Datasets:
davanstrien
/
test_card

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id
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2
115
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⌀
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68.7k
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EgilKarlsen/Thunderbird_GPT2_FT
2023-09-04T16:03:52.000Z
[ "region:us" ]
EgilKarlsen
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - 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name: train num_bytes: 115576722 num_examples: 37500 - name: test num_bytes: 38525585 num_examples: 12500 download_size: 211865268 dataset_size: 154102307 --- # Dataset Card for "Thunderbird_GPT2_FT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EgilKarlsen/Thunderbird_GPTNEO_FT
2023-09-04T16:21:55.000Z
[ "region:us" ]
EgilKarlsen
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - name: '32' dtype: float32 - name: '33' dtype: float32 - name: '34' dtype: float32 - name: '35' dtype: float32 - name: '36' dtype: float32 - name: '37' dtype: float32 - name: '38' dtype: float32 - name: '39' dtype: float32 - name: '40' dtype: float32 - name: '41' dtype: float32 - name: '42' dtype: float32 - name: '43' dtype: float32 - name: '44' dtype: float32 - name: '45' dtype: float32 - name: '46' dtype: float32 - name: '47' dtype: float32 - name: '48' dtype: float32 - name: '49' dtype: float32 - name: '50' dtype: float32 - name: '51' dtype: float32 - name: '52' dtype: float32 - name: '53' dtype: float32 - name: '54' dtype: float32 - name: '55' dtype: float32 - name: '56' dtype: float32 - name: '57' dtype: float32 - name: '58' dtype: float32 - name: '59' dtype: float32 - name: '60' dtype: float32 - name: '61' dtype: float32 - name: '62' dtype: float32 - name: '63' dtype: float32 - name: '64' dtype: float32 - name: '65' dtype: float32 - name: '66' dtype: float32 - name: '67' dtype: float32 - 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name: '1932' dtype: float32 - name: '1933' dtype: float32 - name: '1934' dtype: float32 - name: '1935' dtype: float32 - name: '1936' dtype: float32 - name: '1937' dtype: float32 - name: '1938' dtype: float32 - name: '1939' dtype: float32 - name: '1940' dtype: float32 - name: '1941' dtype: float32 - name: '1942' dtype: float32 - name: '1943' dtype: float32 - name: '1944' dtype: float32 - name: '1945' dtype: float32 - name: '1946' dtype: float32 - name: '1947' dtype: float32 - name: '1948' dtype: float32 - name: '1949' dtype: float32 - name: '1950' dtype: float32 - name: '1951' dtype: float32 - name: '1952' dtype: float32 - name: '1953' dtype: float32 - name: '1954' dtype: float32 - name: '1955' dtype: float32 - name: '1956' dtype: float32 - name: '1957' dtype: float32 - name: '1958' dtype: float32 - name: '1959' dtype: float32 - name: '1960' dtype: float32 - name: '1961' dtype: float32 - name: '1962' dtype: float32 - name: '1963' dtype: float32 - name: '1964' dtype: float32 - name: '1965' dtype: float32 - name: '1966' dtype: float32 - name: '1967' dtype: float32 - name: '1968' dtype: float32 - name: '1969' dtype: float32 - name: '1970' dtype: float32 - name: '1971' dtype: float32 - name: '1972' dtype: float32 - name: '1973' dtype: float32 - name: '1974' dtype: float32 - name: '1975' dtype: float32 - name: '1976' dtype: float32 - name: '1977' dtype: float32 - name: '1978' dtype: float32 - name: '1979' dtype: float32 - name: '1980' dtype: float32 - name: '1981' dtype: float32 - name: '1982' dtype: float32 - name: '1983' dtype: float32 - name: '1984' dtype: float32 - name: '1985' dtype: float32 - name: '1986' dtype: float32 - name: '1987' dtype: float32 - name: '1988' dtype: float32 - name: '1989' dtype: float32 - name: '1990' dtype: float32 - name: '1991' dtype: float32 - name: '1992' dtype: float32 - name: '1993' dtype: float32 - name: '1994' dtype: float32 - name: '1995' dtype: float32 - name: '1996' dtype: float32 - name: '1997' dtype: float32 - name: '1998' dtype: float32 - name: '1999' dtype: float32 - name: '2000' dtype: float32 - name: '2001' dtype: float32 - name: '2002' dtype: float32 - name: '2003' dtype: float32 - name: '2004' dtype: float32 - name: '2005' dtype: float32 - name: '2006' dtype: float32 - name: '2007' dtype: float32 - name: '2008' dtype: float32 - name: '2009' dtype: float32 - name: '2010' dtype: float32 - name: '2011' dtype: float32 - name: '2012' dtype: float32 - name: '2013' dtype: float32 - name: '2014' dtype: float32 - name: '2015' dtype: float32 - name: '2016' dtype: float32 - name: '2017' dtype: float32 - name: '2018' dtype: float32 - name: '2019' dtype: float32 - name: '2020' dtype: float32 - name: '2021' dtype: float32 - name: '2022' dtype: float32 - name: '2023' dtype: float32 - name: '2024' dtype: float32 - name: '2025' dtype: float32 - name: '2026' dtype: float32 - name: '2027' dtype: float32 - name: '2028' dtype: float32 - name: '2029' dtype: float32 - name: '2030' dtype: float32 - name: '2031' dtype: float32 - name: '2032' dtype: float32 - name: '2033' dtype: float32 - name: '2034' dtype: float32 - name: '2035' dtype: float32 - name: '2036' dtype: float32 - name: '2037' dtype: float32 - name: '2038' dtype: float32 - name: '2039' dtype: float32 - name: '2040' dtype: float32 - name: '2041' dtype: float32 - name: '2042' dtype: float32 - name: '2043' dtype: float32 - name: '2044' dtype: float32 - name: '2045' dtype: float32 - name: '2046' dtype: float32 - name: '2047' dtype: float32 - name: label dtype: string splits: - name: train num_bytes: 307576722 num_examples: 37500 - name: test num_bytes: 102525585 num_examples: 12500 download_size: 565396538 dataset_size: 410102307 --- # Dataset Card for "Thunderbird_GPTNEO_FT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vhtran/en-de-2023
2023-09-05T01:00:23.000Z
[ "license:cc-by-4.0", "region:us" ]
vhtran
null
null
null
0
5
--- license: cc-by-4.0 --- Translate German to English
pieken/labeling
2023-09-11T06:13:38.000Z
[ "region:us" ]
pieken
null
null
null
0
5
kavinilavan/BQ
2023-09-05T10:43:22.000Z
[ "region:us" ]
kavinilavan
null
null
null
0
5
Entry not found
gurprbebo/BEBO_DS_UPDATED
2023-09-05T11:16:01.000Z
[ "region:us" ]
gurprbebo
null
null
null
0
5
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2755 num_examples: 9 download_size: 2776 dataset_size: 2755 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "BEBO_DS_UPDATED" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TahmidH/Bengali_Sentence_Construction
2023-09-05T13:25:02.000Z
[ "size_categories:1K<n<10K", "language:bn", "license:cc0-1.0", "region:us" ]
TahmidH
null
null
null
0
5
--- license: cc0-1.0 language: - bn size_categories: - 1K<n<10K ---
OmkarB/Synthetically-generated-SQL-GQL-Translations-with-Schema
2023-09-05T17:29:14.000Z
[ "region:us" ]
OmkarB
null
null
null
0
5
Entry not found
iamshnoo/alpaca-cleaned-greek
2023-09-15T23:22:28.000Z
[ "region:us" ]
iamshnoo
null
null
null
0
5
--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 53753481 num_examples: 51760 download_size: 25664903 dataset_size: 53753481 --- Translated from yahma/alpaca-cleaned using NLLB-1.3B # Dataset Card for "alpaca-cleaned-greek" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mohammadh128/common_voice_fa_preprocessed_and_augmented_training_and_evaluation_11_0
2023-09-06T14:41:50.000Z
[ "region:us" ]
mohammadh128
null
null
null
0
5
--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 51773704672.0 num_examples: 53902 - name: validation num_bytes: 9881781552.0 num_examples: 10288 download_size: 8718461806 dataset_size: 61655486224.0 --- # Dataset Card for "common_voice_fa_preprocessed_and_augmented_training_and_evaluation_11_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
breadlicker45/tayai-chat
2023-09-06T11:09:33.000Z
[ "region:us" ]
breadlicker45
null
null
null
0
5
Entry not found
nampdn-ai/devdocs.io
2023-09-21T21:03:20.000Z
[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "code", "region:us" ]
nampdn-ai
null
null
null
0
5
--- task_categories: - text-generation language: - en tags: - code pretty_name: devdocs.io size_categories: - 100K<n<1M --- 189k (~1GB of raw clean text) documents of various programming language & tech stacks by [DevDocs](https://devdocs.io/), it combines multiple API documentations in a fast, organized, and searchable interface. DevDocs is free and open source by FreeCodeCamp. I've converted it into Markdown format for the standard of training data.
