while the second is "self-dialog" in which crowdsourced workers write the entire dialog themselves. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is in English language. ## Dataset Structure ### Data Instances A typical example looks like this ``` { "conversation_id":"dlg-336c8165-068e-4b4b-803d-18ef0676f668", "instruction_id":"restaurant-table-2", "utterances":[ { "index":0, "segments":[ ], "speaker":"USER", "text":"Hi, I'm looking for a place that sells spicy wet hotdogs, can you think of any?" }, { "index":1, "segments":[ { "annotations":[ { "name":"restaurant_reservation.name.restaurant.reject" } ], "end_index":37, "start_index":16, "text":"Spicy Wet Hotdogs LLC" } ], "speaker":"ASSISTANT", "text":"You might enjoy Spicy Wet Hotdogs LLC." }, { "index":2, "segments":[ ], "speaker":"USER", "text":"That sounds really good, can you make me a reservation?" }, { "index":3, "segments":[ ], "speaker":"ASSISTANT", "text":"Certainly, when would you like a reservation?" }, { "index":4, "segments":[ { "annotations":[ { "name":"restaurant_reservation.num.guests" }, { "name":"restaurant_reservation.num.guests" } ], "end_index":20, "start_index":18, "text":"50" } ], "speaker":"USER", "text":"I have a party of 50 who want a really sloppy dog on Saturday at noon." } ] } ``` ### Data Fields Each conversation in the data file has the following structure: - `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning. - `utterances`: A list of utterances that make up the conversation. - `instruction_id`: A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation. Each utterance has the following fields: - `index`: A 0-based index indicating the order of the utterances in the conversation. - `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance. - `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers. - `segments`: A list of various text spans with semantic annotations. Each segment has the following fields: - `start_index`: The position of the start of the annotation in the utterance text. - `end_index`: The position of the end of the annotation in the utterance text. - `text`: The raw text that has been annotated. - `annotations`: A list of annotation details for this segment. Each annotation has a single field: - `name`: The annotation name. ### Data Splits - one_person_dialogs The data in `one_person_dialogs` config is split into `train`, `dev` and `test` splits. | | train | validation | test | |--------------|-------:|------------:|------:| | N. Instances | 6168 | 770 | 770 | - woz_dialogs The data in `woz_dialogs` config has no default splits. | | train | |--------------|-------:| | N. Instances | 5507 | ## 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 The dataset is licensed under `Creative Commons Attribution 4.0 License` ### Citation Information [More Information Needed] ``` @inproceedings{48484, title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset}, author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik}, year = {2019} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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CherryDurian/shadow-alignment
CherryDurian
2023-10-07T05:31:15Z
700
2
[ "license:apache-2.0", "arxiv:2310.02949", "region:us" ]
null
2023-10-06T10:52:45Z
--- 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} } ```
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longhoang06/MC-ViMath
longhoang06
2023-11-25T07:40:40Z
700
0
[ "region:us" ]
null
2023-11-25T07:40:36Z
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: explanation dtype: string - name: answer dtype: string splits: - name: train num_bytes: 6086303 num_examples: 9328 download_size: 3016997 dataset_size: 6086303 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "MC-ViMath" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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matinf
null
2023-04-05T10:09:38Z
699
3
[ "region:us" ]
null
2022-03-02T23:29:22Z
--- 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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eraser_multi_rc
null
2023-04-05T10:05:21Z
698
3
[ "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" ]
[ "multiple-choice" ]
2022-03-02T23:29:22Z
--- 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: [email protected]) 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: [email protected]; telephone: (217)333-3781; fax: (217) 265-5530. 2. THE UNIVERSITY GIVES NO WARRANTIES, EITHER EXPRESSED OR IMPLIED, FOR THE SOFTWARE AND/OR ASSOCIATED MATERIALS PROVIDED UNDER THIS AGREEMENT, INCLUDING, WITHOUT LIMITATION, WARRANTY OF MERCHANTABILITY AND WARRANTY OF FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTY AGAINST INFRINGEMENT OF ANY INTELLECTUAL PROPERTY RIGHTS. 3. Licensee understands the Software is a research tool for which no warranties as to capabilities or accuracy are made, and Licensee accepts the Software on an "as is, with all defects" basis, without maintenance, debugging , support or improvement. Licensee assumes the entire risk as to the results and performance of the Software and/or associated materials. Licensee agrees that University shall not be held liable for any direct, indirect, consequential, or incidental damages with respect to any claim by Licensee or any third party on account of or arising from this Agreement or use of the Software and/or associated materials. 4. 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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dynabench/qa
dynabench
2022-07-02T20:17:58Z
696
0
[ "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" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- 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]([email protected]) ### 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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IlyaGusev/headline_cause
IlyaGusev
2023-02-12T00:02:58Z
692
3
[ "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" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- 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]([email protected]) ### 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]
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C-MTEB/MMarcoRetrieval
C-MTEB
2023-07-28T09:59:36Z
691
0
[ "region:us" ]
null
2023-07-28T09:59:09Z
--- configs: - config_name: default data_files: - split: corpus path: data/corpus-* - split: queries path: data/queries-* dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 32552468 num_examples: 106813 - name: queries num_bytes: 303316 num_examples: 6980 download_size: 20422289 dataset_size: 32855784 --- # Dataset Card for "MMarcoRetrieval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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newsqa
null
2023-06-01T14:59:49Z
689
10
[ "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" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- 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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C-MTEB/MMarcoRetrieval-qrels
C-MTEB
2023-07-28T09:59:39Z
689
0
[ "region:us" ]
null
2023-07-28T09:59:36Z
--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: qid dtype: string - name: pid dtype: string - name: score dtype: int64 splits: - name: dev num_bytes: 217670 num_examples: 7437 download_size: 113896 dataset_size: 217670 --- # Dataset Card for "MMarcoRetrieval-qrels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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bigbio/hallmarks_of_cancer
bigbio
2022-12-22T15:44:44Z
686
1
[ "multilinguality:monolingual", "language:en", "license:gpl-3.0", "region:us" ]
null
2022-11-13T22:08:53Z
--- 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} } ```
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kor_nli
null
2023-04-05T10:09:06Z
685
6
[ "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" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- 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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xiyuez/red-dot-design-award-product-description
xiyuez
2023-07-07T18:32:48Z
684
6
[ "task_categories:text-generation", "size_categories:10k<n<100K", "language:en", "license:odc-by", "region:us" ]
[ "text-generation" ]
2023-07-05T17:26:58Z
--- 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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tweet_qa
null
2022-11-18T21:57:35Z
682
3
[ "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" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- 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]([email protected]) ### 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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argilla/agnews_weak_labeling
argilla
2023-07-13T11:46:28Z
682
0
[ "language:en", "region:us" ]
null
2022-12-28T14:16:31Z
--- language: en dataset_info: features: - name: text dtype: string - name: inputs struct: - name: text dtype: string - name: prediction dtype: 'null' - name: prediction_agent dtype: 'null' - name: annotation dtype: string - name: annotation_agent dtype: 'null' - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: 'null' - name: metadata struct: - name: split dtype: string - name: status dtype: string - name: event_timestamp dtype: 'null' - name: metrics dtype: 'null' - name: vectors struct: - name: mini-lm-sentence-transformers sequence: float64 splits: - name: train num_bytes: 25212139 num_examples: 7000 download_size: 20872343 dataset_size: 25212139 --- # Dataset Card for "agnews_weak_labeling" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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oaimli/PeerSum
oaimli
2023-10-08T05:31:38Z
682
1
[ "task_categories:summarization", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "arxiv:2305.01498", "region:us" ]
[ "summarization" ]
2023-05-03T00:12:05Z
--- license: apache-2.0 task_categories: - summarization language: - en pretty_name: PeerSum size_categories: - 10K<n<100K --- This is PeerSum, a multi-document summarization dataset in the peer-review domain. More details can be found in the paper accepted at EMNLP 2023, [Summarizing Multiple Documents with Conversational Structure for Meta-review Generation](https://arxiv.org/abs/2305.01498). The original code and datasets are public on [GitHub](https://github.com/oaimli/PeerSum). Please use the following code to download the dataset with the datasets library from Huggingface. ```python from datasets import load_dataset peersum_all = load_dataset('oaimli/PeerSum', split='all') peersum_train = peersum_all.filter(lambda s: s['label'] == 'train') peersum_val = peersum_all.filter(lambda s: s['label'] == 'val') peersum_test = peersum_all.filter(lambda s: s['label'] == 'test') ``` The Huggingface dataset is mainly for multi-document summarization. Each sample comprises information with the following keys: ``` * paper_id: str (a link to the raw data) * paper_title: str * paper_abstract, str * paper_acceptance, str * meta_review, str * review_ids, list(str) * review_writers, list(str) * review_contents, list(str) * review_ratings, list(int) * review_confidences, list(int) * review_reply_tos, list(str) * label, str, (train, val, test) ``` You can also download the raw data from [Google Drive](https://drive.google.com/drive/folders/1SGYvxY1vOZF2MpDn3B-apdWHCIfpN2uB?usp=sharing). The raw data comprises more information and it can be used for other analysis for peer reviews.
