Datasets:
davanstrien
/
dataset_cards_with_long_context_embeddins

Dataset card Data Studio Files
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chiayewken/flan-v2
2023-09-01T05:19:13.000Z
[ "region:us" ]
chiayewken
null
null
3
26
2023-08-31T18:13:51
--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: task_name dtype: string - name: task_source dtype: string - name: template_type dtype: string - name: template_idx dtype: int64 splits: - name: train num_bytes: 44316029472 num_examples: 23173509 download_size: 0 dataset_size: 44316029472 --- # Dataset Card for "flan-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
552
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Arabic-Clip/ImageCaptions-7M-Translations-Arabic-subset-150000
2023-09-21T09:53:24.000Z
[ "region:us" ]
Arabic-Clip
null
null
0
26
2023-09-20T13:33:40
Entry not found
15
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dim/grammarly_coedit
2023-09-21T16:25:22.000Z
[ "region:us" ]
dim
null
null
1
26
2023-09-21T16:25:13
--- dataset_info: features: - name: _id dtype: string - name: task dtype: string - name: src dtype: string - name: tgt dtype: string splits: - name: train num_bytes: 19943349 num_examples: 82466 download_size: 11658767 dataset_size: 19943349 --- # Dataset Card for "grammarly_coedit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
458
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sauravjoshi23/aws-documentation-chunked
2023-09-23T17:41:26.000Z
[ "region:us" ]
sauravjoshi23
null
null
1
26
2023-09-23T09:13:23
Entry not found
15
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lhallee/uniref50_50-512
2023-09-26T19:14:45.000Z
[ "region:us" ]
lhallee
null
null
0
26
2023-09-26T18:58:21
--- dataset_info: features: - name: uniref dtype: string splits: - name: train num_bytes: 10696656442 num_examples: 51521691 download_size: 10582703793 dataset_size: 10696656442 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "uniref50_50-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
461
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mrabhi0505/rule_code
2023-09-29T11:32:48.000Z
[ "region:us" ]
mrabhi0505
null
null
0
26
2023-09-29T11:29:59
Entry not found
15
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yashnbx/l27b-E02-large-b05-0584-3
2023-09-30T17:13:20.000Z
[ "region:us" ]
yashnbx
null
null
0
26
2023-09-30T17:13:04
--- dataset_info: features: - name: id dtype: int64 - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: test num_bytes: 1011775 num_examples: 146 - name: train num_bytes: 4032267 num_examples: 584 download_size: 831330 dataset_size: 5044042 --- # Dataset Card for "l27b-E02-large-b05-0584-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
531
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danielpark/smiles_plc50
2023-10-03T20:07:01.000Z
[ "license:unknown", "region:us" ]
danielpark
null
null
0
26
2023-10-03T19:53:49
--- license: unknown --- Github Repository: [SMILES Featurizer](https://github.com/dsdanielpark/SMILES-featurizer) ``` curl -LO https://raw.githubusercontent.com/dsdanielpark/smiles-featurizer/master/dataset/smiles-plc50-train.csv ``` Alternatively, you can use the dataset uploaded to Hugging Face as follows. ```python from datasets import load_dataset dataset = load_dataset("danielpark/smiles_plc50", "csv") df = pd.DataFrame(dataset['train']) ```
457
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CHOJW1004/kochatgpt_RM2
2023-10-04T07:45:31.000Z
[ "region:us" ]
CHOJW1004
null
null
0
26
2023-10-04T07:45:27
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 15758172.0 num_examples: 27594 - name: test num_bytes: 1750908.0 num_examples: 3066 download_size: 9270108 dataset_size: 17509080.0 --- # Dataset Card for "kochatgpt_RM" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
622
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sordonia/wikipedia-en
2023-10-10T21:16:05.000Z
[ "region:us" ]
sordonia
Wikipedia with math and latex included.
null
0
26
2023-10-08T05:09:09
Entry not found
15
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heegyu/chart2text_pew
2023-10-12T05:08:48.000Z
[ "size_categories:1K<n<10K", "language:en", "license:gpl-3.0", "region:us" ]
heegyu
null
null
0
26
2023-10-12T05:06:05
--- dataset_info: features: - name: id dtype: int64 - name: old_id dtype: string - name: title dtype: string - name: imgPath dtype: string - name: caption dtype: string - name: URL dtype: string - name: dataPath dtype: string - name: chartType dtype: string - name: complexity dtype: string - name: topic dtype: string - name: bboxesPath dtype: string - name: image dtype: image - name: data dtype: string splits: - name: train num_bytes: 280009472 num_examples: 6500 - name: val num_bytes: 62717503.096 num_examples: 1392 - name: test num_bytes: 61265523.23 num_examples: 1393 download_size: 400276057 dataset_size: 403992498.32600003 license: gpl-3.0 language: - en size_categories: - 1K<n<10K --- # Dataset Card for "chart2text_pew" original dataset: https://github.com/vis-nlp/Chart-to-text
914
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erbacher/AmbigNQ-clarifying-question
2023-10-12T16:58:14.000Z
[ "region:us" ]
erbacher
null
null
0
26
2023-10-12T16:57:59
--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: index dtype: int64 - name: clar dtype: string - name: question dtype: string - name: ambig dtype: bool - name: input_passage dtype: string - name: intent dtype: string - name: answer dtype: string splits: - name: train num_bytes: 62693997.0 num_examples: 10000 - name: dev num_bytes: 6291036.0 num_examples: 1001 - name: test num_bytes: 64783344.0 num_examples: 1000 download_size: 75095693 dataset_size: 133768377.0 --- # Dataset Card for "AmbigNQ-clarifying-question" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
758
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royzhong/sample-threat-scenarios
2023-10-16T19:46:26.000Z
[ "region:us" ]
royzhong
null
null
0
26
2023-10-16T06:05:21
Entry not found
15
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Mouli07/ROCO_Chest_Xray_v1
2023-10-17T17:37:34.000Z
[ "region:us" ]
Mouli07
null
null
0
26
2023-10-17T17:36:07
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 38241164.86 num_examples: 1735 download_size: 39693229 dataset_size: 38241164.86 --- # Dataset Card for "ROCO_Chest_Xray_v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
455
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Danielbrdz/Barcenas-lmsys-Dataset
2023-10-18T15:49:46.000Z
[ "language:es", "region:us" ]
Danielbrdz
null
null
0
26
2023-10-18T01:39:28
--- language: - es --- Dataset made on the basis of lmsys/lmsys-chat-1m With data only for the Spanish language.
113
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Globaly/glfamiliasPrendasDeVestir
2023-10-18T17:12:28.000Z
[ "region:us" ]
Globaly
null
null
0
26
2023-10-18T16:55:32
Entry not found
15
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advancedcv/Food500Cap
2023-10-19T02:01:16.000Z
[ "region:us" ]
advancedcv
null
null
0
26
2023-10-19T01:25:20
--- dataset_info: features: - name: image dtype: image - name: cat dtype: string - name: caption dtype: string splits: - name: train num_bytes: 3004559279.747 num_examples: 19877 - name: test num_bytes: 601407879.384 num_examples: 4938 download_size: 3000710601 dataset_size: 3605967159.131 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "caps_data_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
630
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skvarre/movie_posters
2023-10-19T01:35:45.000Z
[ "region:us" ]
skvarre
null
null
2
26
2023-10-19T01:31:05
--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: title dtype: string - name: genres list: - name: id dtype: int64 - name: name dtype: string - name: overview dtype: string - name: popularity dtype: float64 - name: release_date dtype: string - name: budget dtype: int64 - name: revenue dtype: int64 - name: tagline dtype: string - name: original_language dtype: string - name: runtime dtype: int64 splits: - name: train num_bytes: 4559499046.67 num_examples: 9955 download_size: 4558666511 dataset_size: 4559499046.67 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "movie_posters" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
922
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noxneural/kashaloti
2023-10-25T09:51:37.000Z
[ "task_categories:question-answering", "task_categories:translation", "task_categories:summarization", "task_categories:conversational", "size_categories:100K<n<1M", "language:sq", "region:us" ]
noxneural
null
null
2
26
2023-10-21T02:23:51
--- task_categories: - question-answering - translation - summarization - conversational language: - sq pretty_name: Kashaloti_V0.1 size_categories: - 100K<n<1M --- # Kashaloti_V0.1 **Task Categories**: - Question-Answering - Translation - Summarization - Conversational **Language**: sq **Size Categories**: 100K < n < 1M --- # Dataset Card for "Your Dataset Name in Albanian" ## Dataset Summary This dataset is a translated version of the OpenOrca dataset into Albanian. The initial dataset consisted of augmented FLAN Collection data, primarily used for training and evaluation in the natural language processing field. This version specifically utilizes the ~1M GPT-4 completions, translated using the OPUS-MT model from English to Albanian, with subsequent refinement to ensure clarity and coherence. ## Dataset Attribution ### Translation Process: The translation was executed using the OPUS-MT model from English to Albanian. The dataset was then refined and cleaned to ensure semantic accuracy and coherence. ## Supported Tasks and Leaderboards This dataset can be utilized for tasks such as Albanian language modeling, text generation, text augmentation, and other NLP tasks in Albanian. As it's a translated version, it can also be used for cross-lingual understanding. ## Languages The dataset is now in Albanian. ## Dataset Structure ### Data Instances A data instance in this dataset represents entries from the FLAN collection, which have been translated into Albanian and augmented by querying either GPT-4 or GPT-3.5. The response is then input into the response field. ### Data Fields - 'id': a unique identifier - 'system_prompt': System Prompt presented to the GPT model - 'question': a question entry translated into Albanian - 'response': the model's response to the question ### Data Splits The data is unsplit. ## Dataset Creation ### Curation Rationale The dataset was translated to offer Albanian researchers and developers an augmented text data source. By leveraging the "reasoning trace" augmentation from GPT-3.5 and GPT-4, it provides insights into the model's reasoning capabilities in Albanian. ### Source Data The original dataset is the OpenOrca dataset, particularly the ~1M GPT-4 completions. ## Dataset Use ### Use Cases Potential applications include Albanian language understanding, model training, performance evaluation, and other NLP tasks. ### Usage Caveats Given the translation process, users are encouraged to validate the dataset's accuracy and relevance for specific tasks. Regularly checking for updates and improvements is recommended. ### Getting Started The dataset can be accessed via the Hugging Face datasets library. Streaming is advised due to the potential size of the files. Keep an eye on the dataset's repository on Hugging Face for any updates. ________________________________________ **Original dataset contributors**: - Teknium - WingLian/Caseus - Eric Hartford - NanoBit - Pankaj - Winddude - Rohan **Acknowledgment to the following organizations**: - [AlignmentLab.ai](http://AlignmentLab.ai) - Autometa - Entropi - AtlasUnified - NeverendingToast - NanoBit - WingLian/Caseus **Special mention**: TheBloke for supporting the community. https://huggingface.co/TheBloke
3,287
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Tochi2023/kolizo-designs-dataset
2023-10-21T11:35:42.000Z
[ "region:us" ]
Tochi2023
null
null
0
26
2023-10-21T11:35:40
--- 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: 200160.0 num_examples: 10 download_size: 197786 dataset_size: 200160.0 --- # Dataset Card for "kolizo-designs-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
485
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Leekp/dataset
2023-10-21T14:05:30.000Z
[ "region:us" ]
Leekp
null
null
0
26
2023-10-21T13:58:10
Entry not found
15
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davidfant/natural-questions-chunk-0
2023-10-22T22:48:50.000Z
[ "region:us" ]
davidfant
null
null
0
26
2023-10-22T22:45:10
--- dataset_info: features: - name: id dtype: string - name: document struct: - name: html dtype: string - name: title dtype: string - name: tokens sequence: - name: end_byte dtype: int64 - name: is_html dtype: bool - name: start_byte dtype: int64 - name: token dtype: string - name: url dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: short_answers sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: train num_bytes: 4705302627 num_examples: 10000 download_size: 1826111395 dataset_size: 4705302627 --- # Dataset Card for "natural-questions-chunk-0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,818
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chrisgru/dolphin
2023-10-23T09:17:46.000Z
[ "region:us" ]
chrisgru
null
null
0
26
2023-10-23T08:58:42
--- dataset_info: features: - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 15782.991668743547 num_examples: 9 download_size: 10003 dataset_size: 15782.991668743547 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dolphin" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
535
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toilaluan/t2i_topic_comparision_db_v2
2023-10-27T00:11:11.000Z
[ "region:us" ]
toilaluan
null
null
0
26
2023-10-26T16:21:51
--- dataset_info: features: - name: image dtype: image - name: topic dtype: string - name: prompt dtype: string - name: request_id dtype: int64 - name: model_type dtype: string splits: - name: train num_bytes: 335176439.2 num_examples: 7200 download_size: 653813254 dataset_size: 335176439.2 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "t2i_topic_comparision_db_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
611
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Sree1994/ddb_baseprompts
2023-10-26T22:52:37.000Z
[ "region:us" ]
Sree1994
null
null
0
26
2023-10-26T22:52:33
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: Base_prompt dtype: string - name: Prompt dtype: string splits: - name: train num_bytes: 14886028 num_examples: 51602 - name: test num_bytes: 2096918 num_examples: 7299 - name: valid num_bytes: 4301342 num_examples: 14817 download_size: 10829614 dataset_size: 21284288 --- # Dataset Card for "ddb_baseprompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
691
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Adminhuggingface/LORA_ONE_DATA
2023-10-27T06:18:33.000Z
[ "region:us" ]
Adminhuggingface
null
null
0
26
2023-10-27T06:18:32
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2493084.0 num_examples: 6 download_size: 2495157 dataset_size: 2493084.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "LORA_ONE_DATA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
475
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manishiitg/airoboros-2.2.1-hi
2023-11-03T01:08:47.000Z
[ "region:us" ]
manishiitg
null
null
0
26
2023-10-28T07:44:16
--- dataset_info: features: - name: instruction dtype: string - name: response dtype: string - name: system dtype: string - name: skip_prompt_formatting dtype: bool - name: category dtype: string - name: instruction_hindi dtype: string - name: response_hindi dtype: string - name: system_hindi dtype: string splits: - name: train num_bytes: 102711269 num_examples: 13641 download_size: 44765656 dataset_size: 102711269 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "airoboros-2.2.1-hi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
747
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riddhiparakh/mannbot
2023-10-28T15:04:25.000Z
[ "region:us" ]
riddhiparakh
null
null
0
26
2023-10-28T12:32:36
Entry not found
15
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josedonoso/apples-dataset-v1
2023-10-28T23:35:52.000Z
[ "region:us" ]
josedonoso
null
null
0
26
2023-10-28T23:35:50
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2704421.0 num_examples: 192 - name: test num_bytes: 646648.0 num_examples: 48 download_size: 3236890 dataset_size: 3351069.0 --- # Dataset Card for "apples-dataset-v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
579
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kunishou/hh-rlhf-49k-ja-single-turn
2023-11-02T14:30:34.000Z
[ "license:mit", "region:us" ]
kunishou
null
null
0
26
2023-10-31T17:47:50
--- license: mit --- This dataset was created by automatically translating part of "Anthropic/hh-rlhf" into Japanese, and selected for single turn conversations. You can use this dataset for RLHF and DPO. hh-rlhf repository https://github.com/anthropics/hh-rlhf Anthropic/hh-rlhf https://huggingface.co/datasets/Anthropic/hh-rlhf
333
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Ujan/github_classification
2023-11-01T08:43:54.000Z
[ "region:us" ]
Ujan
null
null
0
26
2023-11-01T08:43:19
--- 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: names dtype: string - name: readmes dtype: string - name: topics dtype: string - name: labels dtype: string splits: - name: train num_bytes: 51303107.05622984 num_examples: 10414 - name: validation num_bytes: 6414119.971885082 num_examples: 1302 - name: test num_bytes: 6414119.971885082 num_examples: 1302 download_size: 29047991 dataset_size: 64131347.00000001 --- # Dataset Card for "github_classification" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
814
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magnifi/hl-codellama-chat-response
2023-11-02T16:45:00.000Z
[ "region:us" ]
magnifi
null
null
0
26
2023-11-02T13:36:09
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: Query dtype: string - name: Result dtype: string - name: chat_response dtype: string splits: - name: train num_bytes: 1321860.461185117 num_examples: 1523 - name: test num_bytes: 567627.5388148829 num_examples: 654 download_size: 0 dataset_size: 1889488.0 --- # Dataset Card for "hl-codellama-chat-response" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
646
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kiamehr74/CoarseWSD-20
2021-08-10T09:48:50.000Z
[ "region:us" ]
kiamehr74
The CoarseWSD-20 dataset is a coarse-grained sense disambiguation built from Wikipedia (nouns only) targetting 2 to 5 senses of 20 ambiguous words. It was specifically designed to provide an ideal setting for evaluating WSD models (e.g. no senses in test sets missing from training), both quantitavely and qualitatively.