AdamCodd/emotion-balanced
2023-09-08T19:18:43.000Z
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:other", "emotion-classific...
AdamCodd
null
null
null
0
5
--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: emotion pretty_name: Emotion tags: - emotion-classification dataset_info: - config_name: split features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 1968209 num_examples: 16000 - name: validation num_bytes: 247888 num_examples: 2000 - name: test num_bytes: 244379 num_examples: 2000 download_size: 740883 dataset_size: 2173481 - config_name: unsplit features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 10792185 num_examples: 89754 download_size: 10792185 dataset_size: 10792185 train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "emotion" ## 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/AdamCodd/emotion-dataset](https://github.com/AdamCodd/emotion-dataset) - **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:** 10.54 MB ### Dataset Summary Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. ### 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 An example looks as follows. ``` { "text": "im feeling quite sad and sorry for myself but ill snap out of it soon", "label": 0 } ``` ### Data Fields The data fields are: - `text`: a `string` feature. - `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5). ### Data Splits The dataset has 2 configurations: - split: with a total of 20_000 examples split into train, validation and test - unsplit: with a total of 89_754 examples in a single train split | name | train | validation | test | |---------|-------:|-----------:|-----:| | split | 16000 | 2000 | 2000 | | unsplit | 89754 | n/a | n/a | ## Dataset Creation ### Curation Rationale This dataset is designed for training machine learning models to perform emotion analysis. It contains text samples from Twitter labeled with six different emotions: sadness, joy, love, anger, fear, and surprise. The dataset is balanced, meaning that it has an equal number of samples for each label. This dataset is originally sourced from [dair-ai's emotion dataset](https://huggingface.co/datasets/dair-ai/emotion), but the initial dataset was unbalanced and had some duplicate samples. Thus, this dataset has been deduplicated and balanced to ensure an equal number of samples for each emotion label. ### 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 should be used for educational and research purposes only. ### Citation Information If you use this dataset, please cite: ``` @inproceedings{saravia-etal-2018-carer, title = "{CARER}: Contextualized Affect Representations for Emotion Recognition", author = "Saravia, Elvis and Liu, Hsien-Chi Toby and Huang, Yen-Hao and Wu, Junlin and Chen, Yi-Shin", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1404", doi = "10.18653/v1/D18-1404", pages = "3687--3697", abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.", } ```
bilalahmadai/open_assistant_dataset_QA
2023-09-07T07:26:08.000Z
[ "region:us" ]
bilalahmadai
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 782135 num_examples: 2000 download_size: 483861 dataset_size: 782135 --- # Dataset Card for "open_assistant_dataset_QA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
garcianacho/DPI
2023-09-07T11:47:30.000Z
[ "region:us" ]
garcianacho
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 106579679 num_examples: 150000 download_size: 96335003 dataset_size: 106579679 --- # Dataset Card for "DPI" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
erfanzar/UltraChat-Mini
2023-09-07T12:13:32.000Z
[ "region:us" ]
erfanzar
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: dialog sequence: string - name: user sequence: string - name: assistant sequence: string - name: system dtype: string - name: id dtype: int64 - name: llama2_prompt dtype: string splits: - name: train num_bytes: 6005323184 num_examples: 239641 download_size: 2964129142 dataset_size: 6005323184 --- # Dataset Card for "UltraChat-Mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bilalahmadai/open_assistant_dataset_llama2
2023-09-07T12:13:00.000Z
[ "region:us" ]
bilalahmadai
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 303916 num_examples: 700 - name: validation num_bytes: 176400 num_examples: 300 download_size: 179286 dataset_size: 480316 --- # Dataset Card for "open_assistant_dataset_llama2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
breadlicker45/bread-qa-updated
2023-09-07T20:12:25.000Z
[ "region:us" ]
breadlicker45
null
null
null
0
5
Entry not found
Minglii/v
2023-09-08T23:27:29.000Z
[ "region:us" ]
Minglii
null
null
null
0
5
--- dataset_info: features: - name: data struct: - name: conversations list: - name: from dtype: string - name: markdown struct: - name: answer dtype: string - name: index dtype: int64 - name: type dtype: string - name: text dtype: string - name: value dtype: string - name: id dtype: string splits: - name: train num_bytes: 644558921 num_examples: 117213 download_size: 262396682 dataset_size: 644558921 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "v" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/pixarstyle_prompts
2023-09-09T09:08:29.000Z
[ "region:us" ]
Falah
null
null
null
0
5
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 45415433 num_examples: 100000 download_size: 5581919 dataset_size: 45415433 --- # Dataset Card for "pixarstyle_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Isaacks/tissue-masker-dataset-without-damage
2023-09-09T09:47:46.000Z
[ "region:us" ]
Isaacks
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 32167415.0 num_examples: 80 - name: validation num_bytes: 3412353.0 num_examples: 9 download_size: 34278312 dataset_size: 35579768.0 --- # Dataset Card for "tissue-masker-dataset-without-damage" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sdadas/gpt-exams
2023-09-09T12:06:12.000Z
[ "task_categories:question-answering", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:pl", "license:cc-by-nc-sa-4.0", "region:us" ]
sdadas
null
null
null
0
5
--- language: - pl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K task_categories: - question-answering pretty_name: GPT-exams dataset_info: features: - name: _id dtype: int32 - name: question dtype: string - name: answer dtype: string - name: domain dtype: string splits: - name: train num_bytes: 17237681 num_examples: 8131 --- # GPT-exams ### Dataset summary The dataset contains 8131 multi-domain question-answer pairs. It was created semi-automatically using the `gpt-3.5-turbo-0613` model available in the OpenAI API. The process of building the dataset was as follows: 1. We manually prepared a list of 409 university-level courses from various fields. For each course, we instructed the model with the prompt: "Wygeneruj 20 przykładowych pytań na egzamin z [nazwa przedmiotu]" (Generate 20 sample questions for the [course name] exam). 2. We then parsed the outputs of the model to extract individual questions and performed their deduplication. 3. In the next step, we requested the model to generate the answer to each of the collected questions. We used the following prompt: "Odpowiedz na następujące pytanie z dziedziny [nazwa przedmiotu]: [treść pytania]" (Answer the following question from [course name]: [question content]). Along with the prompt, we also sent the following system message: "Jesteś ekspertem w dziedzinie [nazwa przedmiotu]. Udzielasz specjalistycznych i wyczerpujących odpowiedzi na pytania." (You are an expert in [course name]. You provide knowledgeable and comprehensive answers to questions). 4. In the last step, we manually removed from the dataset the cases in which the model refused to answer the question. We searched for occurrences of phrases such as "model języka" (language model), "nie jestem" (I'm not), or "nie mogę" (I can't). ### Data Instances Example instance: ``` { "_id": 2338, "domain": "wzorców projektowych w oprogramowaniu", "question": "Co to jest dependency injection i jak może być wykorzystane w kontekście wzorców projektowych?", "answer": "Dependency injection (DI) to technika wstrzykiwania zależności, która polega na dostarczaniu obiektowi (...)" } ``` ### Data Fields - _id: record id - question: question text - answer: answer text - domain: name of the course / field / domain
schen357/corpjargon
2023-09-11T02:15:01.000Z
[ "size_categories:n<1K", "language:en", "region:us" ]
schen357
null
null
null
0
5
--- language: - en size_categories: - n<1K ---
strumber/newLetsMODDataset
2023-09-14T15:06:43.000Z
[ "region:us" ]
strumber
null
null
null
0
5
Entry not found
shwetkm/TextCaps-VQA
2023-09-20T15:53:44.000Z
[ "region:us" ]
shwetkm
null
null
null
0
5
--- dataset_info: features: - name: image_id dtype: string - name: question dtype: string - name: answer dtype: string - name: summary dtype: string - name: image_url dtype: string - name: question_id dtype: string splits: - name: train num_bytes: 6476845 num_examples: 13895 download_size: 3307541 dataset_size: 6476845 --- # Dataset Card for "TextCaps-VQA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
prognosis/symptoms_disease_v1
2023-09-11T15:33:24.000Z
[ "region:us" ]
prognosis
null
null
null
0
5
Entry not found
SodaDQ/cache_test
2023-09-11T18:31:51.000Z
[ "region:us" ]
SodaDQ
null
null
null
0
5
--- dataset_info: features: - name: sodacl dtype: string - name: response dtype: string splits: - name: train num_bytes: 2075 num_examples: 5 - name: test num_bytes: 145801 num_examples: 308 download_size: 74408 dataset_size: 147876 --- # Dataset Card for "cache_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pietrolesci/wikitoxic
2023-09-13T12:03:54.000Z
[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|other", "language:en", "license:cc0-1.0", "wikipedia", "toxicity", "tox...