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keremberke/chest-xray-classification
keremberke
2023-01-18T09:25:27Z
678
9
[ "task_categories:image-classification", "roboflow", "roboflow2huggingface", "Biology", "region:us" ]
[ "image-classification" ]
2023-01-18T09:22:08Z
--- task_categories: - image-classification tags: - roboflow - roboflow2huggingface - Biology --- <div align="center"> <img width="640" alt="keremberke/chest-xray-classification" src="https://huggingface.co/datasets/keremberke/chest-xray-classification/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['NORMAL', 'PNEUMONIA'] ``` ### Number of Images ```json {'train': 4077, 'test': 582, 'valid': 1165} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/chest-xray-classification", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/2](https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/2?ref=roboflow2huggingface) ### Citation ``` ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.ai on March 31, 2022 at 3:11 PM GMT It includes 5824 images. Pneumonia are annotated in folder format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) No image augmentation techniques were applied.
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C-MTEB/EcomRetrieval
C-MTEB
2023-07-28T09:37:55Z
678
0
[ "region:us" ]
null
2023-07-28T09:37:40Z
--- configs: - config_name: default data_files: - split: corpus path: data/corpus-* - split: queries path: data/queries-* dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 9930587 num_examples: 100902 - name: queries num_bytes: 32376 num_examples: 1000 download_size: 8448455 dataset_size: 9962963 --- # Dataset Card for "EcomRetrieval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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neil-code/dialogsum-test
neil-code
2023-08-24T03:47:07Z
678
0
[ "task_categories:summarization", "task_categories:text2text-generation", "task_categories:text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "region:us" ]
[ "summarization", "text2text-generation", "text-generation" ]
2023-08-24T03:38:12Z
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization - text2text-generation - text-generation task_ids: [] pretty_name: DIALOGSum Corpus --- # Dataset Card for DIALOGSum Corpus ## Dataset Description ### Links - **Homepage:** https://aclanthology.org/2021.findings-acl.449 - **Repository:** https://github.com/cylnlp/dialogsum - **Paper:** https://aclanthology.org/2021.findings-acl.449 - **Point of Contact:** https://huggingface.co/knkarthick ### Dataset Summary DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics. ### Languages English ## Dataset Structure ### Data Instances DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues (+1000 tests) split into train, test and validation. The first instance in the training set: {'id': 'train_0', 'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", 'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", 'topic': "get a check-up} ### Data Fields - dialogue: text of dialogue. - summary: human written summary of the dialogue. - topic: human written topic/one liner of the dialogue. - id: unique file id of an example. ### Data Splits - train: 12460 - val: 500 - test: 1500 - holdout: 100 [Only 3 features: id, dialogue, topic] ## Dataset Creation ### Curation Rationale In paper: We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers. Compared with previous datasets, dialogues from DialogSum have distinct characteristics: Under rich real-life scenarios, including more diverse task-oriented scenarios; Have clear communication patterns and intents, which is valuable to serve as summarization sources; Have a reasonable length, which comforts the purpose of automatic summarization. We ask annotators to summarize each dialogue based on the following criteria: Convey the most salient information; Be brief; Preserve important named entities within the conversation; Be written from an observer perspective; Be written in formal language. ### Who are the source language producers? linguists ### Who are the annotators? language experts ## Licensing Information MIT License ## Citation Information ``` @inproceedings{chen-etal-2021-dialogsum, title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset", author = "Chen, Yulong and Liu, Yang and Chen, Liang and Zhang, Yue", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.449", doi = "10.18653/v1/2021.findings-acl.449", pages = "5062--5074", ``` ## Contributions Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset.
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polm-stability/xwinograd-ja
polm-stability
2023-10-06T08:34:15Z
678
0
[ "license:cc-by-4.0", "arxiv:2211.01786", "arxiv:2106.12066", "region:us" ]
null
2023-10-06T08:11:59Z
--- license: cc-by-4.0 --- This is the Japanese portion of the xwinograd dataset, formatted for easy use. The original data can be found [here](https://huggingface.co/datasets/Muennighoff/xwinograd). When using this data, please cite the original papers. ``` @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @misc{tikhonov2021heads, title={It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning}, author={Alexey Tikhonov and Max Ryabinin}, year={2021}, eprint={2106.12066}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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facebook/emu_edit_test_set_generations
facebook
2023-11-19T07:36:56Z
674
14
[ "region:us" ]
null
2023-11-13T16:53:50Z
--- configs: - config_name: default data_files: - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: instruction dtype: string - name: image dtype: image - name: task dtype: string - name: split dtype: string - name: idx dtype: int64 - name: hash dtype: string - name: input_caption dtype: string - name: output_caption dtype: string - name: edited_image dtype: image - name: model dtype: string splits: - name: validation num_bytes: 2739856920.3 num_examples: 3589 - name: test num_bytes: 1538831841.4 num_examples: 2022 download_size: 1424484094 dataset_size: 4278688761.7000003 --- # Dataset Card for the Emu Edit Generations on Emu Edit Test Set ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage: https://emu-edit.metademolab.com/** - **Paper: https://emu-edit.metademolab.com/assets/emu_edit.pdf** ### Dataset Summary This dataset contains Emu Edit's generations on the [Emu Edit test set](https://huggingface.co/datasets/facebook/emu_edit_test_set). For more information please read our [paper](https://emu-edit.metademolab.com/assets/emu_edit.pdf) or visit our [homepage](https://emu-edit.metademolab.com/). ### Licensing Information Licensed with CC-BY-NC 4.0 License available [here](https://creativecommons.org/licenses/by-nc/4.0/legalcode?fbclid=IwAR2SYZjLRywwUMblkWg0LyAxHVVTloIFlvC-ju3BthIYtOM2jpQHgbeXOsM). ### Citation Information ``` @inproceedings{Sheynin2023EmuEP, title={Emu Edit: Precise Image Editing via Recognition and Generation Tasks}, author={Shelly Sheynin and Adam Polyak and Uriel Singer and Yuval Kirstain and Amit Zohar and Oron Ashual and Devi Parikh and Yaniv Taigman}, year={2023}, url={https://api.semanticscholar.org/CorpusID:265221391} } ```
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CodedotAI/code_clippy_github
CodedotAI
2022-08-05T02:57:36Z
673
10
[ "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" ]
[ "sequence-modeling" ]
2022-03-02T23:29:22Z
--- 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 [O*NET 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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C-MTEB/EcomRetrieval-qrels
C-MTEB
2023-07-28T09:37:58Z
673
0
[ "region:us" ]
null
2023-07-28T09:37:55Z
--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: qid dtype: string - name: pid dtype: string - name: score dtype: int64 splits: - name: dev num_bytes: 27890 num_examples: 1000 download_size: 14540 dataset_size: 27890 --- # Dataset Card for "EcomRetrieval-qrels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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conceptnet5
null
2023-06-01T14:59:50Z
672
15
[ "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" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- 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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id_liputan6
null
2022-11-18T20:08:31Z
670
5
[ "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" ]
[ "summarization" ]
2022-03-02T23:29:22Z
--- 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:[email protected]), [Jey Han Lau](mailto:[email protected]), [Timothy Baldwin](mailto:[email protected]), ### 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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bianet
null
2023-06-01T14:59:50Z
667
0
[ "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" ]
[ "translation" ]
2022-03-02T23:29:22Z
--- 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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docred
null
2023-06-14T14:07:55Z
667
8
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:mit", "arxiv:1906.06127", "region:us" ]