@misc{loureiro2021analysis, title={Analysis and Evaluation of Language Models for Word Sense Disambiguation}, author={Daniel Loureiro and Kiamehr Rezaee and Mohammad Taher Pilehvar and Jose Camacho-Collados}, year={2021}, eprint={2008.11608}, archivePrefix={arXiv}, primaryClass={cs.CL} }
1
25
2022-03-02T23:29:22
Entry not found
15
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sc2qa/sc2q_commoncrawl_large
2022-03-30T18:34:11.000Z
[ "arxiv:2109.04689", "region:us" ]
sc2qa
\
@article{zhou2021generating, author = {Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri}, title = "{Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning}", conference = {The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)}, year = 2021, }
2
25
2022-03-02T23:29:22
For details, please refer to the following links. Github repo: https://github.com/amazon-research/SC2QA-DRIL Paper: [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://arxiv.org/pdf/2109.04689.pdf)
270
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stjokerli/TextToText_wic_seqio
2022-03-18T04:56:49.000Z
[ "region:us" ]
stjokerli
null
null
0
25
2022-03-13T09:30:58
Entry not found
15
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Yaxin/SemEval2015Task12Raw
2022-08-14T16:01:41.000Z
[ "region:us" ]
Yaxin
A collection of SemEval2015 specifically designed to aid research in Aspect Based Sentiment Analysis.
@inproceedings{pontiki2015semeval, title={Semeval-2015 task 12: Aspect based sentiment analysis}, author={Pontiki, Maria and Galanis, Dimitrios and Papageorgiou, Harris and Manandhar, Suresh and Androutsopoulos, Ion}, booktitle={Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015)}, pages={486--495}, year={2015} }
2
25
2022-04-21T14:03:59
Entry not found
15
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Splend1dchan/NMSQA_testupload2
2022-06-02T15:31:29.000Z
[ "region:us" ]
Splend1dchan
null
null
0
25
2022-06-02T11:52:11
Entry not found
15
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nateraw/pizza_not_pizza
2022-07-07T19:58:03.000Z
[ "license:other", "region:us" ]
nateraw
null
null
1
25
2022-07-07T19:57:37
--- license: - other kaggle_id: carlosrunner/pizza-not-pizza --- # Dataset Card for Pizza or Not Pizza? ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://kaggle.com/datasets/carlosrunner/pizza-not-pizza - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Who doesn't like pizza? This dataset contains about 1000 images of pizza and 1000 images of dishes other than pizza. It can be used for a simple binary image classification task. All images were rescaled to have a maximum side length of 512 pixels. This is a subset of the Food-101 dataset. Information about the original dataset can be found in the following paper: Bossard, Lukas, Matthieu Guillaumin, and Luc Van Gool. "Food-101 – Mining Discriminative Components with Random Forests." In *European conference on computer vision*, pp. 446-461. Springer, Cham, 2014. The original dataset can be found in the following locations: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/ https://www.kaggle.com/datasets/dansbecker/food-101 https://paperswithcode.com/dataset/food-101 https://www.tensorflow.org/datasets/catalog/food101 Number of instances in each class: Pizza: 983 Not Pizza: 983 ##Acknowledgements The Food-101 data set consists of images from Foodspotting [1] which are not property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond scientific fair use must be negociated with the respective picture owners according to the Foodspotting terms of use [2]. [1] http://www.foodspotting.com/ [2] http://www.foodspotting.com/terms/ ### 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 This dataset was shared by [@carlosrunner](https://kaggle.com/carlosrunner) ### Licensing Information The license for this dataset is other ### Citation Information ```bibtex [More Information Needed] ``` ### Contributions [More Information Needed]
3,871
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bigscience/xP3all
2023-05-30T15:51:40.000Z
[ "task_categories:other", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "multilinguality:multilingual", "size_categories:100M<n<1B", "language:ak", "language:ar", "language:as", "language:bm", "language:bn", "language:ca", "language:code", "language:en", "lan...
bigscience
xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.
@misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} }
17
25
2022-07-30T21:05:02
--- annotations_creators: - expert-generated - crowdsourced language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3 size_categories: - 100M<n<1B task_categories: - other --- # Dataset Card for xP3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigscience-workshop/xmtf - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co) ### Dataset Summary > xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility. - **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3)) - **xP3 Dataset Family:** <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "inputs": "Sentence 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\nSentence 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nQuestion: Can we rewrite Sentence 1 to Sentence 2? Yes or No?", "targets": "Yes" } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate ### Data Splits The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. |Language|Kilobytes|%|Samples|%| |--------|------:|-:|---:|-:| |tw|106288|0.11|265071|0.33| |bm|107056|0.11|265180|0.33| |ak|108096|0.11|265071|0.33| |ca|110608|0.11|271191|0.33| |eu|113008|0.11|281199|0.35| |fon|113072|0.11|265063|0.33| |st|114080|0.11|265063|0.33| |ki|115040|0.12|265180|0.33| |tum|116032|0.12|265063|0.33| |wo|122560|0.12|365063|0.45| |ln|126304|0.13|365060|0.45| |as|156256|0.16|265063|0.33| |or|161472|0.16|265063|0.33| |kn|165456|0.17|265063|0.33| |ml|175040|0.18|265864|0.33| |rn|192992|0.19|318189|0.39| |nso|229712|0.23|915051|1.13| |tn|235536|0.24|915054|1.13| |lg|235936|0.24|915021|1.13| |rw|249360|0.25|915043|1.13| |ts|250256|0.25|915044|1.13| |sn|252496|0.25|865056|1.07| |xh|254672|0.26|915058|1.13| |zu|263712|0.26|915061|1.13| |ny|272128|0.27|915063|1.13| |ig|325232|0.33|950097|1.17| |yo|352784|0.35|918416|1.13| |ne|393680|0.39|315754|0.39| |pa|523248|0.52|339210|0.42| |gu|560688|0.56|347499|0.43| |sw|566656|0.57|1130481|1.4| |mr|666240|0.67|417269|0.52| |bn|832720|0.83|428843|0.53| |ta|926912|0.93|415433|0.51| |te|1343232|1.35|584590|0.72| |ur|1918272|1.92|855756|1.06| |vi|3102512|3.11|1672106|2.07| |code|4330752|4.34|2707724|3.34| |hi|4403568|4.41|1554667|1.92| |zh|4599440|4.61|3589234|4.43| |id|4612256|4.62|2643418|3.27| |ar|4683456|4.69|2160181|2.67| |fr|6591120|6.6|5316403|6.57| |pt|6886800|6.9|3752156|4.63| |es|8587920|8.6|5413205|6.69| |en|39252528|39.33|32740750|40.44| |total|99807184|100.0|80956089|100.0| ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) #### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for HumanEval) - Natural Language Inference - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Program Synthesis - [HumanEval](https://huggingface.co/datasets/openai_humaneval) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) #### Additional [xP3all](https://huggingface.co/datasets/bigscience/xP3all) datasets - Coreference Resolution - [WSC (Fixed)](https://huggingface.co/datasets/super_glue) - Sentence Completion - [HellaSwag](https://huggingface.co/datasets/hellaswag) - Translation - [MultiEurlex](https://huggingface.co/datasets/multi_eurlex) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset.
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copenlu/scientific-exaggeration-detection
2022-08-17T13:45:14.000Z
[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:gpl-3.0", "scientific text", ...
copenlu
null
null
3
25
2022-08-17T13:29:27
--- annotations_creators: - expert-generated language: - en language_creators: - found license: - gpl-3.0 multilinguality: - monolingual paperswithcode_id: semi-supervised-exaggeration-detection-of pretty_name: Scientific Exaggeration Detection size_categories: - n<1K source_datasets: [] tags: - scientific text - scholarly text - inference - fact checking - misinformation task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification --- # Dataset Card for Scientific Exaggeration Detection ## Dataset Description - **Homepage:** https://github.com/copenlu/scientific-exaggeration-detection - **Repository:** https://github.com/copenlu/scientific-exaggeration-detection - **Paper:** https://aclanthology.org/2021.emnlp-main.845.pdf ### Dataset Summary Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging. We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task. ## Dataset Structure The training and test data are derived from the InSciOut studies from [Sumner et al. 2014](https://www.bmj.com/content/349/bmj.g7015) and [Bratton et al. 2019](https://pubmed.ncbi.nlm.nih.gov/31728413/#:~:text=Results%3A%20We%20found%20that%20the,inference%20from%20non%2Dhuman%20studies.). The splits have the following fields: ``` original_file_id: The ID of the original spreadsheet in the Sumner/Bratton data where the annotations are derived from press_release_conclusion: The conclusion sentence from the press release press_release_strength: The strength label for the press release abstract_conclusion: The conclusion sentence from the abstract abstract_strength: The strength label for the abstract exaggeration_label: The final exaggeration label ``` The exaggeration label is one of `same`, `exaggerates`, or `downplays`. The strength label is one of the following: ``` 0: Statement of no relationship 1: Statement of correlation 2: Conditional statement of causation 3: Statement of causation ``` ## Dataset Creation See section 4 of the [paper](https://aclanthology.org/2021.emnlp-main.845.pdf) for details on how the dataset was curated. The original InSciOut data can be found [here](https://figshare.com/articles/dataset/InSciOut/903704) ## Citation ``` @inproceedings{wright2021exaggeration, title={{Semi-Supervised Exaggeration Detection of Health Science Press Releases}}, author={Dustin Wright and Isabelle Augenstein}, booktitle = {Proceedings of EMNLP}, publisher = {Association for Computational Linguistics}, year = 2021 } ``` Thanks to [@dwright37](https://github.com/dwright37) for adding this dataset.
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rajistics/electricity_demand
2022-10-19T21:03:02.000Z
[ "task_categories:time-series-forecasting", "region:us" ]
rajistics
null
null
2
25
2022-09-18T19:06:12
--- task_categories: - time-series-forecasting --- The Victoria electricity demand dataset from the [MAPIE github repository](https://github.com/scikit-learn-contrib/MAPIE/tree/master/examples/data). It consists of hourly electricity demand (in GW) of the Victoria state in Australia together with the temperature (in Celsius degrees).
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tomekkorbak/detoxify-pile-chunk3-500000-550000
2022-10-04T17:42:07.000Z
[ "region:us" ]
tomekkorbak
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tomekkorbak/detoxify-pile-chunk3-550000-600000
2022-10-04T17:46:16.000Z
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tomekkorbak
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tomekkorbak/detoxify-pile-chunk3-800000-850000
2022-10-04T22:47:07.000Z
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tomekkorbak
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tomekkorbak/detoxify-pile-chunk3-700000-750000
2022-10-04T17:50:07.000Z
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tomekkorbak
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tomekkorbak/detoxify-pile-chunk3-600000-650000
2022-10-04T17:51:35.000Z
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tomekkorbak
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tomekkorbak/detoxify-pile-chunk3-650000-700000
2022-10-04T18:03:56.000Z
[ "region:us" ]
tomekkorbak
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tomekkorbak/detoxify-pile-chunk3-1200000-1250000
2022-10-04T23:47:33.000Z
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tomekkorbak
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2022-10-04T23:47:25
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tomekkorbak/detoxify-pile-chunk3-1250000-1300000
2022-10-05T00:28:11.000Z
[ "region:us" ]
tomekkorbak
null
null
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2022-10-05T00:28:02
Entry not found
15
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carlosejimenez/stsb_corpus
2022-10-28T00:27:31.000Z
[ "region:us" ]
carlosejimenez
null
null
0
25
2022-10-28T00:27:24
Entry not found
15
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lewtun/music_genres_small
2022-11-03T13:36:49.000Z
[ "region:us" ]
lewtun
null
null
2
25
2022-11-03T13:36:11
--- dataset_info: features: - name: audio dtype: audio - name: song_id dtype: int64 - name: genre_id dtype: int64 - name: genre dtype: string splits: - name: train num_bytes: 392427659.9527852 num_examples: 1000 download_size: 390675126 dataset_size: 392427659.9527852 --- # Dataset Card for "music_genres_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
487
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Norod78/simpsons-blip-captions
2022-11-09T16:27:19.000Z
[ "task_categories:text-to-image", "annotations_creators:machine-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]
Norod78
null
null
2
25
2022-11-06T11:11:36
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 51605730.0 num_examples: 755 download_size: 50553165 dataset_size: 51605730.0 pretty_name: 'Simpsons BLIP captions' size_categories: - n<1K tags: [] task_categories: - text-to-image license: cc-by-nc-sa-4.0 annotations_creators: - machine-generated language: - en language_creators: - other multilinguality: - monolingual --- # Dataset Card for "simpsons-blip-captions"
531
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bigbio/iepa
2022-12-22T15:44:47.000Z
[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]
bigbio
The IEPA benchmark PPI corpus is designed for relation extraction. It was created from 303 PubMed abstracts, each of which contains a specific pair of co-occurring chemicals.
@ARTICLE{ding2001mining, title = "Mining {MEDLINE}: abstracts, sentences, or phrases?", author = "Ding, J and Berleant, D and Nettleton, D and Wurtele, E", journal = "Pac Symp Biocomput", pages = "326--337", year = 2002, address = "United States", language = "en" }
1
25
2022-11-13T22:09:00
--- language: - en bigbio_language: - English license: unknown multilinguality: monolingual bigbio_license_shortname: UNKNOWN pretty_name: IEPA homepage: http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html bigbio_pubmed: True bigbio_public: True bigbio_tasks: - RELATION_EXTRACTION --- # Dataset Card for IEPA ## Dataset Description - **Homepage:** http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html - **Pubmed:** True - **Public:** True - **Tasks:** RE The IEPA benchmark PPI corpus is designed for relation extraction. It was created from 303 PubMed abstracts, each of which contains a specific pair of co-occurring chemicals. ## Citation Information ``` @ARTICLE{ding2001mining, title = "Mining {MEDLINE}: abstracts, sentences, or phrases?", author = "Ding, J and Berleant, D and Nettleton, D and Wurtele, E", journal = "Pac Symp Biocomput", pages = "326--337", year = 2002, address = "United States", language = "en" } ```
1,014
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bigbio/umnsrs
2022-12-22T15:47:36.000Z
[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]
bigbio
UMNSRS, developed by Pakhomov, et al., consists of 725 clinical term pairs whose semantic similarity and relatedness. The similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch a bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness. The following subsets are available: - similarity: A set of 566 UMLS concept pairs manually rated for semantic similarity (e.g. whale-dolphin) using a continuous response scale. - relatedness: A set of 588 UMLS concept pairs manually rated for semantic relatedness (e.g. needle-thread) using a continuous response scale. - similarity_mod: Modification of the UMNSRS-Similarity dataset to exclude control samples and those pairs that did not match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper (Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley, Reed McEwan, Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644). The resulting dataset contains 449 pairs. - relatedness_mod: Modification of the UMNSRS-Relatedness dataset to exclude control samples and those pairs that did not match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper (Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley, Reed McEwan, Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644). The resulting dataset contains 458 pairs.