pietrolesci
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - config_name: embedding_all-MiniLM-L12-v2 data_files: - split: train path: embedding_all-MiniLM-L12-v2/train-* - split: validation path: embedding_all-MiniLM-L12-v2/validation-* - split: test path: embedding_all-MiniLM-L12-v2/test-* - config_name: embedding_all-mpnet-base-v2 data_files: - split: train path: embedding_all-mpnet-base-v2/train-* - split: validation path: embedding_all-mpnet-base-v2/validation-* - split: test path: embedding_all-mpnet-base-v2/test-* - config_name: embedding_multi-qa-mpnet-base-dot-v1 data_files: - split: train path: embedding_multi-qa-mpnet-base-dot-v1/train-* - split: validation path: embedding_multi-qa-mpnet-base-dot-v1/validation-* - split: test path: embedding_multi-qa-mpnet-base-dot-v1/test-* dataset_info: - config_name: default features: - name: id dtype: string - name: text dtype: string - name: labels dtype: class_label: names: '0': non '1': tox - name: uid dtype: int64 splits: - name: train num_bytes: 55430581 num_examples: 127656 - name: validation num_bytes: 13936861 num_examples: 31915 - name: test num_bytes: 27474227 num_examples: 63978 download_size: 62548640 dataset_size: 96841669 - config_name: embedding_all-MiniLM-L12-v2 features: - name: uid dtype: int64 - name: embedding_all-MiniLM-L12-v2 sequence: float32 splits: - name: train num_bytes: 197611488 num_examples: 127656 - name: validation num_bytes: 49404420 num_examples: 31915 - name: test num_bytes: 99037944 num_examples: 63978 download_size: 484421377 dataset_size: 346053852 - config_name: embedding_all-mpnet-base-v2 features: - name: uid dtype: int64 - name: embedding_all-mpnet-base-v2 sequence: float32 splits: - name: train num_bytes: 393691104 num_examples: 127656 - name: validation num_bytes: 98425860 num_examples: 31915 - name: test num_bytes: 197308152 num_examples: 63978 download_size: 827919212 dataset_size: 689425116 - config_name: embedding_multi-qa-mpnet-base-dot-v1 features: - name: uid dtype: int64 - name: embedding_multi-qa-mpnet-base-dot-v1 sequence: float32 splits: - name: train num_bytes: 393691104 num_examples: 127656 - name: validation num_bytes: 98425860 num_examples: 31915 - name: test num_bytes: 197308152 num_examples: 63978 download_size: 827907964 dataset_size: 689425116 annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc0-1.0 multilinguality: - monolingual pretty_name: Toxic Wikipedia Comments size_categories: - 100K<n<1M source_datasets: - extended|other tags: - wikipedia - toxicity - toxic comments task_categories: - text-classification task_ids: - hate-speech-detection --- This is the same dataset as [`OxAISH-AL-LLM/wiki_toxic`](https://huggingface.co/datasets/OxAISH-AL-LLM/wiki_toxic). The only differences are 1. Addition of a unique identifier, `uid` 1. Addition of the indices, that is 3 columns with the embeddings of 3 different sentence-transformers - `all-mpnet-base-v2` - `multi-qa-mpnet-base-dot-v1` - `all-MiniLM-L12-v2` 1. Renaming of the `label` column to `labels` for easier compatibility with the transformers library
wza/FinVis
2023-09-14T01:52:51.000Z
[ "license:apache-2.0", "region:us" ]
wza
null
null
null
0
5
--- license: apache-2.0 --- Dataset for paper： FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis（ https://github.com/wwwadx/FinVis-GPT ） The .zip file contains images
silverliningeda/silverliningeda-dataset-test
2023-09-14T23:54:09.000Z
[ "region:us" ]
silverliningeda
null
null
null
0
5
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 163420 num_examples: 500 download_size: 3073 dataset_size: 163420 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "silverliningeda-dataset-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TonyJPk7/Chat-PCR_CNNDaily_clear
2023-09-12T07:21:21.000Z
[ "region:us" ]
TonyJPk7
null
null
null
0
5
Entry not found
GokhanAI/AGENT_V2
2023-09-12T10:58:55.000Z
[ "region:us" ]
GokhanAI
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 53849344.788422026 num_examples: 83179 - name: test num_bytes: 1294782.211577971 num_examples: 2000 download_size: 19239055 dataset_size: 55144127.0 --- # Dataset Card for "AGENT_V2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Lancelot53/xlsum
2023-09-12T18:01:16.000Z
[ "region:us" ]
Lancelot53
null
null
null
0
5
--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: image_paths sequence: string splits: - name: train num_bytes: 982097374 num_examples: 306522 - name: test num_bytes: 35146245.0 num_examples: 11535 - name: validation num_bytes: 35382527.0 num_examples: 11535 download_size: 648046091 dataset_size: 1052626146.0 --- # Dataset Card for "xlsum" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ingeniumacademy/reuters_articles
2023-09-12T22:14:36.000Z
[ "region:us" ]
ingeniumacademy
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073411 dataset_size: 17042155 --- # Dataset Card for "reuters_articles" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Abhay1212/test
2023-09-13T02:47:19.000Z
[ "license:creativeml-openrail-m", "region:us" ]
Abhay1212
null
null
null
0
5
--- license: creativeml-openrail-m dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 693 num_examples: 5 download_size: 2456 dataset_size: 693 configs: - config_name: default data_files: - split: train path: data/train-* ---
fridge12/SumitranandanPant
2023-09-13T06:39:19.000Z
[ "region:us" ]
fridge12
null
null
null
0
5
Entry not found
harish03/english_hinglist_sentences
2023-09-13T06:48:09.000Z
[ "license:apache-2.0", "region:us" ]
harish03
null
null
null
0
5
--- license: apache-2.0 ---
mesolitica/bge-large-en-embedding
2023-09-17T16:39:59.000Z
[ "region:us" ]
mesolitica
null
null
null
0
5
Entry not found
davidadamczyk/election
2023-09-13T15:07:20.000Z
[ "region:us" ]
davidadamczyk
null
null
null
0
5
--- dataset_info: features: - name: text dtype: string - name: text_label dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 65745.4 num_examples: 350 - name: test num_bytes: 28176.6 num_examples: 150 download_size: 50277 dataset_size: 93922.0 --- # Dataset Card for "election" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ps1293/job_description
2023-09-13T16:30:34.000Z
[ "region:us" ]
ps1293
null
null
null
0
5
Entry not found
lucasmartinho/reddit-topics-targz
2023-09-13T17:23:52.000Z
[ "region:us" ]
lucasmartinho
Demo...