[ "text-retrieval" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual paperswithcode_id: docred pretty_name: DocRED size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-retrieval task_ids: - entity-linking-retrieval dataset_info: features: - name: title dtype: string - name: sents sequence: sequence: string - name: vertexSet list: list: - name: name dtype: string - name: sent_id dtype: int32 - name: pos sequence: int32 - name: type dtype: string - name: labels sequence: - name: head dtype: int32 - name: tail dtype: int32 - name: relation_id dtype: string - name: relation_text dtype: string - name: evidence sequence: int32 splits: - name: validation num_bytes: 3425030 num_examples: 998 - name: test num_bytes: 2843877 num_examples: 1000 - name: train_annotated num_bytes: 10413156 num_examples: 3053 - name: train_distant num_bytes: 346001876 num_examples: 101873 download_size: 458040413 dataset_size: 362683939 --- # Dataset Card for DocRED ## 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/thunlp/DocRED](https://github.com/thunlp/DocRED) - **Paper:** [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://arxiv.org/abs/1906.06127) - **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:** 21.00 MB - **Size of the generated dataset:** 20.12 MB - **Total amount of disk used:** 41.14 MB ### Dataset Summary Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: - DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text. - DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. - Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios. ### 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:** 21.00 MB - **Size of the generated dataset:** 20.12 MB - **Total amount of disk used:** 41.14 MB An example of 'train_annotated' looks as follows. ``` { "labels": { "evidence": [[0]], "head": [0], "relation_id": ["P1"], "relation_text": ["is_a"], "tail": [0] }, "sents": [["This", "is", "a", "sentence"], ["This", "is", "another", "sentence"]], "title": "Title of the document", "vertexSet": [[{ "name": "sentence", "pos": [3], "sent_id": 0, "type": "NN" }, { "name": "sentence", "pos": [3], "sent_id": 1, "type": "NN" }], [{ "name": "This", "pos": [0], "sent_id": 0, "type": "NN" }]] } ``` ### Data Fields The data fields are the same among all splits. #### default - `title`: a `string` feature. - `sents`: a dictionary feature containing: - `feature`: a `string` feature. - `name`: a `string` feature. - `sent_id`: a `int32` feature. - `pos`: a `list` of `int32` features. - `type`: a `string` feature. - `labels`: a dictionary feature containing: - `head`: a `int32` feature. - `tail`: a `int32` feature. - `relation_id`: a `string` feature. - `relation_text`: a `string` feature. - `evidence`: a `list` of `int32` features. ### Data Splits | name |train_annotated|train_distant|validation|test| |-------|--------------:|------------:|---------:|---:| |default| 3053| 101873| 998|1000| ## 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{yao-etal-2019-docred, title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset", author = "Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu, Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1074", doi = "10.18653/v1/P19-1074", pages = "764--777", } ``` ### Contributions Thanks to [@ghomasHudson](https://github.com/ghomasHudson), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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codeparrot/xlcost-text-to-code
codeparrot
2022-10-25T09:30:47Z
667
25
[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "language:code", "license:cc-by-sa-4.0", "arxiv:2206.08474", "region:us" ]
[ "text-generation" ]
2022-07-13T18:13:17Z
--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: - language-modeling pretty_name: xlcost-text-to-code --- # XLCost for text-to-code synthesis ## Dataset Description This is a subset of [XLCoST benchmark](https://github.com/reddy-lab-code-research/XLCoST), for text-to-code generation at snippet level and program level for **7** programming languages: `Python, C, C#, C++, Java, Javascript and PHP`. ## Languages The dataset contains text in English and its corresponding code translation. Each program is divided into several code snippets, so the snipppet-level subsets contain these code snippets with their corresponding comments, for program-level subsets, the comments were concatenated in one long description. Moreover, programs in all the languages are aligned at the snippet level and the comment for a particular snippet is the same across all the languages. ## Dataset Structure To load the dataset you need to specify a subset among the **14 exiting instances**: `LANGUAGE-snippet-level/LANGUAGE-program-level` for `LANGUAGE` in `[Python, C, Csharp, C++, Java, Javascript and PHP]`. By default `Python-snippet-level` is loaded. ```python from datasets import load_dataset load_dataset("codeparrot/xlcost-text-to-code", "Python-program-level") DatasetDict({ train: Dataset({ features: ['text', 'code'], num_rows: 9263 }) test: Dataset({ features: ['text', 'code'], num_rows: 887 }) validation: Dataset({ features: ['text', 'code'], num_rows: 472 }) }) ``` ```python next(iter(data["train"])) {'text': 'Maximum Prefix Sum possible by merging two given arrays | Python3 implementation of the above approach ; Stores the maximum prefix sum of the array A [ ] ; Traverse the array A [ ] ; Stores the maximum prefix sum of the array B [ ] ; Traverse the array B [ ] ; Driver code', 'code': 'def maxPresum ( a , b ) : NEW_LINE INDENT X = max ( a [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( a ) ) : NEW_LINE INDENT a [ i ] += a [ i - 1 ] NEW_LINE X = max ( X , a [ i ] ) NEW_LINE DEDENT Y = max ( b [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( b ) ) : NEW_LINE INDENT b [ i ] += b [ i - 1 ] NEW_LINE Y = max ( Y , b [ i ] ) NEW_LINE DEDENT return X + Y NEW_LINE DEDENT A = [ 2 , - 1 , 4 , - 5 ] NEW_LINE B = [ 4 , - 3 , 12 , 4 , - 3 ] NEW_LINE print ( maxPresum ( A , B ) ) NEW_LINE'} ``` Note that the data undergo some tokenization hence the additional whitespaces and the use of NEW_LINE instead of `\n` and INDENT instead of `\t`, DEDENT to cancel indentation... ## Data Fields * text: natural language description/comment * code: code at snippet/program level ## Data Splits Each subset has three splits: train, test and validation. ## Citation Information ``` @misc{zhu2022xlcost, title = {XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence}, url = {https://arxiv.org/abs/2206.08474}, author = {Zhu, Ming and Jain, Aneesh and Suresh, Karthik and Ravindran, Roshan and Tipirneni, Sindhu and Reddy, Chandan K.}, year = {2022}, eprint={2206.08474}, archivePrefix={arXiv} } ```
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flax-community/german_common_crawl
flax-community
2023-10-02T16:46:37Z
665
0
[ "language:de", "region:us" ]
null
2022-03-02T23:29:22Z
--- 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
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C-MTEB/DuRetrieval
C-MTEB
2023-07-28T09:48:49Z
665
0
[ "region:us" ]
null
2023-07-28T09:47:41Z
--- configs: - config_name: default data_files: - split: corpus path: data/corpus-* - split: queries path: data/queries-* dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 91213303 num_examples: 100001 - name: queries num_bytes: 131354 num_examples: 2000 download_size: 64531170 dataset_size: 91344657 --- # Dataset Card for "DuRetrieval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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kaist-ai/CoT-Collection
kaist-ai
2023-10-14T12:10:16Z
664
45
[ "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" ]
[ "text-generation", "text-classification" ]
2023-06-05T07:11:17Z
--- 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:[email protected]** ### 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} } ```
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bc2gm_corpus
null
2023-08-30T12:13:12Z
662
5
[ "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" ]
[ "token-classification" ]
2022-03-02T23:29:22Z
--- 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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sharc_modified
null
2022-11-03T16:31:23Z
659
0
[ "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" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- 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&amp;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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id_nergrit_corpus
null
2023-01-25T14:32:40Z
657
3
[ "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" ]
[ "token-classification" ]
2022-03-02T23:29:22Z
--- 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:[email protected]) ### 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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sharc
null
2022-11-03T16:16:40Z
657
1
[ "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:original", "language:en", "license:cc-by-sa-3.0", "conversational-qa", "arxiv:1809.01494", "region:us" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: sharc pretty_name: Shaping Answers with Rules through Conversation tags: - conversational-qa dataset_info: 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: negative_question dtype: bool_ - name: negative_scenario dtype: bool_ config_name: sharc splits: - name: train num_bytes: 15088577 num_examples: 21890 - name: validation num_bytes: 1469172 num_examples: 2270 download_size: 5230207 dataset_size: 16557749 --- # Dataset Card for Shaping Answers with Rules through Conversation ## 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:** [ShARC](https://sharc-data.github.io/index.html) - **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]() - **Paper:** [Interpretation of Natural Language Rules in Conversational Machine Reading](https://arxiv.org/abs/1809.01494) - **Leaderboard:** [leaderboard](https://sharc-data.github.io/leaderboard.html) - **Point of Contact:** [Marzieh Saeidi]([email protected]), [Max Bartolo]([email protected]), [Patrick Lewis]([email protected]), [Sebastian Riedel]([email protected]) ### 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 [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 [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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communityai/apt-chat-micro-dataset-llm-v2-714k
communityai
2023-11-11T09:34:18Z
657
0
[ "region:us" ]
null
2023-11-11T09:32:36Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: id dtype: int64 - name: source dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1722474450.0482376 num_examples: 713591 - name: test num_bytes: 1206905.9517624504 num_examples: 500 download_size: 903669025 dataset_size: 1723681356.0 --- # Dataset Card for "apt-chat-micro-dataset-llm-v2-714k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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JackBAI/bert_pretrain_datasets
JackBAI
2023-11-28T20:19:37Z
656
0
[ "region:us" ]
null
2023-10-09T22:43:45Z
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 24500165181 num_examples: 80462898 download_size: 14400389487 dataset_size: 24500165181 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "bert_pretrain_datasets" This dataset is essentially a concatenation of the training set of the English Wikipedia (wikipedia.20220301.en.train) and the Book Corpus (bookcorpus.train). This is exactly how I get this dataset: ``` from datasets import load_dataset, concatenate_datasets, load_from_disk cache_dir = "/data/haob2/cache/" # book corpus bookcorpus = load_dataset("bookcorpus", split="train", cache_dir=cache_dir) # english wikipedia wiki = load_dataset("wikipedia", "20220301.en", split="train", cache_dir=cache_dir) wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"]) # # concatenation concat = concatenate_datasets([bookcorpus, wiki]) concat.push_to_hub("JackBAI/bert_pretrain_datasets") ``` Note that this is a naive reproduction of the dataset that BERT is using. We believe the official BERT checkpoint is pretrained on a much more engineered dataset.