@inproceedings{pakhomov2010semantic, title={Semantic similarity and relatedness between clinical terms: an experimental study}, author={Pakhomov, Serguei and McInnes, Bridget and Adam, Terrence and Liu, Ying and Pedersen, Ted and Melton, Genevieve B}, booktitle={AMIA annual symposium proceedings}, volume={2010}, pages={572}, year={2010}, organization={American Medical Informatics Association} }
1
25
2022-11-13T22:12:42
--- language: - en bigbio_language: - English license: cc0-1.0 multilinguality: monolingual bigbio_license_shortname: CC0_1p0 pretty_name: UMNSRS homepage: https://conservancy.umn.edu/handle/11299/196265/ bigbio_pubmed: False bigbio_public: True bigbio_tasks: - SEMANTIC_SIMILARITY --- # Dataset Card for UMNSRS ## Dataset Description - **Homepage:** https://conservancy.umn.edu/handle/11299/196265/ - **Pubmed:** False - **Public:** True - **Tasks:** STS UMNSRS, developed by Pakhomov, et al., consists of 725 clinical term pairs whose semantic similarity and relatedness. The similarity and relatedness of each term pair was annotated based on a continuous scale by having the resident touch a bar on a touch sensitive computer screen to indicate the degree of similarity or relatedness. The following subsets are available: - similarity: A set of 566 UMLS concept pairs manually rated for semantic similarity (e.g. whale-dolphin) using a continuous response scale. - relatedness: A set of 588 UMLS concept pairs manually rated for semantic relatedness (e.g. needle-thread) using a continuous response scale. - similarity_mod: Modification of the UMNSRS-Similarity dataset to exclude control samples and those pairs that did not match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper (Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley, Reed McEwan, Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644). The resulting dataset contains 449 pairs. - relatedness_mod: Modification of the UMNSRS-Relatedness dataset to exclude control samples and those pairs that did not match text in clinical, biomedical and general English corpora. Exact modifications are detailed in the paper (Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms. Serguei V.S. Pakhomov, Greg Finley, Reed McEwan, Yan Wang, and Genevieve B. Melton. Bioinformatics. 2016; 32(23):3635-3644). The resulting dataset contains 458 pairs. ## Citation Information ``` @inproceedings{pakhomov2010semantic, title={Semantic similarity and relatedness between clinical terms: an experimental study}, author={Pakhomov, Serguei and McInnes, Bridget and Adam, Terrence and Liu, Ying and Pedersen, Ted and Melton, Genevieve B}, booktitle={AMIA annual symposium proceedings}, volume={2010}, pages={572}, year={2010}, organization={American Medical Informatics Association} } ```
2,540
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r-three/fib
2022-11-19T15:57:58.000Z
[ "region:us" ]
r-three
null
null
4
25
2022-11-19T15:22:00
# Dataset Card for FIB ## Dataset Summary The FIB benchmark consists of 3579 examples for evaluating the factual inconsistency of large language models. Each example consists of a document and a pair of summaries: a factually consistent one and a factually inconsistent one. It is based on documents and summaries from XSum and CNN/DM. Since this dataset is intended to evaluate the factual inconsistency of large language models, there is only a test split. Accuracies should be reported separately for examples from XSum and for examples from CNN/DM. This is because the behavior of models on XSum and CNN/DM are expected to be very different. The factually inconsistent summaries are model-extracted from the document for CNN/DM but are model-generated for XSum. ### Citation Information ``` @article{tam2022fib, title={Evaluating the Factual Consistency of Large Language Models Through Summarization}, author={Tam, Derek and Mascarenhas, Anisha and Zhang, Shiyue and Kwan, Sarah and Bansal, Mohit and Raffel, Colin}, journal={arXiv preprint arXiv:2211.08412}, year={2022} } ``` ### Licensing Information license: cc-by-4.0
1,145
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piuba-bigdata/articles_and_comments
2023-02-04T00:32:48.000Z
[ "region:us" ]
piuba-bigdata
null
null
2
25
2022-11-29T01:25:15
--- dataset_info: features: - name: tweet_id dtype: string - name: text dtype: string - name: title dtype: string - name: url dtype: string - name: user dtype: string - name: body dtype: string - name: created_at dtype: string - name: comments list: - name: created_at dtype: string - name: prediction struct: - name: APPEARANCE dtype: int64 - name: CALLS dtype: int64 - name: CLASS dtype: int64 - name: CRIMINAL dtype: int64 - name: DISABLED dtype: int64 - name: LGBTI dtype: int64 - name: POLITICS dtype: int64 - name: RACISM dtype: int64 - name: WOMEN dtype: int64 - name: text dtype: string - name: tweet_id dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 4141280942 num_examples: 537201 download_size: 1984419392 dataset_size: 4141280942 --- # Dataset Card for "articles_and_comments" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,193
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nielsr/coco-panoptic-val2017
2022-12-25T17:26:14.000Z
[ "region:us" ]
nielsr
null
null
0
25
2022-12-25T16:56:03
--- dataset_info: features: - name: label dtype: image - name: segments_info list: - name: id dtype: int64 - name: category_id dtype: int64 - name: iscrowd dtype: int64 - name: bbox sequence: int64 - name: area dtype: int64 - name: image_id dtype: int64 - name: image dtype: image splits: - name: train num_bytes: 850795822.0 num_examples: 5000 download_size: 849210800 dataset_size: 850795822.0 --- # Dataset Card for "coco-panoptic-val2017" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
667
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irds/gov_trec-web-2002
2023-01-05T03:04:33.000Z
[ "task_categories:text-retrieval", "source_datasets:irds/gov", "region:us" ]
irds
null
null
0
25
2023-01-05T03:04:27
--- pretty_name: '`gov/trec-web-2002`' viewer: false source_datasets: ['irds/gov'] task_categories: - text-retrieval --- # Dataset Card for `gov/trec-web-2002` The `gov/trec-web-2002` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/gov#gov/trec-web-2002). # Data This dataset provides: - `queries` (i.e., topics); count=50 - `qrels`: (relevance assessments); count=56,650 - For `docs`, use [`irds/gov`](https://huggingface.co/datasets/irds/gov) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/gov_trec-web-2002', 'queries') for record in queries: record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...} qrels = load_dataset('irds/gov_trec-web-2002', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Craswell2002TrecWeb, title={Overview of the TREC-2002 Web Track}, author={Nick Craswell and David Hawking}, booktitle={TREC}, year={2002} } ```
1,314
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qanastek/frenchmedmcqa
2023-06-08T12:39:22.000Z
[ "task_categories:question-answering", "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "task_ids:open-domain-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1k<n<10k", "source_datasets:original", "lan...
qanastek
FrenchMedMCQA
@unpublished{labrak:hal-03824241, TITLE = {{FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain}}, AUTHOR = {Labrak, Yanis and Bazoge, Adrien and Dufour, Richard and Daille, Béatrice and Gourraud, Pierre-Antoine and Morin, Emmanuel and Rouvier, Mickael}, URL = {https://hal.archives-ouvertes.fr/hal-03824241}, NOTE = {working paper or preprint}, YEAR = {2022}, MONTH = Oct, PDF = {https://hal.archives-ouvertes.fr/hal-03824241/file/LOUHI_2022___QA-3.pdf}, HAL_ID = {hal-03824241}, HAL_VERSION = {v1}, }
2
25
2023-01-08T20:22:47
--- annotations_creators: - no-annotation language_creators: - expert-generated language: - fr license: - apache-2.0 multilinguality: - monolingual size_categories: - 1k<n<10k source_datasets: - original task_categories: - question-answering - multiple-choice task_ids: - multiple-choice-qa - open-domain-qa paperswithcode_id: frenchmedmcqa pretty_name: FrenchMedMCQA --- # Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain ## Table of Contents - [Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain](#dataset-card-for-frenchmedmcqa--a-french-multiple-choice-question-answering-corpus-for-medical-domain) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contact](#contact) ## Dataset Description - **Homepage:** https://deft2023.univ-avignon.fr/ - **Repository:** https://deft2023.univ-avignon.fr/ - **Paper:** [FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain](https://hal.science/hal-03824241/document) - **Leaderboard:** Coming soon - **Point of Contact:** [Yanis LABRAK](mailto:yanis.labrak@univ-avignon.fr) ### Dataset Summary This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online. ### Supported Tasks and Leaderboards Multiple-Choice Question Answering (MCQA) ### Languages The questions and answers are available in French. ## Dataset Structure ### Data Instances ```json { "id": "1863462668476003678", "question": "Parmi les propositions suivantes, laquelle (lesquelles) est (sont) exacte(s) ? Les chylomicrons plasmatiques :", "answers": { "a": "Sont plus riches en cholestérol estérifié qu'en triglycérides", "b": "Sont synthétisés par le foie", "c": "Contiennent de l'apolipoprotéine B48", "d": "Contiennent de l'apolipoprotéine E", "e": "Sont transformés par action de la lipoprotéine lipase" }, "correct_answers": [ "c", "d", "e" ], "subject_name": "pharmacie", "type": "multiple" } ``` ### Data Fields - `id` : a string question identifier for each example - `question` : question text (a string) - `answer_a` : Option A - `answer_b` : Option B - `answer_c` : Option C - `answer_d` : Option D - `answer_e` : Option E - `correct_answers` : Correct options, i.e., A, D and E - `choice_type` ({"single", "multiple"}): Question choice type. - "single": Single-choice question, where each choice contains a single option. - "multiple": Multi-choice question, where each choice contains a combination of multiple options. ### Data Splits | # Answers | Training | Validation | Test | Total | |:---------:|:--------:|:----------:|:----:|:-----:| | 1 | 595 | 164 | 321 | 1,080 | | 2 | 528 | 45 | 97 | 670 | | 3 | 718 | 71 | 141 | 930 | | 4 | 296 | 30 | 56 | 382 | | 5 | 34 | 2 | 7 | 43 | | Total | 2171 | 312 | 622 | 3,105 | ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers. ### Personal and Sensitive Information The corpora is free of personal or sensitive information. ## Additional Information ### Dataset Curators The dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael. ### Licensing Information Apache 2.0 ### Citation Information If you find this useful in your research, please consider citing the dataset paper : ```latex @inproceedings{labrak-etal-2022-frenchmedmcqa, title = "{F}rench{M}ed{MCQA}: A {F}rench Multiple-Choice Question Answering Dataset for Medical domain", author = "Labrak, Yanis and Bazoge, Adrien and Dufour, Richard and Daille, Beatrice and Gourraud, Pierre-Antoine and Morin, Emmanuel and Rouvier, Mickael", booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.louhi-1.5", pages = "41--46", abstract = "This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.", } ``` ### Contact Thanks to contact [Yanis LABRAK](https://github.com/qanastek) for more information about this dataset.
7,913
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IlyaGusev/rulm
2023-03-20T23:53:53.000Z
[ "task_categories:text-generation", "size_categories:10M<n<100M", "language:ru", "region:us" ]
IlyaGusev
null
null
12
25
2023-01-25T18:14:38
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 78609111353 num_examples: 14811026 - name: test num_bytes: 397130292 num_examples: 74794 - name: validation num_bytes: 395354867 num_examples: 74691 download_size: 24170140196 dataset_size: 79401596512 task_categories: - text-generation language: - ru size_categories: - 10M<n<100M --- # Dataset for training Russian language models Overall: 75G Scripts: https://github.com/IlyaGusev/rulm/tree/master/data_processing | Website | Char count (M) | Word count (M) | |-----------------|---------------|---------------| | pikabu | 14938 | 2161 | | lenta | 1008 | 135 | | stihi | 2994 | 393 | | stackoverflow | 1073 | 228 | | habr | 5112 | 753 | | taiga_fontanka | 419 | 55 | | librusec | 10149 | 1573 | | buriy | 2646 | 352 | | ods_tass | 1908 | 255 | | wiki | 3473 | 469 | | math | 987 | 177 |
1,234
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mrm8488/CHISTES_spanish_jokes
2023-02-17T10:26:57.000Z
[ "task_categories:text-classification", "task_categories:text-generation", "language:es", "region:us" ]
mrm8488
null
null
1
25
2023-02-15T07:19:30
--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: keywords dtype: string - name: funny dtype: int64 - name: category dtype: string splits: - name: train num_bytes: 814817 num_examples: 2419 download_size: 504749 dataset_size: 814817 task_categories: - text-classification - text-generation language: - es pretty_name: chistes --- # Dataset Card for "CHISTES_spanish_jokes" Dataset from [Workshop for NLP introduction with Spanish jokes](https://github.com/liopic/chistes-nlp) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
694
[ [ -0.0330810546875, -0.0224761962890625, 0.01546478271484375, 0.044891357421875, -0.035797119140625, -0.0018529891967773438, -0.00026607513427734375, -0.02581787109375, 0.0606689453125, 0.038787841796875, -0.06640625, -0.059173583984375, -0.031890869140625, 0....
lansinuote/diffusion.2.textual_inversion
2023-02-24T06:16:59.000Z
[ "region:us" ]
lansinuote
null
null
0
25
2023-02-24T05:29:07
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 1740639.0 num_examples: 6 download_size: 0 dataset_size: 1740639.0 --- # Dataset Card for "diffusion.2.textual_inversion" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
366
[ [ -0.0291900634765625, -0.0667724609375, 0.032012939453125, 0.031585693359375, -0.00567626953125, -0.01430511474609375, 0.0036029815673828125, 0.0025482177734375, 0.035186767578125, 0.037261962890625, -0.0455322265625, -0.051025390625, -0.058441162109375, -0.0...
urialon/gov_report_test
2023-02-28T15:42:26.000Z
[ "region:us" ]
urialon
null
null
0
25
2023-02-28T15:42:18
Entry not found
15
[ [ -0.0213775634765625, -0.0149993896484375, 0.05718994140625, 0.02880859375, -0.0350341796875, 0.046478271484375, 0.052490234375, 0.005069732666015625, 0.051361083984375, 0.01702880859375, -0.0521240234375, -0.01494598388671875, -0.06036376953125, 0.0379028320...
instruction-tuning-sd/low-level-image-proc
2023-05-11T15:22:12.000Z
[ "task_categories:image-to-image", "size_categories:1K<n<10K", "language:en", "region:us" ]
instruction-tuning-sd
null
null
4
25
2023-03-04T05:45:03
--- dataset_info: features: - name: instruction dtype: string - name: input_image dtype: image - name: ground_truth_image dtype: image splits: - name: train num_bytes: 3463030772.619 num_examples: 1917 download_size: 3528678684 dataset_size: 3463030772.619 task_categories: - image-to-image language: - en size_categories: - 1K<n<10K --- # Instruction-prompted low-level image processing dataset To construct this dataset, we took different number of samples from the following datasets for each task and constructed a single dataset with prompts added like so: | **Task** | **Prompt** | **Dataset** | **Number of samples** | |---|---|---|---| | Deblurring | “deblur the blurry image” | REDS (train_blur and<br> train_sharp) | 1200 | | Deraining | “derain the image” | Rain13k | 686 | | Denoising | “denoise the noisy image” | SIDD | 8 | | Low-light <br>image enhancement | "enhance the low-light image” | LOL | 23 | To know more about how this sampling was performed, refer to [this notebook](https://huggingface.co/datasets/instruction-tuning-sd/low-level-image-proc/blob/main/data_preparation/sample_dataset.ipynb).This notebook outputs a CSV file which was then used for generating the final version of the dataset ([notebook](https://huggingface.co/datasets/instruction-tuning-sd/low-level-image-proc/blob/main/data_preparation/final_data_preparation.ipynb)). ## Known limitations and biases Since this dataset was derived from various datasets, as mentioned above, it inherits their limitations and biases as well. ## Licensing Since this dataset was derived from various datasets, as mentioned above, it inherits the licensing policies of those datasets.
1,709
[ [ -0.04205322265625, -0.052276611328125, 0.0171966552734375, 0.01425933837890625, -0.0234222412109375, -0.0021800994873046875, 0.007476806640625, -0.02984619140625, -0.0036334991455078125, 0.04693603515625, -0.06500244140625, -0.053009033203125, -0.035980224609375...
MichiganNLP/svo_probes
2023-06-18T05:28:20.000Z
[ "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "region:us" ]
MichiganNLP
null
null
1
25
2023-03-22T20:57:44
--- license: cc-by-4.0 language: - en pretty_name: SVO-Probes size_categories: - 10K<n<100K --- # SVO-Probes This dataset comes from https://github.com/deepmind/svo_probes. ## Usage ```python from datasets import load_dataset # Note that the following line says "train" split, but there are actually no splits in this dataset. dataset = load_dataset("MichiganNLP/svo_probes", split="train") # To see an example, access the first element of the dataset with `dataset[0]`. ```
481
[ [ -0.0220489501953125, -0.0028839111328125, 0.0202178955078125, -0.00185394287109375, -0.030975341796875, 0.012542724609375, 0.021484375, 0.004764556884765625, 0.038360595703125, 0.04437255859375, -0.076171875, -0.0254669189453125, -0.0243072509765625, 0.01055...
sklearn-docs/digits
2023-04-06T19:05:28.000Z
[ "size_categories:1K<n<10K", "license:cc0-1.0", "region:us" ]
sklearn-docs
null
null
0
25
2023-04-01T14:09:07
--- license: cc0-1.0 size_categories: - 1K<n<10K --- # Dataset Card for digits dataset Optical recognition of handwritten digits dataset ## Dataset Description - **Homepage:** https://scikit-learn.org/stable/datasets/toy_dataset.html#digits-dataset ## Note - How to load this dataset directly with the datasets library ``` from datasets import load_dataset dataset = load_dataset("sklearn-docs/digits",header=None) ``` ### Dataset Summary This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits The data set contains images of hand-written digits: 10 classes where each class refers to a digit. Preprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions. For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994. ### Data Instances Number of Instances: 1797 Number of Attributes: 64 Attribute Information: 8x8 image of integer pixels in the range 0..16. Missing Attribute Values: None Creator: 5. Alpaydin (alpaydin ‘@’ boun.edu.tr) Date: July; 1998 ### Citation Information References C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their Applications to Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika. Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005. Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000.
2,290
[ [ -0.02618408203125, -0.002872467041015625, 0.02734375, -0.0025959014892578125, -0.03289794921875, 0.007076263427734375, 0.00464630126953125, -0.035552978515625, 0.01097869873046875, 0.033416748046875, -0.03533935546875, -0.0311431884765625, -0.0367431640625, ...
Akajackson/donut_synthdog_rus
2023-04-01T18:52:22.000Z
[ "region:us" ]
Akajackson
null
null
1
25
2023-04-01T17:46:40
--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 8522173356.748 num_examples: 96204 - name: validation num_bytes: 1062440747.78 num_examples: 11820 - name: test num_bytes: 1107229186.768 num_examples: 11976 download_size: 10700638276 dataset_size: 10691843291.296 --- # Dataset Card for "donut_rus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
555
[ [ -0.012908935546875, -0.00992584228515625, 0.0200042724609375, 0.003688812255859375, -0.0007915496826171875, 0.0157012939453125, 0.006710052490234375, -0.002285003662109375, 0.05902099609375, 0.0279541015625, -0.057373046875, -0.05682373046875, -0.0443115234375, ...
InstaDeepAI/multi_species_genomes
2023-11-01T14:07:25.000Z
[ "DNA", "Genomics", "Nucleotide", "region:us" ]
InstaDeepAI
Dataset made of diverse genomes available on NCBI and coming from ~850 different species. Test and validation are made of 50 species each. The rest of the genomes are used for training. Default configuration "6kbp" yields chunks of 6.2kbp (100bp overlap on each side). Similarly, the "12kbp"configuration yields chunks of 12.2kbp. The chunks of DNA are cleaned and processed so that they can only contain the letters A, T, C, G and N.