@article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, Konstantin and {Liang}, Percy}, title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", journal = {arXiv e-prints}, year = 2016, eid = {arXiv:1606.05250}, pages = {arXiv:1606.05250}, archivePrefix = {arXiv}, eprint = {1606.05250}, }
null
0
5
Entry not found
MKDonnelly/langchain-docs
2023-09-13T20:10:49.000Z
[ "region:us" ]
MKDonnelly
null
null
null
0
5
Entry not found
187ro/incelset
2023-09-18T08:43:30.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "size_categories:100K<n<1M", "language:en", "license:unknown", "not-for-all-audiences", "region:us" ]
187ro
null
null
null
2
5
--- license: unknown task_categories: - text-generation - fill-mask tags: - not-for-all-audiences pretty_name: Incel Dataset 🎭 size_categories: - 100K<n<1M language: - en --- # Dataset Card for IncelSet ### Dataset Summary This dataset is based off the incels.is forum and is ⚠️HIGHLY OFFENSIVE⚠️ A compilation of almost 3 years worth of posts, highlighting topics such as (self-described) celibatism, self-views, life-improvement (attempts or advice), suicide, perceived failure, views on women, views on society, views on politcs - from the members' perspective. Co-Authored by inmate & curly for Universiteit van Amsterdam [Politics, Psychology, Law and Economics (PPLE)](https://pple.uva.nl) ### Languages English with a lot of racial slurs, misoginy, mentions of sexual assault and general hatred - do not view or use if easily offended. ## Dataset Structure The dataset consists of 2 colums, "title" - representing the thread title & "text" - representing the user replies (posts) under the thread title ### Source Data Incels.is Forum. #### Initial Data Collection and Normalization 1. We first built a script in GoLang that scrapes all the content of the incel.is Forum. We downloaded roughly 150.000 threads - containing almost 2.1 Million posts - in approximately 9 hours from start to finish - using a dedicated server with 72 cores. 2. We then took the scraped data and started processing it, firstly building a script in Python that processed the data & formatted it into the JSON data format according to (RFC 8259) standards. 3. We then started the removal process of PII (Personal Identifiable Information) - thus anonymizing user posts in the dataset. This wasn't hard to do as users already set up monikers for themselves & never gave out personal information such as full names, addresses or social security numbers, nevertheless we still validated the removal of such data. 4. We then proceeded to remove leftover non-human readable text such as HTML tags or base64 encodings, along URLs users may have posted in their discussions. 5. We now begin the dataset formatting process of compiling all 143.501 files left (threads) & ~2.1M posts in Parquet. 6. Final results yield approx 1bil characters on ~144k rows. #### Who are the source language producers? Self-described incels / members of the incels.is website (not to be taken in the mot-a-mot sense of the word) ### Personal and Sensitive Information Includes details of the users' (tragic & tragically self-perceived) lifes. No personal information contained in itself but touches on many sensitive subjects. ## Considerations for Using the Data Go wild with it. Keep in mind that we are not trying to expose, radicalize or even remotely harm this community. We have compiled almost 3 years worth of posts on this forum so we could better study this phenomena for a University project. We will be taking into consideration the actual publishing of the model trained on this data, but we do not see a potential scientific gain that would convince us to do so. ### Social Impact of Dataset Public Awareness and Education: Pro: Publishing a dataset might bring greater public awareness to the issue and could be used for educational purposes, enlightening people about the intricacies of this community. Greater understanding might foster empathy and encourage supportive interventions. Con: It might also inadvertently glamorize or sensationalize the community, leading to an increased interest in and potential growth of such ideologies. Source: Marwick, A., & Caplan, R. (2018). Drinking male tears: Language, the manosphere, and networked harassment. Feminist Media Studies, 18(4), 543-559. Potential Stigmatization and Alienation: Pro: Identifying problematic behaviors and attitudes can help professionals develop targeted interventions. Con: Generalizing or pathologizing the behaviors of this community might further stigmatize and alienate its members. Labeling can reinforce undesirable behavior if individuals internalize these negative identities. Source: Dovidio, J. F., Major, B., & Crocker, J. (2000). Stigma: Introduction and overview. In T. F. Heatherton, R. E. Kleck, M. R. Hebl, & J. G. Hull (Eds.), The social psychology of stigma (p. 1–28). Misuse of Data: Pro: When used responsibly, such a dataset can be a treasure trove for academic research. Con: However, there's always a risk of data being misused, misinterpreted, or cherry-picked to support harmful narratives or agendas. Source: boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679. Ethical Concerns: Pro: Revealing problematic beliefs might serve a greater good. Con: There are ethical concerns, especially if data was collected without consent. Respect for individuals' autonomy and privacy is paramount in research ethics. (Data is collected under anonymity from a free-to-view, no-signup required, non-scrape blocking Forum - as per their ToS) Source: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. Psychological Impact on Incels: Pro: Confronting one's views might lead to self-reflection and change. Con: Conversely, it might entrench their beliefs further if they feel attacked or misunderstood, a phenomenon supported by the backfire effect. Source: Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303-330. ### Discussion of Biases The authors compiled only the first 150.000 of the 270.000 threads in the "Inceldom discussion" part of the forum. As a consequence, older posts have been left out and the dataset may not thoroughly represent the full extent of incel discourse. The authors declare no further biases or conflicts of interest - the data was scraped and processed as it appears on the forum.
eitoi/elk_deer_test_jpg
2023-09-14T20:00:41.000Z
[ "region:us" ]
eitoi
null
null
null
0
5
Entry not found
HydraLM/filter-delete-1
2023-09-14T00:20:06.000Z
[ "region:us" ]
HydraLM
null
null
null
0
5
--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_conversation_id dtype: string - name: embedding sequence: float32 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1403707526 num_examples: 230443 download_size: 1340424028 dataset_size: 1403707526 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "deleted-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
strumber/objectDatasetLetsMOD
2023-09-14T15:33:06.000Z
[ "region:us" ]
strumber
null
null
null
0
5
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {} --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### 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]
DhruvShek/synapsellm-v0-1-llama2
2023-09-16T10:08:06.000Z
[ "region:us" ]
DhruvShek
null
null
null
0
5
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5261590 num_examples: 9446 download_size: 3238425 dataset_size: 5261590 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "synapsellm-v0-1-llama2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/SkunkData-002-convid-cluster
2023-09-14T23:49:59.000Z
[ "region:us" ]
HydraLM
null
null
null
0
5
--- dataset_info: features: - name: unique_conversation_id dtype: string - name: cluster dtype: int32 splits: - name: train num_bytes: 89257780 num_examples: 1472917 download_size: 17951475 dataset_size: 89257780 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "SkunkData-002-convid-cluster" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ericwang/samromur_children_test
2023-09-25T19:08:10.000Z
[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:is", "license:cc-by-4.0", "samromur", "children's speech", "icelandic: iceland"...
Ericwang
null
null
null
0
5
--- annotations_creators: - crowdsourced language: - is language_creators: - crowdsourced license: - cc-by-4.0 multilinguality: - monolingual pretty_name: "Samrómur Children Icelandic Speech 1.0" size_categories: - 100K<n<1M source_datasets: - original tags: - "samromur" - children's speech - 'icelandic: iceland' - icelandic children - icelandic kids - kids task_categories: - automatic-speech-recognition task_ids: [] --- # Dataset Card for samromur_children ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data 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:** [Samrómur Children Icelandic Speech 1.0](https://samromur.is/) - **Repository:** [LDC](https://catalog.ldc.upenn.edu/LDC2022S11) - **Paper:** [Samrómur Children: An Icelandic Speech Corpus](https://aclanthology.org/2022.lrec-1.105.pdf) - **Point of Contact:** [Carlos Mena](mailto:carlos.mena@ciempiess.org), [Jón Guðnason](mailto:jg@ru.is) ### Dataset Summary The Samrómur Children Corpus consists of audio recordings and metadata files containing prompts read by the participants. It contains more than 137000 validated speech-recordings uttered by Icelandic children. The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021). ### Example Usage The Samrómur Children Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name: ```python from datasets import load_dataset samromur_children = load_dataset("language-and-voice-lab/samromur_children") ``` To load an specific split (for example, the validation split) do: ```python from datasets import load_dataset samromur_children = load_dataset("language-and-voice-lab/samromur_children",split="validation") ``` ### Supported Tasks automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). ### Languages The audio is in Icelandic. The reading prompts were gathered from a variety of sources, mainly from the [Icelandic Gigaword Corpus](http://clarin.is/en/resources/gigaword). The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the [Icelandic Web of Science](https://www.visindavefur.is/). ## Dataset Structure ### Data Instances ```python { 'audio_id': '015652-0717240', 'audio': { 'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/2c6b0d82de2ef0dc0879732f726809cccbe6060664966099f43276e8c94b03f2/test/015652/015652-0717240.flac', 'array': array([ 0. , 0. , 0. , ..., -0.00311279, -0.0007019 , 0.00128174], dtype=float32), 'sampling_rate': 16000 }, 'speaker_id': '015652', 'gender': 'female', 'age': '11', 'duration': 4.179999828338623, 'normalized_text': 'eiginlega var hann hin unga rússneska bylting lifandi komin' } ``` ### Data Fields * `audio_id` (string) - id of audio segment * `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally). * `speaker_id` (string) - id of speaker * `gender` (string) - gender of speaker (male or female) * `age` (string) - range of age of the speaker: Younger (15-35), Middle-aged (36-60) or Elderly (61+). * `duration` (float32) - duration of the audio file in seconds. * `normalized_text` (string) - normalized audio segment transcription. ### Data Splits The corpus is split into train, dev, and test portions. Lenghts of every portion are: train = 127h25m, test = 1h50m, dev=1h50m. To load