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poleval2019_mt
null
2022-11-18T21:39:08Z
654
0
[ "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" ]
[ "translation" ]
2022-03-02T23:29:22Z
--- 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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jglaser/binding_affinity
jglaser
2022-03-12T00:29:11Z
653
5
[ "molecules", "chemistry", "SMILES", "region:us" ]
null
2022-03-02T23:29:22Z
--- 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. Final concatenation and filtering Run the steps in the notebook `combine_dbs.ipynb`
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dennlinger/eur-lex-sum
dennlinger
2022-11-11T14:25:06Z
653
23
[ "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" ]
[ "translation", "summarization" ]
2022-10-10T08:07:37Z
--- 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:[email protected]) ### 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} } ```
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flaviagiammarino/vqa-rad
flaviagiammarino
2023-06-03T18:38:48Z
653
8
[ "task_categories:visual-question-answering", "size_categories:1K<n<10K", "language:en", "license:cc0-1.0", "medical", "region:us" ]
[ "visual-question-answering" ]
2023-06-03T14:33:55Z
--- license: cc0-1.0 task_categories: - visual-question-answering language: - en paperswithcode_id: vqa-rad tags: - medical pretty_name: VQA-RAD size_categories: - 1K<n<10K dataset_info: features: - name: image dtype: image - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 95883938.139 num_examples: 1793 - name: test num_bytes: 23818877.0 num_examples: 451 download_size: 34496718 dataset_size: 119702815.139 --- # Dataset Card for VQA-RAD ## Dataset Description VQA-RAD is a dataset of question-answer pairs on radiology images. The dataset is intended to be used for training and testing Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions. The dataset is built from [MedPix](https://medpix.nlm.nih.gov/), which is a free open-access online database of medical images. The question-answer pairs were manually generated by a team of clinicians. **Homepage:** [Open Science Framework Homepage](https://osf.io/89kps/)<br> **Paper:** [A dataset of clinically generated visual questions and answers about radiology images](https://www.nature.com/articles/sdata2018251)<br> **Leaderboard:** [Papers with Code Leaderboard](https://paperswithcode.com/sota/medical-visual-question-answering-on-vqa-rad) ### Dataset Summary The dataset was downloaded from the [Open Science Framework Homepage](https://osf.io/89kps/) on June 3, 2023. The dataset contains 2,248 question-answer pairs and 315 images. Out of the 315 images, 314 images are referenced by a question-answer pair, while 1 image is not used. The training set contains 3 duplicate image-question-answer triplets. The training set also has 1 image-question-answer triplet in common with the test set. After dropping these 4 image-question-answer triplets from the training set, the dataset contains 2,244 question-answer pairs on 314 images. #### Supported Tasks and Leaderboards This dataset has an active leaderboard on [Papers with Code](https://paperswithcode.com/sota/medical-visual-question-answering-on-vqa-rad) where models are ranked based on three metrics: "Close-ended Accuracy", "Open-ended accuracy" and "Overall accuracy". "Close-ended Accuracy" is the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Open-ended accuracy" is the accuracy of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated answers across all questions. #### Languages The question-answer pairs are in English. ## Dataset Structure ### Data Instances Each instance consists of an image-question-answer triplet. ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=566x555>, 'question': 'are regions of the brain infarcted?', 'answer': 'yes' } ``` ### Data Fields - `'image'`: the image referenced by the question-answer pair. - `'question'`: the question about the image. - `'answer'`: the expected answer. ### Data Splits The dataset is split into training and test. The split is provided directly by the authors. | | Training Set | Test Set | |-------------------------|:------------:|:---------:| | QAs |1,793 |451 | | Images |313 |203 | ## Additional Information ### Licensing Information The authors have released the dataset under the CC0 1.0 Universal License. ### Citation Information ``` @article{lau2018dataset, title={A dataset of clinically generated visual questions and answers about radiology images}, author={Lau, Jason J and Gayen, Soumya and Ben Abacha, Asma and Demner-Fushman, Dina}, journal={Scientific data}, volume={5}, number={1}, pages={1--10}, year={2018}, publisher={Nature Publishing Group} } ```
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LeoLM/MMLU_de
LeoLM
2023-06-15T01:41:53Z
653
0
[ "license:mit", "region:us" ]
null
2023-06-03T22:07:16Z
--- 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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C-MTEB/DuRetrieval-qrels
C-MTEB
2023-07-28T09:48:53Z
653
0
[ "region:us" ]
null
2023-07-28T09:48:49Z
--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: qid dtype: string - name: pid dtype: string - name: score dtype: int64 splits: - name: dev num_bytes: 787120 num_examples: 9839 download_size: 420443 dataset_size: 787120 --- # Dataset Card for "DuRetrieval-qrels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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ShrinivasSK/hi_en_1
ShrinivasSK
2023-11-06T19:06:53Z
653
0
[ "region:us" ]
null
2023-11-06T18:54:23Z
--- dataset_info: features: - name: idx dtype: int64 - name: tgt dtype: string - name: src dtype: string splits: - name: train num_bytes: 6349061.7 num_examples: 18000 - name: test num_bytes: 705451.3 num_examples: 2000 download_size: 3779852 dataset_size: 7054513.0 --- # Dataset Card for "hi_en_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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C-MTEB/CmedqaRetrieval
C-MTEB
2023-07-28T09:40:17Z
650
0
[ "region:us" ]
null
2023-07-28T09:39:17Z
--- configs: - config_name: default data_files: - split: corpus path: data/corpus-* - split: queries path: data/queries-* dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 84962605 num_examples: 100001 - name: queries num_bytes: 728106 num_examples: 3999 download_size: 61319407 dataset_size: 85690711 --- # Dataset Card for "CmedqaRetrieval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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C-MTEB/CovidRetrieval
C-MTEB
2023-07-28T09:44:36Z
650
0
[ "region:us" ]
null
2023-07-28T09:43:30Z
--- configs: - config_name: default data_files: - split: corpus path: data/corpus-* - split: queries path: data/queries-* dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 91531256 num_examples: 100001 - name: queries num_bytes: 111094 num_examples: 949 download_size: 65093081 dataset_size: 91642350 --- # Dataset Card for "CovidRetrieval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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mediabiasgroup/BABE-v3
mediabiasgroup
2023-08-23T05:37:34Z
650
0
[ "license:cc-by-nc-sa-4.0", "region:us" ]
null
2023-08-23T05:25:25Z
--- license: cc-by-nc-sa-4.0 --- Original BABE dataset enriched with sentences from two annotations rounds: NewsUnfold project and Media Bias Game project. # Please cite as ``` @InProceedings{Spinde2021f, title = "Neural Media Bias Detection Using Distant Supervision With {BABE} - Bias Annotations By Experts", author = "Spinde, Timo and Plank, Manuel and Krieger, Jan-David and Ruas, Terry and Gipp, Bela and Aizawa, Akiko", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.101", doi = "10.18653/v1/2021.findings-emnlp.101", pages = "1166--1177", } ```
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Divyanshu/indicxnli
Divyanshu
2022-10-06T15:26:00Z
646
1
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:1M<n<10M", "source_datasets:original", "language:as", "language:bn", "language:gu", "language:hi", "language:kn", "language:ml", "language:mr", "language:or", "language:pa", "language:ta", "language:te", "license:cc0-1.0", "arxiv:2204.08776", "region:us" ]
[ "text-classification" ]
2022-04-17T17:48:10Z
--- annotations_creators: - machine-generated language_creators: - machine-generated language: - as - bn - gu - hi - kn - ml - mr - or - pa - ta - te license: - cc0-1.0 multilinguality: - multilingual pretty_name: IndicXNLI size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference --- # Dataset Card for "IndicXNLI" ## Table of Contents - [Dataset Card for "IndicXNLI"](#dataset-card-for-indicxnli) - [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 Description - **Homepage:** <https://github.com/divyanshuaggarwal/IndicXNLI> - **Paper:** [IndicXNLI: Evaluating Multilingual Inference for Indian Languages](https://arxiv.org/abs/2204.08776) - **Point of Contact:** [Divyanshu Aggarwal](mailto:[email protected]) ### Dataset Summary INDICXNLI is similar to existing XNLI dataset in shape/form, but focusses on Indic language family. INDICXNLI include NLI data for eleven major Indic languages that includes Assamese (‘as’), Gujarat (‘gu’), Kannada (‘kn’), Malayalam (‘ml’), Marathi (‘mr’), Odia (‘or’), Punjabi (‘pa’), Tamil (‘ta’), Telugu (‘te’), Hindi (‘hi’), and Bengali (‘bn’). ### Supported Tasks and Leaderboards **Tasks:** Natural Language Inference **Leaderboards:** Currently there is no Leaderboard for this dataset. ### Languages - `Assamese (as)` - `Bengali (bn)` - `Gujarati (gu)` - `Kannada (kn)` - `Hindi (hi)` - `Malayalam (ml)` - `Marathi (mr)` - `Oriya (or)` - `Punjabi (pa)` - `Tamil (ta)` - `Telugu (te)` ## Dataset Structure ### Data Instances One example from the `hi` dataset is given below in JSON format. ```python {'premise': 'अवधारणात्मक रूप से क्रीम स्किमिंग के दो बुनियादी आयाम हैं-उत्पाद और भूगोल।', 'hypothesis': 'उत्पाद और भूगोल क्रीम स्किमिंग का काम करते हैं।', 'label': 1 (neutral) } ``` ### Data Fields - `premise (string)`: Premise Sentence - `hypothesis (string)`: Hypothesis Sentence - `label (integer)`: Integer label `0` if hypothesis `entails` the premise, `2` if hypothesis `negates` the premise and `1` otherwise. ### Data Splits <!-- Below is the dataset split given for `hi` dataset. ```python DatasetDict({ train: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 392702 }) test: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 5010 }) validation: Dataset({ features: ['premise', 'hypothesis', 'label'], num_rows: 2490 }) }) ``` --> Language | ISO 639-1 Code |Train | Test | Dev | --------------|----------------|-------|-----|------| Assamese | as | 392,702 | 5,010 | 2,490 | Bengali | bn | 392,702 | 5,010 | 2,490 | Gujarati | gu | 392,702 | 5,010 | 2,490 | Hindi | hi | 392,702 | 5,010 | 2,490 | Kannada | kn | 392,702 | 5,010 | 2,490 | Malayalam | ml |392,702 | 5,010 | 2,490 | Marathi | mr |392,702 | 5,010 | 2,490 | Oriya | or | 392,702 | 5,010 | 2,490 | Punjabi | pa | 392,702 | 5,010 | 2,490 | Tamil | ta | 392,702 | 5,010 | 2,490 | Telugu | te | 392,702 | 5,010 | 2,490 | <!-- The dataset split remains same across all languages. --> ## Dataset usage Code snippet for using the dataset using datasets library. ```python from datasets import load_dataset dataset = load_dataset("Divyanshu/indicxnli") ``` ## Dataset Creation Machine translation of XNLI english dataset to 11 listed Indic Languages. ### Curation Rationale [More information needed] ### Source Data [XNLI dataset](https://cims.nyu.edu/~sbowman/xnli/) #### Initial Data Collection and Normalization [Detailed in the paper](https://arxiv.org/abs/2204.08776) #### Who are the source language producers? [Detailed in the paper](https://arxiv.org/abs/2204.08776) #### Human Verification Process [Detailed in the paper](https://arxiv.org/abs/2204.08776) ## Considerations for Using the Data ### Social Impact of Dataset [Detailed in the paper](https://arxiv.org/abs/2204.08776) ### Discussion of Biases [Detailed in the paper](https://arxiv.org/abs/2204.08776) ### Other Known Limitations [Detailed in the paper](https://arxiv.org/abs/2204.08776) ### Dataset Curators Divyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan ### Licensing Information Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders. ### Citation Information If you use any of the datasets, models or code modules, please cite the following paper: ``` @misc{https://doi.org/10.48550/arxiv.2204.08776, doi = {10.48550/ARXIV.2204.08776}, url = {https://arxiv.org/abs/2204.08776}, author = {Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {IndicXNLI: Evaluating Multilingual Inference for Indian Languages}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ### Contributions -->