@article{o2016reference, title={Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation}, author={O'Leary, Nuala A and Wright, Mathew W and Brister, J Rodney and Ciufo, Stacy and Haddad, Diana and McVeigh, Rich and Rajput, Bhanu and Robbertse, Barbara and Smith-White, Brian and Ako-Adjei, Danso and others}, journal={Nucleic acids research}, volume={44}, number={D1}, pages={D733--D745}, year={2016}, publisher={Oxford University Press} }
7
25
2023-04-06T19:05:46
--- tags: - DNA - Genomics - Nucleotide pretty_name: Human Reference Genome --- # Dataset Card for the Multi-species genome ## Dataset Description - **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer) - **Paper:** [The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics](https://www.biorxiv.org/content/10.1101/2023.01.11.523679v1) ### Dataset Summary The Multi-species dataset was constructed by parsing the genomes available on [NCBI](https://www.ncbi.nlm.nih.gov/), before arbitrarily selecting only one species from each genus. Plant and virus genomes were not taken into account, as their regulatory elements differ from those of interest in the paper's tasks. The resulting collection of genomes was downsampled to a total of 850 species, in which several genomes that are heavily studied in the literature have been incorporated. The collection represents 174B nucleotides, resulting in roughly 29B tokens. The distribution of each genomics class in the dataset is displayed below: ``` | Class | Number of species | Number of nucleotides (B) | | ---------------------| -------------------| --------------------------| | Bacteria | 667 | 17.1 | | Fungi | 46 | 2.3 | | Invertebrate | 39 | 20.8 | | Protozoa | 10 | 0.5 | | Mammalian Vertebrate | 31 | 69.8 | | Other Vertebrate | 57 | 63.4 | ``` ### Supported Tasks and Leaderboards This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch. ### Languages DNA ## Dataset Structure [N/A] ### Data Instances For each instance, there is a string representing the sequence, a string indicating the description of the sequence, two integers representing the index of the first and last nucleotide respectively and the link to the genome's fasta URL. An instance is shown below: ```python {'sequence': 'AAACTACCACTGGCTAAATTTCGACCATCTGGGCTAATAGCAACTGACCGCACCCAATATTTATGTCCTTTAAGTGTGCGAATTAGCTTTCCTGTGCCTAAATTCCAAACTTTGAGAGTGTTGTCATCGCTACCACTCACCAAAATTTTCCCATTAGGACTAATTGTTAATGCTTGAATGGAGTCAGTATGTCCTGTTAATGTGTAGACTATTTTACCTGTTGCCAAATTCCAGGCTTTAATAGTTTGATCATCACTCCCGCTAACCAAAGTTTTGCCATTGGGACTGATAGCCACAGCATTAACTTTTTGCGAATGTCCACTCAGGGTTAGTATTTCTTTTCCTGTGGTCAGATTCCACATTTTAATTATGCGTTCCCCTTCGCCACTACTAGCAATTGTCTGCCCATCGGGACTAATGGCGACAGAGACAACAGATTTTGCCCCACCTTTGAGGGTGTTAGCTAAGGAAATATTTTTAACTGGAACATTGGGTGACTGACCAAAAACAACTTCACCCTGAGTAGGACTGTAATTTCCTGGCTTTAGTCTCGATAACAAACTGGTTTGAATTTGGTGATATTTTTGATACCAAGTATCACTAAAACCAAATAACAAAATGAAAGCAGCGCCTAAAACTAAACTTTTGACAAAAGCATATTTAAAGGAGAACTTTGCACTCGGTTGAGTTACGGTGAATTTTCCTGATGATTGTCCGGCGGCTGGTAAGGCGCGTGGGAGTGATGGAATCAAATCTTTAATCACTTCATCGGCTGACTGGTAGCGTTGACTTAAGTCTTTTTGCAACAGCTTCGTCATCACCCCTTCCAATTCTGGCGACAAAGGACTACGCAAATATTCCCGCCAACTGTTCGCCCAGCCATAGCCATGTTCCATCCACAATTGAAAAGGGGATGTTCCTGTTAAGAGATGAAAACAGGTAGCCCCCAAACTGAACAAATCACTAGCTGGGTAAGCTTTACCGTCTCTGATTTGTTCCAGTGGAGAATAACCATGCGAACCAATGGATGTACCATTTTTATTCTTGACTTTTTCGGTTAATTGCTTAGAAGAACCAAAATCAATCAAGCTAAGTCGCCCATCATAACGACAGCGAATTAAATTTTCTGGTTTAATGTCTCGGTGAATCACACCGCGATCGTGAATGAATTTGAGTACAGGCAGTAAATCAAGTAAAATTGCTTGAATTTCATTCGCTTTATAGACTTTGCGCTGTTGTAATTCTTTTAACAAGTTCTGCCCATTAATAAACTGTTGTACCAAATAAAGGCAGTTATCTTGTTCAAAGTAAGCAATCAGTGTAGGAATTTGCGGATGTTCGCCGAGTTCTTGCAGTCGCTTGGCTTCTTCTGCAAATAACTCCATTGCTTTTTTCTGCGACCAAGTTCCTTGAAATTTCGGTGCTAATTGCTTAATTACACACAGTTCATTGAGTTTATCGGTATCTTCAGATAAATAAGTTCTGCCAAATCCCCCCTCATCGGAAAGCACCCGAATCACTCGAAAGCGATTTCTTAATAGTGGCACCAAGGGGGTGCTACAAGTTTGGCATGACTGCTTTCCTTTGGGATTTAGGGGATTTGGACAATCGGGATTTAAGCAGCAGATCATTATCTGACAGGCGCAACTGCATAAAAATTTTTACTAAATTAACCCCGATATTTCCCTAGATGATGATTGACTCTCACGTATTGATGGTAGATCCCGCTGGTAGTGGGGAGTGGGGAATCAATTATATAGTCAATTTTGGTAAATGCTCATAAGTTTTCTTCAATGCAGGAAAACTACGAGAGTCATCAGCTGAATTTTATCGATTATAGCAGCAGGCAAAAGTAGCAGACAGGTTAAGAGTGTCATTAGTCAAGACAAATGACTCATGACTAATGACTCATGACTAATAACTAAGGCTTTTGGGTGGCGATCGCTAATTTTGCCCCCTGGACTTGTCTGACTTGATCCATCACTGCCACTACTTTACCGTGGGTGACTGTTGCATCAGCATTCACAATTACTAATGCTTCTTGGTTATCGCCTACCAAGGTACGCAATTGTCCGGCTAAACCGTCAACAGTGCTTGGTTGACGGTTAACACTTACTATTCCATCTTTATCTACTGTGACGGTAATTTTGGCTGGAACTTGCTGCTGTTTGGCTGTCGCCGCTTTGGGTAAGTTGACGGGTAAACCTTCTGAGCGAGTTAAAAATAACGTTGACATGATAAAAAATGTCAAAATCGCAAATATCACATCAATCATTGGCACGATGTTGATTTGCGGTGGTAAATCTGGCTCATCTTGTAGACGCATAGGTTCTGTCTCCTCGTTCAAAGCGGCGGCGATAGAGCAGTTCTAATTGTCCACCATATTCTTGTATTGCGGCAATCTGTCGTTGATATAACCCTCGAAAGGTATTAGCAAATAAAAGTATAAAAATAGCCACAATTAAACCTGAAGCTGTAGATACCAGCGCTTCACTAATACCTGCGGTAACTCCTGCGGTTTTTGTCCCGCCTACATCACCCAAGTTTAATGATGCAAAAGAAGCAATCAAACCTAATACAGTACCCAGTAGACCTAAAAGTGGTGCAAGACCAATAATTGTGTCAAACATATTTTGAAAACGTTTGAGAACTGGGATTTCGGCTTGCGCTTCACTTTCTAGTGCAAGCCGAAATTCTTCTGGGGTTGGTTCTTCTAATTGCAACGCCGCTAAAAAAATCCGTGTCATGGGCAAATCTGCATTCTTTTGCAATTTATCCAACGCGCCAACAACATTATCAAGGCGGTAAAGATTCAACACTTCTCTGACTATGCGGTTTTGCCGAGTATTGATGCGATACCAAAAGCGGACTCGCTCGATAATTAAAGCAATTCCCACCACACTAAACGCCAGCAGGGGCCACATGACTACGCCACCTGCTACAAACAACTCATACATGGGCAATATCTCTAGGAACTAAATGGACAACGTTACAGTTAGACTAGCAGTTTACGGTACTAAATGATATATCTTATCAATAAGGAGTAGACAAAATAAAAAGCTATGTCAAATTCGGTTGAGTTTTGATGACATAATTATTCATTCTTGTTCAAGGCTTGATTCGCTACAATCCTGATGATGAAAGTATTTGTGTAAGTATACAGTTGATGAAAGCTAACTCAGGAATTTTTTTCTTTATTGCTTGACTTTTGCGAGAGATGGTTTTGAACAGAGTAATTACTAATAAGAACTTGCAATAAATTTAAACAGAACAGTAGTTTGTAGCTTTGCTTGAGAAGCGATCGCCCGACGTTGAGAGTTAAAGTATATTTTGCGTACTAACTTACCCAACGCCCAAAAAATTACATCATTTGAATATCGTCAATTTGTACTCTTAATCATCTATGGCTAAACTATTTGACTCAATCACAGAAGAACTGCAAGAGTTTATTGCAGCCCAAAACCTTTTCTTTGTAGGAACCGCGCCTCTGAGTGCTACAGGTCACGTTAATTTATCTCCCAAAGGTCTCGATTGCTTGCGGATTTTATCACCCCACAAAGTCGCCTATCTCGATCTCACAGGTAGCGGTAACGAAACTTCAGCCCATCTGCAAGAAAATGGTCGCATTACCTTCATGTTTTGCGCCTTCACTGAACCAGCGCGCATCTTGCGACTTTACGGTCAAGGACACGTAATTTTACCTAGCTATCCTGATTGGGATTCTGTATATTCAGTGTTTCCGCCGCTACCAGGAACTCGTCAAATTATCGTAGCTGATATTGAGATTGTGCAAAGTTCCTGTGGTTTCGGCGTTCCTCTTTACGAATACCAAGGTCAACGCCAAACACTAGTAAATTGGGCTGCTAAAAAAGGCGAACAGGGAGTCCGAGAATATCAACAACAAAAAAACAGCATCAGCATTGATGGTTTACCGACACCATTAGGCCAATTATCTGACGGTTAAAGCGGCGTTTCATATATTTTTAGTTAATCTGAACCAAAAAATCTCAAATTTTTTGTCAATAGTCTCTAGTCCAAAGAAGCTTGATTTTTGACCATAGATTGTAGGCTTTTGACAAAAATAACCTTTATAGAGAAAATTTATCCTTGCTGACACTCTATAACTAAGTTTATAAAACATAGCGTCAAAAATCGATACATATCAGTTCTATTTTCTGCCTCTATTCCTAATTAAATTTGGTGTAAAGGAACTATTATGCGGTTTCCGTGTCTTGACGTAATGATTTGCAACGAATTATGATTCGAGTTTAGTCCGGATCAACCGAGACATCCTCGAAAATTGGTGCAAGTAAATTCAACTTTCGCTCTACATAATCACACGCATGAGATTACGCTTATTTCTGTTTAGCGTTGTCAGTATTGTCCTGCTTTCTTCTCCAGTAAGAGCATCTCGCTTAGAATCTTGGAGCTTTGACACCGCACAAAATCAACTGAATATTACTACTGTATCTGGTGTTAAACCAAGAGCATTTTTAATTCAAAATCCCACGCGGTTAGTTATCGATCTTCCTGGTACACAACTGAACACAAATACAGTTCGGAAAAACTTTGGTTCCACAGTACGTGAAATCCGTGTTGGTAAGGTTGACGATAACACAACAAGATTAGTAGTTGAATTAGCACCTGGATACACTGTAGACCCTAACAAGTTACTGCTGCAAGGTGATTCTTCCACTCATTGGATAGTGAAATTTCCATCGGTAGAACGGGTTCAAAATCCTGTTGATAATAATTTTTCTTTATCTAGTGAAGAGCAAATTCCGGTTTCTGTGAGTGATGTTTCTTTGTTTGCGGGAGTTGTACCGTTAGGTAAGGAAATACCACAATTGCGATCGCAGGTACAAGCCTTAGCTGCTCGTTATCGTTCCCTGGATGCAGGAATGTTCTTTTTAGATTTAGATACTGGTAACTATCTAGATTTAAATGGTGAGAAAGTCTTTCCTGCTGCTAGTACAATAAAGTTTCCCATTTTAGTAGCGTTATTTCAAGAAGTAGATGCAGGTAGAGTCAAACTGAATGAAACCTTAGTTATGCGGCGCGACTTAATAACTGGAGGTTCTGGAGAATTTCAATACAAGCGTGCAGGAAGTCGTTTTAGTCTGATAGAAACCGTGACTAAGATGATTACCATCAGCGACAACACAGCTACCAATATGGTAATTGACCGATTAGGTGGTAAAGCTAAGTTAAATCAGCGTTTTCGTGGTTGGGGTCTGCAAAACACCGTTGTGCGGAATTTACTCGGCGACTTTAAGGGAACGAATACAACTAGCGCCAAAGATTTAGTCAGGCTGTCTGCGTTGGTTGCAAAAAATCAATTATTGACTGATTCCAGCCGTAGCAAAGTTTTGGATATTATGCAGCGTGTTCACAACACCAAGTTATTACCTGCTGGTTTGGGTAAAGGTGCGGTAATTGCTCACAAAACCGGAACTCTAGGCATTGTACTAGGTGATGCCGGGATTATTCAAATGCCATCTGGTAAGCGCTACTTAGCCGGAATTTTTGTCAGAAGACCTTTTAATGATTTAAAAGCGCGAGATTTTATCAATCAAGTTTCTCGAATTGTTTACGGCTATTTAGACCAACCAAGAGTCGCCAGCAAGCCTTAATACTCCTGATGTAAAAAAGAAAAATTTTAATTGACGTAAGCCCCTGATATTCATTAATATCTAGGGGTTTTTGCATATCTATTTATAGCAGTGCTTAACGCACCCTATCTCTCAGTGCGTTACGGCTAATCCTTATTCTCTTAAACTAACAAATTCTTGCATAGCCGTAACACATTCTAATTCATATTGGCTTTGAAGGATATTGACTGTATTCCTGCCAAGTTGGCTACATATACCTAAGCCGCACTGCTAAATTATGAATGGGAAATAACTTGCGGGCTTGATAAACCAACTTTTACTACACTAAACATGCTAAAGCATTAACAACGGACGGATTTAGGTTAGTTGCTTATTTTGCTCACTCTTGTGAGAGATTGCTGCTGTTTTTATTGTAGCGATCGACATCAAACTTCTTTATCTCTAAAAGGACAAATATAACAGGAAGTCCTCATTGATTACTCCTATCCTCACCTCGTTCATCGCAAAATGTACGAGGGCTTTTTTTATTTGGCAGAATTTACCCCTATTACGCCAATGATAATTAAAGCTATCGAGAAAAGTTTGGTAAGAGACATTGATTCACGAAACCAAATTACCCCAATAGTAGCGATTACAGTTGTGCCTAAACCTGACCAAACAGCATACGCAATGCTGACTTCAATTTTTTTAAGAGCTAAAGTTAAAAAACTAAAACAAATTCCATAACAGATAAAAATTAAAACCGAGGGAATAGTTCTTGTAAACCCCTCAGACAATTTCATGGAAGTTGTACCAGCGACTTCAAATAAGATTGCTGCAATGAGATAAAGCCAACTATTTACCATGTTTATTGATTGATTATAAGGTGATGATGGGAATATGATTTTTCGACAAGCATAATGAGTCAAAATTCTATATTTAATCTATTAACTAATTCTGCTATTTTGACAACATTTATAGTTAGCTGATGAGATAGGCAAAAATCAAAATATTCATATTTCCGAATTAGTAAAGAAGTTGGTAATCTCTAAAGTTCAGTTTACCACACCAATATTATGGGGGTTTACCGTACTAATACTAAGGTTCGGAAATCATGATGTAATTGGTGATAAAAACCGAATTTACACTGTACTGGATTGTGAATACTATAAAAACAACGCAAATGATTTAAACCTAAATCAACTACACAAAATTAGAAATTAAACGAGGTGGAGACATGACATTAGTGCGTTGGAATCCTTGGCAAGAAATGAACACTCTCCAAAGACAAATCAACAATTTATTTGCAGACGAAATGCTCCCATCTACTTTACTTGAAAGAAGCCTTACAAAAGTTCCGGCGGCTGAATTACACGAATCTGAAGAAGCTATTCATCTCAAGCTAGAATTACCAGGAATTGAAGCCAAAGACCTAGATGTGCAAGTTACAGAAAAAGCTGTGTATATCAGCGGTGAACGGAAATCTGAAACTAAAACAGAAGGGAAAGGTGTAACCAAGAGTGAATTTCATTATGGGAAATTCCAACGTTTGATTCCTTTACCAACTCGCATTCAAAATACCAATGTTACTGCTGATTATAAAGATGGTATTTTGACTCTGACTTTGCCTAAAGCCGAAGAAGAAAAGAAAAAGGTTGTCAAGCTGAATCTTGAATCTATTGGCTAATATCAATTTTGGATTAGCGCTAAAATACCCGACTTCTTTAAGAAGTCGGGTATTTTGTTGTTCACTAATGATTTAAAATTGCTATAAGCTGCGATTTCTGCCTGTTGATTGTTGTCTGTCTACGGGAAAAACGTCAAAATCGAAAGTTGCAATTAGACGCTCATCAACGTATACCTGTATTTTATGCTTACCAGGAGGATCACCTGCGGCGATCGTCCAATAGTTTTCAATTACACCATCATTAGCTATAGTTTTGCGCCTCATTACCGACTCTGTACCGTCAGCGGAGACTGTGAAGTTTTCACCATCATCTGTAGCCCAAGTTTCTGGGGGTTTTGGTAAGCGTAGGACTTCTCGCCATGTAACTTCGCCTTGGTAGTCTTTGAGTTGAATTCGCCACCCATATTTACTACCTTCTTGTAGTGGGACTCTGAATGTGGGGATGAAGTTAACTTTACCTCTAGCATCGACTCTCGCTATGCCAAACTCAGCTTTGTCGATCGCTACCGACTTTTTAGTATTGTTTGCTTGAGAAATTGACCCTGATGATGCTATTTTTTCGTCGGAGATCGCTACTGTAGCATTGATTGGCTGAGACGCTACCAACCCGGAAACTAGCCAAGAAGAAGTTAGTACAACTATTGCAGTCCAAATTCTCATCAGCAAAATTTTTGGTCATTTACTAGTACTTATTCCCGCCTTCCCATTGGCTTCCGGGTACAGTCCCGATAAATAGCCAAGTTGGCAGAATAAAAGTTGCAGAATTAATAGTCAGTTTATAGTTAAATCGGCAACACCAGATCAAGCCACTCAAACTACTTTACTCTCGGGCCAGTTGCCAGAACTGCGAAAACTATCATCGCAGGTTTTCGGTGTAGGTGCTAAATATGCGTTTATTCTTAACTATTTTGTGTTCAATACGGAATTTTTAATATGTAAGCAATTGCTGACAGTCGGCTATTTGATCAATTGTCATTTCCTAGAGTTTCATCCCCTTGAGGGGAAGGAGTTTGGGAAATGTCAAAAACTGTCAAATGCTTAATGCAAAGATTAACAGTTGTGCCTAAGTGCGATCGCACTTAGGCATGACAAAGCATCAAAAATTAGCATTGGAGAACCGATATTTTCCTATTACCTGACTGCTATATATTGATAGTGAGGCGTTTTTGAGCAGCAAACAGCATGGCAGATATTCCAAATTCCATCGCATCATACCGTGCCTTAGCACTGCAAGTTACCTGTCATGCTGTGAATCAAGCGAGCGATCGCCACGCTGTCCAAGAAATCATTCATCATACTATCAACCGCCTGGCGCAACAAATCGCCGCCAGTATTGCTTTTATTGGTTTTGACTGTCGTTTAATTGTTTTACCAGAATATTTTCTGACAGGTTTCCCGATGGGTGAACCTTTGGCTGTTTGGGGAGAAAAGGCTTGTATAGAAATGCACGGTGCCGAGTATGAAGCCCTCAGTAAAATTGCTCAAAAACATCAGATATTTTTAGCTGGTAACGCCTACGAACTCGACCCCAATTTTCCTGGCTTATACTTTCAAACTTGCTTTGTGATTGACCCGGCTGGTGCTATTGTCTTGCGGTATCGGCGGCTAAATTCGTTATTTGCACCCACACCTCATGATGTTTGGGATAAATATCTTGATTGTTACGGCCTAGAAGGGGTGTTTCCTGTAGCGAAAACTGCAATTGGCAATTTAGCCGCTTTAGCTTCCGAAGAAATTTTGTATCCAGAAGTAGCGCGGTGTTTAGCAATGCGTGGTGCAGAAATTTTTCTGCATTCCACTTCTGAAATTTATAGCAAAAACCTCACACCTAAAGATGCGGCGAAAATTTCTCGCGCTGTGGAAAATATGGCTTACGTTGTGTCTGCGAATACCGCAGGTCTAGCTAATAGTTCTATACCCAGCGCTTCTGTTGATGGTGGCTCAAAAATAGTTGACTATCGCGGTATCGTATTAGCAGAAACAGGTGCAGGCGAAAGTATGGCAGCTTTTGCAGAGATAGATTTAACTGCTTTAAGACGCGATCGCCGTCGTCCAGGGTTAAATAATTTACTGTCTCGCCAGCGATTTGAACTCTACGCCCAAAGCTACAGCCAGTCACAATTTTATCCAGCAAACACTATGCTAAATCAAGAATGCGATCGCCAACACTTCATCCAAACACAGCAACAAACCATAGAACGTCTATCTCAGTTAGGAGTGATTTAAAAGTCTAAAGTCTGAAATTAGATTCTTTTGACCATTGACTATTGACAAATGACAAATGACAAAACCAATCGAAGTCCGTAACCCGCGAACGGGAAAATATGATTATGTAATTATCCCACCGCCGCCGAAACTGCTGGCGCAGCAATGTAACCGAGCGCGAAGGGCGCAAGTGCGTTGGCAAAAACTGGGCGTAGAAGGGAGAGTTGCAGCTTTAAAAGAATGGAAGCAAGCAGTTTTGGCTGGACGCGAAAAGCTCACAGATGCTTTGGTCAATGATACGGGTAGATTATCTATATCAGTGATGGAAATCGACTCATTCCTTTCTAGCATCGATCGCTGGTGTGGATTAGCGCCAGATTTATTACAAGATTCGGCCAAAAATACATCAATTCCGTTCATCGCCTTACAACAAACATCAACGCCTTACCCTGTAGTTGGGGTAATTAGTCCTTGGAATTTCCCTCTGTTGCTGTCTACGATAGATACCATTCCCGCACTGTTGGCGGGTTGTGCTGTAGTTGTCAAACCCAGTGAAATTGCACCGCGTTTCATCGCCCCACTGATAGCTGCAATTAATCAAGTACCCGCCTTGCGCGATGTTTTCAGTTTTGTGGAAGGTGCGGGAGAAACTGGCGCGGCTTTGATGGAGAATGTAGATTTAGTTTGTTTTACCGGTAGTGTCGCTACTGGACGCAAAGTTGCAGAAGTCGCCGCACAAAGATTTATCCCCGCTTTTTTGGAATTGGGCGGGAAAGATCCGGCGATCGTGTTGGAATCTGCCGATTTAGAATTAGCCACATCAGCGATTTTATGGGGTTCCGTCGTTAACACCGGACAGTCTTGTTTATCAATTGAGCGTATTTACGTTGCCGAATCTATCTTTGAAAAGTTTTATCATCAGTTAGTAGCCAAAGCACATCGCCTACAACTAGCCCATCCCACCATTGAAAGTGGCGAAATCGGCCCCATTATTGCTGAAAGACAAGCTGGCATAATTAACGAGCATATCTCCGATGCAGTGCAAAAAGGTGCAGTAATTCATTGTGGCGGTAAAGTTGAAGAGTTAGGCGGTGGTTGGTGGTGTCATCCCACAGTGCTGACTCATGTTAACCATACAATGAAAGTCATGACCGAAGAGACTTTTGGCCCGATCATGCCAATCATGCCTTTTGCCACAGTAGAGGAAGCTGTTAACTTAGCCAACGATTCAATTTATGGACTGAGTGCGGCGGTGTTTGCGGAAACCGAAACTGAAGCGTTAACAGTTGCCCAGCAAATAGATGCAGGTGCTATCAGTATTAATGATGCCGCCCTCACCGCCATTATGCACGAAGGTGAAAAAAACGCTTTCAAATTATCCGGTTTAGGCGGTTCACGTATGGGTGCAGCCGCCATCAAACGATTTTTGCGGAAAAAAGCGTTTTTGATTAAAACCAACTCAAATCAAGACCCTTGGTGGTTTGAGCCTAAAGTGTAGTGCAATCTTCTCTCAGCGACCTCTGCGTCTCTGTAGTTCGTTAAAAACCGTATTAGATTCTGTTTGTTGGGTTTCGCTGTCGCTTCACCCAACCTACTTTCCTTAAACCCCTACTACAGATTCATTCACAGTTTCACTAGCCGCAACACCATTAGTCAAAATCGCTTGCCGAGTTTTCAGGTTAAATTTATAACCATGTGGCAAAATATGCAGCTTCGCACCACAAATTGCCAAAGGTTCATCCCGGAGAATTGTATCTGCGTTGTTATATGTAGATTCAGACTCATCCACAATGGTGACTGAACCTTCACCAATAATTTCGATTTGGTCATCAGTCACGGCGATCGCTGTATTCTCATCAATCCCAAATCCTAACACCGCAGGTTCATGAATTAAAGCTGTAATTAAACGCCCTAAGCGTCCCCGTTGTAAGAAATGTTGGTCAATCACCACCCCTGGGAGAAAACCCATACCAGGCCCCATTTCCACAATTTCCATCCGTGGTGTACTTTGAGAATCACCCTCAACAATCATTTTATCGGGCATCACAGCCGCACCCGCACTAGTACCTGCAATTACTGCACCTTCAGCATAGCGTTGGTGAATAGCCGCATCGATTTCGGTATCCTTGAGGATACTAGTAATTCGCGCTTGGTCTCCTCCAGTAAAAAATATCCCAGTCGCCTTAGCAATAGCTTCTAAAGCCGTAGAAGACCTAGCATCTTCACGAGTTTCTGTATCAATAATGCGAACGTGTTCTGCACCTAGCCGTTCAAAAACTCTAATATAATTTTCCCCCACTTCTCTAGGCAGTTCTGTGGCGGCCGTCATAATTACAATATTGGCTTTTGTACCCCCAGCCCGACGGACAAATTCTCGCAGAATCACACAATCTCCTTCTTTATCTTCTGCGCCACCAATAATTACCAACTGGCGTTTATGTGCAGTTTCTGTCATAATGCCCCCCGGATAACCGGATTAGAATTTAATTTAGATTAATTTCAATAAAACATGACAATTATCACAATCAAATCATCCATTTGATAGATTAATTTTTAATGGCAAAAGTTAAATTATATATAACTTTATGTATATATAAACTCTTGCCAAATTTAGCATTTTTAATAATTGGTAATTCATTTAGCAGAATTACCAATTACTTATACAGTAATAATTTATGTATAACTCTTCTCAAGTAATAGCACTAAAATCTCATAGT', 'description': 'NZ_AP018174.1 Anabaenopsis circularis NIES-21 DNA, nearly complete genome', 'start_pos': 1824000, 'end_pos': 1836200, 'fasta_url': 'https://ftp.ncbi.nlm.nih.gov/genomes/refseq/bacteria/Anabaenopsis_circularis/latest_assembly_versions/GCF_002367975.1_ASM236797v1/GCF_002367975.1_ASM236797v1_genomic.fna.gz'} ``` ### Data Fields - `sequence`: a string containing a DNA sequence from the human reference genome - `desciption`: a string indicating the Species of the sequence as well as the NCBI id. - `start_pos`: an integer indicating the index of the sequence's first nucleotide - `end_pos`: an integer indicating the index of the sequence's last nucleotide - `fasta_url`: a string indicating the URL used to download the fasta from which the sequence was taken. ### Data Splits The Multi-species dataset has 3 splits: train, validation, and test. | ## Dataset Creation [N/A] ### Curation Rationale [N/A] ### Source Data #### Initial Data Collection and Normalization The data consists of sequences cut from the the whole genome sequences of the 850 species sampled that can be found in the `urls.csv` file of this dataset's repository. #### Who are the source language producers? [N/A] ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset [N/A] ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators [N/A] ### Licensing Information [N/A] ### Citation Information ```bibtex @article{dalla2023nucleotide, title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics}, author={Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza Revilla, Javier and Lopez Carranza, Nicolas and Henryk Grywaczewski, Adam and Oteri, Francesco and Dallago, Christian and Trop, Evan and Sirelkhatim, Hassan and Richard, Guillaume and others}, journal={bioRxiv}, pages={2023--01}, year={2023}, publisher={Cold Spring Harbor Laboratory} } ```
17,218
[ [ -0.055419921875, -0.022705078125, 0.00609588623046875, -0.0032215118408203125, -0.0239410400390625, 0.01087188720703125, -0.0113525390625, 0.00141143798828125, 0.032958984375, 0.0229949951171875, -0.038055419921875, -0.043609619140625, -0.047943115234375, 0....
bakhitovd/ML_arxiv
2023-05-19T21:47:33.000Z
[ "task_categories:summarization", "size_categories:10K<n<100K", "language:en", "license:cc0-1.0", "region:us" ]
bakhitovd
null
null
1
25
2023-04-06T21:46:29
--- license: cc0-1.0 task_categories: - summarization language: - en pretty_name: ML Articles Subset of Scientific Papers size_categories: - 10K<n<100K --- # Dataset Card for 'ML Articles Subset of Scientific Papers' Dataset ## Dataset Summary The dataset consists of 32,621 instances from the 'Scientific papers' dataset, a selection of scientific papers and summaries from ArXiv repository. This subset focuses on articles that are semantically, vocabulary-wise, structurally, and meaningfully closest to articles describing machine learning. This subset was created using sentence embeddings and K-means clustering. ## Supported Tasks and Leaderboards The dataset supports tasks related to text summarization. Particularly, the dataset was created for fine-tuning transformer models for summarization. There are no established leaderboards at this moment. ## Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances An instance in the dataset includes a scientific paper and its summary, both in English. ### Data Fields article: The full text of the scientific paper.\ abstract: The summary of the paper. ### Data Splits The dataset is split into:\ -training subset: 30280 articles\ -validation subset: 1196 articles\ -test subset: 1145 articles ## Dataset Creation ### Methods The subset was created using sentence embeddings from a transformer model, SciBERT. The embeddings were clustered into 6 clusters using the K-means clustering algorithm. The cluster closest to articles strongly related to the machine learning area by cosine similarity was chosen to form this dataset. ### Source Data The dataset is a subset of the 'Scientific papers' dataset, which includes scientific papers from the ArXiv repository. ### Social Impact This dataset could help improve the quality of summarization models for machine learning research articles, which in turn can make such content more accessible. ### Discussion of Biases As the dataset focuses on machine learning articles, it may not be representative of scientific papers in general or other specific domains. ### Other Known Limitations As the dataset has been selected based on a specific methodology, it may not include all machine learning articles or may inadvertently include non-machine learning articles. ### Dataset Curators The subset was created as part of a project aimed to build an effective summarization model for Machine Learning articles.
2,458
[ [ -0.0214996337890625, -0.03656005859375, 0.0211181640625, 0.0152435302734375, -0.0200042724609375, -0.001407623291015625, -0.005931854248046875, -0.00936126708984375, 0.028472900390625, 0.0338134765625, -0.022674560546875, -0.055023193359375, -0.050262451171875, ...
andreabac3/StackOverflow-Italian-Fauno-Baize
2023-04-08T15:49:40.000Z
[ "license:gpl-3.0", "arxiv:2304.01196", "region:us" ]
andreabac3
null
null
1
25
2023-04-08T15:46:42
--- license: gpl-3.0 --- # StackOverflow-Italian-Fauno-Baize This dataset is an Italian translation of the StackOverflow dataset presented by Baize's authors. ## Dataset Description - **Paper:** https://arxiv.org/abs/2304.01196 ### Languages Italian ## Dataset Structure ### Data Instances Sentences 57,046 average number of turns 3.6 response lengths of each turn 36.0 ### Data Fields topic, input ### Data Splits Train ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization https://github.com/project-baize/baize-chatbot ## Additional Information ### Dataset Curators [Andrea Bacciu](https://andreabac3.github.io/), Dr. [Giovanni Trappolini](https://sites.google.com/view/giovannitrappolini), [Andrea Santilli](https://www.santilli.xyz/), and Professor [Fabrizio Silvestri](https://sites.google.com/diag.uniroma1.it/fabriziosilvestri/home). ### Licensing Information This project is a derivative of Baize, and we adhere to the licensing constraints imposed by Baize's creators. ### Citation Information ```bibtex @misc{fauno, author = {Andrea Bacciu, Giovanni Trappolini, Andrea Santilli, Fabrizio Silvestri}, title = {Fauno: The Italian Large Language Model that will leave you senza parole!}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/andreabac3/Fauno-Italian-LLM}}, } ``` ```bibtex @article{xu2023baize, title={Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data}, author={Xu, Canwen and Guo, Daya and Duan, Nan and McAuley, Julian}, journal={arXiv preprint arXiv:2304.01196}, year={2023} } ```
1,669
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BAAI/COIG
2023-07-12T15:38:35.000Z
[ "language:zh", "license:apache-2.0", "arxiv:2204.07705", "arxiv:2212.10560", "arxiv:2212.09689", "arxiv:2304.07987", "region:us" ]
BAAI
We propose the Chinese Open Instruction Generalist (COIG) project to maintain a harmless, helpful, and diverse set of Chinese instruction corpora. We welcome all researchers in the community to contribute to the corpus set and collaborate with us. We only release the first chip of COIG to help the Chinese LLMs' development in the exploration stage and appeal to more researchers joining us in building COIG. We introduce a manually verified translated general instruction corpus, a manually annotated exam instruction corpus, a human value alignment instruction corpus, a multi-round counterfactual correction chat corpus, and a leetcode instruction corpus. We provide these new instruction corpora to assist the community with instruction tuning on Chinese LLMs. These instruction corpora are also template workflows for how new Chinese instruction corpora can be built and expanded effectively.