an specific portion please see the above section "Example Usage". ## Dataset Creation ### Curation Rationale In the field of Automatic Speech Recognition (ASR) is a known fact that the children's speech is particularly hard to recognise due to its high variability produced by developmental changes in children's anatomy and speech production skills. For this reason, the criteria of selection for the train/dev/test portions have to take into account the children's age. Nevertheless, the Samrómur Children is an unbalanced corpus in terms of gender and age of the speakers. This means that the corpus has, for example, a total of 1667 female speakers (73h38m) versus 1412 of male speakers (52h26m). These unbalances impose conditions in the type of the experiments than can be performed with the corpus. For example, a equal number of female and male speakers through certain ranges of age is impossible. So, if one can't have a perfectly balance corpus in the training set, at least one can have it in the test portion. The test portion of the Samrómur Children was meticulously selected to cover ages between 6 to 16 years in both female and male speakers. Every of these range of age in both genders have a total duration of 5 minutes each. The development portion of the corpus contains only speakers with an unknown gender information. Both test and dev sets have a total duration of 1h50m each. In order to perform fairer experiments, speakers in the train and test sets are not shared. Nevertheless, there is only one speaker shared between the train and development set. It can be identified with the speaker ID=010363. However, no audio files are shared between these two sets. ### Source Data #### Initial Data Collection and Normalization The data was collected using the website https://samromur.is, code of which is available at https://github.com/cadia-lvl/samromur. The age range selected for this corpus is between 4 and 17 years. The original audio was collected at 44.1 kHz or 48 kHz sampling rate as *.wav files, which was down-sampled to 16 kHz and converted to *.flac. Each recording contains one read sentence from a script. The script contains 85.080 unique sentences and 90.838 unique tokens. There was no identifier other than the session ID, which is used as the speaker ID. The corpus is distributed with a metadata file with a detailed information on each utterance and speaker. The madata file is encoded as UTF-8 Unicode. The prompts were gathered from a variety of sources, mainly from The Icelandic Gigaword Corpus, which is available at http://clarin.is/en/resources/gigaword. The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the [Icelandic Web of Science](https://www.visindavefur.is/). ### Annotations #### Annotation process Prompts were pulled from these corpora if they met the criteria of having only letters which are present in the Icelandic alphabet, and if they are listed in the [DIM: Database Icelandic Morphology](https://aclanthology.org/W19-6116.pdf). There are also synthesised prompts consisting of a name followed by a question or a demand, in order to simulate a dialogue with a smart-device. #### Who are the annotators? The audio files content was manually verified against the prompts by one or more listener (summer students mainly). ### Personal and Sensitive Information The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset This is the first ASR corpus of Icelandic children. ### Discussion of Biases * The utterances were recorded by a smartphone or the web app. * Participants self-reported their age group, gender, and the native language. * Participants are aged between 4 to 17 years. * The corpus contains 137597 utterances from 3175 speakers, totalling 131 hours. * The amount of data due to female speakers is 73h38m, the amount of data due to male speakers is 52h26m and the amount of data due to speakers with an unknown gender information is 05h02m * The number of female speakers is 1667, the number of male speakers is 1412. The number of speakers with an unknown gender information is 96. * The audios due to female speakers are 78993, the audios due to male speakers are 53927 and the audios due to speakers with an unknown gender information are 4677. ### Other Known Limitations "Samrómur Children: Icelandic Speech 21.09" by the Language and Voice Laboratory (LVL) at the Reykjavik University is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. ## Additional Information ### Dataset Curators The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021). The corpus was curated by Carlos Daniel Hernández Mena in 2021. ### Licensing Information [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @misc{menasamromurchildren2021, title={Samrómur Children Icelandic Speech 1.0}, ldc_catalog_no={LDC2022S11}, DOI={https://doi.org/10.35111/frrj-qd60}, author={Hernández Mena, Carlos Daniel and Borsky, Michal and Mollberg, David Erik and Guðmundsson, Smári Freyr and Hedström, Staffan and Pálsson, Ragnar and Jónsson, Ólafur Helgi and Þorsteinsdóttir, Sunneva and Guðmundsdóttir, Jóhanna Vigdís and Magnúsdóttir, Eydís Huld and Þórhallsdóttir, Ragnheiður and Guðnason, Jón}, publisher={Reykjavík University} journal={Linguistic Data Consortium, Philadelphia}, year={2019}, url={https://catalog.ldc.upenn.edu/LDC2022S11}, } ``` ### Contributions This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture. The verification for the dataset was funded by the the Icelandic Directorate of Labour's Student Summer Job Program in 2020 and 2021. Special thanks for the summer students for all the hard work.
TangTide/arbovirusdata
2023-09-15T04:56:32.000Z
[ "region:us" ]
TangTide
null
null
null
0
5
FunDialogues/customer-service-apple-picker-maintenance
2023-09-15T06:03:50.000Z
[ "task_categories:question-answering", "task_categories:conversational", "size_categories:n<1K", "language:en", "license:apache-2.0", "fictitious dialogues", "prototyping", "customer service", "region:us" ]
FunDialogues
null
null
null
0
5
--- license: apache-2.0 task_categories: - question-answering - conversational language: - en tags: - fictitious dialogues - prototyping - customer service pretty_name: customer-service-apple-picker-maintenance size_categories: - n<1K --- # fun dialogues A library of fictitious dialogues that can be used to train language models or augment prompts for prototyping and educational purposes. Fun dialogues currently come in json and csv format for easy ingestion or conversion to popular data structures. Dialogues span various topics such as sports, retail, academia, healthcare, and more. The library also includes basic tooling for loading dialogues and will include quick chatbot prototyping functionality in the future. Visit the Project Repo: https://github.com/eduand-alvarez/fun-dialogues/ # This Dialogue Comprised of fictitious examples of dialogues between a technician and an expert on maintaining automated apple picker machines. Check out the example below: ``` "id": 1, "description": "Machine not picking apples", "dialogue": "Technician: Hello, one of our apple picker machines is not picking apples. What should I do to fix it?\n\nExpert: Check the picking arms for any obstructions or damage. Clean or replace them if necessary. Also, ensure the collection basket is not overfilled." ``` # How to Load Dialogues Loading dialogues can be accomplished using the fun dialogues library or Hugging Face datasets library. ## Load using fun dialogues 1. Install fun dialogues package `pip install fundialogues` 2. Use loader utility to load dataset as pandas dataframe. Further processing might be required for use. ``` from fundialogues import dialoader # load as pandas dataframe bball_coach = dialoader('"FunDialogues/customer-service-apple-picker-maintenance") ``` ## Loading using Hugging Face datasets 1. Install datasets package 2. Load using datasets ``` from datasets import load_dataset dataset = load_dataset("FunDialogues/customer-service-apple-picker-maintenance") ``` ## How to Contribute If you want to contribute to this project and make it better, your help is very welcome. Contributing is also a great way to learn more about social coding on Github, new technologies and and their ecosystems and how to make constructive, helpful bug reports, feature requests and the noblest of all contributions: a good, clean pull request. ### Contributing your own Lifecycle Solution If you want to contribute to an existing dialogue or add a new dialogue, please open an issue and I will follow up with you ASAP! ### Implementing Patches and Bug Fixes - Create a personal fork of the project on Github. - Clone the fork on your local machine. Your remote repo on Github is called origin. - Add the original repository as a remote called upstream. - If you created your fork a while ago be sure to pull upstream changes into your local repository. - Create a new branch to work on! Branch from develop if it exists, else from master. - Implement/fix your feature, comment your code. - Follow the code style of the project, including indentation. - If the component has tests run them! - Write or adapt tests as needed. - Add or change the documentation as needed. - Squash your commits into a single commit with git's interactive rebase. Create a new branch if necessary. - Push your branch to your fork on Github, the remote origin. - From your fork open a pull request in the correct branch. Target the project's develop branch if there is one, else go for master! If the maintainer requests further changes just push them to your branch. The PR will be updated automatically. Once the pull request is approved and merged you can pull the changes from upstream to your local repo and delete your extra branch(es). And last but not least: Always write your commit messages in the present tense. Your commit message should describe what the commit, when applied, does to the code – not what you did to the code. # Disclaimer The dialogues contained in this repository are provided for experimental purposes only. It is important to note that these dialogues are assumed to be original work by a human and are entirely fictitious, despite the possibility of some examples including factually correct information. The primary intention behind these dialogues is to serve as a tool for language modeling experimentation and should not be used for designing real-world products beyond non-production prototyping. Please be aware that the utilization of fictitious data in these datasets may increase the likelihood of language model artifacts, such as hallucinations or unrealistic responses. Therefore, it is essential to exercise caution and discretion when employing these datasets for any purpose. It is crucial to emphasize that none of the scenarios described in the fun dialogues dataset should be relied upon to provide advice or guidance to humans. These scenarios are purely fictitious and are intended solely for demonstration purposes. Any resemblance to real-world situations or individuals is entirely coincidental. The responsibility for the usage and application of these datasets rests solely with the individual or entity employing them. By accessing and utilizing these dialogues and all contents of the repository, you acknowledge that you have read and understood this disclaimer, and you agree to use them at your own discretion and risk.