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open-llm-leaderboard/details_medalpaca__medalpaca-7b
open-llm-leaderboard
2023-10-13T02:38:07Z
646
0
[ "region:us" ]
null
2023-08-18T11:18:43Z
--- pretty_name: Evaluation run of medalpaca/medalpaca-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [medalpaca/medalpaca-7b](https://huggingface.co/medalpaca/medalpaca-7b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_medalpaca__medalpaca-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-13T02:37:55.174881](https://huggingface.co/datasets/open-llm-leaderboard/details_medalpaca__medalpaca-7b/blob/main/results_2023-10-13T02-37-55.174881.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.1761744966442953,\n\ \ \"em_stderr\": 0.003901474629801755,\n \"f1\": 0.24214345637583887,\n\ \ \"f1_stderr\": 0.003972046949089224,\n \"acc\": 0.37112196044335327,\n\ \ \"acc_stderr\": 0.008725686094881443\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.1761744966442953,\n \"em_stderr\": 0.003901474629801755,\n\ \ \"f1\": 0.24214345637583887,\n \"f1_stderr\": 0.003972046949089224\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.030326004548900682,\n \ \ \"acc_stderr\": 0.004723487465514772\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7119179163378059,\n \"acc_stderr\": 0.012727884724248115\n\ \ }\n}\n```" repo_url: https://huggingface.co/medalpaca/medalpaca-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|arc:challenge|25_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T16:30:25.304813.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_13T02_37_55.174881 path: - '**/details_harness|drop|3_2023-10-13T02-37-55.174881.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-13T02-37-55.174881.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_13T02_37_55.174881 path: - '**/details_harness|gsm8k|5_2023-10-13T02-37-55.174881.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-13T02-37-55.174881.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hellaswag|10_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:30:25.304813.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:30:25.304813.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T16_30_25.304813 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T16:30:25.304813.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T16:30:25.304813.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_13T02_37_55.174881 path: - '**/details_harness|winogrande|5_2023-10-13T02-37-55.174881.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-13T02-37-55.174881.parquet' - config_name: results data_files: - split: 2023_07_19T16_30_25.304813 path: - results_2023-07-19T16:30:25.304813.parquet - split: 2023_10_13T02_37_55.174881 path: - results_2023-10-13T02-37-55.174881.parquet - split: latest path: - results_2023-10-13T02-37-55.174881.parquet --- # Dataset Card for Evaluation run of medalpaca/medalpaca-7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/medalpaca/medalpaca-7b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [medalpaca/medalpaca-7b](https://huggingface.co/medalpaca/medalpaca-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_medalpaca__medalpaca-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-13T02:37:55.174881](https://huggingface.co/datasets/open-llm-leaderboard/details_medalpaca__medalpaca-7b/blob/main/results_2023-10-13T02-37-55.174881.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.1761744966442953, "em_stderr": 0.003901474629801755, "f1": 0.24214345637583887, "f1_stderr": 0.003972046949089224, "acc": 0.37112196044335327, "acc_stderr": 0.008725686094881443 }, "harness|drop|3": { "em": 0.1761744966442953, "em_stderr": 0.003901474629801755, "f1": 0.24214345637583887, "f1_stderr": 0.003972046949089224 }, "harness|gsm8k|5": { "acc": 0.030326004548900682, "acc_stderr": 0.004723487465514772 }, "harness|winogrande|5": { "acc": 0.7119179163378059, "acc_stderr": 0.012727884724248115 } } ``` ### 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 [More Information Needed]
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indonlp/nusatranslation_senti
indonlp
2023-09-07T12:58:31Z
646
0
[ "license:apache-2.0", "region:us" ]
null
2023-09-07T12:57:23Z
--- license: apache-2.0 ---
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smangrul/chat-instruct-mixer
smangrul
2023-09-08T05:44:19Z
646
4
[ "region:us" ]
null
2023-09-08T03:03:01Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 169947792.7111158 num_examples: 73302 - name: test num_bytes: 48395025.62775446 num_examples: 23318 download_size: 123606462 dataset_size: 218342818.33887026 --- # Chat-Instruct-Mixer Dataset This dataset is focused on improving LLM logical reasoning skills and conversation skills. It is comprised of the following datasets: | Dataset Name | Train Mixing Percentage/Samples | Test Mixing Percentage/Samples | |--------------------------------------------------------------|--------------|------------------| | [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | 100% | 300 samples | | [GAIR/lima](https://huggingface.co/datasets/GAIR/lima) | 100% | 518 samples | | [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) | 100% minus the samples set aside for test split | 2500 samples | | [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) | 10000 samples from GPT-4 split | 5000 samples | | [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) | 10000 samples from GPT-4 split | 5000 samples | | [stingning/ultrachat](https://huggingface.co/datasets/stingning/ultrachat) | 10000 samples | 5000 samples | | [jondurbin/airoboros-2.2](https://huggingface.co/datasets/jondurbin/airoboros-2.2) | 10000 Samples while filtering out samples with `skip_prompt_formatting==True` | 5000 samples | Code for Creating this dataset: [ToDo]()
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feradauto/MoralExceptQA
feradauto
2022-10-27T15:42:04Z
645
1
[ "task_categories:text-classification", "arxiv:2210.01478", "region:us" ]
[ "text-classification" ]
2022-10-26T00:26:07Z
--- pretty_name: MoralExceptQA task_categories: - text-classification --- # Dataset Card for MoralExceptQA ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [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) - [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) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [MoralCoT](https://github.com/feradauto/MoralCoT) - **Paper:** [When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment](https://arxiv.org/abs/2210.01478) - **Point of Contact:** [Fernando Gonzalez](mailto:[email protected]) , [Zhijing Jin](mailto:[email protected]) ### Dataset Summary Challenge set consisting of moral exception question answering of cases that involve potentially permissible moral exceptions. Our challenge set, MoralExceptQA, is drawn from a series of recent moral psychology studies designed to investigate the flexibility of human moral cognition – specifically, the ability of humans to figure out when it is permissible to break a previously established or well-known rule. ### Languages The language in the dataset is English. ## Dataset Structure ### Data Instances Each instance is a rule-breaking scenario acompanied by an average human response. ### Data Fields - `study`: The moral psychology study. Studies were designed to investigate the ability of humans to figure out when it is permissible to break a previously established or well-known rule. - `context`: The context of the scenario. Different context within the same study are potentially governed by the same rule. - `condition`: Condition in the scenario. - `scenario`: Text description of the scenario. - `human.response`: Average human response (scale 0 to 1) equivalent to the % of people that considered that breaking the rule is permissible. ### Data Splits MoralExceptQA contains one split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data Information about the data collection and annotators can be found in the appendix of [our paper](https://arxiv.org/abs/2210.01478). ### Personal and Sensitive Information The MoralExceptQA dataset does not have privacy concerns. ## Considerations for Using the Data ### Social Impact of Dataset The intended use of this work is to contribute to AI safety research. We do not intend this work to be developed as a tool to automate moral decision-making on behalf of humans, but instead as a way of mitigating risks caused by LLMs’ misunderstanding of human values. The MoralExceptQA dataset does not have privacy concerns or offensive content. ### Discussion of Biases Our subjects are U.S. residents, and therefore our conclusions are limited to this population. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The MoralExceptQA dataset is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). ### Citation Information ``` @misc{https://doi.org/10.48550/arxiv.2210.01478, doi = {10.48550/ARXIV.2210.01478}, url = {https://arxiv.org/abs/2210.01478}, author = {Jin, Zhijing and Levine, Sydney and Gonzalez, Fernando and Kamal, Ojasv and Sap, Maarten and Sachan, Mrinmaya and Mihalcea, Rada and Tenenbaum, Josh and Schölkopf, Bernhard}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Computers and Society (cs.CY), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution Share Alike 4.0 International} } ```
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mlabonne/guanaco-llama2
mlabonne
2023-07-26T14:49:17Z
645
9
[ "region:us" ]
null
2023-07-23T13:53:10Z
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 15409089 num_examples: 9846 - name: test num_bytes: 815811 num_examples: 518 download_size: 9461517 dataset_size: 16224900 --- # Guanaco: Lazy Llama 2 Formatting This is the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 model in a Google Colab.