@misc{zhang2023chinese, title={Chinese Open Instruction Generalist: A Preliminary Release}, author={Ge Zhang and Yemin Shi and Ruibo Liu and Ruibin Yuan and Yizhi Li and Siwei Dong and Yu Shu and Zhaoqun Li and Zekun Wang and Chenghua Lin and Wenhao Huang and Jie Fu}, year={2023}, eprint={2304.07987}, archivePrefix={arXiv}, primaryClass={cs.CL} }
330
25
2023-04-16T11:09:32
--- license: apache-2.0 arxiv: 2304.07987 language: - zh --- # This is the Chinese Open Instruction Generalist project We propose the Chinese Open Instruction Generalist (**COIG**) project to maintain a harmless, helpful, and diverse set of Chinese instruction corpora. We welcome all researchers in the community to contribute to the corpus set and collaborate with us. We only release the first chip of COIG to help the Chinese LLMs' development in the exploration stage and appeal to more researchers joining us in building COIG. We introduce a manually verified translated general instruction corpus, a manually annotated exam instruction corpus, a human value alignment instruction corpus, a multi-round counterfactual correction chat corpus, and a leetcode instruction corpus. We provide these new instruction corpora to assist the community with instruction tuning on Chinese LLMs. These instruction corpora are also template workflows for how new Chinese instruction corpora can be built and expanded effectively. It is best to download the individual data files directly that you wish to use instead of using HF load_datasets. All datasets can be downloaded from: https://huggingface.co/datasets/BAAI/COIG/tree/main This dataset card is modified from [OIG](https://huggingface.co/datasets/laion/OIG). ### Translated Instructions (66,858) There are 66,858 instructions in total, which are composed of 1,616 task descriptions in [Super-NaturalInstructions](https://arxiv.org/abs/2204.07705) along with a single instance for each of them, 175 seed tasks in [Self-Instruct](https://arxiv.org/abs/2212.10560), and 66,007 instructions from [Unnatural Instructions](https://arxiv.org/abs/2212.09689). To reduce the cost and further improve the quality of the instruction corpus, we separate the translation procedure into three phases: automatic translation, manual verification, and manual correction. These strict quality verification procedures assure the reliability of the translated corpus. ### Exam Instructions (63,532) The Chinese National College Entrance Examination, Middle School Entrance Examinations, and Civil Servant Examination are the main Chinese commonsense tests. These exams contain various question formats and detailed analysis that can be used as the Chain-of-Thought (**CoT**) corpus. We extract six informative elements from original exam questions, including instruction, question context, question, answer, answer analysis, and coarse-grained subject. There are six main coarse-grained subjects: Chinese, English, Politics, Biology, History, and Geology. There are very few Math, Physics, and Chemistry questions in the corpus because these questions are often with complex symbols which are hard to annotate. For many choice questions, we recommend that the researchers utilize this corpus to further post-process it using prompts or post-process it to blank-filling questions to increase the instructions' diversity further. ### Human Value Alignment Instructions (34,471) To respect and reflect the major difference caused by different cultural backgrounds, different from other tasks in COIG that leverage one unified collection of instruction-following samples, we categorize the value alignment data into two separate series: - A set of samples that present shared human values in the Chinese-speaking world. In total, we choose 50 instructions as the augmentation seeds, and produce 3k resulting instructions following samples for general-purpose value alignment in the Chinese-speaking world. - Some additional sets of samples that present regional-culture or country-specific human values. ### Counterfactural Correction Multi-round Chat (13,653) The Counterfactual Correction Multi-round Chat dataset (CCMC) is constructed based on the [CN-DBpedia knowledge graph dataset](https://link.springer.com/chapter/10.1007/978-3-319-60045-1_44) with the aim of alleviating and resolving the pain points of hallucination and factual inconsistency in current LLMs. The CCMC dataset includes 5 rounds of role-playing chat between a student and a teacher, and the corresponding knowledge they refer to. The dataset contains ~13,000 dialogues with an average of 5 rounds per dialogue, resulting in ~65,000 rounds of chat. ### Leetcode Instructions (11,737) Given that the code-related tasks potentially contribute to the ability emergence of LLMs, we argue that code-related tasks aligned with the Chinese natural language should be considered in our datasets. Therefore, we build the Leetcode instructions from a **CC-BY-SA-4.0** license [collection](https://github.com/doocs/leetcode) of 2,589 programming questions. The questions contain problem descriptions, multiple programming languages, and explanations (834 questions do not have explanations). ## Support this project Your contributions and feedback support the open source ecosystem, improve the bot and provide datasets for future AI research. To participate you can: Submit Github issues, track issues and help create datasets that need improvement. https://github.com/BAAI-Zlab/COIG ## Update: May 27, 2023 - v0.3: Update counterfactural_correction_multi_round_chat.tar.gz and make sure all round responses can be decoded as json. - v0.2: Update exam_instructions.jsonl, translated_instructions.jsonl and human_value_alignment_instructions_part2.json. - v0.1: Release the five datasets of COIG. ## Disclaimer These datasets contain synthetic data and in some cases data that includes humans trying to get the language model to say toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to reduce or eliminate undesirable content from the instruction tuning datasets. ## License The COIG dataset that is authored by BAAI is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses such as unnatural instructions data which is licensed under MIT License, or web-crawled data which is used under fair use principles. ## BibTeX & Citation ``` @misc{zhang2023chinese, title={Chinese Open Instruction Generalist: A Preliminary Release}, author={Ge Zhang and Yemin Shi and Ruibo Liu and Ruibin Yuan and Yizhi Li and Siwei Dong and Yu Shu and Zhaoqun Li and Zekun Wang and Chenghua Lin and Wenhao Huang and Jie Fu}, year={2023}, eprint={2304.07987}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
6,642
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pietrolesci/pubmed-20k-rct
2023-05-12T10:04:08.000Z
[ "task_categories:text-classification", "language:en", "region:us" ]
pietrolesci
null
null
0
25
2023-05-11T18:28:35
--- task_categories: - text-classification language: - en dataset_info: features: - name: abstract_id dtype: string - name: labels dtype: class_label: names: '0': background '1': conclusions '2': methods '3': objective '4': results - name: text dtype: string - name: sentence_id dtype: int64 - name: uid dtype: int64 - name: embedding_all-mpnet-base-v2 sequence: float32 - name: embedding_multi-qa-mpnet-base-dot-v1 sequence: float32 - name: embedding_all-MiniLM-L12-v2 sequence: float32 splits: - name: train num_bytes: 1392522399 num_examples: 176642 - name: validation num_bytes: 233905609 num_examples: 29672 - name: test num_bytes: 233146005 num_examples: 29578 download_size: 0 dataset_size: 1859574013 --- This is the same dataset as [`armanc/pubmed-rct20k`](https://huggingface.co/datasets/armanc/pubmed-rct20k). 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
1,331
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silk-road/Wizard-LM-Chinese-instruct-evol
2023-05-15T00:13:52.000Z
[ "task_categories:text-generation", "task_categories:question-answering", "size_categories:10K<n<100K", "language:zh", "language:en", "license:cc-by-4.0", "region:us" ]
silk-road
null
null
59
25
2023-05-15T00:04:30
--- license: cc-by-4.0 task_categories: - text-generation - question-answering language: - zh - en size_categories: - 10K<n<100K --- Wizard-LM-Chinese是在MSRA的Wizard-LM数据集上，对指令进行翻译，然后再调用GPT获得答案的数据集 Wizard-LM包含了很多难度超过Alpaca的指令。 中文的问题翻译会有少量指令注入导致翻译失败的情况 中文回答是根据中文问题再进行问询得到的。 我们会陆续将更多数据集发布到hf，包括 - [ ] Coco Caption的中文翻译 - [ ] CoQA的中文翻译 - [ ] CNewSum的Embedding数据 - [ ] 增广的开放QA数据 - [x] WizardLM的中文翻译 如果你也在做这些数据集的筹备，欢迎来联系我们，避免重复花钱。 # 骆驼(Luotuo): 开源中文大语言模型 [https://github.com/LC1332/Luotuo-Chinese-LLM](https://github.com/LC1332/Luotuo-Chinese-LLM) 骆驼(Luotuo)项目是由[冷子昂](https://blairleng.github.io) @ 商汤科技, 陈启源 @ 华中师范大学 以及 李鲁鲁 @ 商汤科技 发起的中文大语言模型开源项目，包含了一系列语言模型。 ( 注意: [陈启源](https://qiyuan-chen.github.io/) 正在寻找2024推免导师，欢迎联系 ) 骆驼项目**不是**商汤科技的官方产品。 ## Citation Please cite the repo if you use the data or code in this repo. ``` @misc{alpaca, author={Ziang Leng, Qiyuan Chen and Cheng Li}, title = {Luotuo: An Instruction-following Chinese Language model, LoRA tuning on LLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/LC1332/Luotuo-Chinese-LLM}}, } ```
1,139
[ [ -0.026153564453125, -0.0684814453125, 0.00750732421875, 0.0247955322265625, -0.0177154541015625, -0.01442718505859375, -0.00582122802734375, -0.03192138671875, 0.040740966796875, 0.0391845703125, -0.032470703125, -0.04766845703125, -0.0247344970703125, 0.008...
argilla/comparison-dataset-dolly-curated-falcon
2023-06-02T14:52:40.000Z
[ "region:us" ]
argilla
null
null
0
25
2023-06-02T11:51:48
--- {} --- ## Guidelines These guidelines are based on the paper [Training Language Models to Follow Instructions with Human Feedback](https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf) **You are given a text-based description of a task, submitted by a user.** This task description may be in the form of an explicit instruction (e.g. "Write a story about a wise frog."). The task may also be specified indirectly, for example by using several examples of the desired behavior (e.g. given a sequence of movie reviews followed by their sentiment, followed by one movie review without a sentiment, you can assume the task is to predict the sentiment of the final review), or by generating the start of a desired output (e.g. given "There once was a wise frog named Julius," you can assume the task is to continue the story). **You will also be given several text outputs, intended to help the user with their task.** Your job is to evaluate these outputs to ensure that they are helpful, truthful, and harmless. For most tasks, being truthful and harmless is more important than being helpful. By **helpful**, we mean that the output should follow the user's intention and help the user solve their task. Some examples of helpful behavior: - Writing in clear language. - Answering the question they meant to ask, even if they mis-asked it. - Being sensitive to internationality (e.g., "football" shouldn't mean American football, and "the president" doesn't necessarily mean the US president). - If an instruction is too confusing, asking for clarification and explaining why the instruction is confusing. - Not giving overly long or rambling answers, or repeating information from the question. - Not assuming extraneous extra context outside of what's given (besides things like facts about the world), unless that's an implied part of the task. For example, if asked to "write a polite email response to this email: email body," the output shouldn't assume "I can't make it this time, but am free next weekend." But if asked to "Write an email to Socrates," then you can safely assume the output can make up a context. By **truthful**, we mean that the output contains accurate information and doesn't mislead the user. Some examples of truthful behavior: - On tasks like summarization, where the output should only use information from input, not making up details that are not part of the input description. - Not producing clearly false information about the world (e.g., making up facts or promoting conspiracies). For example, the output should not state that Hillary Clinton has served time in prison. - Avoid generating misleading information or information with questionable authenticity. For example, if the input asks "Why did Hillary Clinton go to jail?", the output should not say "It's not totally clear," but rather should refute the premise of the question. By **harmless**, we mean that the output should not cause physical, psychological, or social harm to people; damage to or loss of equipment or property; damage to the environment; or harm to institutions or resources necessary to human well-being. Some examples of harmless behavior: - Treating other humans with kindness, respect, and consideration; not denigrating members of certain groups or using biased language against a particular group. - Not generating abusive, threatening, or offensive language or promoting violence. - Not writing sexual or violent content if it's not asked for. - Not giving bad real-world advice or promoting illegal activity. Evaluating model outputs may involve making trade-offs between these criteria. These trade-offs will depend on the task. ## Fields * `prompt` is of type <class 'str'> * `response-1` is of type <class 'str'> * `response-2` is of type <class 'str'> ## Questions * `response_ranking` : Select response 1 or 2. Select 3 if no response is suitable. \If you select 3, provide a response using the field below, or discard the record. Helpful: output follows the user's intention. Truthful: output contains accurate information and doesn't mislead the user. Harmless: the output should not cause physical, psychological, or social harm to people, property, environment, or institutions ## Load with Argilla To load this dataset with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface('argilla/comparison-dataset-dolly-curated-falcon') ``` ## Load with Datasets To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset('argilla/comparison-dataset-dolly-curated-falcon') ```
4,870
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Nadav/pixel_glue_sst2
2023-06-12T11:08:22.000Z
[ "region:us" ]
Nadav
null
null
0
25
2023-06-07T23:18:44
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 404363205.375 num_examples: 67349 - name: validation num_bytes: 7130426.0 num_examples: 872 download_size: 348047558 dataset_size: 411493631.375 --- # Dataset Card for "pixel_glue_sst2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
539
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skeskinen/books3_basic_paragraphs
2023-06-14T12:55:02.000Z
[ "region:us" ]
skeskinen
null
null
0
25
2023-06-12T05:47:39
--- dataset_info: features: - name: text dtype: string - name: book dtype: string - name: pos dtype: float64 - name: smog_index dtype: float64 splits: - name: train num_bytes: 1366299770 num_examples: 6639751 download_size: 676098743 dataset_size: 1366299770 --- # Dataset Card for "books3_basic_paragraphs" the_pile books3, books with smog grade difficulty estimate of 6.5 or under. Split into paragraphs and filtered out most 'non-paragraphs' like titles, tables of content, etc.