AfonsoBiscaia/CH
2023-09-15T11:55:33.000Z
[ "language:pt", "region:us" ]
AfonsoBiscaia
null
null
null
0
5
--- language: - pt pretty_name: ChTweets ---
elsheikhams/WikiNewsTruth
2023-09-15T13:40:28.000Z
[ "region:us" ]
elsheikhams
null
null
null
0
5
Entry not found
elsheikhams/Shakkelha
2023-09-15T14:17:22.000Z
[ "region:us" ]
elsheikhams
null
null
null
0
5
--- dataset_info: features: - name: text dtype: string - name: undiacrtizied dtype: string splits: - name: train num_bytes: 579339698 num_examples: 533384 download_size: 276101045 dataset_size: 579339698 --- # Dataset Card for "Shakkelha" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/spacecraft_prompts
2023-09-15T16:31:18.000Z
[ "region:us" ]
Falah
null
null
null
0
5
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 5116462 num_examples: 10000 download_size: 622894 dataset_size: 5116462 --- # Dataset Card for "spacecraft_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jason-lee08/TinyStoriesExclamationValidation
2023-09-15T20:25:32.000Z
[ "region:us" ]
jason-lee08
null
null
null
0
5
--- dataset_info: features: - name: validation dtype: string splits: - name: train num_bytes: 322761 num_examples: 405 download_size: 100666 dataset_size: 322761 --- # Dataset Card for "TinyStoriesExclamationValidation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cky07/cross_domain_paraphrase_detection
2023-10-08T19:37:25.000Z
[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "region:us" ]
cky07
null
null
null
1
5
--- task_categories: - text-classification language: - en pretty_name: MPQS size_categories: - 10K<n<100K --- Cross Domain Paraphrase Detection 'MRPC', 'twitterPPDB', 'QQP', 'SemEval2015-Task1' 4 Domains: 1) MRPC (https://huggingface.co/datasets/SetFit/mrpc) - Contain all the train, validation, and test sets. 2) twitterPPDB (https://git.uwaterloo.ca/jimmylin/Castor-data/-/tree/sst/twitterPPDB) - Randomly sample 3500 instances for each class from the training set with random seed 24. 3) QQP (https://huggingface.co/datasets/SetFit/qqp) - There are too many data points in this dataset. In order to create a balanced cross-domain dataset, we randomly sampled 7000 data instances from the 40.4k validation sets with random seed 24. 4) SemEval2015-Task1 (https://github.com/cocoxu/SemEval-PIT2015) - Similarly, we wanted to create a domain for 7000 data instances. We sampled all the equivalent data (around 3500) and randomly sampled the rest from the non-equivalent data points with random seed 24.
Falah/luxury_prompts
2023-09-16T07:36:14.000Z
[ "region:us" ]
Falah
null
null
null
0
5
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 1944817 num_examples: 10000 download_size: 64348 dataset_size: 1944817 --- # Dataset Card for "luxury_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Emma92/emails_1k
2023-09-16T09:27:55.000Z
[ "region:us" ]
Emma92
null
null
null
0
5
Entry not found
knowrohit07/ArithmeLogic
2023-09-16T11:53:47.000Z
[ "license:openrail", "region:us" ]
knowrohit07
null
null
null
11
5
--- license: openrail --- 1. Dataset Description: Purpose: The dataset aims to train models to solve math word problems, providing step-by-step calculations with expected output. 2. Data Collection and Processing: Source: GPT 4 Processing: The dataset is structured with math problems given as "instruction" and their step-by-step solutions as "output". 3. Data Attributes: instruction (String): A textual representation of the math word problem. output (String): Detailed step-by-step calculations leading to the solution. It appears that placeholders like <<>> are used to indicate calculations, and "####" is used to present the final answer. 4. Sample Data Point: { "instruction": "Rohit is saving money for a new wallet which costs $100. Rohit has only half of the money he needs. His parents decided to give him $15 for that purpose, and his grandparents twice as much as his parents. How much more money does Rohit need to buy the wallet?", "output": "Rohit has only 100 * 0.5 = $<<100*0.5=50>>50.\nRohit's grandparents gave him 15 * 2 = $<<15*2=30>>30.\nIn total, Rohit needs 100 - 50 - 30 - 15 = $<<100-50-30-15=5>>5 more.\n#### 5" } 5. Potential Uses: Training models to comprehend and solve math word problems. Evaluating models' ability to perform mathematical operations based on textual context. 6. Potential Biases, Ethical Considerations, and Limitations: Scope: The provided samples seem to revolve around basic arithmetic. If this pattern holds for the entire dataset, it might not cover more complex math problems or higher-level mathematics. Simplicity: Some real-world math problems might require more advanced problem-solving techniques than simple arithmetic. 7. Dataset Maintenance and Updates: will try to keep in loop offers several merits for LLMs: 1. Structured Problem Solving: Merit: The dataset encourages structured problem-solving. Each solution is broken down into steps, reinforcing the idea that problems often need a sequential approach. Learning: Transformers excel at learning sequences and patterns. By observing structured step-by-step solutions, they can learn the logical progression of tackling mathematical problems. 2. Varied Expression: Merit: The dataset offers multiple ways to solve the same problem, emphasizing that there's often more than one way to approach a solution. Learning: This can enhance the generalization capabilities of transformers. They can learn that while different paths may be taken, they can still lead to the same solution. This reduces overfitting to a singular method of problem-solving. 3. Explicit Arithmetic Computations: Merit: The use of placeholders like <<>> clearly indicates where arithmetic operations occur, making it explicit what computations are being performed. Learning: Transformers can utilize such explicit markers to better identify and learn arithmetic patterns, focusing on these sections for numeric computations. 4. Clear Answer Indication: Merit: The "####" tag provides a clear indication of the final answer, differentiating it from the intermediate steps. Learning: This can help the model discern between intermediate computations and final outcomes. When queried, the model can then prioritize presenting such clear answers. 5. Contextual Comprehension: Merit: Math problems are embedded in worded instructions, demanding not just mathematical ability but also linguistic comprehension. Learning: Transformers can fine-tune their contextual understanding by discerning relevant information from word problems, integrating their language model training with arithmetic capabilities. In essence, the dataset's design provides a comprehensive approach to training transformers on mathematical problem-solving, offering both linguistic comprehension and arithmetic execution.