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C-MTEB/CovidRetrieval-qrels
C-MTEB
2023-07-28T09:44:39Z
645
0
[ "region:us" ]
null
2023-07-28T09:44:36Z
--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: qid dtype: string - name: pid dtype: string - name: score dtype: int64 splits: - name: dev num_bytes: 76720 num_examples: 959 download_size: 62785 dataset_size: 76720 --- # Dataset Card for "CovidRetrieval-qrels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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michaljunczyk/pl-asr-bigos-v2
michaljunczyk
2023-11-16T07:18:15Z
645
0
[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "annotations_creators:other", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "source_datasets:extended|common_voice", "source_datasets:extended|multilingual_librispeech", "source_datasets:extended|minds14", "language:pl", "license:cc-by-sa-4.0", "benchmark", "polish", "asr", "speech", "region:us" ]
[ "automatic-speech-recognition" ]
2023-11-10T17:46:32Z
--- annotations_creators: - crowdsourced - expert-generated - other - machine-generated language: - pl language_creators: - crowdsourced - expert-generated - other license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: pl-asr-bigos-v2 size_categories: - 1K<n<10K source_datasets: - original - extended|common_voice - extended|multilingual_librispeech - extended|minds14 tags: - benchmark - polish - asr - speech task_categories: - automatic-speech-recognition task_ids: [] extra_gated_prompt: |- Original datasets used for curation of BIGOS have specific terms of usage that must be understood and agreed to before use. Below are the links to the license terms and datasets the specific license type applies to: * [Creative Commons 0](https://creativecommons.org/share-your-work/public-domain/cc0) which applies to [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) * [Creative Commons By Attribution Share Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/), which applies to [Clarin Cyfry](https://clarin-pl.eu/dspace/handle/11321/317), [Azon acoustic speech resources corpus](https://zasobynauki.pl/zasoby/korpus-nagran-probek-mowy-do-celow-budowy-modeli-akustycznych-dla-automatycznego-rozpoznawania-mowy,53293/). * [Creative Commons By Attribution 3.0](https://creativecommons.org/licenses/by/3.0/), which applies to [CLARIN Mobile database](https://clarin-pl.eu/dspace/handle/11321/237), [CLARIN Studio database](https://clarin-pl.eu/dspace/handle/11321/236), [PELCRA Spelling and Numbers Voice Database](http://pelcra.pl/new/snuv) and [FLEURS dataset](https://huggingface.co/datasets/google/fleurs) * [Creative Commons By Attribution 4.0](https://creativecommons.org/licenses/by/4.0/), which applies to [Multilingual Librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech) and [Poly AI Minds 14](https://huggingface.co/datasets/PolyAI/minds14) * [Proprietiary License of Munich AI Labs dataset](https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset) * Public domain mark, which applies to [PWR datasets](https://www.ii.pwr.edu.pl/~sas/ASR/) To use selected dataset, you also need to fill in the access forms on the specific datasets pages: * Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0 extra_gated_fields: I hereby confirm that I have read and accepted the license terms of datasets comprising BIGOS corpora: checkbox I hereby confirm that I have registered on the original Common Voice page and agree to not attempt to determine the identity of speakers in the Common Voice dataset: checkbox --- # Dataset Card for Polish ASR BIGOS corpora ## 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://huggingface.co/datasets/michaljunczyk/pl-asr-bigos-v2 - **Repository:** https://github.com/goodmike31/pl-asr-bigos-tools - **Paper:** https://annals-csis.org/proceedings/2023/drp/1609.html - **Leaderboard:** https://huggingface.co/spaces/michaljunczyk/pl-asr-bigos-benchmark - **Point of Contact:** [email protected] ### Dataset Summary The BIGOS (Benchmark Intended Grouping of Open Speech) corpora aims at simplifying the access and use of publicly available ASR speech datasets for Polish.<br> Version 1 consists of 1900 test recordings from 10 public datasets [details](https://huggingface.co/datasets/michaljunczyk/pl-asr-bigos)<br> Version 2 (V2) consists of dev, train and test splits repurposed from 12 datasets.<br> BIGOS V2 is intended for benchmarking of community provided ASR systems under PolEval challenge.<br> References in test splits will remain masked until the PolEval challenge is completed.<br> For more details please refer to the PolEval challenge website.<br> ### Supported Tasks and Leaderboards The leaderboard with benchmark of publicly available Polish ASR systems evaluated on BIGOS corpora is [under construction](https://huggingface.co/spaces/michaljunczyk/pl-asr-bigos-benchmark/).<br> Evaluation results of 3 commercial and 5 freely available on BIGOS V1 can be found in the [paper](https://annals-csis.org/proceedings/2023/drp/1609.html). ### Languages Polish ## Dataset Structure Dataset consists audio recordings in WAV format and corresponding metadata.<br> Audio and metadata can be used in raw format (TSV) or via hugging face datasets library. ### Data Instances TODO ### Data Fields Available fields: * file_id - file identifier * dataset_id - source dataset identifier * audio - binary representation of audio file * ref_orig - original transcription of audio file <br><br> Fields to be added in the next release: * ref_rig_norma - automatically normalized original transcriptions * ref_spoken - manual transcription in a spoken format * ref_written - manual transcription in a written format Transcription protocol can be found [here]() ### Data Splits TODO - add metada with split size per subset ## Dataset Creation ### Curation Rationale [Polish ASR Speech Data Catalog](https://github.com/goodmike31/pl-asr-speech-data-survey) was used to identify suitable datasets which can be repurposed and included in the BIGOS corpora.<br> The following mandatory criteria were considered: * Dataset must be downloadable. * The license must allow for free, noncommercial use. * Transcriptions must be available and align with the recordings. * The sampling rate of audio recordings must be at least 8 kHz. * Audio encoding using a minimum of 16 bits per sample. ### Source Data 13 datasets that meet the criteria were chosen as sources for the BIGOS dataset. * The latest (version 15) Common Voice dataset (mozilla-common_voice15-23) * The Multilingual LibriSpeech (MLS) dataset (fair-mls-20) * The Clarin Studio Corpus (pjatk-clarin_studio-15) * The Clarin Mobile Corpus (pjatk-clarin_mobile-15) * The Jerzy Sas PWR datasets from Politechnika Wrocławska (pwr-viu-unk, pwr-shortwords-unk, pwr-maleset-unk). More info [here](https://www.ii.pwr.edu.pl/) * The Munich-AI Labs Speech corpus (mailabs-librispeech_corpus-19) * The AZON Read and Spontaneous Speech Corpora (pwr-azon_spont-20, pwr_azon-read-20) More info [here](https://zasobynauki.pl/zasoby/korpus-nagran-probek-mowy-do-celow-budowy-modeli-akustycznych-dla-automatycznego-rozpoznawania-mowy) * The PolyAI Minds 14 dataset (polyai-minds14-21) * The Google FLEURS dataset (google-fleurs-22) Additional source datasets will be added in the future. #### Initial Data Collection and Normalization Source text and audio files were extracted and encoded in a unified format.<br> Dataset-specific transcription norms are preserved, including punctuation and casing. <br> #### Who are the source language producers? 1. Clarin corpora - Polish Japanese Academy of Technology 2. Common Voice - Mozilla foundation 3. Multilingual librispeech - Facebook AI research lab 4. Jerzy Sas and AZON datasets - Politechnika Wrocławska 5. Minds 14 - Poly AI Limited 6. FLEURS - Google ### Annotations #### Annotation process Current release contains original transcriptions. Manual transcriptions are planned for subsequent releases. #### Who are the annotators? Depends on the source dataset. ### Personal and Sensitive Information This corpus does not contain PII or Sensitive Information. All IDs pf speakers are anonymized. ## Considerations for Using the Data ### Social Impact of Dataset To be updated. ### Discussion of Biases To be updated. ### Other Known Limitations The dataset in the initial release contains only a subset of recordings from original datasets. ## Additional Information ### Dataset Curators Original authors of the source datasets - please refer to [source-data](#source-data) for details. Michał Junczyk ([email protected]) - curator of BIGOS corpora. ### Licensing Information The BIGOS corpora is available under [Creative Commons By Attribution Share Alike 4.0 license.](https://creativecommons.org/licenses/by-sa/4.0/) Original datasets used for curation of BIGOS have specific terms of usage that must be understood and agreed to before use. Below are the links to the license terms and datasets the specific license type applies to: * [Creative Commons 0](https://creativecommons.org/share-your-work/public-domain/cc0) which applies to [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) * [Creative Commons By Attribution Share Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/), which applies to [Clarin Cyfry](https://clarin-pl.eu/dspace/handle/11321/317), [Azon acoustic speech resources corpus](https://zasobynauki.pl/zasoby/korpus-nagran-probek-mowy-do-celow-budowy-modeli-akustycznych-dla-automatycznego-rozpoznawania-mowy,53293/). * [Creative Commons By Attribution 3.0](https://creativecommons.org/licenses/by/3.0/), which applies to [CLARIN Mobile database](https://clarin-pl.eu/dspace/handle/11321/237), [CLARIN Studio database](https://clarin-pl.eu/dspace/handle/11321/236), [PELCRA Spelling and Numbers Voice Database](http://pelcra.pl/new/snuv) and [FLEURS dataset](https://huggingface.co/datasets/google/fleurs) * [Creative Commons By Attribution 4.0](https://creativecommons.org/licenses/by/4.0/), which applies to [Multilingual Librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech) and [Poly AI Minds 14](https://huggingface.co/datasets/PolyAI/minds14) * [Proprietiary License of Munich AI Labs dataset](https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset) * Public domain mark, which applies to [PWR datasets](https://www.ii.pwr.edu.pl/~sas/ASR/) ### Citation Information Please cite [BIGOS V1 paper](https://annals-csis.org/proceedings/2023/drp/1609.html). Once BIGOS V2 paper is available, new citation reference will be provided. ### Contributions Thanks to [@goodmike31](https://github.com/goodmike31) for adding this dataset.