524
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bias-amplified-splits/mnli
2023-07-04T11:48:21.000Z
[ "task_categories:text-classification", "size_categories:100K<n<1M", "language:en", "license:cc-by-4.0", "arxiv:2305.18917", "arxiv:1704.05426", "region:us" ]
bias-amplified-splits
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
@inproceedings{wang2019glue, title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.}, note={In the Proceedings of ICLR.}, year={2019} }
0
25
2023-07-03T19:32:08
--- license: cc-by-4.0 dataset_info: - config_name: minority_examples features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 splits: - name: train.biased num_bytes: 58497575 num_examples: 309873 - name: train.anti_biased num_bytes: 16122071 num_examples: 82829 - name: validation_matched.biased num_bytes: 1443678 num_examples: 7771 - name: validation_matched.anti_biased num_bytes: 390105 num_examples: 2044 - name: validation_mismatched.biased num_bytes: 1536381 num_examples: 7797 - name: validation_mismatched.anti_biased num_bytes: 412850 num_examples: 2035 download_size: 92308759 dataset_size: 78402660 - config_name: partial_input features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 splits: - name: train.biased num_bytes: 59529986 num_examples: 309873 - name: train.anti_biased num_bytes: 15089660 num_examples: 82829 - name: validation_matched.biased num_bytes: 1445996 num_examples: 7745 - name: validation_matched.anti_biased num_bytes: 387787 num_examples: 2070 - name: validation_mismatched.biased num_bytes: 1529878 num_examples: 7758 - name: validation_mismatched.anti_biased num_bytes: 419353 num_examples: 2074 download_size: 92308759 dataset_size: 78402660 task_categories: - text-classification language: - en pretty_name: MultiNLI size_categories: - 100K<n<1M --- # Dataset Card for Bias-amplified Splits for MultiNLI ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Annotations](#annotations) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Citation Information](#citation-information) ## Dataset Description - **Repository:** [Fighting Bias with Bias repo](https://github.com/schwartz-lab-nlp/fight-bias-with-bias) - **Paper:** [arXiv](https://arxiv.org/abs/2305.18917) - **Point of Contact:** [Yuval Reif](mailto:yuval.reif@mail.huji.ac.il) - **Original Dataset's Paper:** [MultiNLI](https://arxiv.org/abs/1704.05426) ### Dataset Summary Bias-amplified splits is a novel evaluation framework to assess model robustness, by amplifying dataset biases in the training data and challenging models to generalize beyond them. This framework is defined by a bias-amplified training set and a hard, anti-biased test set, which we automatically extract from existing datasets using model-based methods. Our experiments show that the identified anti-biased examples are naturally challenging for models, and moreover, models trained on bias-amplified data exhibit dramatic performance drops on anti-biased examples, which are not mitigated by common approaches to improve generalization. Here we apply our framework to **MultiNLI**, a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. Our evaluation framework can be applied to any existing dataset, even those considered obsolete, to test model robustness. We hope our work will guide the development of robust models that do not rely on superficial biases and correlations. #### Evaluation Results (DeBERTa-large) ##### For splits based on minority examples: | Training Data \ Test Data | Original test | Anti-biased test | |---------------------------|---------------|------------------| | Original training split | 91.1 | 74.3 | | Biased training split | 88.7 | 57.5 | ##### For splits based on partial-input model: | Training Data \ Test Data | Original test | Anti-biased test | |---------------------------|---------------|------------------| | Original training split | 91.1 | 81.4 | | Biased training split | 89.5 | 71.8 | #### Loading the Data ``` from datasets import load_dataset # choose which bias detection method to use for the bias-amplified splits: either "minority_examples" or "partial_input" dataset = load_dataset("bias-amplified-splits/mnli", "minority_examples") # use the biased training split and anti-biased test split train_dataset = dataset['train.biased'] eval_dataset = dataset['validation_matched.anti_biased'] ``` ## Dataset Structure ### Data Instances Data instances are taken directly from MultiNLI (GLUE version), and re-split into biased and anti-biased subsets. Here is an example of an instance from the dataset: ``` { "idx": 0, "premise": "Your contribution helped make it possible for us to provide our students with a quality education.", "hypothesis": "Your contributions were of no help with our students' education.", "label": 2 } ``` ### Data Fields - `idx`: unique identifier for the example within its original data splits (e.g., validation matched) - `premise`: a piece of text - `hypothesis`: a piece of text that may be true, false, or whose truth conditions may not be knowable when compared to the premise - `label`: one of `0`, `1` and `2` (`entailment`, `neutral`, and `contradiction`) ### Data Splits Bias-amplified splits require a method to detect *biased* and *anti-biased* examples in datasets. We release bias-amplified splits based created with each of these two methods: - **Minority examples**: A novel method we introduce that leverages representation learning and clustering for identifying anti-biased *minority examples* (Tu et al., 2020)—examples that defy common statistical patterns found in the rest of the dataset. - **Partial-input baselines**: A common method for identifying biased examples containing annotation artifacts in a dataset, which examines the performance of models that are restricted to using only part of the input. Such models, if successful, are bound to rely on unintended or spurious patterns in the dataset. Using each of the two methods, we split each of the original train and test splits into biased and anti-biased subsets. See the [paper](https://arxiv.org/abs/2305.18917) for more details. #### Minority Examples | Dataset Split | Number of Instances in Split | |-------------------------------------|------------------------------| | Train - biased | 309873 | | Train - anti-biased | 82829 | | Validation matched - biased | 7771 | | Validation matched - anti-biased | 2044 | | Validation mismatched - biased | 7797 | | Validation mismatched - anti-biased | 2035 | #### Partial-input Baselines | Dataset Split | Number of Instances in Split | |-------------------------------------|------------------------------| | Train - biased | 309873 | | Train - anti-biased | 82829 | | Validation matched - biased | 7745 | | Validation matched - anti-biased | 2070 | | Validation mismatched - biased | 7758 | | Validation mismatched - anti-biased | 2074 | ## Dataset Creation ### Curation Rationale NLP models often rely on superficial cues known as *dataset biases* to achieve impressive performance, and can fail on examples where these biases do not hold. To develop more robust, unbiased models, recent work aims to filter bisased examples from training sets. We argue that in order to encourage the development of robust models, we should in fact **amplify** biases in the training sets, while adopting the challenge set approach and making test sets anti-biased. To implement our approach, we introduce a simple framework that can be applied automatically to any existing dataset to use it for testing model robustness. ### Annotations #### Annotation process No new annotations are required to create bias-amplified splits. Existing data instances are split into *biased* and *anti-biased* splits based on automatic model-based methods to detect such examples. ## Considerations for Using the Data ### Social Impact of Dataset Bias-amplified splits were created to promote the development of robust NLP models that do not rely on superficial biases and correlations, and provide more challenging evaluation of existing systems. ### Discussion of Biases We propose to use bias-amplified splits to complement benchmarks with challenging evaluation settings that test model robustness, in addition to the dataset’s main training and test sets. As such, while existing dataset biases are *amplified* during training with bias-amplified splits, these splits are intended primarily for model evaluation, to expose the bias-exploiting behaviors of models and to identify more robsut models and effective robustness interventions. ## Additional Information ### Dataset Curators Bias-amplified splits were introduced by Yuval Reif and Roy Schwartz from the [Hebrew University of Jerusalem](https://schwartz-lab-huji.github.io). MultiNLI was developed by Adina Williams, Nikita Nangia and Samuel Bowman. ### Citation Information ``` @misc{reif2023fighting, title = "Fighting Bias with Bias: Promoting Model Robustness by Amplifying Dataset Biases", author = "Yuval Reif and Roy Schwartz", month = may, year = "2023", url = "https://arxiv.org/pdf/2305.18917", } ``` Source dataset: ``` @InProceedings{N18-1101, author = "Williams, Adina and Nangia, Nikita and Bowman, Samuel", title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference", booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)", year = "2018", publisher = "Association for Computational Linguistics", pages = "1112--1122", location = "New Orleans, Louisiana", url = "http://aclweb.org/anthology/N18-1101" } ```
10,953
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talby/spamassassin
2023-07-11T18:36:22.000Z
[ "license:unknown", "region:us" ]
talby
Welcome to the SpamAssassin public mail corpus. This is a selection of mail messages, suitable for use in testing spam filtering systems. Pertinent points: - All headers are reproduced in full. Some address obfuscation has taken place, and hostnames in some cases have been replaced with "spamassassin.taint.org" (which has a valid MX record). In most cases though, the headers appear as they were received. - All of these messages were posted to public fora, were sent to me in the knowledge that they may be made public, were sent by me, or originated as newsletters from public news web sites. - relying on data from public networked blacklists like DNSBLs, Razor, DCC or Pyzor for identification of these messages is not recommended, as a previous downloader of this corpus might have reported them! - Copyright for the text in the messages remains with the original senders. OK, now onto the corpus description. It's split into three parts, as follows: - spam: 500 spam messages, all received from non-spam-trap sources. - easy_ham: 2500 non-spam messages. These are typically quite easy to differentiate from spam, since they frequently do not contain any spammish signatures (like HTML etc). - hard_ham: 250 non-spam messages which are closer in many respects to typical spam: use of HTML, unusual HTML markup, coloured text, "spammish-sounding" phrases etc. - easy_ham_2: 1400 non-spam messages. A more recent addition to the set. - spam_2: 1397 spam messages. Again, more recent. Total count: 6047 messages, with about a 31% spam ratio.
null
0
25
2023-07-10T17:59:18
--- license: unknown --- # Dataset Card for the SpamAssassin public mail corpus ## Dataset Description - **Homepage:** https://spamassassin.apache.org/old/publiccorpus/readme.html ### Dataset Summary This is a selection of mail messages, suitable for use in testing spam filtering systems assembled by members of the SpamAssassin project. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances - The `text` config normalizes all character sets to utf8 and dumps the MIME tree as a JSON list of lists. - The `unprocessed` config does not parse messages at all, leaving the full headers and content as binary. ### Data Fields - `label`: `spam` or `ham` - `group`: SpamAssassin has grouped these samples into categories {'hard_ham', 'spam_2', 'spam', 'easy_ham', 'easy_ham_2'} - `text`: normalized text of the message bodies - `raw`: full binary headers and contents of messages ### Data Splits Only a _train_ split has been provided. ## Dataset Creation ### Curation Rationale It is hoped this dataset can help verify that modern NLP tools can solve old NLP problems. ### Source Data #### Initial Data Collection and Normalization [The upstream corpus description](https://spamassassin.apache.org/old/publiccorpus/readme.html) goes into detail on collection methods. The work here to recover text bodies is largely done with [email.parser](https://docs.python.org/3/library/email.parser.html) and [ftfy](https://pypi.org/project/ftfy/). #### 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]
2,258
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taesiri/arxiv_qa
2023-11-03T01:20:42.000Z
[ "task_categories:question-answering", "language:en", "license:mit", "arxiv:2311.00618", "arxiv:2311.00613", "arxiv:2311.00571", "arxiv:2311.00522", "arxiv:2311.00430", "arxiv:2311.00272", "arxiv:2311.00257", "arxiv:2311.00176", "arxiv:2311.00059", "arxiv:2311.00047", "arxiv:2310.20707", ...
taesiri
null
null
112
25
2023-07-11T16:14:06
--- license: mit task_categories: - question-answering language: - en pretty_name: ArXiv QA --- # ArXiv QA (TBD) Automated ArXiv question answering via large language models [Github](https://github.com/taesiri/ArXivQA) | [Homepage](https://arxiv.taesiri.xyz/) | [Simple QA - Hugging Face Space](https://huggingface.co/spaces/taesiri/ClaudeReadsArxiv) --- # Automated Question Answering with ArXiv Papers ## Latest 25 Papers - De-Diffusion Makes Text a Strong Cross-Modal Interface - [[Arxiv](https://arxiv.org/abs/2311.00618)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00618.md)] - Controllable Music Production with Diffusion Models and Guidance Gradients - [[Arxiv](https://arxiv.org/abs/2311.00613)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00613.md)] - LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing - [[Arxiv](https://arxiv.org/abs/2311.00571)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00571.md)] - Text Rendering Strategies for Pixel Language Models - [[Arxiv](https://arxiv.org/abs/2311.00522)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00522.md)] - Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling - [[Arxiv](https://arxiv.org/abs/2311.00430)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00430.md)] - ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation - [[Arxiv](https://arxiv.org/abs/2311.00272)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00272.md)] - AMSP: Super-Scaling LLM Training via Advanced Model States Partitioning - [[Arxiv](https://arxiv.org/abs/2311.00257)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00257.md)] - ChipNeMo: Domain-Adapted LLMs for Chip Design - [[Arxiv](https://arxiv.org/abs/2311.00176)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00176.md)] - The Generative AI Paradox: "What It Can Create, It May Not Understand" - [[Arxiv](https://arxiv.org/abs/2311.00059)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00059.md)] - Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans? - [[Arxiv](https://arxiv.org/abs/2311.00047)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2311.00047.md)] - What's In My Big Data? - [[Arxiv](https://arxiv.org/abs/2310.20707)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.20707.md)] - SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction - [[Arxiv](https://arxiv.org/abs/2310.20700)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.20700.md)] - Learning From Mistakes Makes LLM Better Reasoner - [[Arxiv](https://arxiv.org/abs/2310.20689)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.20689.md)] - LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B - [[Arxiv](https://arxiv.org/abs/2310.20624)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.20624.md)] - Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning - [[Arxiv](https://arxiv.org/abs/2310.20587)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.20587.md)] - CapsFusion: Rethinking Image-Text Data at Scale - [[Arxiv](https://arxiv.org/abs/2310.20550)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.20550.md)] - Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models - [[Arxiv](https://arxiv.org/abs/2310.20499)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.20499.md)] - Does GPT-4 Pass the Turing Test? - [[Arxiv](https://arxiv.org/abs/2310.20216)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.20216.md)] - Beyond U: Making Diffusion Models Faster &amp; Lighter - [[Arxiv](https://arxiv.org/abs/2310.20092)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.20092.md)] - The Impact of Depth and Width on Transformer Language Model Generalization - [[Arxiv](https://arxiv.org/abs/2310.19956)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.19956.md)] - Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks - [[Arxiv](https://arxiv.org/abs/2310.19909)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.19909.md)] - CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models - [[Arxiv](https://arxiv.org/abs/2310.19784)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.19784.md)] - MM-VID: Advancing Video Understanding with GPT-4V(ision) - [[Arxiv](https://arxiv.org/abs/2310.19773)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.19773.md)] - VideoCrafter1: Open Diffusion Models for High-Quality Video Generation - [[Arxiv](https://arxiv.org/abs/2310.19512)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.19512.md)] - Text-to-3D with classifier score distillation - [[Arxiv](https://arxiv.org/abs/2310.19415)] [[QA](https://github.com/taesiri/ArXivQA/blob/main/papers/2310.19415.md)] ## List of Papers by Year - [Papers for 2023](https://github.com/taesiri/ArXivQA/blob/main/Papers-2023.md) - [Papers for 2022](https://github.com/taesiri/ArXivQA/blob/main/Papers-2022.md) - [Papers for 2021](https://github.com/taesiri/ArXivQA/blob/main/Papers-2021.md) - [Papers for 2020](https://github.com/taesiri/ArXivQA/blob/main/Papers-2020.md) - [Papers for 2019](https://github.com/taesiri/ArXivQA/blob/main/Papers-2019.md) - [Papers for 2018](https://github.com/taesiri/ArXivQA/blob/main/Papers-2018.md) - [Papers for 2017](https://github.com/taesiri/ArXivQA/blob/main/Papers-2017.md) - [Papers for 2016](https://github.com/taesiri/ArXivQA/blob/main/Papers-2016.md) - [Papers for 2015](https://github.com/taesiri/ArXivQA/blob/main/Papers-2015.md) - [Papers for 2014](https://github.com/taesiri/ArXivQA/blob/main/Papers-2014.md) - [Papers for 2013](https://github.com/taesiri/ArXivQA/blob/main/Papers-2013.md) - [Papers for 2012](https://github.com/taesiri/ArXivQA/blob/main/Papers-2012.md) - [Papers for 2010](https://github.com/taesiri/ArXivQA/blob/main/Papers-2010.md) - [Papers for 2009](https://github.com/taesiri/ArXivQA/blob/main/Papers-2009.md) ## Acknowledgements This project is made possible through the generous support of [Anthropic](https://www.anthropic.com/), who provided free access to the `Claude-2.0` API.