nikchar/claim_detection_paper_test_bert
2023-09-16T14:01:52.000Z
[ "region:us" ]
nikchar
null
null
null
0
5
--- dataset_info: features: - name: label dtype: string - name: claim dtype: string - name: evidence_wiki_url dtype: string - name: Is_Claim dtype: string - name: Claim_detection_result dtype: string splits: - name: train num_bytes: 1175941 num_examples: 11073 download_size: 507279 dataset_size: 1175941 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "claim_detection_paper_test_bert" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nikchar/Large_training_set_40kclaims
2023-09-16T19:45:34.000Z
[ "region:us" ]
nikchar
null
null
null
0
5
--- dataset_info: features: - name: label dtype: string - name: claim dtype: string - name: evidence_wiki_url dtype: string splits: - name: train num_bytes: 3252366 num_examples: 39752 download_size: 1954676 dataset_size: 3252366 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Large_training_set_40kclaims" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nikchar/Large_training_set_55kdocs
2023-09-16T19:45:36.000Z
[ "region:us" ]
nikchar
null
null
null
0
5
--- dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 37559617 num_examples: 56816 download_size: 23914506 dataset_size: 37559617 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Large_training_set_55kdocs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
stealthwriter/newAIHumanGPT3.5V2
2023-09-17T13:12:20.000Z
[ "region:us" ]
stealthwriter
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 4751074 num_examples: 36000 - name: validation num_bytes: 528788 num_examples: 4000 download_size: 3478514 dataset_size: 5279862 --- # Dataset Card for "newAIHumanGPT3.5V2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DoctorSlimm/mozart-api
2023-09-19T18:35:39.000Z
[ "region:us" ]
DoctorSlimm
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
elsheikhams/mt_gender_ar
2023-09-17T14:38:42.000Z
[ "region:us" ]
elsheikhams
null
null
null
0
5
Entry not found
qazisaad/rw_processed_ds
2023-09-17T19:26:07.000Z
[ "region:us" ]
qazisaad
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: float64 splits: - name: train num_bytes: 79056000 num_examples: 16200 - name: test num_bytes: 8784000 num_examples: 1800 download_size: 16937368 dataset_size: 87840000 --- # Dataset Card for "rw_processed_ds" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tomazws/testing
2023-09-18T03:27:23.000Z
[ "region:us" ]
tomazws
null
null
null
0
5
Entry not found
Jackoon/dataset_advanced
2023-09-18T07:40:58.000Z
[ "region:us" ]
Jackoon
null
null
null
0
5
Entry not found
Jackoon/JSON_expert
2023-09-18T07:41:16.000Z
[ "region:us" ]
Jackoon
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 35954 num_examples: 36 download_size: 13720 dataset_size: 35954 --- # Dataset Card for "JSON_expert" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
oemd001/financeDataset
2023-09-18T08:42:53.000Z
[ "region:us" ]
oemd001
null
null
null
1
5
Entry not found
nchen909/hugcode-codesft
2023-09-19T05:20:33.000Z
[ "region:us" ]
nchen909
null
null
null
3
5
所有数据都是单轮代码指令数据 140696条英语，42816条中文。 --- license: cc ---
hungeni/amrutaDB
2023-09-18T11:11:08.000Z
[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "language:vi", "language:hi", "license:other", "region:us" ]
hungeni
null
null
null
0
5
--- license: other task_categories: - text-generation language: - en - vi - hi size_categories: - 1K<n<10K --- This dataset clone from amruta.org for training LLM Contact: hungbui@sahajayoga.edu.vn By the grace of Our H.H. Shri Mataji Nirmala Devi
nguyenthanhdo/vhac_v2_chai_format_80k
2023-09-18T17:05:59.000Z
[ "region:us" ]
nguyenthanhdo
null
null
null
0
5
--- dataset_info: features: - name: model_input dtype: string - name: model_output dtype: string splits: - name: train num_bytes: 272113279.4640063 num_examples: 80000 download_size: 130456890 dataset_size: 272113279.4640063 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vhac_v2_chai_format_80k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mariapaulaf/RegulatoryReqs
2023-09-18T21:40:14.000Z
[ "region:us" ]
mariapaulaf
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 151700.0 num_examples: 37 - name: test num_bytes: 20500.0 num_examples: 5 download_size: 71240 dataset_size: 172200.0 --- # Dataset Card for "RegulatoryReqs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alexandrainst/nordjylland-news-summarization
2023-09-19T13:05:48.000Z
[ "region:us" ]
alexandrainst
null
null
null
1
5
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: text_len dtype: int64 - name: summary_len dtype: int64 splits: - name: train num_bytes: 118935809 num_examples: 75219 - name: val num_bytes: 6551332 num_examples: 4178 - name: test num_bytes: 6670392 num_examples: 4178 download_size: 81334629 dataset_size: 132157533 --- # Dataset Card for "nordjylland-news-summarization" ## Dataset Description - **Point of Contact:** [Oliver Kinch](mailto:oliver.kinch@alexandra.dk) - **Size of dataset:** 148 MB ### Dataset Summary This dataset consists of pairs containing text and corresponding summaries extracted from the Danish newspaper [TV2 Nord](https://www.tv2nord.dk/). ### Supported Tasks and Leaderboards Summarization is the intended task for this dataset. No leaderboard is active at this point. ### Languages The dataset is available in Danish (`da`). ## Dataset Structure An example from the dataset looks as follows. ``` { "text": "some text", "summary": "some summary", "text_len": <number of chars in text>, "summary_len": <number of chars in summary> } ``` ### Data Fields - `text`: a `string` feature. - `summary`: a `string` feature. - `text_len`: an `int64` feature. - `summary_len`: an `int64` feature. ### Dataset Statistics #### Number of samples - Train: 75219 - Val: 4178 - Test: 4178 #### Text Length Distribution - Minimum length: 21 - Maximum length: 35164 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61e0713ac50610f535ed2c88/YBO73NHfW5Ufh0svopGbc.png) #### Summary Length Distribution - Minimum length: 12 - Maximum length: 499 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61e0713ac50610f535ed2c88/tSLeODADes_r-V7sED2tH.png) ## Potential Dataset Issues Within the dataset, there are 181 instances where the length of the summary exceeds the length of the corresponding text. ## Dataset Creation ### Curation Rationale There are not many large-scale summarization datasets in Danish. ### Source Data The dataset has been collected through the TV2 Nord API, which can be accessed [here](https://developer.bazo.dk/#876ab6f9-e057-43e3-897a-1563de34397e). ## Additional Information ### Dataset Curators [Oliver Kinch](https://huggingface.co/oliverkinch) from the [The Alexandra Institute](https://alexandra.dk/) ### Licensing Information The dataset is licensed under the [CC0 license](https://creativecommons.org/share-your-work/public-domain/cc0/).
mohsen2/snappfood4
2023-09-19T09:26:46.000Z
[ "region:us" ]
mohsen2
null
null
null
0
5
Entry not found
distil-whisper/earnings22
2023-09-19T15:35:29.000Z
[ "arxiv:2203.15591", "region:us" ]
distil-whisper
null
null
null
0
5
--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: audio dtype: audio - name: file_id dtype: string - name: ticker_symbol dtype: string - name: country_by_ticker dtype: string - name: un_defined dtype: string - name: major_dialect_family dtype: string - name: language_family dtype: string - name: file_length dtype: string - name: sampling_rate dtype: string - name: transcription dtype: string splits: - name: test num_bytes: 1913805510.0 num_examples: 125 download_size: 1889076368 dataset_size: 1913805510.0 --- # Dataset Card for Earnings 22 ## 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:** [revdotcom Speech Datasets](https://github.com/revdotcom/speech-datasets) - **Paper:** [Earnings-22: A Practical Benchmark for Accents in the Wild](https://arxiv.org/abs/2203.15591) - **Point of Contact:** [Miguel Del Rio Fernandez](miguel.delrio@rev.com) ### Dataset Summary Earnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research. This dataset contains 125 files totalling roughly 119 hours of English language earnings calls from global countries. This dataset provides the full audios, transcripts, and accompanying metadata such as ticker symbol, headquarters country, and our defined "Language Region". ### Supported Tasks and Leaderboards The dataset is intended to be used to **evaluate** Automatic Speech Recognition (ASR) models. The model is presented with an long audio file, ranging from several minutes to tens of minutes, and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER), averaged over the 125 audio files. ### Languages The audio is in English, with speakers from seven different langauge regions and a total of 27 unique countries. As such, there is large diversity in the speakers and accents. ## Dataset Structure ### Data Instances A typical data point comprises the audio input, denoted by the key `audio`, and its transcription, denoted by `transcription. Some additional information about the speaker, accent and passage which contains the transcription is provided as metadata: ```python {'audio': {'path': '/fsx/sanchit/speech-datasets/earnings22/media/4468679.mp3', 'array': array([ 0.00000000e+00, -3.36748518e-09, -3.54287222e-09, ..., 4.77626486e-07, -7.80206960e-07, -8.02787653e-07]), 'sampling_rate': 16000}, 'file_id': '4468679', 'ticker_symbol': 'PAM', 'country_by_ticker': 'Argentina', 'un_defined': 'Latin America and Caribbean', 'major_dialect_family': 'Other', 'language_family': 'Spanish/Portuguese', 'file_length': '3300', 'sampling_rate': '16000', 'transcription': "Good morning ladies and gentlemen, and thank you for waiting. I'm Margarita Chun from IR, and we would like to welcome everyone to Pampa Energia's Third Quarter 2021 Results Video Conference... ``` ### Data Fields - audio: 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]`. - file_id: unique id of the data sample. - ticker_symbol: ticker symbol of the company from which the earning call was taken. - country_by_ticker: country to which the ticker symbol belongs (i.e. where the company is registered). - un_defined: UN defined language region. - major_dialect_family: the large-span (major) dialect family to which the country belongs. - language_family: the Earnings-22 assigned language family. One of seven possible values: African, Asian, English, Germanic, Other Romance, Slavic, Spanish / Portuguese. - file_length: length of the audio in seconds. - sampling_rate: sampling rate at which the audio data was saved. - transcription: the target transcription of the audio file. ### Data Splits The Earnings-22 dataset is intended to be used as a test-only split for evaluating ASR systems. As such, only one split is provided: the test split. <!--- ## 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 this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ---> ### Citation Information ``` @misc{delrio2022earnings22, title={"Earnings-22: A Practical Benchmark for Accents in the Wild"}, author={Miguel Del Rio and Peter Ha and Quinten McNamara and Corey Miller and Shipra Chandra}, year={2022}, eprint={2203.15591}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@sanchit-gandhi](https://hf.co/sanchit-gandhi) for adding this dataset.