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kilt_wikipedia
null
2023-04-05T10:08:59Z
642
10
[ "region:us" ]
null
2022-03-02T23:29:22Z
--- paperswithcode_id: null pretty_name: KiltWikipedia dataset_info: features: - name: kilt_id dtype: string - name: wikipedia_id dtype: string - name: wikipedia_title dtype: string - name: text sequence: - name: paragraph dtype: string - name: anchors sequence: - name: paragraph_id dtype: int32 - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string - name: href dtype: string - name: wikipedia_title dtype: string - name: wikipedia_id dtype: string - name: categories dtype: string - name: wikidata_info struct: - name: description dtype: string - name: enwikiquote_title dtype: string - name: wikidata_id dtype: string - name: wikidata_label dtype: string - name: wikipedia_title dtype: string - name: aliases sequence: - name: alias dtype: string - name: history struct: - name: pageid dtype: int32 - name: parentid dtype: int32 - name: revid dtype: int32 - name: pre_dump dtype: bool - name: timestamp dtype: string - name: url dtype: string config_name: '2019-08-01' splits: - name: full num_bytes: 29372535718 num_examples: 5903530 download_size: 37318876722 dataset_size: 29372535718 --- # Dataset Card for "kilt_wikipedia" ## 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/facebookresearch/KILT](https://github.com/facebookresearch/KILT) - **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:** 37.32 GB - **Size of the generated dataset:** 29.37 GB - **Total amount of disk used:** 66.69 GB ### Dataset Summary KILT-Wikipedia: Wikipedia pre-processed for KILT. ### 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 #### 2019-08-01 - **Size of downloaded dataset files:** 37.32 GB - **Size of the generated dataset:** 29.37 GB - **Total amount of disk used:** 66.69 GB An example of 'full' looks as follows. ``` { "anchors": { "end": [], "href": [], "paragraph_id": [], "start": [], "text": [], "wikipedia_id": [], "wikipedia_title": [] }, "categories": "", "history": { "pageid": 0, "parentid": 0, "pre_dump": true, "revid": 0, "timestamp": "", "url": "" }, "kilt_id": "", "text": { "paragraph": [] }, "wikidata_info": { "aliases": { "alias": [] }, "description": "", "enwikiquote_title": "", "wikidata_id": "", "wikidata_label": "", "wikipedia_title": "" }, "wikipedia_id": "", "wikipedia_title": "" } ``` ### Data Fields The data fields are the same among all splits. #### 2019-08-01 - `kilt_id`: a `string` feature. - `wikipedia_id`: a `string` feature. - `wikipedia_title`: a `string` feature. - `text`: a dictionary feature containing: - `paragraph`: a `string` feature. - `anchors`: a dictionary feature containing: - `paragraph_id`: a `int32` feature. - `start`: a `int32` feature. - `end`: a `int32` feature. - `text`: a `string` feature. - `href`: a `string` feature. - `wikipedia_title`: a `string` feature. - `wikipedia_id`: a `string` feature. - `categories`: a `string` feature. - `description`: a `string` feature. - `enwikiquote_title`: a `string` feature. - `wikidata_id`: a `string` feature. - `wikidata_label`: a `string` feature. - `wikipedia_title`: a `string` feature. - `aliases`: a dictionary feature containing: - `alias`: a `string` feature. - `pageid`: a `int32` feature. - `parentid`: a `int32` feature. - `revid`: a `int32` feature. - `pre_dump`: a `bool` feature. - `timestamp`: a `string` feature. - `url`: a `string` feature. ### Data Splits | name | full | |----------|------:| |2019-08-01|5903530| ## 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{fb_kilt, author = {Fabio Petroni and Aleksandra Piktus and Angela Fan and Patrick Lewis and Majid Yazdani and Nicola De Cao and James Thorne and Yacine Jernite and Vassilis Plachouras and Tim Rockt"aschel and Sebastian Riedel}, title = {{KILT:} a {B}enchmark for {K}nowledge {I}ntensive {L}anguage {T}asks}, journal = {CoRR}, archivePrefix = {arXiv}, year = {2020}, ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.
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TheBritishLibrary/blbooksgenre
TheBritishLibrary
2023-06-01T14:59:51Z
641
4
[ "task_categories:text-classification", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:topic-classification", "task_ids:multi-label-classification", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "language:en", "language:fr", "language:nl", "license:cc0-1.0", "region:us" ]
[ "text-classification", "text-generation", "fill-mask" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - de - en - fr - nl license: - cc0-1.0 multilinguality: - multilingual size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original task_categories: - text-classification - text-generation - fill-mask task_ids: - topic-classification - multi-label-classification - language-modeling - masked-language-modeling pretty_name: British Library Books Genre dataset_info: - config_name: title_genre_classifiction features: - name: BL record ID dtype: string - name: title dtype: string - name: label dtype: class_label: names: '0': Fiction '1': Non-fiction splits: - name: train num_bytes: 187600 num_examples: 1736 download_size: 20111420 dataset_size: 187600 - config_name: annotated_raw features: - name: BL record ID dtype: string - name: Name dtype: string - name: Dates associated with name dtype: string - name: Type of name dtype: string - name: Role dtype: string - name: All names sequence: string - name: Title dtype: string - name: Variant titles dtype: string - name: Series title dtype: string - name: Number within series dtype: string - name: Country of publication sequence: string - name: Place of publication sequence: string - name: Publisher dtype: string - name: Date of publication dtype: string - name: Edition dtype: string - name: Physical description dtype: string - name: Dewey classification dtype: string - name: BL shelfmark dtype: string - name: Topics dtype: string - name: Genre dtype: string - name: Languages sequence: string - name: Notes dtype: string - name: BL record ID for physical resource dtype: string - name: classification_id dtype: string - name: user_id dtype: string - name: subject_ids dtype: string - name: annotator_date_pub dtype: string - name: annotator_normalised_date_pub dtype: string - name: annotator_edition_statement dtype: string - name: annotator_FAST_genre_terms dtype: string - name: annotator_FAST_subject_terms dtype: string - name: annotator_comments dtype: string - name: annotator_main_language dtype: string - name: annotator_other_languages_summaries dtype: string - name: annotator_summaries_language dtype: string - name: annotator_translation dtype: string - name: annotator_original_language dtype: string - name: annotator_publisher dtype: string - name: annotator_place_pub dtype: string - name: annotator_country dtype: string - name: annotator_title dtype: string - name: Link to digitised book dtype: string - name: annotated dtype: bool - name: Type of resource dtype: class_label: names: '0': Monograph '1': Serial - name: created_at dtype: timestamp[s] - name: annotator_genre dtype: class_label: names: '0': Fiction '1': Can't tell '2': Non-fiction '3': The book contains both Fiction and Non-Fiction splits: - name: train num_bytes: 3583138 num_examples: 4398 download_size: 20111420 dataset_size: 3583138 - config_name: raw features: - name: BL record ID dtype: string - name: Name dtype: string - name: Dates associated with name dtype: string - name: Type of name dtype: string - name: Role dtype: string - name: All names sequence: string - name: Title dtype: string - name: Variant titles dtype: string - name: Series title dtype: string - name: Number within series dtype: string - name: Country of publication sequence: string - name: Place of publication sequence: string - name: Publisher dtype: string - name: Date of publication dtype: string - name: Edition dtype: string - name: Physical description dtype: string - name: Dewey classification dtype: string - name: BL shelfmark dtype: string - name: Topics dtype: string - name: Genre dtype: string - name: Languages sequence: string - name: Notes dtype: string - name: BL record ID for physical resource dtype: string - name: classification_id dtype: string - name: user_id dtype: string - name: subject_ids dtype: string - name: annotator_date_pub dtype: string - name: annotator_normalised_date_pub dtype: string - name: annotator_edition_statement dtype: string - name: annotator_FAST_genre_terms dtype: string - name: annotator_FAST_subject_terms dtype: string - name: annotator_comments dtype: string - name: annotator_main_language dtype: string - name: annotator_other_languages_summaries dtype: string - name: annotator_summaries_language dtype: string - name: annotator_translation dtype: string - name: annotator_original_language dtype: string - name: annotator_publisher dtype: string - name: annotator_place_pub dtype: string - name: annotator_country dtype: string - name: annotator_title dtype: string - name: Link to digitised book dtype: string - name: annotated dtype: bool - name: Type of resource dtype: class_label: names: '0': Monograph '1': Serial '2': Monographic component part - name: created_at dtype: string - name: annotator_genre dtype: string splits: - name: train num_bytes: 27518816 num_examples: 55343 download_size: 20111420 dataset_size: 27518816 config_names: - annotated_raw - raw - title_genre_classifiction --- # Dataset Card for blbooksgenre ## Table of Contents - [Dataset Card for blbooksgenre](#dataset-card-for-blbooksgenre) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Supervised tasks](#supervised-tasks) - [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) - [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://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312) - **Repository:** [https://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312) - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset consists of metadata relating to books [digitised by the British Library in partnership with Microsoft](https://www.bl.uk/collection-guides/google-books-digitised-printed-heritage). Some of this metadata was exported from the British Library catalogue whilst others was generated as part of a crowdsourcing project. The text of this book and other metadata can be found on the [date.bl](https://data.bl.uk/bl_labs_datasets/#3) website. The majority of the books in this collection were published in the 18th and 19th Century but the collection also includes 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 a variety of languages. For the subsection of the data which contains additional crowsourced annotations the date of publication breakdown is as follows: | | Date of publication | | ---- | ------------------- | | 