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jjonhwa/V3
2023-07-13T06:09:28.000Z
[ "region:us" ]
jjonhwa
null
null
0
25
2023-07-13T06:06:47
--- dataset_info: features: - name: context dtype: string - name: question dtype: string splits: - name: train num_bytes: 1063468075 num_examples: 571983 download_size: 164853265 dataset_size: 1063468075 --- # Dataset Card for "V3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
394
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BigSuperbPrivate/SpeakerVerification_Aishell1Train
2023-07-17T18:20:09.000Z
[ "region:us" ]
BigSuperbPrivate
null
null
0
25
2023-07-13T17:53:26
--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: file2 dtype: string - name: instruction dtype: string - name: label dtype: string splits: - name: train num_bytes: 17452497240.0 num_examples: 120418 - name: validation num_bytes: 2087682679.0 num_examples: 14331 download_size: 19207085144 dataset_size: 19540179919.0 --- # Dataset Card for "SpeakerVerification_AISHELL1Train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
607
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refugee-law-lab/canadian-legal-data
2023-07-30T22:47:52.000Z
[ "size_categories:100K<n<1M", "language:en", "language:fr", "license:cc-by-nc-4.0", "arxiv:2207.00220", "region:us" ]
refugee-law-lab
null
null
0
25
2023-07-16T02:12:31
--- license: cc-by-nc-4.0 language: - en - fr size_categories: - 100K<n<1M --- # Refugee Law Lab: Canadian Legal Data ## Dataset Summary The [Refugee Law Lab](https://refugeelab.ca) supports bulk open-access to Canadian legal data to facilitate research and advocacy. Bulk open-access helps avoid asymmetrical access-to-justice and amplification of marginalization that results when commercial actors leverage proprietary legal datasets for profit -- a particular concern in the border control setting. The Canadian Legal Data dataset includes the unofficial full text of thousands of court and tribunal decisions at the federal level. It can be used for legal analytics (i.e. identifying patterns in legal decision-making), to test ML and NLP tools on a bilingual dataset of Canadian legal materials, and to pretrain language models for various tasks. ## Dataset Structure ### Data Instances #### Court Decisions - SCC: Full text of Supreme Court of Canada decisions, based on the Refugee Law Lab's [Supreme Court of Canada Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/scc/) (1877 – 2023) - FCA: Full text of Federal Court of Appeal (Canada) decisions that have been given a neutral citation, based on the Refugee Law Lab's [Federal Court of Appeal Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/fca/) (2001-2023) - FC: Full text of Federal Court (Canada) decisions that have been given a neutral citation, based on the Refugee Law Lab's [Federal Court Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/fc/) (2001-2023) - TCC: Full text of Tax Court of Canada decisions that have been given a neutral citation, based on the Refugee Law Lab's [Tax Court of Canada Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/tcc/) (2003-2023) #### Tribunal Decisions - RLLR: Full text of Immigration and Refugee Board, Refugee Protection Division Decisions, as reported in the [Refugee Law Lab Reporter](https://refugeelab.ca/rllr), based on the Refugee Law Lab's [RLLR Bulk Decisions Dataset](https://refugeelab.ca/bulk-data/rllr/) (2019 – 2022) ### Data Fields - citation1 (string): Legal citation for the document (neutral citation where available) - citation2 (string): For some documents multiple citations are available (e.g. for some periods the Supreme Court of Canada provided both official reported citation and neutral citation) - dataset (string): Name of the data instance (e.g. "SCC", "FCA", "FC", "TCC", etc) - year (int32): Year of the document date, which can be useful for filtering - name (string): Name of the document, typically the style of cause of a case - language (string): Language of the document, "en" for English, "fr" for French, "" for no language specified - document_date (string): Date of the document, typically the date of a decision (yyyy-mm-dd) - source_url (string): URL where the document was scraped and where the official version can be found - scraped_timestamp (string): Date the document was scraped (yyyy-mm-dd) - unofficial_text (string): Full text of the document (unofficial version, for official version see source_url) - other (string): Field for additional metadata in JSON format, currently a blank string for most datasets ### Data Languages Many documents are available in both English and French. Some are only available in one of the two languages. ### Data Splits The data has not been split, so all files are in the train split. If splitting for training/validation, some thought should be given to whether it is necessary to limit to one language or to ensure that both English and French versions of the same documents (where available) are put into the same split. ### Data Loading To load all data instances: ```python from datasets import load_dataset dataset = load_dataset("refugee-law-lab/canadian-legal-data", split="train") ``` To load only a specific data instance, for example only the SCC data instance: ```python from datasets import load_dataset dataset = load_dataset("refugee-law-lab/canadian-legal-data", split="train", data_dir="SCC") ``` ## Dataset Creation ### Curation Rationale The dataset includes all the [Bulk Legal Data](https://refugeelab.ca/bulk-data) made publicly available by the Refugee Law Lab. The Lab has focused on federal courts (e.g. Supreme Court of Canada, Federal Court of Appeal, Federal Court) as well as federal administrative tribunals (e.g. Immigration and Refugee Board) because immigration and refugee law, which is the main area of interest of the Lab, operates mostly at the federal level. ### Source Data #### Initial Data Collection and Normalization Details (including links to github repos with code) are available via links on the Refugee Law Lab's [Bulk Legal Data](https://refugeelab.ca/bulk-data/) page. ### Personal and Sensitive Information Documents may include personal and sensitive information. All documents have been published online or otherwise released publicly by the relevant court or tribunal. While the open court principle mandates that court (and some tribunal) materials be made available to the public, there are privacy risks when these materials become easily and widely available. These privacy risks are particularly acute for marginalized groups, including refugees and other non-citizens whose personal and sensitive information is included in some of the documents in this dataset. For example, imagine a repressive government working with private data aggregators to collect information that is used to target families of political opponents who have sought asylum abroad. One mechanism used to try to achieve a balance between the open court principle and privacy is that in publishing the documents in this dataset, the relevant courts and tribunals prohibit search engines from indexing the documents. Users of this data are required to do the same. ### Non-Official Versions Documents included in this dataset are unofficial copies. For official versions published by the Government of Canada, please see the source URLs. ### Non-Affiliation / Endorsement The reproduction of documents in this dataset was not done in affiliation with, or with the endorsement of the Government of Canada. ## Considerations for Using the Data ### Social Impact of Dataset The Refugee Law Lab recognizes that this dataset -- and further research using the dataset -- raises challenging questions about how to balance protecting privacy, enhancing government transparency, addressing information asymmetries, and building technologies that leverage data to advance the rights and interests of refugees and other displaced people, as well as assisting those working with them (rather than technologies that [enhance the power of states](https://citizenlab.ca/2018/09/bots-at-the-gate-human-rights-analysis-automated-decision-making-in-canadas-immigration-refugee-system/) to control the movement of people across borders). More broadly, the Refugee Law Lab also recognizes that considerations around privacy and data protection are complex and evolving. When working on migration, refugee law, data, technology and surveillance, we strive to foreground intersectional understandings of the systemic harms perpetuated against groups historically made marginalized. We encourage other users to do the same. We also encourage users to try to avoid participating in building technologies that harm refugees and other marginalized groups, as well as to connect with [community organizations](https://www.migrationtechmonitor.com/ways-to-help) working in this space, and to [listen directly](https://www.migrationtechmonitor.com/about-us) and learn from people who are affected by new technologies. We will review the use these datasets periodically to examine whether continuing to publicly release these datasets achieves the Refugee Law Lab's goals of advancing the rights and interests of refugees and other marginalized groups without creating disproportionate risks and harms, including risks related to privacy and human rights. ### Discussion of Biases The dataset reflects many biases present in legal decision-making, including biases based on race, immigration status, gender, sexual orientation, religion, disability, socio-economic class, and other intersecting categories of discrimination. ### Other Known Limitations Publicly available court and tribunal decisions are not a representative sample of legal decision-making -- and in some cases may reflect significantly skewed samples. To give one example, the vast majority of Federal Court judicial reviews of refugee determinations involve negative first instance decisions even thought most first instance decisions are positive (this occurs because the government seldom applies for judicial reviews of positive first instance decisions whereas claimants frequently apply for judicial review of negative decisions). As such, generative models built partly on this dataset risk amplifying negative refugee decision-making (rather than more common positive refugee decision-making). Due to the ways that legal datasets may be skewed, users of this dataset are encouraged to collaborate with or consult domain experts. ## Additional Information ### Licensing Information Attribution-NonCommercial 4.0 International ([CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)) NOTE: Users must also comply with upstream licensing for the [SCC](https://www.scc-csc.ca/terms-avis/notice-enonce-eng.aspx), [FCA](https://www.fca-caf.gc.ca/en/pages/important-notices) & [FC](https://www.fct-cf.gc.ca/en/pages/important-notices) data instances, as well as requests on source urls not to allow indexing of the documents by search engines to protect privacy. As a result, users must not make the data available in formats or locations that can be indexed by search engines. ### Warranties / Representations We make no warranties or representations that the data included in this dataset is complete or accurate. Data were obtained through academic research projects, including projects that use automated processes. While we try to make the data as accurate as possible, our methodologies may result in inaccurate or outdated data. As such, data should be viewed as preliminary information aimed to prompt further research and discussion, rather than as definitive information. ### Dataset Curators [Sean Rehaag](https://www.osgoode.yorku.ca/faculty-and-staff/rehaag-sean), Osgoode Hall Law School Professor & Director of the Refugee Law Lab ### Citation Information Sean Rehaag, "Refugee Law Lab: Canadian Legal Data" (2023) online: Hugging Face: <https://huggingface.co/datasets/refugee-law-lab/canadian-legal-data>. ### Acknowledgements This project draws on research supported by the Social Sciences and Humanities Research Council and the Law Foundation of Ontario. The project was inspired in part by the excellent prior work by [pile-of-law](https://huggingface.co/datasets/pile-of-law/pile-of-law) (Peter Henderson et al, "Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset" (2022), online: arXiv: https://arxiv.org/abs/2207.00220).
11,226
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rusheeliyer/uk-abs
2023-08-11T16:40:28.000Z
[ "region:us" ]
rusheeliyer
null
null
0
25
2023-08-11T16:10:46
--- dataset_info: features: - name: judgement dtype: string - name: summary dtype: string splits: - name: train num_bytes: 52800141 num_examples: 589 - name: test num_bytes: 8174530 num_examples: 100 - name: validation num_bytes: 10432092 num_examples: 104 download_size: 32973908 dataset_size: 71406763 --- # Dataset Card for "uk-abs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
518
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sartmis1/text2sql-spider
2023-08-18T04:01:41.000Z
[ "region:us" ]
sartmis1
null
null
0
25
2023-08-18T04:00:10
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 1319601 num_examples: 7000 download_size: 339745 dataset_size: 1319601 --- # Dataset Card for "text2sql-spider-processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
490
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LawChat-tw/PT
2023-08-24T03:49:38.000Z
[ "region:us" ]
LawChat-tw
null
null
0
25
2023-08-24T03:44:56
Entry not found
15
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Flmc/DISC-Med-SFT
2023-08-29T12:54:14.000Z
[ "task_categories:question-answering", "task_categories:conversational", "size_categories:100K<n<1M", "language:zh", "license:apache-2.0", "medical", "region:us" ]
Flmc
null
null
37
25
2023-08-29T10:20:50
--- license: apache-2.0 task_categories: - question-answering - conversational language: - zh tags: - medical size_categories: - 100K<n<1M --- This is a repository containing a subset of the DISC-Med-SFT Dataset. Check [DISC-MedLLM](https://github.com/FudanDISC/DISC-MedLLM) for more information.
298
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zelros/insurance-fr
2023-10-21T13:19:38.000Z
[ "insurance", "region:us" ]
zelros
null
null
0
25
2023-09-01T07:16:26
--- tags: - insurance --- This dataset contains question/answer pairs from a French home insurance (MRH: Multi-Risk Home Insurance). It comes from structuring the following open sources: - https://www.mma.fr/assurance-habitation.html - https://cap.mma.fr/files/live/sites/mmafr/files/documents-cg/cg410/Habitation_MMA_410p.pdf The objective of this dataset is to contribute to open source research projects aiming to, for instance: * fine-tune LLMs on high-quality datasets, specializing them in the insurance domain * develop new question/answer applications using Retrieval Augmented Generation (RAG) for insurance contracts * assess the knowledge of language models in the insurance field * more generally, apply LLMs to the insurance domain for better understanding and increased transparency of this industry. Other datasets of the same kind (but on other types of insurance, other languages, or from different sources) are also available - or will be available soon - and are part of this research effort.
1,015
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mHossain/end_bn_summary_v1
2023-09-04T11:43:51.000Z
[ "region:us" ]
mHossain
null
null
0
25
2023-09-04T11:42:38
Entry not found
15
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Tristan/flickr30k_test
2023-09-04T22:36:06.000Z
[ "region:us" ]
Tristan
null
null
0
25
2023-09-04T22:34:11
--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: caption list: string - name: sentids list: string - name: split dtype: string - name: img_id dtype: string - name: filename dtype: string splits: - name: test num_bytes: 142117238.54065907 num_examples: 1000 download_size: 141466584 dataset_size: 142117238.54065907 --- # Dataset Card for "flickr30k_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
639
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maximegmd/medmcqa_alpaca_format
2023-09-12T11:29:11.000Z
[ "region:us" ]
maximegmd
null
null
0
25
2023-09-12T11:28:37
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: solution dtype: string splits: - name: train num_bytes: 120644997 num_examples: 182822 - name: test num_bytes: 1077057 num_examples: 6150 - name: validation num_bytes: 2009220 num_examples: 4183 download_size: 79503290 dataset_size: 123731274 --- # Dataset Card for "medmcqa_alpaca_format" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
752
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skaltenp/textworld_turn_top_demonstrations
2023-09-18T10:06:22.000Z
[ "region:us" ]
skaltenp
null
null
0
25
2023-09-18T10:06:09
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: demonstration sequence: sequence: string - name: moves dtype: int64 - name: score dtype: int64 splits: - name: train num_bytes: 16265368 num_examples: 4440 - name: valid num_bytes: 787252 num_examples: 222 - name: test num_bytes: 2640791 num_examples: 514 download_size: 3417454 dataset_size: 19693411 --- # Dataset Card for "textworld_turn_top_demonstrations" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
786
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backblaze/Drive_Stats
2023-10-05T04:46:26.000Z
[ "annotations_creators:machine-generated", "size_categories:100M<n<1B", "license:other", "region:us" ]
backblaze
null
null
0
25
2023-09-20T20:51:43
--- license: - other license_details: 'https://www.backblaze.com/cloud-storage/resources/hard-drive-test-data#howYouCanUseTheData' annotations_creators: - 'machine-generated' pretty_name: 'Drive Stats' size_categories: - '100M<n<1B' --- # Drive Stats [**Drive Stats**](https://www.backblaze.com/cloud-storage/resources/hard-drive-test-data) is a public data set of daily metrics on the hard drives in Backblaze’s [cloud storage infrastructure](https://www.backblaze.com/cloud-storage) that Backblaze has open-sourced since April 2013. Currently, Drive Stats comprises over 388 million records, rising by over 240,000 records per day. Drive Stats is an append-only dataset effectively logging daily statistics that once written are never updated or deleted. This is our first Hugging Face dataset; feel free to suggest improvements by creating a new discussion on the [Community](https://huggingface.co/datasets/backblaze/Drive_Stats/discussions)! ## Drive Stats Q2 2023 Snapshot * Drive Count: 240,940 * Drive Failures: 1,339 * Drive Days: 21.1M * Annualized Failure Rate: 2.28% ## Overview of the Hard Drive Data Each day in the Backblaze data center, we take a snapshot of each operational hard drive. This snapshot includes basic drive information along with the S.M.A.R.T. statistics reported by that drive. The daily snapshot of one drive is one record or row of data. All of the drive snapshots for a given day are collected into a file consisting of a row for each active hard drive. The format of this file is a "csv" (Comma Separated Values) file. Each day this file is named in the format YYYY-MM-DD.csv, for example, 2013-04-10.csv. The first row of the each file contains the column names, the remaining rows are the actual data. The columns are as follows: * Date – The date of the snapshot in yyyy-mm-dd format. * Serial Number – The manufacturer-assigned serial number of the drive. * Model – The manufacturer-assigned model number of the drive. * Capacity – The drive capacity in bytes. * Failure – Contains a “0” if the drive is OK. Contains a “1” if this is the last day the drive was operational before failing. * SMART Stats: * 2013-2014: 80 columns of data, that are the Raw and Normalized values for 40 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2015-2017: 90 columns of data, that are the Raw and Normalized values for 45 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2018 (Q1): 100 columns of data, that are the Raw and Normalized values for 50 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2018 (Q2): 104 columns of data, that are the Raw and Normalized values for 52 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2018 (Q4): 124 columns of data, that are the Raw and Normalized values for 62 different SMART stats as reported by the given drive. Each value is the number reported by the drive. ## Helpful Hints and Caveats ### Schema Changes The schema may change from quarter to quarter. The basic information: date, serial_number, model, capacity_bytes, and failure will not change. All of the changes will be in the number of SMART attributes reported for all of the drives in a given quarter. There will never be more than 255 pair of SMART attributes reported. When you load the CSV files for each quarter you will need to account for the potential of a different number of SMART attributes from the previous quarter. ## How You Can Use the Data You can download and use this data for free for your own purpose, all we ask is three things: * you cite Backblaze as the source if you use the data, * you accept that you are solely responsible for how you use the data, and * you do not sell this data to anyone, it is free.
3,921
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hasangoni/Electron_microscopy_dataset
2023-09-25T07:57:56.000Z
[ "task_categories:image-segmentation", "size_categories:10K<n<100K", "language:en", "microscopy", "EPFL", "image segmentation", "region:us" ]
hasangoni
null
null
0
25
2023-09-22T16:54:18
--- task_categories: - image-segmentation language: - en tags: - microscopy - EPFL - image segmentation pretty_name: electron microscopy patch image size_categories: - 10K<n<100K --- The dataset: - Is a patch from the existing dataset available at https://www.epfl.ch/labs/cvlab/data/data-em/. - Contains patches of size (256, 256). - Removes any patches with empty masks to ensure quality. - Has the same license applied as the original dataset. - Please refer to the license for information on allowed usage. - If you have any questions or concerns about the dataset, please do not hesitate to contact me.
608
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Tural/processed_bert_dataset
2023-10-04T23:04:01.000Z
[ "region:us" ]
Tural
null
null
0
25
2023-10-04T22:54:22
--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 24943076880 num_examples: 27349865 download_size: 5901536405 dataset_size: 24943076880 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "processed_bert_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
609
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ContextualAI/nq_open
2023-10-07T00:34:08.000Z
[ "region:us" ]
ContextualAI
null
null
0
25
2023-10-07T00:33:44
--- dataset_info: features: - name: query dtype: string - name: gold_generation sequence: string splits: - name: train num_bytes: 5990520 num_examples: 79168 - name: dev num_bytes: 660716 num_examples: 8757 - name: test num_bytes: 313829 num_examples: 3610 download_size: 4681299 dataset_size: 6965065 --- # Dataset Card for "nq_open" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
517
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PocketDoc/Choose-Your-Story-Long-Text-Adventures
2023-10-16T04:39:05.000Z
[ "task_categories:conversational", "language:en", "not-for-all-audiences", "region:us" ]
PocketDoc
null
null
5
25
2023-10-07T20:04:56
--- tags: - not-for-all-audiences task_categories: - conversational language: - en pretty_name: Choose Your Story Novel Format Text Adventures --- This is the 'CYS' text adventure dataset converted to a chat format with system messages. The system messages were randomly constructed from a table of phrases and templates. The original data can be found in the .7z archive. **Credits:** Thank you to VE Forbryderne from KoboldAI for scraping the dataset.
455
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hippocrates/DDI2013_test
2023-10-12T19:21:33.000Z
[ "region:us" ]
hippocrates
null
null
0
25
2023-10-08T22:20:53
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: train num_bytes: 20658927 num_examples: 18779 - name: valid num_bytes: 8739656 num_examples: 7244 - name: test num_bytes: 6455758 num_examples: 5761 download_size: 3113073 dataset_size: 35854341 --- # Dataset Card for "DDI2013_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
782
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darcycao/finaldataset
2023-10-09T10:19:10.000Z
[ "region:us" ]
darcycao
null
null
0
25
2023-10-09T10:18:56
Entry not found
15
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jamescalam/ai-arxiv
2023-10-10T12:57:37.000Z
[ "region:us" ]
jamescalam
null
null
9
25
2023-10-09T21:07:32
Entry not found
15
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open-phi/ft-sample
2023-10-12T05:10:35.000Z
[ "region:us" ]
open-phi
null
null
4
25
2023-10-10T03:04:33
--- dataset_info: features: - name: topic dtype: string - name: model dtype: string - name: concepts sequence: string - name: outline sequence: string - name: markdown dtype: string splits: - name: train num_bytes: 299599047 num_examples: 4121 download_size: 92594714 dataset_size: 299599047 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ft-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
593
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