mtc/faithfulness_benchmark_sanity_check_extrinsic_only_gold_annotation
2023-09-19T13:05:46.000Z
[ "region:us" ]
mtc
null
null
null
0
5
--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: article_id dtype: int64 - name: system dtype: string - name: sentence_ord dtype: int64 - name: Comments sequence: string - name: pre_context dtype: string - name: post_context dtype: string - name: article_with_lead dtype: string - name: is_faithful dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 579827 num_examples: 213 download_size: 112696 dataset_size: 579827 --- # Dataset Card for "faithfulness_benchmark_sanity_check_extrinsic_only_gold_annotation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
abhinavrai/therapy
2023-09-19T18:09:45.000Z
[ "license:mit", "region:us" ]
abhinavrai
null
null
null
0
5
--- license: mit ---
Iir/Just-test
2023-09-19T21:23:56.000Z
[ "region:us" ]
Iir
null
null
null
0
5
Entry not found
aelneima/iSarcasmEval_custom
2023-09-19T22:43:50.000Z
[ "region:us" ]
aelneima
null
null
null
0
5
Entry not found
bsen26/jtbd-needs
2023-09-20T03:02:16.000Z
[ "task_categories:text-classification", "language:en", "social", "region:us" ]
bsen26
null
null
null
0
5
--- task_categories: - text-classification language: - en tags: - social ---
Trelis/touch-rugby-rules-unsupervised
2023-09-20T14:39:47.000Z
[ "task_categories:text-generation", "size_categories:n<1K", "language:en", "fine-tuning", "touch rugby", "region:us" ]
Trelis
null
null
null
0
5
--- task_categories: - text-generation language: - en tags: - fine-tuning - touch rugby size_categories: - n<1K --- # Touch Rugby Rules Dataset train.csv is taken from the [International Touch Website](https://cdn.internationaltouch.org/public/FIT%205th%20Edition%20Rulebook.pdf) All text is chunked to a length of 250 tokens, aiming to keep sentences whole where possible. For educational and non-commercial use only.
tuankg1028/nghiem_dataset_20_9
2023-09-20T13:47:59.000Z
[ "region:us" ]
tuankg1028
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1051961 num_examples: 500 download_size: 311097 dataset_size: 1051961 --- # Dataset Card for "nghiem_dataset_20_9" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
amktk/ktkDataSet
2023-09-20T14:25:29.000Z
[ "region:us" ]
amktk
null
null
null
0
5
--- dataset_info: features: - name: audio dtype: audio - name: transctiption dtype: string splits: - name: train num_bytes: 71647032.0 num_examples: 10 download_size: 60508649 dataset_size: 71647032.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ktkDataSet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NexaAIDev/opensource_model_images_new_text
2023-09-21T23:20:34.000Z
[ "region:us" ]
NexaAIDev
null
null
null
0
5
--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: text_blip dtype: string splits: - name: train num_bytes: 2293613435.125 num_examples: 33959 download_size: 2241674834 dataset_size: 2293613435.125 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "opensource_model_images_new_text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
larryvrh/OASST_Top1_2023-08-25-Zh_Only
2023-09-20T19:33:28.000Z
[ "task_categories:text-generation", "task_categories:conversational", "size_categories:n<1K", "language:zh", "region:us" ]
larryvrh
null
null
null
0
5
--- dataset_info: features: - name: conversation list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 1008722 num_examples: 662 download_size: 603882 dataset_size: 1008722 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-generation - conversational language: - zh size_categories: - n<1K --- # Dataset Card for "OASST_Top1_2023-08-25-Zh_Only" Filtered from [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25).
MaxReynolds/Lee_Souder_Dataset
2023-09-20T21:46:40.000Z
[ "region:us" ]
MaxReynolds
null
null
null
0
5
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 804279.0 num_examples: 9 download_size: 805499 dataset_size: 804279.0 --- # Dataset Card for "Lee_Souder_Dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ticoAg/hh_rlhf_helpful_cn_train
2023-09-21T14:37:57.000Z
[ "region:us" ]
ticoAg
null
null
null
0
5
# Note > some rm data from public dataset - format ```json { "history": [ "query1", "answer1", "query2", "answer2" ], "prompt": "query", "input": "input for query", "output": [ "output rank1", "output rank2", "output rank3" ] } ``` Thanks - [beyond/rlhf-reward-single-round-trans_chinese](https://huggingface.co/datasets/beyond/rlhf-reward-single-round-trans_chinese) : - [dikw/hh_rlhf_cn](https://huggingface.co/datasets/dikw/hh_rlhf_cn) - [liyucheng/zhihu_rlhf_3k](https://huggingface.co/datasets/liyucheng/zhihu_rlhf_3k)
chargoddard/coedit-reworded
2023-09-21T07:14:35.000Z
[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "arxiv:2305.09857", "region:us" ]
chargoddard
null
null
null
1
5
--- dataset_info: features: - name: task dtype: string - name: id dtype: string - name: original_instruction dtype: string - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 24317220 num_examples: 82466 download_size: 12064503 dataset_size: 24317220 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - text-generation language: - en size_categories: - 10K<n<100K --- # coedit-reworded This is Grammarly's [coedit](https://huggingface.co/datasets/grammarly/coedit) dataset parsed into Alpaca-style `instruction`, `input`, and `output` rows, with the original `instruction` values replaced with a more diverse set of procedurally generated instructions. Contains 23930 unique values of `instruction`, as compared to the original 144. See [`coedit_reword.py`](https://huggingface.co/datasets/chargoddard/coedit-reworded/blob/main/coedit_reword.py) for how these were generated. All credit to the original authors of this dataset. # Citation ``` @article{raheja2023coedit, title={CoEdIT: Text Editing by Task-Specific Instruction Tuning}, author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang}, year={2023}, eprint={2305.09857}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
CaxtonEmeraldS/Mod_10M
2023-09-21T08:21:04.000Z
[ "region:us" ]
CaxtonEmeraldS
null
null
null
0
5
Entry not found
FreedomIntelligence/Preference-Data
2023-09-21T09:13:49.000Z
[ "region:us" ]
FreedomIntelligence
null
null
null
0
5
Entry not found
swaroopajit/next-dataset-refined-batch-1000
2023-09-21T10:06:42.000Z
[ "region:us" ]
swaroopajit
null
null
null
0
5
--- dataset_info: features: - name: caption dtype: string - name: image dtype: image splits: - name: train num_bytes: 292983973.0 num_examples: 1000 download_size: 263093694 dataset_size: 292983973.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "next-dataset-refined-batch-1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
swaroopajit/next-dataset-refined-batch-2000
2023-09-21T10:29:14.000Z
[ "region:us" ]
swaroopajit
null
null
null
0
5
--- dataset_info: features: - name: caption dtype: string - name: image dtype: image splits: - name: train num_bytes: 303690944.0 num_examples: 1000 download_size: 275266590 dataset_size: 303690944.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "next-dataset-refined-batch-2000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
swaroopajit/next-dataset-refined-batch-3000
2023-09-21T10:57:45.000Z
[ "region:us" ]
swaroopajit
null
null
null
0
5
--- dataset_info: features: - name: caption dtype: string - name: image dtype: image splits: - name: train num_bytes: 297746292.0 num_examples: 999 download_size: 268205162 dataset_size: 297746292.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "next-dataset-refined-batch-3000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
swaroopajit/next-dataset-refined-batch-4000
2023-09-21T11:19:35.000Z
[ "region:us" ]
swaroopajit
null
null
null
0
5
--- dataset_info: features: - name: caption dtype: string - name: image dtype: image splits: - name: train num_bytes: 316595519.0 num_examples: 1000 download_size: 289227918 dataset_size: 316595519.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "next-dataset-refined-batch-4000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
swaroopajit/next-dataset-refined-batch-5000
2023-09-21T11:40:37.000Z
[ "region:us" ]
swaroopajit
null
null
null
0
5
--- dataset_info: features: - name: caption dtype: string - name: image dtype: image splits: - name: train num_bytes: 307226208.0 num_examples: 1000 download_size: 278805299 dataset_size: 307226208.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "next-dataset-refined-batch-5000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
swaroopajit/next-dataset-refined-batch-6000
2023-09-21T12:01:46.000Z
[ "region:us" ]
swaroopajit
null
null
null
0
5
--- dataset_info: features: - name: caption dtype: string - name: image dtype: image splits: - name: train num_bytes: 315307268.0 num_examples: 999 download_size: 288501432 dataset_size: 315307268.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "next-dataset-refined-batch-6000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
swaroopajit/next-dataset-refined-batch-7000
2023-09-21T12:20:06.000Z
[ "region:us" ]
swaroopajit
null
null
null
0
5
--- dataset_info: features: - name: caption dtype: string - name: image dtype: image splits: - name: train num_bytes: 320953791.0 num_examples: 1000 download_size: 294115368 dataset_size: 320953791.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "next-dataset-refined-batch-7000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)