1630 | 8 | | 1690 | 4 | | 1760 | 10 | | 1770 | 5 | | 1780 | 5 | | 1790 | 18 | | 1800 | 45 | | 1810 | 96 | | 1820 | 152 | | 1830 | 182 | | 1840 | 259 | | 1850 | 400 | | 1860 | 377 | | 1870 | 548 | | 1880 | 776 | | 1890 | 1484 | | 1900 | 17 | | 1910 | 1 | | 1970 | 1 | [More Information Needed] ### Supported Tasks and Leaderboards The digitised books collection which this dataset describes has been used in a variety of digital history and humanities projects since being published. This dataset is suitable for a variety of unsupervised tasks and for a 'genre classification task'. #### Supervised tasks The main possible use case for this dataset is to develop and evaluate 'genre classification' models. The dataset includes human generated labels for whether a book is 'fiction' or 'non-fiction'. This has been used to train models for genre classifcation which predict whether a book is 'fiction' or 'non-fiction' based on its title. ### Languages [More Information Needed] ## Dataset Structure The dataset currently has three configurations intended to support a range of tasks for which this dataset could be used for: - `title_genre_classifiction` : this creates a de-duplicated version of the dataset with the `BL record`, `title` and `label`. - `annotated_raw`: This version of the dataset includes all fields from the original dataset which are annotated. This includes duplication from different annotators" - `raw`: This version of the dataset includes all the data from the original data including data without annotations. ### Data Instances An example data instance from the `title_genre_classifiction` config: ```python {'BL record ID': '014603046', 'title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]', 'label': 0} ``` An example data instance from the `annotated_raw` config: ```python {'BL record ID': '014603046', 'Name': 'Yates, William Joseph H.', 'Dates associated with name': '', 'Type of name': 'person', 'Role': '', 'All names': ['Yates, William Joseph H. [person] ', ' Y, W. J. H. [person]'], 'Title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]', 'Variant titles': '', 'Series title': '', 'Number within series': '', 'Country of publication': ['England'], 'Place of publication': ['London'], 'Publisher': '', 'Date of publication': '1879', 'Edition': '', 'Physical description': 'pages not numbered, 21 cm', 'Dewey classification': '', 'BL shelfmark': 'Digital Store 11601.f.36. (1.)', 'Topics': '', 'Genre': '', 'Languages': ['English'], 'Notes': 'In verse', 'BL record ID for physical resource': '004079262', 'classification_id': '267476823.0', 'user_id': '15.0', 'subject_ids': '44369003.0', 'annotator_date_pub': '1879', 'annotator_normalised_date_pub': '1879', 'annotator_edition_statement': 'NONE', 'annotator_FAST_genre_terms': '655 7 ‡aPoetry‡2fast‡0(OCoLC)fst01423828', 'annotator_FAST_subject_terms': '60007 ‡aAlice,‡cGrand Duchess, consort of Ludwig IV, Grand Duke of Hesse-Darmstadt,‡d1843-1878‡2fast‡0(OCoLC)fst00093827', 'annotator_comments': '', 'annotator_main_language': '', 'annotator_other_languages_summaries': 'No', 'annotator_summaries_language': '', 'annotator_translation': 'No', 'annotator_original_language': '', 'annotator_publisher': 'NONE', 'annotator_place_pub': 'London', 'annotator_country': 'enk', 'annotator_title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]', 'Link to digitised book': 'http://access.bl.uk/item/viewer/ark:/81055/vdc_00000002842E', 'annotated': True, 'Type of resource': 0, 'created_at': datetime.datetime(2020, 8, 11, 14, 30, 33), 'annotator_genre': 0} ``` ### Data Fields The data fields differ slightly between configs. All possible fields for the `annotated_raw` config are listed below. For the `raw` version of the dataset datatypes are usually string to avoid errors when processing missing values. - `BL record ID`: an internal ID used by the British Library, this can be useful for linking this data to other BL collections. - `Name`: name associated with the item (usually author) - `Dates associated with name`: dates associated with above e.g. DOB - `Type of name`: whether `Name` is a person or an organization etc. - `Role`: i.e. whether `Name` is `author`, `publisher` etc. - `All names`: a fuller list of names associated with the item. - `Title`: The title of the work - `Variant titles` - `Series title` - `Number within series` - `Country of publication`: encoded as a list of countries listed in the metadata - `Place of publication`: encoded as a list of places listed in the metadata - `Publisher` - `Date of publication`: this is encoded as a string since this field can include data ranges i.e.`1850-1855`. - `Edition` - `Physical description`: encoded as a string since the format of this field varies - `Dewey classification` - `BL shelfmark`: a British Library shelf mark - `Topics`: topics included in the catalogue record - `Genre` the genre information included in the original catalogue record note that this is often missing - `Languages`; encoded as a list of languages - `Notes`: notes from the catalogue record - `BL record ID for physical resource` The following fields are all generated via the crowdsourcing task (discussed in more detail below) - `classification_id`: ID for the classification in the annotation task - `user_id` ID for the annotator - `subject_ids`: internal annotation task ID - `annotator_date_pub`: an updated publication data - `annotator_normalised_date_pub`: normalized version of the above - `annotator_edition_statement` updated edition - `annotator_FAST_genre_terms`: [FAST classification genre terms](https://www.oclc.org/research/areas/data-science/fast.html) - `annotator_FAST_subject_terms`: [FAST subject terms](https://www.oclc.org/research/areas/data-science/fast.html) - `annotator_comments`: free form comments - `annotator_main_language` - `annotator_other_languages_summaries` - `'annotator_summaries_language` - `annotator_translation` - `annotator_original_language` - `annotator_publisher` - `annotator_place_pub` - `annotator_country` - `annotator_title` - `Link to digitised book` - `annotated`: `bool` flag to indicate if row has annotations or not - `created_at`: when the annotation was created - `annotator_genre`: the updated annotation for the `genre` of the book. Finally the `label` field of the `title_genre_classifiction` configuration is a class label with values 0 (Fiction) or 1 (Non-fiction). [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 where there is a goal to quickly digitise large volumes of materials shape the selection of materials to include in a number of ways. Some consideratoins which are often involved in the decision of whether to include items for digitization 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 digitize 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 digitized. Large volumes are likely to be correlated to content to at least some extent so excluding them from digitization will mean that material is under represented. Similarly copyright status is often (but not only) determined by publication data. 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 with the aim of creating a representative sample of the British Library's holdings. Some material will be over-represented and other under-represented. Similarly, the collection should not be considered a representative sample of what was published across the time period covered by the dataset (nor that that the relative proportions of the data for each time period represent a proportional sample of publications from that period). [More Information Needed] ### Source Data The original 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). [More Information Needed] #### Initial Data Collection and Normalization This version of the dataset was created partially from data exported from British Library catalogue records and partially via data generated from a crowdsourcing task involving British Library staff. #### Who are the source language producers? [More Information Needed] ### Annotations The data does includes metadata associated with the books these are produced by British Library staff. The additional annotations were carried out during 2020 as part of an internal crowdsourcing task. #### Annotation process New annotations were produced via a crowdsourcing tasks. Annotators have the option to pick titles from a particular language subset from the broader digitized 19th century books collection. As a result the annotations are not random and overrepresent some languages. [More Information Needed] #### Who are the annotators? Staff working at the British Library. Most of these staff work with metadata as part of their jobs and so could be considered expert 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 bias towards particular languages etc. It is also important to note that library metadata is not static. The metadata held in library catalogues is updated and changed over time for a variety of reasons. The way in which different institutions catalogue items also varies. As a result it is important to evaluate the performance of any models trained on this data before applying to a new collection. [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases The text in this collection is derived from historic text. As a result the text will reflect to social beliefs and attitudes of this time period. The titles of the book give some sense of their content. Examples of book titles which 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'] Whilst using titles alone, is obviously insufficient to integrate bias in this collection it gives some insight into the topics covered by books in the corpus. Further looking into 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 We can see even in the above random sample of titles examples of colonial attitudes. We can try and interrogate this further by searching for the name of countries which were part of the British Empire at the time 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 colonization, 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'] Whilst this dataset doesn't include the underlying text it is important to consider the potential attitudes represented in the title of the books, or the full text if you are using this dataset in conjunction with the full text. [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 ```bibtex @misc{british library_genre, title={ 19th Century Books - metadata with additional crowdsourced annotations}, url={https://doi.org/10.23636/BKHQ-0312}, author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annelies and Wollner, Ildi}, year={2021}} ``` ### Contributions Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
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